The present application is being filed along with a Sequence Listing in electronic format. The Sequence Listing is provided as a file entitled 68EB-298684-US12, created Feb. 8, 2023, which is 2,543,616 bytes in size. The information in the electronic format of the Sequence Listing is incorporated herein by reference in its entirety.
Methods, compositions and products for counting individual molecules by stochastic attachment of diverse labels from a set of labels, followed by amplification and detection are disclosed.
Many processes are characterized or regulated by the absolute or relative amounts of a plurality of items. For example, in biology, the level of expression of particular genes or groups of genes or the number of copies of chromosomal regions can be used to characterize the status of a cell or tissue. Analog methods such as microarray hybridization methods and real-time PCR are alternatives, but digital readout methods such as those disclosed herein have advantages over analog methods. Methods for estimating the abundance or relative abundance of genetic material having increased accuracy of counting would be beneficial.
The availability of convenient and efficient methods for the accurate identification of genetic variation and expression patterns among large sets of genes may be applied to understanding the relationship between an organism's genetic make-up and the state of its health or disease, Collins et al, Science, 282: 682-689 (1998). In this regard, techniques have been developed for the analysis of large populations of polynucleotides based either on specific hybridization of probes to microarrays, e.g. Lockhart et al. Hacia et al, Nature Genetics, 21: 4247 (1999), or on the counting of tags or signatures of DNA fragments, e.g. Velculescu et al, Science, 270: 484487 (1995); Brenner et al, Nature Biotechnology, 18: 630-634 (2000). These techniques have been used in discovery research to identify subsets of genes that have coordinated patterns of expression under a variety of circumstances or that are correlated with, and predictive of events, of interest, such as toxicity, drug responsiveness, risk of relapse, and the like, e.g. Golub et al, Science, 286: 531-537 (1999); Alizadeh et al, Nature, 403: 503-511 (2000); Perou et al, Nature, 406: 747-752 (2000); Shipp et al, Nature Medicine, 8: 68-74 (2002); Hakak et al, Proc. Natl. Acad. Sci., 98: 47454751 (2001); Thomas et al, Mol. Pharmacol., 60: 1189-1194 (2001); De Primo et al, BMC Cancer 2003, 3:3; and the like. Not infrequently the subset of genes found to be relevant has a size in the range of from ten or under to a few hundred.
In addition to gene expression, techniques have also been developed to measure genome-wide variation in gene copy number. For example, in the field of oncology, there is interest in measuring genome-wide copy number variation of local regions that characterize many cancers and that may have diagnostic or prognostic implications. For a review see Zhang et al. Annu. Rev. Genomics Hum. Genet. 2009. 10:451-81.
While such hybridization-based techniques offer the advantages of scale and the capability of detecting a wide range of gene expression or copy number levels, such measurements may be subject to variability relating to probe hybridization differences and cross-reactivity, element-to-element differences within microarrays, and microarray-to-microarray differences, Audic and Claverie, Genomic Res., 7: 986-995 (1997); Wittes and Friedman, J. Natl. Cancer Inst. 91: 400-401 (1999).
On the other hand, techniques that provide digital representations of abundance, such as SAGE (Velculescu et al, cited above) or MPSS (Brenner et al, cited above), are statistically more robust; they do not require repetition or standardization of counting experiments as counting statistics are well-modeled by the Poisson distribution, and the precision and accuracy of relative abundance measurements may be increased by increasing the size of the sample of tags or signatures counted, e.g. Audic and Claverie (cited above).
Both digital and non-digital hybridization-based assays have been implemented using oligonucleotide tags that are hybridized to their complements, typically as part of a detection or signal generation schemes that may include solid phase supports, such as microarrays, microbeads, or the like, e.g. Brenner et al, Proc. Natl. Acad. Sci., 97: 1665-1670 (2000); Church et al, Science, 240: 185-188 (1988); Chee, Nucleic Acids Research, 19: 3301-3305 (1991); Shoemaker et al., Nature Genetics, 14: 450456 (1996); Wallace, U.S. Pat. No. 5,981,179; Gerry et al, J. Mol. Biol., 292: 251-262 (1999); Fan et al., Genome Research, 10: 853-860 (2000); Ye et al., Human Mutation, 17: 305-316 (2001); and the like. Bacterial transcript imaging by hybridization of total RNA to nucleic acid arrays may be conducted as described in Saizieu et al., Nature Biotechnology, 16:45-48 (1998). Accessing genetic information using high density DNA arrays is further described in Chee et al., Science 274:610-614 (1996). Tagging approaches have also been used in combination with next-generation sequencing methods, see for example, Smith et al. NAR (May 11, 2010), 1-7.
A common feature among all of these approaches is a one-to-one correspondence between probe sequences and oligonucleotide tag sequences. That is, the oligonucleotide tags have been employed as probe surrogates for their favorable hybridizations properties, particularly under multiplex assay conditions.
Determining small numbers of biological molecules and their changes is essential when unraveling mechanisms of cellular response, differentiation or signal transduction, and in performing a wide variety of clinical measurements. Although many analytical methods have been developed to measure the relative abundance of different molecules through sampling (e.g., microarrays and sequencing), few techniques are available to determine the absolute number of molecules in a sample. This can be an important goal, for example in single cell measurements of copy number or stochastic gene expression, and is especially challenging when the number of molecules of interest is low in a background of many other species. As an example, measuring the relative copy number or expression level of a gene across a wide number of genes can currently be performed using PCR, hybridization to a microarray or by direct sequence counting. PCR and microarray analysis rely on the specificity of hybridization to identify the target of interest for amplification or capture respectively, then yield an analog signal proportional to the original number of molecules. A major advantage of these approaches is in the use of hybridization to isolate the specific molecules of interest within the background of many other molecules, generating specificity for the readout or detection step. The disadvantage is that the readout signal to noise is proportional to all molecules (specific and non-specific) specified by selective amplification or hybridization. The situation is reversed for sequence counting. No intended sequence specificity is imposed in the sequence capture step, and all molecules are sequenced. The major advantage is that the detection step simply yields a digital list of those sequences found, and since there is no specificity in the isolation step, all sequences must be analyzed at a sufficient statistical depth in order to learn about a specific sequence. Although very major technical advances in sequencing speed and throughput have occurred, the statistical requirements imposed to accurately measure small changes in concentration of a specific gene within the background of many other sequences requires measuring many sequences that don't matter to find the ones that do matter. Each of these techniques, PCR, array hybridization and sequence counting is a comparative technique in that they primarily measure relative abundance, and do not typically yield an absolute number of molecules in a solution. A method of absolute counting of nucleic acids is digital PCR (B. Vogelstein, K. W. Kinzler, Proc Natl Acad Sci USA 96, 9236 (Aug. 3, 1999)), where solutions are progressively diluted into individual compartments until there is an average probability of one molecule per two wells, then detected by PCR. Although digital PCR can be used as a measure of absolute abundance, the dilutions must be customized for each type of molecule, and thus in practice is generally limited to the analysis of a small number of different molecules.
High-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules is disclosed. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. The uniqueness of each labeled molecule is determined by the statistics of random choice, and depends on the number of copies of identical molecules in the collection compared to the diversity of labels. The size of the resulting set of labeled molecules is determined by the stochastic nature of the labeling process, and analysis reveals the original number of molecules. When the number of copies of a molecule to the diversity of labels is low, the labeled molecules are highly unique, and the digital counting efficiency is high. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of yes/no digital questions detecting whether preprogrammed labels are present. The conceptual framework for stochastic mapping of a variety of molecule types is developed and the utility of the methods are demonstrated by stochastically labeling 360,000 different fragments of the human genome. The labeled fragments for a target molecule of choice are detected with high specificity using a microarray readout system, and with DNA sequencing. The results are consistent with a stochastic process, and yield highly precise relative and absolute counting statistics of selected molecules within a vast background of other molecules.
Methods are disclosed herein for digital counting of individual molecules of one or more targets. In preferred embodiments the targets are nucleic acids, but may be a variety of biological or non-biological elements. Targets are labeled so that individual occurrences of the same target are marked by attachment of a different label to difference occurrences. The attachment of the label confers a separate, determinable identity to each occurrence of targets that may otherwise be indistinguishable. Preferably the labels are different sequences that tag or mark each target occurrence uniquely. The resulting modified target comprises the target sequence and the unique identifier (which may be referred to herein as tag, counter, label, or marker). The junction of the target and identifier forms a uniquely detectable mechanism for counting the occurrence of that copy of the target. The attachment of the identifier to each occurrence of the target is a random sampling event. Each occurrence of target could choose any of the labels. Each identifier is present in multiple copies so selection of one copy does not remove that identifier sequence from the pool of identifiers so it is possible that the same identifier will be selected twice. The probability of that depends on the number of target occurrences relative to the number of different identifier sequences.
Each stochastic attachment event, where a target occurrence is attached to a unique identifier, results in the creation of a novel sequence formed at the junction of the identifier and the target. For a given target, all resulting products will contain the same target portion, but each will contain a different identifier sequence (T1L1, T1L2, . . . T1LN where N is the number of different occurrences of target 1, “T1” and L is the identifier, L1, L2 . . . LN). In preferred aspects the occurrences are detected by hybridization. In some aspects the methods and systems include a probe array comprising features, wherein each feature has a different combination of target sequence with identifiers, 1 to N wherein N is the number of unique identifiers in the pool of identifiers. The array has N features for each target, so if there are 8 targets to be analyzed there are 8 times N features on the array to interrogate the 8 targets.
Reference will now be made in detail to exemplary embodiments of the invention. While the invention will be described in conjunction with the exemplary embodiments, it will be understood that they are not intended to limit the invention to these embodiments. On the contrary, the invention is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the invention.
The invention has many preferred embodiments and relies on many patents, applications and other references for details known to those of the art. Therefore, when a patent, application, or other reference, such as a printed publication, is cited or repeated below, it should be understood that it is incorporated by reference in its entirety for all purposes and particularly for the proposition that is recited.
As used in this application, the singular form “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “an agent” includes a plurality of agents, including mixtures thereof
An individual is not limited to a human being, but may also be other organisms including, but not limited to, mammals, plants, bacteria, or cells derived from any of the above.
Throughout this disclosure, various aspects of this invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
The practice of the present invention may employ, unless otherwise indicated, conventional techniques and descriptions of organic chemistry, polymer technology, molecular biology (including recombinant techniques), cell biology, biochemistry, and immunology, which are within the skill of the art. Such conventional techniques include polymer array synthesis, hybridization, ligation, and detection of hybridization using a label. Specific illustrations of suitable techniques can be had by reference to the example herein below. However, other equivalent conventional procedures can, of course, also be used. Such conventional techniques and descriptions can be found in standard laboratory manuals such as Genome Analysis: A Laboratory Manual Series (Vols. I-IV), Using Antibodies: A Laboratory Manual, Cells: A Laboratory Manual, PCR Primer: A Laboratory Manual, and Molecular Cloning: A Laboratory Manual (all from Cold Spring Harbor Laboratory Press), Gait, “Oligonucleotide Synthesis: A Practical Approach” 1984, IRL Press, London, Nelson and Cox (2000), Lehninger et al., (2008) Principles of Biochemistry 5th Ed., W.H. Freeman Pub., New York, NY and Berg et al. (2006) Biochemistry, 6th Ed., W.H. Freeman Pub., New York, NY, all of which are herein incorporated in their entirety by reference for all purposes.
The present invention can employ solid substrates, including arrays in some preferred embodiments. Methods and techniques applicable to polymer (including protein) array synthesis have been described in U.S. Patent Pub. No. 20050074787, WO 00/58516, U.S. Pat. Nos. 5,143,854, 5,242,974, 5,252,743, 5,324,633, 5,384,261, 5,405,783, 5,424,186, 5,451,683, 5,482,867, 5,491,074, 5,527,681, 5,550,215, 5,571,639, 5,578,832, 5,593,839, 5,599,695, 5,624,711, 5,631,734, 5,795,716, 5,831,070, 5,837,832, 5,856,101, 5,858,659, 5,936,324, 5,968,740, 5,974,164, 5,981,185, 5,981,956, 6,025,601, 6,033,860, 6,040,193, 6,090,555, 6,136,269, 6,269,846 and 6,428,752, in PCT Publication No. WO 99/36760 and WO 01/58593, which are all incorporated herein by reference in their entirety for all purposes.
Patents that describe synthesis techniques in specific embodiments include U.S. Pat. Nos. 5,412,087, 6,147,205, 6,262,216, 6,310,189, 5,889,165, and 5,959,098. Nucleic acid arrays are described in many of the above patents, but the same techniques may be applied to polypeptide arrays.
The present invention also contemplates many uses for polymers attached to solid substrates. These uses include gene expression monitoring, profiling, library screening, genotyping and diagnostics. Gene expression monitoring and profiling methods can be shown in U.S. Pat. Nos. 5,800,992, 6,013,449, 6,020,135, 6,033,860, 6,040,138, 6,177,248 and 6,309,822. Genotyping and uses therefore are shown in U.S. Patent Publication Nos. 20030036069 and 20070065816 and U.S. Pat. Nos. 5,856,092, 6,300,063, 5,858,659, 6,284,460, 6,361,947, 6,368,799 and 6,333,179. Other uses are embodied in U.S. Pat. Nos. 5,871,928, 5,902,723, 6,045,996, 5,541,061, and 6,197,506.
The present invention also contemplates sample preparation methods in certain embodiments. Prior to or concurrent with analysis, the sample may be amplified by a variety of mechanisms. In some aspects nucleic acid amplification methods such as PCR may be combined with the disclosed methods and systems. See, for example, PCR Technology: Principles and Applications for DNA Amplification (Ed. H. A. Erlich, Freeman Press, NY, NY, 1992); PCR Protocols: A Guide to Methods and Applications (Eds. Innis, et al., Academic Press, San Diego, C A, 1990); Mattila et al., Nucleic Acids Res. 19, 4967 (1991); Eckert et al., PCR Methods and Applications 1, 17 (1991); PCR (Eds. McPherson et al., IRL Press, Oxford); and U.S. Pat. Nos. 4,683,202, 4,683,195, 4,800,159, 4,965,188, and 5,333,675, each of which is incorporated herein by reference in their entireties for all purposes. Enzymes and related methods of use in molecular biology that may be used in combination with the disclosed methods and systems are reviewed, for example, in Rittie and Perbal, J. Cell Commun. Signal. (2008) 2:25-45. The sample may be amplified on the array. See, for example, U.S. Pat. No. 6,300,070 and which is incorporated herein by reference in its entirety for all purposes.
Many of the methods and systems disclosed herein utilize enzyme activities. A variety of enzymes are well known, have been characterized and many are commercially available from one or more supplier. For a review of enzyme activities commonly used in molecular biology see, for example, Rittie and Perbal, J. Cell Commun. Signal. (2008) 2:25-45, incorporated herein by reference in its entirety. Exemplary enzymes include DNA dependent DNA polymerases (such as those shown in Table 1 of Rittie and Perbal), RNA dependent DNA polymerase (see Table 2 of Rittie and Perbal), RNA polymerases, ligases (see Table 3 of Rittie and Perbal), enzymes for phosphate transfer and removal (see Table 4 of Rittie and Perbal), nucleases (see Table 5 of Rittie and Perbal), and methylases.
Other methods of genome analysis and complexity reduction include, for example, AFLP, see U.S. Pat. No. 6,045,994, which is incorporated herein by reference, and arbitrarily primed-PCR (AP-PCR) see McClelland and Welsh, in PCR Primer: A laboratory Manual, (1995) eds. C. Dieffenbach and G. Dveksler, Cold Spring Harbor Lab Press, for example, at p 203, which is incorporated herein by reference in its entirety. Additional methods of sample preparation and techniques for reducing the complexity of a nucleic sample are described in Dong et al., Genome Research 11, 1418 (2001), in U.S. Pat. Nos. 6,361,947, 6,391,592, 6,458,530 and U.S. Patent Publication Nos. 20030039069, 20050079536, 20030096235, 20030082543, 20040072217, 20050142577, 20050233354, 20050227244, 20050208555, 20050074799, 20050042654 and 20040067493, which are each incorporated herein by reference in their entireties.
The design and use of allele-specific probes for analyzing polymorphisms is described by e.g., Saiki et al., Nature 324, 163-166 (1986); Dattagupta, EP 235,726, and WO 89/11548. Allele-specific probes can be designed that hybridize to a segment of target DNA from one individual but do not hybridize to the corresponding segment from another individual due to the presence of different polymorphic forms in the respective segments from the two individuals.
Sample preparation methods are also contemplated in many embodiments. Prior to or concurrent with analysis, the genomic sample may be amplified by a variety of mechanisms, some of which may employ PCR. See, e.g., PCR Technology: Principles and Applications for DNA Amplification (Ed. H. A. Erlich, Freeman Press, NY, NY, 1992); PCR Protocols: A Guide to Methods and Applications (Eds. Innis, et al., Academic Press, San Diego, C A, 1990); Mattila et al., Nucleic Acids Res. 19, 4967 (1991); Eckert et al., PCR Methods and Applications 1, 17 (1991); PCR (Eds. McPherson et al., IRL Press, Oxford); and U.S. Pat. Nos. 4,683,202, 4,683,195, 4,800,159 4,965,188, and 5,333,675, and each of which is incorporated herein by reference in their entireties for all purposes. See also U.S. Pat. No. 6,300,070 which is incorporated herein by reference. Additional methods of sample preparation and techniques for reducing the complexity of a nucleic sample are described in Dong et al., Genome Research 11, 1418 (2001), in U.S. Pat. Nos. 6,361,947, 6,391,592 and U.S. Patent Pub. Nos. 20030096235, 20030082543 and 20030036069.
Other suitable amplification methods include the ligase chain reaction (LCR) (for example, Wu and Wallace, Genomics 4, 560 (1989), Landegren et al., Science 241, 1077 (1988) and Barringer et al. Gene 89:117 (1990)), transcription amplification (Kwoh et al., Proc. Natl. Acad. Sci. USA 86, 1173 (1989) and WO88/10315), self-sustained sequence replication (Guatelli et al., Proc. Nat. Acad. Sci. USA, 87, 1874 (1990) and WO90/06995), selective amplification of target polynucleotide sequences (U.S. Pat. No. 6,410,276), consensus sequence primed polymerase chain reaction (CP-PCR) (U.S. Pat. No. 4,437,975), arbitrarily primed polymerase chain reaction (AP-PCR) (U.S. Pat. Nos. 5,413,909, 5,861,245), rolling circle amplification (RCA) (for example, Fire and Xu, PNAS 92:4641 (1995) and Liu et al., J. Am. Chem. Soc. 118:1587 (1996)) and nucleic acid based sequence amplification (NABSA). (See, U.S. Pat. Nos. 5,409,818, 5,554,517, and 6,063,603, each of which is incorporated herein by reference). Other amplification methods that may be used are described in, U.S. Pat. Nos. 6,582,938, 5,242,794, 5,494,810, 4,988,617, and US Pub. No. 20030143599 each of which is incorporated herein by reference.
Molecular inversion probes may also be used for amplification of selected targets. MIPs may be generated so that the ends of the pre-circle probe are complementary to regions that flank the region to be amplified. The gap can be closed by extension of the end of the probe so that the complement of the target is incorporated into the MIP prior to ligation of the ends to form a closed circle. The closed circle can be amplified as previously disclosed in Hardenbol et al., Genome Res. 15:269-275 (2005) and in U.S. Pat. No. 6,858,412.
In some embodiments, amplification may include the use of a strand displacing polymerase that may be primed by selected primers or by a mixture of primers, for example, random hexamers. See for example Lasken and Egholm, Trends Biotechnol. 2003 21(12):531-5; Barker et al. Genome Res. 2004 May; 14(5):901-7; Dean et al. Proc Natl Acad Sci USA. 2002; 99(8):5261-6; and Paez, J. G., et al. Nucleic Acids Res. 2004; 32(9):e71. Other amplification methods that may be used include: Qbeta Replicase, described in PCT Patent Application No. PCT/US87/00880, isothermal amplification methods such as SDA, described in Walker et al. 1992, Nucleic Acids Res. 20(7):1691-6, 1992, and rolling circle amplification, described in U.S. Pat. No. 5,648,245. DNA may also be amplified by multiplex locus-specific PCR or using adaptor-ligation and single primer PCR. Other available methods of amplification, such as balanced PCR (Makrigiorgos, et al. (2002), Nat Biotechnol, Vol. 20, pp. 936-9), may also be used.
Methods of ligation will be known to those of skill in the art and are described, for example in Sambrook et at. (2001) and the New England BioLabs catalog both of which are incorporated herein by reference for all purposes. Methods include using T4 DNA Ligase which catalyzes the formation of a phosphodiester bond between juxtaposed 5′ phosphate and 3′ hydroxyl termini in duplex DNA or RNA with blunt and sticky ends; Taq DNA Ligase which catalyzes the formation of a phosphodiester bond between juxtaposed 5′ phosphate and 3′ hydroxyl termini of two adjacent oligonucleotides which are hybridized to a complementary target DNA; E. coli DNA ligase which catalyzes the formation of a phosphodiester bond between juxtaposed 5′-phosphate and 3′-hydroxyl termini in duplex DNA containing cohesive ends; and T4 RNA ligase which catalyzes ligation of a 5′ phosphoryl-terminated nucleic acid donor to a 3′ hydroxyl-terminated nucleic acid acceptor through the formation of a 3′→5′ phosphodiester bond, substrates include single-stranded RNA and DNA as well as dinucleoside pyrophosphates; or any other methods described in the art. Fragmented DNA may be treated with one or more enzymes, for example, an endonuclease, prior to ligation of adaptors to one or both ends to facilitate ligation by generating ends that are compatible with ligation.
Fixed content mapping arrays are available from Affymetrix, for example, the SNP 6.0 array. Methods for using mapping arrays see, for example, Kennedy et al., Nat. Biotech. 21:1233-1237 (2003), Matsuzaki et al., Genome Res. 14:414-425 (2004), Matsuzaki et al., Nat. Meth. 1:109-111 (2004) and U.S. Patent Pub. Nos. 20040146890 and 20050042654, each incorporated herein by reference. Applications of microarrays for SNP genotyping have been described in e.g., U.S. Pat. Nos. 6,300,063, 6,361,947, 6,368,799 and US Patent Publication Nos. 20040067493, 20030232353, 20030186279, 20050260628, 20070065816 and 20030186280, all incorporated herein by reference in their entireties for all purposes.
Selected panels of SNPs can also be interrogated using a panel of locus specific probes in combination with a universal array as described in Hardenbol et al., Genome Res. 15:269-275 (2005) and in U.S. Pat. No. 6,858,412. Universal tag arrays and reagent kits for performing such locus specific genotyping using panels of custom molecular inversion probes (MIPs) are available from Affymetrix.
Computer implemented methods for determining genotype using data from mapping arrays are disclosed, for example, in Liu, et al., Bioinformatics 19:2397-2403 (2003), Rabbee and Speed, Bioinformatics, 22:7-12 (2006), and Di et al., Bioinformatics 21:1958-63 (2005). Computer implemented methods for linkage analysis using mapping array data are disclosed, for example, in Ruschendorf and Nurnberg, Bioinformatics 21:2123-5 (2005) and Leykin et al., BMC Genet. 6:7, (2005). Computer methods for analysis of genotyping data are also disclosed in U.S. Patent Pub. Nos. 20060229823, 20050009069, 20040138821, 20060024715, 20050250151 and 20030009292.
Methods for analyzing chromosomal copy number using mapping arrays are disclosed, for example, in Bignell et al., Genome Res. 14:287-95 (2004), Lieberfarb, et al., Cancer Res. 63:4781-4785 (2003), Zhao et al., Cancer Res. 64:3060-71 (2004), Huang et al., Hum Genomics 1:287-299 (2004), Nannya et al., Cancer Res. 65:6071-6079 (2005), Slater et al., Am. J. Hum. Genet. 77:709-726 (2005) and Ishikawa et al., Biochem. and Biophys. Res. Comm., 333:1309-1314 (2005). Computer implemented methods for estimation of copy number based on hybridization intensity are disclosed in U.S. Patent Pub. Nos. 20040157243, 20050064476, 20050130217, 20060035258, 20060134674 and 20060194243.
Additional methods of sample preparation and techniques for reducing the complexity of a nucleic sample are described in Dong et al., Genome Research 11, 1418 (2001), in U.S. Pat. Nos. 6,361,947, 6,391,592 and 6,872,529 and U.S. Patent Publication Nos. 20030036069, 20030096235 and 20030082543. Additional methods of using a genotyping array are disclosed, for example, in U.S. Patent Publication Nos. 20040146883, 20030186280, 20030186279, 20040067493, 20030232353, 20060292597, 20050233354, 20050074799, 20070065816 and 20040185475.
Methods for conducting polynucleotide hybridization assays have been well developed in the art. Hybridization assay procedures and conditions will vary depending on the application and are selected in accordance with known general binding methods, including those referred to in: Maniatis et al. Molecular Cloning: A Laboratory Manual (2nd Ed. Cold Spring Harbor, N.Y, 1989); Berger and Kimmel Methods in Enzymology, Vol. 152, Guide to Molecular Cloning Techniques (Academic Press, Inc., San Diego, C A, 1987); Young and Davis, P.N.A.S, 80: 1194 (1983). Methods and apparatus for carrying out repeated and controlled hybridization reactions have been described in U.S. Pat. Nos. 5,871,928, 5,874,219, 6,045,996 and 6,386,749, 6,391,623 each of which are incorporated herein by reference.
The present invention also contemplates signal detection of hybridization between ligands in certain preferred embodiments. See U.S. Pat. Nos. 5,143,854, 5,578,832, 5,631,734, 5,834,758, 5,936,324, 5,981,956, 6,025,601, 6,141,096, 6,185,030, 6,201,639, 6,218,803, and 6,225,625 in U.S. Patent Pub. No. 20040012676 and in PCT Application PCT/US99/06097 (published as WO99/47964), each of which also is hereby incorporated by reference in its entirety for all purposes.
Methods and apparatus for signal detection and processing of intensity data are disclosed in, for example, U.S. Pat. Nos. 5,143,854, 5,547,839, 5,578,832, 5,631,734, 5,800,992, 5,834,758, 5,856,092, 5,902,723, 5,936,324, 5,981,956, 6,025,601, 6,090,555, 6,141,096, 6,185,030, 6,201,639; 6,218,803; and 6,225,625, in U.S. Patent Pub. Nos. 20040012676 and 20050059062 and in PCT Application PCT/US99/06097 (published as WO99/47964), each of which also is hereby incorporated by reference in its entirety for all purposes.
The practice of the present invention may also employ conventional biology methods, software and systems. Computer software products of the invention typically include computer readable medium having computer-executable instructions for performing the logic steps of the method of the invention. Suitable computer readable medium include floppy disk, CD-ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes, etc. The computer-executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are described in, for example, Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2nd ed., 2001). See U.S. Pat. No. 6,420,108.
The present invention may also make use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. See, U.S. Pat. Nos. 5,593,839, 5,795,716, 5,733,729, 5,974,164, 6,066,454, 6,090,555, 6,185,561, 6,188,783, 6,223,127, 6,229,911 and 6,308,170. Computer methods related to genotyping using high density microarray analysis may also be used in the present methods, see, for example, US Patent Pub. Nos. 20050250151, 20050244883, 20050108197, 20050079536 and 20050042654.
Additionally, the present disclosure may have preferred embodiments that include methods for providing genetic information over networks such as the Internet as shown in U.S. Patent Pub. Nos. 20030097222, 20020183936, 20030100995, 20030120432, 20040002818, 20040126840, and 20040049354.
An allele refers to one specific form of a genetic sequence (such as a gene) within a cell, an individual or within a population, the specific form differing from other forms of the same gene in the sequence of at least one, and frequently more than one, variant sites within the sequence of the gene. The sequences at these variant sites that differ between different alleles are termed “variances”, “polymorphisms”, or “mutations”. At each autosomal specific chromosomal location or “locus” an individual possesses two alleles, one inherited from one parent and one from the other parent, for example one from the mother and one from the father. An individual is “heterozygous” at a locus if it has two different alleles at that locus. An individual is “homozygous” at a locus if it has two identical alleles at that locus.
Single nucleotide polymorphisms (SNPs) are positions at which two alternative bases occur at appreciable frequency (>1%) in a given population. SNPs are a common type of human genetic variation and are useful in the performance of genome wide association studies (GWAS). GWAS may be used, for example for the analysis of biological pathways, see Wang and Hakonarson, Nat. Rev. Genet. 2010, 11:843-854. Other common variation includes single base deletions or insertions of a nucleotide relative to a reference allele. Copy number variants (CNVs), transversions and other rearrangements are also forms of genetic variation.
The term genotyping refers to the determination of the genetic information an individual carries at one or more positions in the genome. For example, genotyping may comprise the determination of which allele or alleles an individual carries for a single SNP or the determination of which allele or alleles an individual carries for a plurality of SNPs or CNVs. A diploid individual may be homozygous for each of the two possible alleles (for example, AA or BB) or heterozygous (for example, AB). For additional information regarding genotyping and genome structure see, Color Atlas of Genetics, Ed. Passarge, Thieme, New York, NY (2001), which is incorporated by reference.
Normal cells that are heterozygous at one or more loci may give rise to tumor cells that are homozygous at those loci. This loss of heterozygosity (LOH) may result from structural deletion of normal genes or loss of the chromosome carrying the normal gene, mitotic recombination between normal and mutant genes, followed by formation of daughter cells homozygous for deleted or inactivated (mutant) genes; or loss of the chromosome with the normal gene and duplication of the chromosome with the deleted or inactivated (mutant) gene.
The term “array” as used herein refers to an intentionally created collection of molecules which can be prepared either synthetically or biosynthetically. The molecules in the array can be identical or different from each other. The array can assume a variety of formats, for example, libraries of soluble molecules; libraries of compounds tethered to resin beads, silica chips, microparticles, nanoparticles or other solid supports.
The term “complementary” as used herein refers to the hybridization or base pairing between nucleotides or nucleic acids, such as, for instance, between the two strands of a double stranded DNA molecule or between an oligonucleotide primer and a primer binding site on a single stranded nucleic acid to be sequenced or amplified. See, M. Kanehisa Nucleic Acids Res. 12:203 (1984), incorporated herein by reference.
The term “copy number variation” or “CNV” refers to differences in the copy number of genetic information. In many aspects it refers to differences in the per genome copy number of a genomic region. For example, in a diploid organism the expected copy number for autosomal genomic regions is 2 copies per genome. Such genomic regions should be present at 2 copies per cell. For a recent review see Zhang et al. Annu. Rev. Genomics Hum. Genet. 2009. 10:451-81. CNV is a source of genetic diversity in humans and can be associated with complex disorders and disease, for example, by altering gene dosage, gene disruption, or gene fusion. They can also represent benign polymorphic variants. CNVs can be large, for example, larger than 1 Mb, but many are smaller, for example between 100 bp and 1 Mb. More than 38,000 CNVs greater than 100 bp (and less than 3 Mb) have been reported in humans. Along with SNPs these CNVs account for a significant amount of phenotypic variation between individuals. In adition to having deleterious impacts, e.g. causing disease, they may also result in advantageous variation.
Digital PCR is a technique where a limiting dilution of the sample is made across a large number of separate PCR reactions so that most of the reactions have no template molecules and give a negative amplification result. Those reactions that are positive at the reaction endpoint are counted as individual template molecules present in the original sample in a 1 to 1 relationship. See Kalina et al. NAR 25:1999-2004 (1997) and Vogelstein and Kinzler, PNAS 96:9236-9241 (1999). This method is an absolute counting method where solutions are partitioned into containers until there is an average probability of one molecule per two containers or when, P0=(1−e−n/c)=½; where n is the number of molecules and c is the number of containers, or n/c is 0.693. Quantitative partitioning is assumed, and the dynamic range is governed by the number of containers available for stochastic separation. The molecules are then detected by PCR and the number of positive containers is counted. Each successful amplification is counted as one molecule, independent of the actual amount of product. PCR-based techniques have the additional advantage of only counting molecules that can be amplified, e.g. that are relevant to the massively parallel PCR step in the sequencing workflow. Because digital PCR has single molecule sensitivity, only a few hundred library molecules are required for accurate quantification. Elimination of the quantification bottleneck reduces the sample input requirement from micrograms to nanograms or less, opening the way for minute and/or precious samples onto the next-generation sequencing platforms without the distorting effects of pre-amplification. Digital PCR has been used to quantify sequencing libraries to eliminate uncertainty associated with the construction and application of standard curves to PCR-based quantification and enable direct sequencing without titration runs. See White et al. BMC Genomics 10: 116 (2009).
To vary dynamic range, micro-fabrication can be used to substantially increase the number of containers. See, Fan et al. Am J Obstet Gynecol 200, 543 el (May, 2009).
Similarly, in stochastic labeling as disclosed herein, the same statistical conditions are met when P0=(1−e−n/m)=½; where m is the number of labels, and one half of the labels will be used at least once when n/m=0.693. The dynamic range is governed by the number of labels used, and the number of labels can be easily increased to extend the dynamic range. The number of containers in digital PCR plays the same role as the number of labels in stochastic labeling and by substituting containers for labels identical statistical equations may be applied. Using the principles of physical separation, digital PCR stochastically expands identical molecules into physical space, whereas the principle governing stochastic labeling is identity based and expands identical molecules into identity space.
The term “hybridization” as used herein refers to the process in which two single-stranded polynucleotides bind noncovalently to form a stable double-stranded polynucleotide; triple-stranded hybridization is also theoretically possible. The resulting (usually) double-stranded polynucleotide is a “hybrid.” The proportion of the population of polynucleotides that forms stable hybrids is referred to herein as the “degree of hybridization.” Hybridizations may be performed under stringent conditions, for example, at a salt concentration of no more than 1 M and a temperature of at least 25° C. For example, conditions of 5×SSPE (750 mM NaCl, 50 mM NaPhosphate, 5 mM EDTA, pH 7.4) and a temperature of 25-30° C. are suitable for allele-specific probe hybridizations. For stringent conditions, see, for example, Sambrook, Fritsche and Maniatis. “Molecular Cloning A laboratory Manual” 2nd Ed. Cold Spring Harbor Press (1989) which is hereby incorporated by reference in its entirety for all purposes above. In some aspects salt concentrations for hybridization are preferably between about 200 mM and about 1M or between about 200 mM and about 500 mM. Hybridization temperatures can be as low as 5° C., but are typically greater than 22° C., more typically greater than about 30° C., and preferably in excess of about 37° C. Longer fragments may require higher hybridization temperatures for specific hybridization. As other factors may affect the stringency of hybridization, including base composition and length of the complementary strands, presence of organic solvents and extent of base mismatching, the combination of parameters is more important than the absolute measure of any one alone.
The term “mRNA” or sometimes refer by “mRNA transcripts” as used herein, include, but not limited to pre-mRNA transcript(s), transcript processing intermediates, mature mRNA(s) ready for translation and transcripts of the gene or genes, or nucleic acids derived from the mRNA transcript(s). Transcript processing may include splicing, editing and degradation. As used herein, a nucleic acid derived from an mRNA transcript refers to a nucleic acid for whose synthesis the mRNA transcript or a subsequence thereof has ultimately served as a template. Thus, a cDNA reverse transcribed from an mRNA, an RNA transcribed from that cDNA, a DNA amplified from the cDNA, an RNA transcribed from the amplified DNA, etc., are all derived from the mRNA transcript and detection of such derived products is indicative of the presence and/or abundance of the original transcript in a sample. Thus, mRNA derived samples include, but are not limited to, mRNA transcripts of the gene or genes, cDNA reverse transcribed from the mRNA, cRNA transcribed from the cDNA, DNA amplified from the genes, RNA transcribed from amplified DNA, and the like.
The term “nucleic acid” as used herein refers to a polymeric form of nucleotides of any length, either ribonucleotides, deoxyribonucleotides or peptide nucleic acids (PNAs), that comprise purine and pyrimidine bases, or other natural, chemically or biochemically modified, non-natural, or derivatized nucleotide bases. The backbone of the polynucleotide can comprise sugars and phosphate groups, as may typically be found in RNA or DNA, or modified or substituted sugar or phosphate groups. A polynucleotide may comprise modified nucleotides, such as methylated nucleotides and nucleotide analogs. The sequence of nucleotides may be interrupted by non-nucleotide components. Thus the terms nucleoside, nucleotide, deoxynucleoside and deoxynucleotide generally include analogs such as those described herein. These analogs are those molecules having some structural features in common with a naturally occurring nucleoside or nucleotide such that when incorporated into a nucleic acid or oligonucleoside sequence, they allow hybridization with a naturally occurring nucleic acid sequence in solution. Typically, these analogs are derived from naturally occurring nucleosides and nucleotides by replacing and/or modifying the base, the ribose or the phosphodiester moiety. The changes can be tailor made to stabilize or destabilize hybrid formation or enhance the specificity of hybridization with a complementary nucleic acid sequence as desired.
The term “oligonucleotide” or sometimes refer by “polynucleotide” as used herein refers to a nucleic acid ranging from at least 2, preferable at least 8, and more preferably at least 20 nucleotides in length or a compound that specifically hybridizes to a polynucleotide. Polynucleotides of the present invention include sequences of deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) which may be isolated from natural sources, recombinantly produced or artificially synthesized and mimetics thereof. A further example of a polynucleotide of the present invention may be peptide nucleic acid (PNA). The invention also encompasses situations in which there is a nontraditional base pairing such as Hoogsteen base pairing which has been identified in certain tRNA molecules and postulated to exist in a triple helix. “Polynucleotide” and “oligonucleotide” are used interchangeably in this application.
The term “polymorphism” as used herein refers to the occurrence of two or more genetically determined alternative sequences or alleles in a population. A polymorphic marker or site is the locus at which divergence occurs. Preferred markers have at least two alleles, each occurring at frequency of greater than 1%, and more preferably greater than 10% or 20% of a selected population. A polymorphism may comprise one or more base changes, an insertion, a repeat, or a deletion. A polymorphic locus may be as small as one base pair. Polymorphic markers include restriction fragment length polymorphisms, variable number of tandem repeats (VNTR's), hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, and insertion elements such as Alu. The first identified allelic form is arbitrarily designated as the reference form and other allelic forms are designated as alternative or variant alleles. The allelic form occurring most frequently in a selected population is sometimes referred to as the wildtype form. Diploid organisms may be homozygous or heterozygous for allelic forms. A diallelic polymorphism has two forms. A triallelic polymorphism has three forms. Single nucleotide polymorphisms (SNPs) are included in polymorphisms.
The term “primer” as used herein refers to a single-stranded oligonucleotide capable of acting as a point of initiation for template-directed DNA synthesis under suitable conditions for example, buffer and temperature, in the presence of four different nucleoside triphosphates and an agent for polymerization, such as, for example, DNA or RNA polymerase or reverse transcriptase. The length of the primer, in any given case, depends on, for example, the intended use of the primer, and generally ranges from 15 to 30 nucleotides. Short primer molecules generally require cooler temperatures to form sufficiently stable hybrid complexes with the template. A primer need not reflect the exact sequence of the template but must be sufficiently complementary to hybridize with such template. The primer site is the area of the template to which a primer hybridizes. The primer pair is a set of primers including a 5′ upstream primer that hybridizes with the 5′ end of the sequence to be amplified and a 3′ downstream primer that hybridizes with the complement of the 3′ end of the sequence to be amplified.
The term “probe” as used herein refers to a surface-immobilized molecule that can be recognized by a particular target. See U.S. Pat. No. 6,582,908 for an example of arrays having all possible combinations of probes with 10, 12, and more bases. Examples of probes that can be investigated by this invention include, but are not restricted to, agonists and antagonists for cell membrane receptors, toxins and venoms, viral epitopes, hormones (for example, opioid peptides, steroids, etc.), hormone receptors, peptides, enzymes, enzyme substrates, cofactors, drugs, lectins, sugars, oligonucleotides, nucleic acids, oligosaccharides, proteins, and monoclonal antibodies.
The term “solid support”, “support”, and “substrate” as used herein are used interchangeably and refer to a material or group of materials having a rigid or semi-rigid surface or surfaces. In many embodiments, at least one surface of the solid support will be substantially flat, although in some embodiments it may be desirable to physically separate synthesis regions for different compounds with, for example, wells, raised regions, pins, etched trenches, or the like. According to other embodiments, the solid support(s) will take the form of beads, resins, gels, microspheres, or other geometric configurations. See U.S. Pat. No. 5,744,305 and US Patent Pub. Nos. 20090149340 and 20080038559 for exemplary substrates.
A stochastic process is the counterpart to a deterministic process. Instead of dealing with only one possible “reality” of how the process might evolve under time, in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition (or starting point) is known, there are many possibilities the process might go to, but some paths are more probable and others less.
In the simplest possible case, a stochastic process amounts to a sequence of random variables known as a time series (for example, see Markov chain). Another basic type of a stochastic process is a random field, whose domain is a region of space, in other words, a random function whose arguments are drawn from a range of continuously changing values. One approach to stochastic processes treats them as functions of one or several deterministic arguments (“inputs”, in most cases regarded as “time”) whose values (“outputs”) are random variables: non-deterministic (single) quantities which have certain probability distributions. Random variables corresponding to various times (or points, in the case of random fields) may be completely different. The main requirement is that these different random quantities all have the same “type”. Although the random values of a stochastic process at different times may be independent random variables, in most commonly considered situations they exhibit complicated statistical correlations.
Familiar examples of processes modeled as stochastic time series include stock market and exchange rate fluctuations, signals such as speech, audio and video, medical data such as a patient's EKG, EEG, blood pressure or temperature, and random movement such as Brownian motion or random walks. Examples of random fields include static images, random terrain (landscapes), or composition variations of an heterogeneous material.
The stochastic labeling process can be generalized as follows. Consider n copies of a given target molecule T, where T={t1, i=1,2, . . . , n}, and a non-depleting reservoir of m diverse labels L, where L={lj, j=1,2, . . . , m}. T reacts with L stochastically, such that each ti will choose exactly one lj(i), 1≤j(i)≤m to take on a new identity tilj(i), and may be identified by its label subscript. Therefore, the new collection of molecules T* may be denoted as T*={tlj(i), i=1,2, . . . , n, 1≤j(i)≤m}.
When different copies of the target molecules react with the same label, j(i) for those molecules will assume the same value, therefore, the number of uniquely labeled target molecules k cannot be greater than m. The stochastic mapping of the set of labels on a target may be described by a stochastic operator S with m members, acting upon a target population of n, such that S(m)T(n)=T*(m,n) generating the set
T*={tlj(i), i=1,2, . . . , n, 1≤j(i)≈m}. For simplicity, we may write T*={tlk}. Furthermore, since S operates on all molecules randomly, it will independently act on many different target sequences and the method can be expanded to count copies of multiple target sequences, w, simultaneously: STw=ST1+ST2+ . . . +STw=T1*+T2*+ . . . +Tw*={tlk}1+ . . . +{tlk}2+ . . . +{tlk}w, where each Ti*, i=1,2, . . . , w consists of a set {tlk}i. The net result of S operating on a specific target population is to map the number of molecules, n, of that target, to the number of labels captured, k, which is a random variable.
Since target molecules randomly react with a label with probability
the probability of a label being captured by exactly x out of n copies of a target molecule can be modeled as a Binomial distribution,
where x! denotes the factorial of x. The probability that a label will not be captured by any copy of the target molecule is P(0)=(1−1/m)n, and the probability that a label will be captured at least once is 1−P(0). When n→∞ and 1/m→0 in the way that n/m→λ, P(x) converges to the Poisson distribution with mean λ, i.e.,
To compute the number of unique labels captured by n copies of a target molecule, we introduce an index random variable, Xi, which is 1 if a label has been captured at least once, and 0 otherwise. The number of unique labels captured is thus
The mean and variance of k can be derived,
Similarly, to compute the number of labels captured by exactly x copies of a target molecule, we introduce another index random variable, Yi, which is 1 if a label has been captured exactly x times, and 0 otherwise. The number of labels captured x times is thus
The mean and variance of t are,
where
and the combination
The equations were experimentally validated by performing numerical simulations with 5000 independent runs for each simulated case. Complete agreement with the analytical solutions was observed.
Stochastic Labeling of Individual Molecules
Methods are disclosed herein that may be applied to determining small numbers of biological molecules and their changes in response to, for example, cellular response, differentiation or signal transduction. The methods may also be used in performing a wide variety of clinical measurements. Although many analytical methods have been developed to measure the relative abundance of different molecules through sampling (e.g., microarrays and sequencing), the methods disclosed herein are able to determine the absolute number of molecules in a sample.
Methods for performing single molecule digital counting by the stochastic labeling of a collection of identical molecules are disclosed. As illustrated in
One embodiment is illustrated schematically in
The stochastic labeling process can be generalized as follows for illustrative purposes. Consider a given target sequence defined as T={t1, t2 . . . tn}; where n is the number of copies of T. A set of labels is defined as L={l1, l2 . . . lm}; where m is the number of different labels. T reacts stochastically with L, such that each t becomes attached to one l. If the l's are in non-depleting excess, each t will choose one l randomly, and will take on a new identity litj; where li, is chosen from L and j is the jth copy from the set of n molecules. We identify each new molecule litj by its label subscript and drop the subscript for the copies of T, because they are identical. The new collection of molecules becomes T*=l1t+l2t+ . . . lit; where li, is the ith choice from the set of m labels. It is important to emphasize that the subscripts of l at this point refer only to the ith choice and provide no information about the identity of each l. In fact, l1 and l2 will have some probability of being identical, depending upon the diversity m of the set of labels. Overall, T* will contain a set of k unique labels resulting from n targets choosing from the non-depleting reservoir of m labels. Or, T*(m,n)={tlk}; where k represents the number of unique labels that have been captured. In all cases, k will be smaller than m, approaching m only when n becomes very large. We can define the stochastic attachment of the set of labels on a target using a stochastic operator S with m members, acting upon a target population of n, such that S(m)T(n)=T*(m,n) generating the set {tlk}. Furthermore, since S operates on all molecules randomly, it can independently act on many different target sequences. Hence, the method can simultaneously count copies of multiple target sequences. The distribution of outcomes generated by the number of trials n, from a diversity of m, can be approximated by the Poisson equation, Px=x/x!e−(n/m), P0 is the probability that a label will not be chosen in n trials, and therefore, 1−P0 is the probability that a label will occur at least once. It follows that the number of unique labels captured is given by k=m(1−P0)=m(1−e−(n/m)).
Given k, we can calculate n. In addition to using the Poisson approximation, the relationship for k, n and m can be described analytically using the binomial distribution, or simulated using a random number generator, each yielding similar results (see SOM).
The outcome of stochastic labeling is illustrated by examining the graph of k verses n (curve 3201 in
The methods and examples below demonstrate that a population of indistinguishable molecules can be stochastically expanded to a population of uniquely identifiable and countable molecules. High-sensitivity threshold detection of single molecules is demonstrated, and the process can be used to count both the absolute and relative number of molecules in a sample. The method should be well suited for determining the absolute number of multiple target molecules in a specified container, for example in high-sensitivity clinical assays, or for determining the number of transcripts in single cells. The approach should also be compatible with other molecular assay systems. For example, antibodies could be stochastically labeled with DNA fragments and those that bind antigen harvested. After amplification, the number of labels detected will reflect the original number of antigens in solutions. In the examples shown here, DNA is used because of the great diversity of sequences available, and because it is easily detectable. In principle, any molecular label could be used, for example fluorescent groups or mass spectroscopy tags, as long as they are easily detected and they have sufficient diversity for the desired application. Although many of the examples refer to populations.
It is instructive to contrast the attributes of stochastic labeling with other quantitative methods. Microarray and sequencing technologies are commonly used to obtain relative abundance of multiple targets in a sample. In the case of microarray analysis, intensity values reflect the relative amount of hybridization bound target and can be used to compare to the intensity of other targets in the sample. In the case of sequencing, the relative number of times a sequence is found is compared to the number of times other sequences are found. Although the techniques differ by using intensity in one case and a digital count in the other, they both provide relative comparisons of the number of molecules in solution. In order to obtain absolute numbers, quantitative capture of all sequences would need to be assured; however in practice the efficiency of capture with microarray and sequencing technologies is unknown.
Digital PCR is an absolute counting method where solutions are stochastically partitioned into multi-well containers until there is an average probability of one molecule per two containers, then detected by PCR(4). This condition is satisfied when, P0=(1−e−n/c)=½; where n is the number of molecules and c is the number of containers, or n/c is 0.693. Quantitative partitioning is assumed, and the dynamic range is governed by the number of containers available for stochastic separation. Once the molecules are partitioned, high efficiency PCR detection gives the yes/no answer and absolute counting enabled. To vary dynamic range, micro-fabrication can be used to substantially increase the number of containers (5). Similarly, in stochastic labeling, the same statistical conditions are met when P0=(1−e−n/m)=½; where m is the number of labels, and one half of the labels will be used at least once when n/m=0.693. The dynamic range is governed by the number of labels used, and the number of labels can be easily increased to extend the dynamic range. The number of containers in digital PCR plays the same role as the number of labels in stochastic labeling and by substituting containers for labels we can write identical statistical equations. Using the principles of physical separation, digital PCR stochastically expands identical molecules into physical space, whereas the principle governing stochastic labeling is identity based and expands identical molecules into identity space.
New methods and compositions for single molecule counting employing the use of stochastic labeling are disclosed herein. In preferred aspects, a diverse set of labels is randomly attached to a population of identical molecules is converted into a population of distinct molecules suitable for threshold detection. Random attachment as used herein refers to a process whereby any label can be attached to a given molecule with the same probability. To demonstrate stochastic labeling methods experimentally the absolute and relative number of selected genes were determined after stochastically labeling 360,000 different fragments of the human genome. The approach does not require the physical separation of molecules and may take advantage of highly parallel methods such as microarray and sequencing technologies to simultaneously count absolute numbers of multiple targets. In some embodiments, stochastic labeling may be used for determining the absolute number of RNA or DNA molecules within single cells.
The methods disclosed herein may be used to take quantitative measurements of copies of identical molecules in a solution by transformation of the information to a digital process for detecting the presence of different labels. The stochastic properties of the method have been measured, and the relative and absolute digital counting of nucleic acid molecules is demonstrated. The method is extremely sensitive, quantitative, and can be multiplexed to high levels. In some aspects a microarray-based detection method is used, but the method is extendable to many other detection formats.
In some aspects, the methods are based on probability theory, where the outcome of chemical reactions occurring between a set of labeling molecules and a set of target molecules is modeled and tested. When all of the molecules in a uniform mixture of fixed volume collide and react randomly, the chemical events follow a stochastic process governed in part by the molecule concentration of each species (D. T. Gillespie, The Journal of Physical Chemistry 81, 2340 (1977)).
Methods for analyzing genomic information often utilize a correlation between a measurement of the amount of material associated with a location. The location can be, for example, a feature of an array that contains a specific sequence that is known or can be determined or any type of solid support such as a bead, particle, membrane, etc. A common aspect to these methods is often hybridization of a target to be measured to a complementary probe attached to the solid support. The probe may be, for example, an oligonucleotide of known or determinable sequence, but may also be BACs, PACs, or PCR amplicons.
Because of the density of different features that can be obtained using synthesis methods such as photolithography, microarrays can be applied to high density applications. For example, at feature sizes of 1 micron square an array can have about 108 features per cm2. Within a feature, depending on the chemistry used for synthesis, the probes are spaced typically at about 10 nm spacing resulting in about 104 molecules in a micron2. At approximately full saturation about 10% of those probes are hybridized with target. There are then about 640 functional molecules in an array having 1 micron2 spacing between features (˜800 nm2 functional area). This relatively small number of functional molecules in a feature limits the dynamic range for estimating relative concentration from hybridization signal intensity.
Methods are disclosed herein to overcome the dynamic range limitations observed with small feature sizes and small numbers of molecules on the array surface, by using a counting or digital readout as a substitute for the typical analog signal resulting from array hybridization.
Methods that use signal intensity to estimate relative concentrations of targets typically label the targets with a detectable label, often after an amplification step, and through hybridization of the labeled target to the probe, the probe and thus the feature is also labeled. The amount of label is detected and correlated with a measurement of the amount of target in the sample. The estimate of amount of a given target in a sample is typically relative to other targets in the sample or to previously obtained measurements and may be based on comparison to targets present in the sample at known or expected levels or to controls within the sample. This type of analysis can and has been used successfully, for example, to estimate genomic copy number to detect copy number variation in individuals or in cell populations (see, for example, Pinkel & Albertson, Annu. Rev. Genomics Hum. Genet. 6, 331-354 (2005), Lucito et al. Genome Res. 13, 229102305 (2004), Sebat et al. Science 305, 525-528 (2004), Zhou et al., Nat. Biotechnol. 19, 78-81 (2001) and Zhao et al. Cancer Res. 65, 5561-5570 (2005) and US Patent Pub. Nos. 20040157243 and 20060035258) or to estimate gene expression levels (see, for example, Lockhart et al., Nat. Biotechnol. 14:1675-1680 (1996), and Wodicka et al., Nat. Biotechnol. 15:1359-1367 (1997)).
Correlating intensity of hybridization signal or signal intensity with concentration of target molecules has limitations and can typically provide only an estimate of the absolute amount of a target, and may not be an accurate count of the actual amount of target present. The estimate may be an under or over estimate, particularly when comparing different targets or different samples. This is the result of many different factors, including but not limited to, differences between probes, feature specific effects, sample specific effects, feature size (as it decreases the ability to correlate accurately decreases) and experimental variation. Much of this variation can be addressed by data analysis methods, but the methods do not provide counting of individual molecules or events and are therefore subject to estimation errors.
In preferred aspects methods are disclosed for attaching a different label-tag sequence to each molecule of a particular target sequence or more preferably a collection of target sequences of interest. For example, a sample having 100 molecules of target type 1 is mixed with an excess, for example, 1000 different label-tag sequences, forming a library of label-tag sequences under ligation conditions. Multiple copies of the library of label-tag sequences are added so there are preferably many copies of each label-tag. Different label-tag sequences from the library are appended to each of the 100 target molecules so that each of the 100 molecules of the first target sequence has a unique label-tag sequence appended thereto. This results in 100 different target-label-tag combinations. The target-label-tag molecules may then be amplified to enrich the target-label-tag products relative to other non-targets. Amplification after labeling alters the absolute amount of the target, but because each occurrence in the original sample has been uniquely labeled this will not alter the count. The amplified target-label-tag products, whether amplified or not, can then be labeled with a detectable label, and hybridized to an array of probes. The features of the array that have target-label-tag hybridized thereto can be detected, for example, by labeling the hybridization complex with a fluorescent label and detecting the presence of signal at the features. In this example, because there are 1000 different labels possible and a single target being analyzed, there are 1000 different possible label-target sequences that might be generated so an array having a different feature for each of the 1000 different possibilities can be used. Assuming each target is labeled and no label is used twice, 100 of the 1000 different features should be detectable, indicating the corresponding label has been used.
Consider 1 copy of a target molecule in solution identified as t1. React this target against a set of 10 labels, Lm={l1, l2, . . . l10}. Each label has a 0.1 probability of being chosen. Next consider multiple copies of the target, tn, reacted against the set of Lm (assume non-depelting reservoir of labels). For simplicity, consider 3 copies of t: t1, t2 and t3. Target t1 will choose a label, t2 has a 0.9 probability of choosing a different label, t3 has a predictable probability of choosing the same label as t1 or t2. For n copies choosing from m labels, outcomes can be modeled by the binomial distribution as discussed above. For 3 targets and 10 labels, the probability of a label not being chosen, P0 is (1−(1/10))3=0.729. The probability P1 of being chosen exactly once is (3/10)(1−(1/10))2=0.243. The probability of being chosen twice, P2 is 0.027 and the probability P3 of being chosen 3 times is 0.001. Since P0 is the probability of not being chosen, 1−P0 is the probability of being chosen at least once. We define k=m(1−P0) as the number of labels we expect to see in an experiment. Conversely, if we know m, and observe k we can solve for the number of molecules. In the previous example where n=3 and m=10 we expect to see 10(1−P0) or 2.71 labels as our most probable outcome. Increasing m dramatically increases our counting efficiency, accuracy and dynamic range, e.g. for m=1,000, k(number of labels expected for n=10, k=9.96, for n=20, k=19.8.
Once the target molecules are labeled with the counter they can be amplified freely without impacting the counting since the readout is either yes, indicating detection or no indication not detected. In one aspect, a simple detector having m elements for each target sequence can be constructed. The detector may be an array. An array having 108 features or elements could assay 105 different targets using 103 different labels, for example. Other detection methods do not require individual elements for each counter, for example, sequencing.
In preferred aspects the “counter library” or “label-tag library” has approximately the same number of copies of each label-tag in the library. The label-tag sequences are not target specific, but are like the tags that have been used for other tagging applications, for example, the Affymetrix GENFLEX tag array. Preferably all label-tags in a set of label-tags will have similar hybridization characteristics so that the label-tags of the set can be detected under similar conditions.
For each target there are a series of features on the array, preferably one feature for each label-tag. In each of these features the portion of the probe that hybridizes to the target (or target complement) is the same but the label-tag complement is different in each feature. For example, to detect a first target RNA, “RNA1”, there would be a series of features each having a different probe (RNA1-tag1, RNA1-tag2 . . . RNA1tagN). For each target to be detected there is a similar set of features, e.g. RNA2-tag1, RNA2-tag2, . . . RNA2-tagN. The set of label-tags is N tags and it is the unique combination of the label-tag with the target sequence that creates a novel sequence to be detected, for example, by hybridization.
Label-tag attachment to individual targets is a stochastic process whereby the probability of any given label-tag being attached to any target is stochastic. There is a random selection of label-tags by attaching the label-tags to the end of a known target sequence in a sequence independent manner. The label-tag is attached without requirement for it to hybridize to any portion of the target so there is no or minimal bias as to which label-tag sequence is attached. Individual molecules all look the same for the purpose of attachment of the label-tag.
The label-tag may be attached to the target by any method available. In one embodiment, the label-tag is attached by ligation of the label-tag to one of the ends of the target. In preferred aspects the probes of the array are complementary to a predicted junction between target and label so it is preferable that the labels are attached to all occurrences of a target at the same position. This is facilitated if the termini of each occurrence of a selected target are the same and are known. In one aspect, target occurrences are fragmented with a restriction enzyme so that defined ends of known sequence are formed.
After label-tag attachment in some embodiments the target-label-tag segment is amplified. Attachment of universal primers to either end followed by PCR amplification is one method for amplifying. The universal primers may be added along with the label or at a subsequent ligation step.
For RNA targets an RNA ligase, such as T4 RNA ligase may be used. T4 RNA ligase 1 catalyses the ligation of a 5′ phosphryl-terminated nucleic acid donor to a 3′ hydroxyl-terminated nucleic acid acceptor. Substrates include single-stranded RNA and DNA. See, for example, Romaniuk, P. and Uhlenbeck, O. (1983) R. Wu, L. Grossman and K. Moldave (Eds.), Methods Enzymol., 100, pp. 52-56. New York: Academic Press and Moore, M. J. and Sharp, P. A. (1992) Science, 256, 992-997. RNA targets may also be circularized and used as template for rolling circle amplification using an enzyme having reverse transcriptase activity. T4 RNA ligase 1 may be used for circularization of RNA by ligating the ends of the molecule together. T4 RNA ligase 1 can also be used to ligated RNA to DNA.
Full-length mRNA can be selected by treating total or poly(A) RNA with calf intestinal phosphatase (CIP) to remove the 5′ phosphate from all molecules which contain free 5′ phosphates (e.g. ribosomal RNA, fragmented mRNA, tRNA and genomic DNA). Full-length mRNAs are not affected. The RNA can them be treated with tobacco acid pyrophosphatase (TAP) to remove the cap structure from the full-length mRNA leaving a 5′-monophosphate. A synthetic RNA adapter can be ligated to the RNA population. Only molecules containing a 5′-phosphate, (i.e. the uncapped, full-length mRNAs) will ligate to the adapters. Preferably the adapter has a variable label sequence, and may also have a constant sequence for priming. Preferably, the constant sequence is 5′ of the variable sequence. In some aspects, the adapter ligated mRNA may then be copied to form a first strand cDNA by, for example, random priming or priming using oligo dT. The cDNA may subsequently be amplified by, for example, PCR.
T4 RNA ligase may also be used for ligation of a DNA oligo to single stranded DNA. See, for example, Troutt et al., (1992) Proc. Natl, Acad. Sci. USA, 89, 9823-9825.
In other aspects, the ligated target-label-tag molecule may be enriched in the sample relative to other nucleic acids or other molecules. This enrichment may be, for example, by preferentially amplifying the target-label-tag methods, using for example, a DNA or RNA polymerase, or by degrading non target-label-tag molecules preferentially.
In one aspect, the target-label-tag molecule may be nuclease resistant while the unligated target and unligated label molecules may be nuclease sensitive. A nuclease can be added to the sample after ligation so that ligated target-label-tag molecules are not digested but non-ligated molecules are digested. For example, the targets may be resistant to a 5′ exonuclease (but not a 3′ exonuclease) while the labels are resistant to a 3′ exonuclease but not a 5′ exonuclease. Ligating target to label generates a molecule that is resistant to 5′ and 3′ exonuclease activity. After ligation the sample may be treated with a 5′ exonuclease activity, a 3′ exonuclease activity or both 5′ and 3′ exonuclease activities. For examples of nucleases see Rittie and Perbal, J. Cell Commun. Signal. (2008) 2:25-45, which is incorporated by reference (in particular see Table 5). Exo VII, for example degrades single stranded DNA from both the 5′ and 3′ ends so the sample could be treated with Exo VII after ligation to degrade molecules that are not ligation products.
In another aspect amplification may include a rolling circle amplification (RCA) step. See for example, Baner et al. (1998) NAR 26:5073, Lizardi et al. (1998) Nat. Genet. 19:225, Fire and Xu, (1995) PNAS 92:4641-5, Zhao et al. Angew Chem Int Ed Engl. 2008; 47:6330-6337 and Nilsson et al. (2008), Trends in Biotechnology, 24:83-88. The targets may be ligated so that they have a label and a universal priming (UP) sequence attached to the 5′ end of the targets. The UP-label-target is then ligated to form a circle. A primer complementary to the UP is then hybridized to the circles and extended using a strand displacing polymerase. The resulting amplification product contains multiple copies of the complement of the circle, UP-target-L.
In another aspect, targets may be labeled in a copying step. For example, a primer having a 3′ target specific region and a 5′ variable label region may be hybridized to the targets, either RNA or DNA, and extended to create a single complimentary copy of the target. Each extension product will have a different label and the junction between the label and the target specific region is known. The extension may be performed in the presence of nuclease resistant nucleotides so that the extension product is resistant to nuclease but the unextended primers are not. After extension the reaction is treated with a 3′-5′ exonuclease activity to digest unextended primer. Exonuclease I, for example, removes nucleotides from single stranded DNA in the 3′ to 5′ direction and Exo III removes nucleotides from the 3′ termini of duplex DNA. Exonuclease T (or RNase T) is a single-stranded RNA or DNA specific nuclease that requires a free 3′ terminus and removes nucleotides in the 3′ to 5′ direction. The extension products are then detected by hybridization to probes that are complementary to the primers and include the unique label portion and the constant target specific portion. If the target is RNA it can be digested with RNase H after extension. The extension product may also be amplified before hybridization.
In some aspects the probability that any two targets are labeled with the same label may be decreased by using two or more labeling steps. For example, a first labeling step where each target has a label selected from a set of labels followed by a second labeling set using the same set of labels. The first labeling event will be independent of the second so the probability that the first and second labeling events will both be the same in two independent targets is the product of the probability of two targets having the same label in either step. If there are N possible labels, and the first target is labeled first with label N1 and then with label N4, the probability that a second target will be labeled also with N1 and then N4 is 1/N2. So if there are 100 different labels, the probability that two targets will be labeled with the same label in the first round and the same label in the second round is 1/10,000.
In another aspect a first round of labeling may be done with 16 probes (for example, all possible 2 base combinations) and then a second round of labeling is done using the same 16 probes. The chance of any one probe attaching to a given target occurrence in the first round is 1 out of 16, the chance that the same probe will attach to the second target is 1/16 and the chance that the same two probes will attach is 1/16×1/16 or 1/256.
In another aspect reversible terminators are used to add a sequence to the end of each target being counted. For example, a 6 base sequence may be added and the chance of two being the same is 1 in 46 or 1 in 4096. See, for example, WO 93/06121 and U.S. Pat. No. 6,140,493 which disclose stochastic methods for synthesizing random oligomers.
There is a finite set of labels, L1-x and each target to be detected is present in the sample at a certain integer occurrence (T11-t1, T21-t2, . . . TN1-t2). In a preferred aspect, the method is used to count the number of each of the different targets, (e.g. how many occurrences of T1, how many of T2, . . . how many of TN) in the sample. The targets are independently labeled with the label molecules. Labeling is stochastic, so that any given target occurrence can be labeled with any one of the labels. For example, T1-1/L689, T1-2/L3, T1-3/L4,567 and so on. For Target 2, any given occurrence can also be labeled with any of the label molecules. This might generate, for example, (T2-1, L5), (T2-2, L198), (T2-3, L34) and so on. There are multiple copies of each label so T2-1 might be labeled with L5 and T1-500 may also be labeled with L5.
The methods disclosed herein may be used to measure random cell-to-cell variations in gene expression within an isogenic population of cells. Such variation can lead to transitions between alternative states for individual cells. For example, cell-to-cell variation in the expression of comK in B. subtilis has been shown to select cells for transition to the competent state in which genes encoding for DNA uptake proteins are expressed. See, Maamar et al. Science 317:526-529 (2007) which is incorporated herein by reference.
In some aspects the labels are generated within the target to be counted. For example, the label may be a unique cleavage site in a target fragment as shown in
In some aspects methods for selecting a collection of labels optimized for use in the disclosed methods is contemplated. For example, a list of all possible 14 mers may be used as a starting pool (414 is ˜268 million different sequences). Different label lengths can be used resulting in different numbers of starting sequences. Eliminate all labels that are not at least 50% GC content. Eliminate all labels that do not use each of the 4 possible nucleotides at least twice. Eliminate all labels that have more than two Gs or Cs in tandem, e.g. a probe with GGG or CCC would be eliminated, or with more than three As or Ts in tandem, e.g. AAAA or TTTT would be removed. Remove labels that contain a selected restriction site. Remove labels having a Tm that is outside of the range (38.5 to 39.5° C.). In other embodiments the range may be about 38 to 40, 38-39, or 39-40. Remove probes that have self complementarity beyond a selected threshold. Perform a hierarchical clustering to maximize sequence differences between labels to minimize cross hybridization, same label to same probe. Minimize self-complementarity within the collection to reduce tendency of two labels binding to each other.
After circularization, the uncircularized fragments can be digested using an exonuclease, for example. The circularized fragments can be amplified using target specific primers to generate amplification product 3113. In the figure the target specific primers are identified as TS primer F and TS primer R. Whereas the primers used to amplify 3111 are common to all adaptor ligated fragments and will amplify all fragments that are in the size range to be amplified using PCR, the TS primers are specific for selected targets to be analyzed. The amplification product 3113 has in the 5′ to 3′ direction, target specific sequence, overhang sequence, a first counter, first adaptor sequence, circularization junction 3115, second adaptor sequence, second counter, second overhang sequence and a second target specific sequence. The first and second counter are different (although they may be the same at a low probability) and the first and second target sequence are different. The product 3113 or preferably fragments thereof can be detected by a variety of methods, for example, an array of probes as exemplified by probe 3117 can be used. The array probe 3117 is complementary to a region of the target, the overhang region and the counter. When hybridized the target will have an overhanging single stranded region that corresponds to the adaptor sequence. A labeled probe 3119 that is complementary to one strand of the adaptor can be hybridized and the ligated to the array probe as shown, and as described below.
The left panel shows the results when target G1 ligated to label L1 to form G1Li hybridizes to the complementary G1L1 probe on the array. The constant region (in white) can hybridize to its labeled complement so that the 3′ end of the labeled complement is juxtaposed with the 5′ end of the L1 region of the probe on the array and the ends can be ligated. In the center panel the target hybridizing to the G1L1 probe is non-cognate, the label region is L2 and not L1 so it does not hybridize to the L1 region of the probe. The labeled oligo can hybridize to the partially hybridized target but it is not juxtaposed with the 5′ end of the L1 region of the probe so it should not ligate to the probe. In the right panel the target shown hybridized has the L1 region and is complementary to the array probe at that region, but the array probe has a G region that is not G1 so the G1L1 target does not hybridize. The labeled oligo can hybridize to the target but because the L1:L1 region is short the duplex is not stable and the labeled oligo does not ligate to the end of the array probe.
If you have N targets T (T1, T2, . . . TN) and each is present at a number of copies C (C1, C2, . . . Cx) where X varies from target to target (XT1, XT2, . . . XTN) and you ligate to a set of Y different labels (L1, L2, . . . LY) then you generate, for example, T1C1L1, T1C2L2, . . . TNCXLXT1, where X<<<Y). So, for example, if T1 is gene A and T2 is gene B and gene A is present in the sample at 500 copies and gene B is present at 100 copies, each copy of gene A, 1 to 500, will be attached to a different label (so there will be—500 different labels attached to the gene A copies), and each copy of gene B, 1 to 100, will be attached to a different label.
A method for counting the number of occurrences of each of a plurality of same targets in a mixture of targets comprising multiple occurrences of each type of a plurality of different targets. In preferred aspects, the mixture of targets is a nucleic acid sample that contains different amounts of multiple target sequences. For example, there may be target sequences 1, 2, 3, 4 and 5 that are expression products from 5 different genes, occur in the sample as follows: 1000 copies of target 1, 100 copies of target 2, 500 copies of target 3, 10 copies of target 4 and 50 copies of target 5. The targets are preferably of known sequence and are treated so that they may be ligated to a label-tag sequence.
The targets are mixed with a collection of label-tag sequences, each label-tag being a different sequence and the collection having a number that is preferably 10 times the number of copies of the most abundant target to be counted. In a preferred aspect, the label-tags are a collection of known sequences such as a collection of all possible 6mers (N6). Each of the label-tag sequences is present in multiple copies in the mixture, but all are present at approximately equal amounts. The label-tag sequences are ligated to the targets. Ligation is random so that any given label-tag has about the same probability of ligating to any one target occurrence. So if there are 1000 different targets each could be ligated to a different label-tag sequence and the probability that any two target occurrences will have the same label-tag ligated is low. Because the ligation is a random stochastic process there is a known probability that if there are C copies of a given target and N different label-tags that any two copies of a target T will have the same label.
T1, T2, . . . TN. C1, C2, . . . CX, L1, L2, . . . LY where T are the different targets and there are N different targets, C are the different copies of a target and there are X copies of that target and L are the different label label-tags and there are Y label tags. X varies for each target and determining X is one of the objects of the present invention. The relationship between X and Y determines the probability that two C's will have the same L. In preferred aspects Y is greater than X for each target to be counted. This reduces the probability of undercounting due to double labeling. If C1 and C2 of T1 are both labeled with L3 both copies will be counted as a single occurrence, resulting in under counting. Undercounting can also be adjusted for by estimating the number of copies that are likely to be multiply labeled and adjusting the final count upwards to take those into account. For example, if there is a likelihood that 5 of 1000 copies will be labeled with the same label tag then the final number should be adjusted up by 0.5%.
In preferred aspects, the detection is by hybridization to an array of probes. The array has a collection of features for each target that includes a different feature for each label tag. For example, if there are X label tags there are X features for each target, T1L1, T1L2, . . . T1LX and the same for target 2, T2L1, T2L2, . . . T2LX, out to TNL1, TNL2, . . . TNLX. The number of features of the array is on the order of X times N. Each probe has a target complementary sequence and a label tag complementary sequence. Within a set of probes for a given target the target segment of the probe would remain constant and the label tag portion varies from feature to feature so that each label tag sequence is represented by at least one feature for each target.
In one aspect, the methods may be used to count the number of copies of each of a plurality of targets in a sample. The amount of target containing sample mixed with the label tags may be diluted so that the number of copies of each target to be counted is less than the number of label tags. For example, if the targets to be counted are present at about 1,000 copies per cell and there are 10,000 label tags you want to have the amount of sample in the mixture to be about the equivalent of one cell's worth of RNA. You can mix that with multiple copies of each label-tag, but you want to keep the absolute number of copies of target below the number of types of label tag sequences. Dilution of the sample and use of an appropriately small amount of starting material may be used. If a target sequence is present at low copy number per cell it is possible to use the nucleic acid from a larger number of cells. For example, to measure the DNA copy number of a chromosomal region relative to other chromosomal regions the expected copy number is low (e.g. 2 for normal) so if there are 10,000 different label tags, the number of genomes that can be added to the sample for attachment of label tags can be high, e.g. 500 to 1000.
In one aspect, the methods are used to identify regions of genomic amplification and chromosomal abnormalities. For example, the methods may be used to detect trisomy. Most of the chromosomal regions will be present in 2 copies per cell and the region of trisomy will be present in 3 copies per cell. You would expect to observe a 3:2 ratio in your count. For example, if you have 500 genomes you would have 1000 copies of most regions and 1500 copies of the trisomy regions. Small errors in the counting, resulting from undercounting, would have little or no effect on the counting.
In some aspects, controls of known copy number may be spiked in to a sample to determine accuracy.
Stochastic labeling of t1,N (collection of essential identical molecules of copy 1, 2 . . . N of target 1) by L1,m (effectively an infinite reservoir of diversity m when m is much greater than N). This allows for complete or near complete resolution of members of t1,N, by imparting separate identities to the members of the collection of t1,N (provided that M is sufficiently smaller than N in the labeling). This provides for a stochastic or random projection of t1,N onto L1,m. In some aspects L1,m is a library and the members of the library that are associated with t1,N can be counted to determine the number of copies of the target. In some aspects the methods can be described as indexing the members of the target. This provides a method to follow individual molecules that are members of a type of molecule that would not otherwise be distinguishable one from another.
Because stochastic labeling can impart identifiability to otherwise non-identifiable molecules it can impart identifiability to any two targets that may be very similar, but different. Examples of targets that may be highly similar but could be separately counted using the disclosed methods, include, for example, alternative splice forms of a gene, and sequences that have one or more variations, including a variation in a single base (e.g. SNP or indels (insertion or deletions of short regions, e.g. 1-5 bases). In some aspects the methods impart a clonal labeling, that allows a single copy to be separately detected and separately isolated from the solution.
Some nucleic acid sequencing reactions use methods that stochastically attach targets to a solid support followed by amplification of the attached target and analysis. The target attaches in an unknown location and the location can be determined by sequencing the amplified target at specific locations. In contrast, the disclosed methods provide for clonal amplification of known targets in a known location. The stochastic nature of the formation of the target-label-tag molecule provides a mechanism for isolating single occurrences of selected targets that can be subsequently amplified and analyzed. In some aspects the label can be used as a handle for isolating clonal populations of targets. The labeling step generates an indexed library that has a variety of applications. For example, the indexed library could be used for sequencing applications. The method adds distinguishability to any set of molecules, even molecules that are not distinguishable by other mechanisms because they may share common regions or even been identical. The indexed library can be stored and used multiple times to generate samples for analysis. Some applications include, for example, genotyping polymorphisms, studying RNA processing, and selecting clonal representatives to do sequencing.
In some aspects the methods are used to stochastically label a polyclonal antibody population. This may be used to identify different polyclonal populations.
The methods may be used to convert an analog readout of hybridization signal intensities on arrays into a measurable process that can be scored digitally on the arrays. The method leverages a random process where the tagging of assayed molecules is governed by stochastic behavior. In a random process, the more copies of a given target, the greater the probability of being tagged with multiple labels. A count of the number of incorporated labels for each target can approximate the abundance level of a given target of interest. The ability to count labels on microarrays would be a clear cost-advantage over the other existing techniques.
Serial analysis of gene expression (SAGE) is another method for analysis of gene expression patterns. SAGE relies on short sequence tags (10-14 bp) within transcripts as an indicator of the presence of a given transcript. The tags are separated from the rest of the RNA and collected. The tags can be linked together to form long serial molecules that can be cloned and sequenced. Quantitation of the number of times a particular tag is observed provides an estimate of the relative expression level of the corresponding transcript, relative to other tagged transcripts. See, for example, Velculescu et al. Science 270, 484-487 (1995) and Velculescu et al. Cell 88 (1997). Again this method provides a relative estimate of the abundance of a transcript and not an actual count of the number of times that transcript appears. Other methods based on counting and estimating relative abundance have also been described. See, for example, Wang et al. Nat. Rev. Genet. 10, 57-63 (2009). Additional methods for digital profiling are disclosed, for example, in U.S. Patent Pub. 20050250147 and U.S. Pat. No. 7,537,897.
A stochastic counting assay system as described herein can also be a sub-system within a much larger bio-analysis system. The bio-analysis system could include all the aspects of sample preparation prior to, for example, optical detection, the post processing of data collected in the optical detection phase and finally decision making based on these results. Sample preparation may include steps such as: extraction of the sample from the tested subject (human, animal, plant environment etc.); separation of different parts of the sample to achieve higher concentration and purity of the molecules under investigation; sample amplification (e.g. through PCR); attachment of fluorescence tags or markers to different parts of the sample; and transfer of the sample or a portion of the sample into a reaction vessel or site on a substrate. The post processing of the collected data may include: normalization; background and noise reduction; and statistical analysis such as averaging over repeated tests or correlation between different tests. The decision making may include: testing against a predefined set of rules and comparison to information stored in external data-bases.
The applications and uses of the stochastic labeling and counting methods and systems described herein can produce one or more result useful to diagnose a disease state of an individual, for example, a patient. In one embodiment, a method of diagnosing a disease comprises reviewing or analyzing data relating to the presence and/or the concentration level of a target in a sample. A conclusion based review or analysis of the data can be provided to a patient, a health care provider or a health care manager. In one embodiment the conclusion is based on the review or analysis of data regarding a disease diagnosis. It is envisioned that in another embodiment that providing a conclusion to a patient, a health care provider or a health care manager includes transmission of the data over a network.
Accordingly, business methods relating to the stochastic labeling and counting methods and methods related to use thereof as described herein are provided. One aspect of the invention is a business method comprising screening patient test samples for the amount of a biologically active analyte present in the sample to produce data regarding the analyte, collecting the analyte data, providing the analyte data to a patient, a health care provider or a health care manager for making a conclusion based on review or analysis of the data regarding a disease diagnosis or prognosis or to determine a treatment regimen. In one embodiment the conclusion is provided to a patient, a health care provider or a health care manager includes transmission of the data over a network.
Applications for the disclosed methods include diagnosing a cancerous condition or diagnosing viral, bacterial, and other pathological or nonpathological infections, as described in U.S. Pat. No. 5,800,992. Additional applications of the disclosed methods and systems include, pathogens detection and classification; chemical/biological warfare real-time detection; chemical concentration control; dangerous substance (e.g., gas, liquid) detection and alarm; sugar and insulin levels detection in diabetic patients; pregnancy testing; detection of viral and bacterial infectious diseases (e.g. AIDS, Bird Flu, SARS, West Nile virus); environmental pollution monitoring (e.g., water, air); and quality control in food processing.
Any available mechanism for detection of the labels may be used. While many of the embodiments discussed above use an array readout form, it will be obvious to one of skill in the art that other methods for readout may be used. For example, sequencing may be preferred in some embodiments.
In some aspects the readout is on an array. The array may be a solid support having immobilized nucleic acid probes attached to the surface in an ordered arrangement. The probes may be, for example, synthesized in situ on the support in known locations using photolithography or the probes may be spotted onto the support in an array format. As discussed above, in some embodiments the array includes a probe feature for each possible label-target combination. A feature preferably includes many copies of a single probe sequence. The feature may also have some probes that are not full length, resulting from truncation of synthesis. The photo activation process may not be 100% efficient so some probes are terminated at each step without having subsequent bases added. These truncated probes have the sequence of a portion of the full length probe.
Sequencing readout. After attachment of the labels to the targets in a stochastic manner, the targets may be amplified according to any of the methods disclosed herein and the amplification product may be subjected to any available sequencing method.
A number of alternative sequencing techniques have been developed and many are available commercially. For a review see, for example, Ansorge New Biotechnology 25(4):195-203 (2009), which is incorporated herein by reference. These include the use of microarrays of genetic material that can be manipulated so as to permit parallel detection of the ordering of nucleotides in a multitude of fragments of genetic material. The arrays typically include many sites formed or disposed on a substrate. Additional materials, typically single nucleotides or strands of nucleotides (oligonucleotides) are introduced and permitted or encouraged to bind to the template of genetic material to be sequenced, thereby selectively marking the template in a sequence dependent manner. Sequence information may then be gathered by imaging the sites. In certain current techniques, for example, each nucleotide type is tagged with a fluorescent tag or dye that permits analysis of the nucleotide attached at a particular site to be determined by analysis of image data.
In another aspect, mass spec analysis may be used to detect the labels and count the targets. The labels can be distinguishable based on size or other property that can be detected. Many of the examples provided herein identify the label based on unique nucleic acid sequence but any distinguishable label may be used, for example, the pool of labels may be labels that are differentially detectable based on fluorescence emission at a unique wavelength.
The left panel shows the results when target G1 ligated to label L1 to form G1L1 hybridizes to the complementary G1L1 probe on the array. The constant region (in white) can hybridize to its labeled complement so that the 3′ end of the labeled complement is juxtaposed with the 5′ end of the L1 region of the probe on the array and the ends can be ligated. In the center panel the target hybridizing to the G1Li probe is non-cognate, the label region is L2 and not L1 so it does not hybridize to the L1 region of the probe. The labeled oligo can hybridize to the partially hybridized target but it is not juxtaposed with the 5′ end of the L1 region of the probe so it should not ligate to the probe. In the right panel the target shown hybridized has the L1 region and is complementary to the array probe at that region, but the array probe has a G region that is not G1 so the G1L1 target does not hybridize. The labeled oligo can hybridize to the target but because the L1:L1 region is short the duplex is not stable and the labeled oligo does not ligate to the end of the array probe.
The methods are broadly applicable to counting a population of molecules by performing a stochastic operation on the population to generate a stochastic population of identifiable molecules. The targets need not be identical. For example, the methods may be used to count the absolute number of members of a group. In one aspect, a sample having an unknown number of copies of a selected nucleic acid target is fragmented randomly so that on average each copy of the target has a different end resulting from a distinct fragmentation event. A common adaptor sequence can be ligated to the end of each fragment and used for amplification of the fragments. Each ligation event generates a new molecule having a junction formed by the end of the random fragment and the adaptor sequence. The new junction can be detected by, for example, sequencing using a primer complementary to the adaptor or a region of the adaptor. Because the fragmentation was a stochastic process the number of different ends detected is a count of the number of different starting target molecules, assuming one fragment per starting target molecule.
The examples provided herein demonstrate the concept of using a stochastic labeling strategy in the high sensitivity detection and counting of individual DNA molecules. The difficult task of quantifying single nucleic acid molecules is converted into a simple qualitative assay that leverages the statistics of random probability; and at the same time, the requirement of single molecule detection sensitivity is achieved with PCR for the robust amplification of single DNA molecules. In some aspects improved methods for amplification will be used. For example, linear amplification methods may be used to mitigate the representation distortions created by exponential cycling in PCR. Given the lack of available techniques for single molecule counting, and the increasing need for its use, the new concept of stochastic labeling is likely to find numerous applications in the near future.
To demonstrate stochastic labeling, we performed an experiment to count small numbers of nucleic acid molecules in solution. Genomic DNA from a male individual with Trisomy 21 was used to determine the absolute and relative number of DNA copies of chromosomes X, 4 and 21, representing 1, 2 and 3 target copies of each chromosome, respectively. Genomic DNA isolated from cultured B-Lymphocytes of a male caucasion with Trisomy 21 was purchased from The Coriell Institute for Medical Research (Catalog #GM01921). The DNA quantity was determined by PICOGREEN dye (Invitrogen) measurements using the lambda phage DNA provided in the kit as reference standard. DNA quality was assessed by agarose gel electrophoresis.
The DNA concentration in the stock solution was measured by quantitative staining with picogreen fluorescent dye, and dilutions containing 3.62 ng, 1.45 ng, 0.36 ng and 0.036 ng were prepared. In each dilution, the number of copies of target molecules in the sample was calculated from a total DNA mass of 3.5 pg per haploid nucleus (see, T. R. Gregory et al., Nucleic Acids Res 35, D332 (2007), and represent approximately 500, 200, 50 and 5 haploid genomes. The absolute quantity of DNA in the sample was determined by optical density measurements and quantitative staining with PICOGREEN fluorescent dye (Invitrogen) prior to making dilutions.
As outlined in
The adaptors have a 5′ overhang of 11 bases in the even numbered SEQ IDs and 4 bases (GATC) in the odd numbered SEQ IDs. Oligonucleotides were synthesized (Integrated DNA Technologies) and annealed to form double-stranded adaptors prior to pooling. For ligation, the digested DNA was diluted to the desired quantity and added to 100 pmoles (equivalent to 6×1013 molecules) of pooled label-adaptors, and 2×106 units (equivalent to 1×1013 molecules) of T4 DNA ligase (NEB) in a 30 μl reaction. The reaction was incubated at 20° C. for 3 hours until inactivation at 65° C. for 20 minutes.
For the stochastic labeling reaction, each DNA fragment-end randomly attaches to a single label by means of enzymatic ligation of compatible cohesive DNA ends to generate labeled fragments 1907. High coupling efficiency is achieved through incubation with a large molar excess of labels and DNA ligase enzyme (˜1013 molecules each). At this stage, the labeling process is complete, and the samples can be amplified as desired for detection. A universal primer may be added added, and the entire population of labeled DNA fragments may be PCR amplified. The PCR reaction preferentially amplifies approximately 80,000 fragments in the 150 bp-2 kb size range (
The purified PCR product was denatured at 95° C. for 3 minutes prior to phosphorylation with T4 polynucleotide kinase (NEB). The phosphorylated DNA was ethanol precipitated and circularized using the CIRCLIGASE II ssDNA Ligase Kit (Epicentre). Circularization was performed at 60° C. for 2 hours followed by 80° C. inactivation for 10 minutes in a 40 μl reaction consisting of 1× CIRCLIGASE II reaction buffer, 2.5 mM MnCl2, 1M betaine, and 200U CIRCLIGASE II ssDNA ligase. Uncirculated DNAs were removed by treatment with 20U Exonuclease I (Epicentre) at 37° C. for 30 minutes. Remaining DNA was purified with ethanol precipitation and quantified with OD260 measurement.
Amplification of gene targets. Three assay regions were tested: One on each of chromosomes 4, 21 and X. The genomic location (fragment starting and ending positions are provided), of the selected fragments are as follows: Chr4 106415806_106416680 (SEQ ID No. 1), Chr21 38298439_38299372 (SEQ ID No. 2), and ChrX 133694723_133695365 (SEQ ID No. 3). The lengths are 875, 934 and 643 bases respectively. The circularized DNA was amplified with gene specific primers (SEQ ID Nos. 4-9) in a multiplex inverse PCR reaction. PCR primers were picked using Primer3 (available from the FRODO web site hosted by MIT) to yield amplicons ranging between 121 and 168 bp. PCR was carried out with 1× TITANIUM Taq PCR buffer (Clontech), 0.3 mM dNTPs, 0.4 μM each primer, 1× TITANIUM Taq DNA Polymerase (Clontech), and ˜200 ng of the circularized DNA. After denaturation at 94° C. for 2 minutes, reactions were cycled 30 times as follows: 94° C. for 20 seconds, 60° C. for 20 seconds, and 68° C. for 20 seconds, with a 68° C. final hold for 4 minutes. PCR products were assessed on a 4-20% gradient polyacrylamide gel (Invitrogen) and precipitated with ethanol.
The amplified DNA was fragmented with DNase I, end-labeled with Biotin, and hybridized to a whole-genome tiling array which spans the entire non-repetitive portion of the genome with uniform coverage at an average probe spacing of ˜200 nt (see Matsuzaki et al., Genome Biol 10, R125 (2009) and Wagner et al. Systematic Biology 43, 250(1994)). Probe intensity (“Array Intensity”) from the whole-genome tiling array (y-axis) is grouped into 200 nt bins by the length of the BamHI fragment on which it resides. High probe intensity demonstrates the amplification of fragments in the 600 bp˜ 1.2 kb size range (x-axis, log-scale). The computed size distribution of BamHI restricted fragments in the reference genome sequence (NCBI Build 36) is shown by the curve labeled “Number of Fragments”. After circularization of the amplified products to obtain circles 1909, three test target fragments were isolated using gene-specific PCR; one on each of chromosomes X, 4, and 21, and prepared for detection.
The three labeled targets were counted using two sampling techniques: DNA microarrays and next-generation sequencing. For the array counting, a custom DNA array detector capable of distinguishing the set of labels bound to the targets was constructed by dedicating one array element for each of the 960 target-label combinations. Each array element consists of a complementary target sequence adjacent to one of the complements of the 960 label sequences (as shown in
Array Design: For each gene target assayed, the array probes tiled consist of all possible combinations of the 960 counter sequences connected to the two BamHI genomic fragment ends (
Arrays were synthesized following standard Affymetrix GENECHIP manufacturing methods utilizing lithography and phosphoramidite nucleoside monomers bearing photolabile 5′-protecting groups. Array probes were synthesized with 5′ phosphate ends to allow for ligation. Fused-silica wafer substrates were prepared by standard methods with trialkoxy aminosilane as previously described in Fodor et al., Science 251:767 (1991). After the final lithographic exposure step, the wafer was de-protected in an ethanolic amine solution for a total of 8 hrs prior to dicing and packaging.
Hybridization to Arrays: PCR products were digested with Stu I (NEB), and treated with Lambda exonuclease (USB). 5 μg of the digested DNA was hybridized to a GeneChip array in 112.5 μl of hybridization solution containing 80 μg denatured Herring sperm DNA (Promega), 25% formamide, 2.5 pM biotin-labeled gridding oligo, and 70 μl hybridization buffer (4.8M TMACl, 15 mM Tris pH 8, and 0.015% Triton X-100). Hybridizations were carried out in ovens for 16 hours at 50° C. with rotation at 30 rpm. Following hybridization, arrays were washed in 0.2×SSPE containing 0.005% Trition X-100 for 30 minutes at 37° C., and with TE (10 mM Tris, 1 mM EDTA, pH 8) for 15 minutes at 37° C. A short biotin-labeled oligonucleotide (see 3119 in
In order to maximize the specificity of target-label hybridization and scoring, we employed a ligation labeling procedure on the captured sequences (
Sampling error calculation. A sampling error can be introduced when preparing dilutions of the stock DNA solution. This error is a direct consequence of random fluctuations in the number of molecules in the volume of solution sampled. For example, when exactly 10 μl of a 100 μl solution containing 100 molecules is measured, the actual number of molecules in the sampled aliquot may not be exactly 10. The lower the concentration of the molecules in the entire solution, the higher the sampling error, and the more likely the actual abundance in the sampled aliquot will deviate from the expected abundance (n=10). To calculate sampling errors, we determined the number of molecules for each chromosome target in our stock DNA solution and performed numerical simulations to follow our dilution steps in preparing the test samples (3.62 ng, 1.45 ng, 0.36 ng and 0.036 ng). To illustrate, if the dilution step is sampling 1 μl of a 25 μl solution containing 250 molecules, we create 25 bins and randomly assign each of the 250 molecules into one of the bins. We randomly choose one bin and count the number of molecules assigned to that bin to simulate the process of sampling 1/25th of the entire solution. If a serial dilution was performed, we would repeat the simulation process accordingly. For each dilution, the observed copy number ratios of Chr 4:X or 21:X for 10,000 independent trials are summarized as observed medians, along with the 10th and 90th percentiles and shown in
As an alternate form of detection, the samples were submitted to two independent DNA sequencing runs (
Validation by DNA sequencing (First SOLID run). DNA targets that were used for hybridization to arrays were converted to libraries for sequencing on the SOLID instrument (ABI). P1 and P2 SOLID amplification primers were added to the DNA ends through adaptor ligation and strand extension from gene-specific primers flanked by P1 or P2 sequences (
Sequencing replication (Second SOLID run). An aliquot of the exact same DNA library originally sequenced by Cofactor Genomics was subsequently re-sequenced by Beckman Coulter Genomics. Approximately 50 million 35 base reads were generated, and processed following the same rules. Approximately 61% of the raw reads passed quality filters, of which 81% uniquely mapped to a reference sequence with a maximum tolerance of 3 color mismatches (An adjusted mismatch tolerance was applied in the alignment step to account for the shorter length of these reads). Of the mapped reads, 91% (22.5 million) are of high mapping quality, i.e., with perfect match in the sample encoder and at most 1 mismatch in the label sequence. These high-quality reads (45% of the total raw reads generated) were used for counting analysis.
Between several hundred thousand to several million reads were used to score the captured labels. Table 1 shows the number of mapped reads from SOLID DNA sequencing.
We set thresholds for the number of sequencing reads observed for each label, and score a label as “present” and counted if the number of sequencing reads exceeded the threshold. Label usage summaries from experimental observations or from the stochastic modeling are shown in Table 2. The number of attached labels, k, detected for each target in each dilution either by microarray counting or sequence counting is presented in Table 2, and plotted in
Several dilutions (3.62 ng, 1.45 ng, 0.36 ng and 0.036 ng) of DNA isolated from cultured of a Trisomy 21 male individual were processed for microarray hybridization (
The counting results span a range of approximately 1,500 to 5 molecules, and it is useful to consider the results in two counting regimes, below and above 200 molecules. There is a striking agreement between the experimentally observed number of molecules and that expected from dilution in the first regime where the ratio of molecules to labels (n/m)<0.2 (Table 2). Below 200 molecules the data are in tight agreement, including the data from the lowest number of molecules, 5, 10 and 15 where the counting results are all within the expected sampling error for the experiment (The sampling error for 10 molecules is estimated to be 10±6.4, where 10 and 6.4 are the mean and two standard deviations from 10,000 independent simulation trials).
In the second regime above 200 molecules, there is an approximate 10-25% undercounting of molecules, increasing as the number of molecules increases. We attribute this deviation to be due to a distortion in the amplification reaction. PCR-introduced distortion occurs from small amounts of any complex template due to the differences in amplification efficiency between individual templates (2, 3). In the present case, stochastic labeling will produce only one (at low n m ratios), and increasingly several copies (at higher n m ratios) of each template. Modeling suggests that simple random dropout of sequences (PCR efficiencies under 100%) generate significant distortion in the final numbers of each molecule after amplification. At any labeling ratio, random dropout of sequences due to PCR efficiency will result in an undercount of the original number of molecules. At high n m ratios, the number of labels residing on multiple targets will increase and have a statistical survival advantage through the PCR reaction causing greater distortion. In support of this argument, we observe a wide range of intensities on the microarray and a wide range in the number of occurrences of specific sequences in the sequencing experiments (
The lymphoblast cell line used in this study provides an internal control for the relative measurement of copy number for genes residing on chromosomes X, 4 and 21.
Overall, the identity of labels detected on the microarrays and in sequencing are in good agreement, with only a small subset of labels unique to each process (Table 7). Despite a high sequencing sampling depth (Table 1), a small number of labels with high microarray intensity appear to be missing or under-represented in the sequencing results. In contrast, labels that appear in high numbers in the sequencing reaction always correlate with high microarray intensities. No trivial explanation could be found for the labels that are missing from any given sequencing experiment. While under-represented in some experiments, the same labels appear as present with high sequence counts in other experiments, suggesting that the sequences are compatible with the sequencing reactions.
PCR validation. We used PCR as an independent method to investigate isolated cases of disagreement, and demonstrated that the labels were present in the samples used for the sequencing runs.
PCR was used to detect the presence of 16 label sequences (Table 3) which were either observed as high or low hybridization intensity on microarrays, and observed with either high or low numbers of mapped reads in SOLID sequencing. The Chr4 gene target was PCR amplified with 3 dilutions (0.1 pg, 1 pg, and 10 pg) of the 3.62 ng NA01921 sample, using the DNA target that was hybridized to microarrays, or the prepared SOLID library template. PCR products were resolved on 4% agarose gels and fluorescent DNA bands were detected after ethidium bromide staining
Table 3. PCR detection for the presence of label sequences in the processed DNA sample that was hybridized to microarray, or in the DNA sequencing library. Each PCR contained 0.1 pg of template, which represents approximately 1×106 DNA molecules. The number of mapped sequencing reads and the microarray intensity of each of the 16 labels for this selected gene target (Chr4, 3.62 ng) are listed. The last 2 columns show the gel lane number containing the indicated sample. Those numbers indicated by an * correspond to reactions where PCR failed to detect the label sequence in the sample.
Although we can clearly confirm their presence in the sequencing libraries, it is unclear as to why these labels are missing or under-represented in the final sequencing data.
To test the stochastic behavior of label selection, we pooled the results of multiple reactions at low target concentrations (0.36 and 0.036 ng), where the probability that a label will be chosen more than once is small.
Furthermore, since each end of a target sequence chooses a label independently, we can compare the likely hood of the same label occurring on both ends of a target at high copy numbers. Table 4 columns 10-11 present the experimentally observed frequency of labels occurring in common across both ends of a target and their expected frequency from numerical simulations. No evidence of non-stochastic behavior was observed in this data.
Labels detected on microarray experiments are quantified in Table 4. Indicated quantities (col. 2) of genomic DNA derived from a Trisomy 21 male sample were tested on 3 chromosome targets (col. 1). The estimated number of copies of target molecules (or haploid genome equivalents, col. 3), the number of labels expected by the stochastic model (col. 4), and the actual number of labels detected on microarrays (col. 6-8) are summarized. Because each gene target fragment paired-end consists of random, independent label ligation events at the left (L) and the right (R) termini, the number of identical labels expected (col. 5) can be predicted from computer simulations, and compared to the number actually detected (col. 11). Given the number of labels detected (col. 8), we obtain the corresponding number of copies of target molecules (col. 9) in our stochastic model, and the predicted occurrences of identical labels across paired-ends (col. 10). The numbers in col. 5 and 10 are the means from 5,000 independent simulation runs along with one standard deviation of the corresponding means, given the number of labels at either end (col. 4 and col. 9).
The detailed column information for Table 4 is as follows: column 1: name of tested gene targets; column 2: estimated number of target molecules at either left or right end, this number is determined by PICOGREEN dye measurement (Molecular Probes, Inc.), the DNA concentration is also listed in this column; column 3: number of labels expected to be observed/used at either end (predicted by theoretical models), given the estimated number of target molecules in 2nd column; column 4: number of labels expected to be observed in common across the paired-ends (predicted by theoretical models), given the estimated number of target molecules in 2nd column; column 5: empirically observed number of labels used at the left end of gene target; column 6: empirically observed number of labels used at the right end of gene target; column 7: empirically observed number of labels used in common across the paired-ends; column 8: number of target molecules predicted by theoretical models, based on the empirically observed number of labels used (i.e., number in 7th column); column 9: number of labels expected to be observed in common across the paired-ends, given the number of target molecules in 8th column; column 10: empirically observed number of labels that were used in common across the paired-ends of the gene target.
Example X: An array was designed having 48 target sequences. Each target was paired with one of 3840 labels or “counters” for a total of 48×3840 or 184,320 probes. The probes were 30 mers (30 nucleotides in length) and the assay was designed to test whether or not the 30 mer imparts sufficient discrimination. Of the 30 bases, 15 bases are from the labels and the other 15 bases are derived from the targets. The probes were assayed to determine if each label-target combination hybridizes specifically. A phage RNA ligase was used to join labels with targets. Universal priming sites of 18 bases were included on the 5′ end of the labels and the 3′ end of the targets, facilitating PCR amplification of the joined label-targets. The method is diagramed in
The 3840 distinct label oligos (counters) were single stranded oligos pooled from the Ddel TACL primer panel (40 primer plates by 96 wells per plate for 3840 different oligos). An example label oligo 301 is shown
The 48 different primers used as “targets” were synthesized using as target 48 different 21 nucleotide sequences from the Affymetrix TrueTag 5K_A array. An example target oligo 307 is shown
The “label” or “counter” oligo has an 18 nt common sequence at the 5′ end and a 15-28 nt “label” (or “counter”) sequence at the 3′ end. An example “label” 305 is shown. The universal primer 303 common to all or a group of the label oligos has sequence
at the 5′ end and each target oligonucleotide has common sequence
at the 3′ end as universal priming sequence. The target oligos vary in sequence at the 5′ ends 309.
A 1:1 dilution of each of the 3840 counters was mixed with various dilutions of each of the 48 target oligos to simulate different expression levels under ligation conditions so that the 5′ end of the target oligos can be ligated to the 3′ end of the label oligos. In preferred aspects T4 RNA ligase may be used to join the ends of the single stranded oligos. The 5′ and 3′ ends of the target oligos are phosphorylated and the 5′ and 3′ ends of the label oligos are hydroxylated. After the ligation the products are amplified by PCR using primers to the universal priming sequences. Only those fragments that have both universal priming sequences 303 and 311 will amplify efficiently.
Each of the 48 target sequences may be tiled with each of the 3,840 counters, resulting in a total number of features on array=48×3,840=184,320. This is the number of different possible combinations of target with label. The product of the ligation and amplification reactions is hybridized to the array. For each target, the number of features that light up is determined and can be compared to the known copy number of each target in the input sample.
To test the digital counting methods, also referred to as stochastic labeling a collection of label-tag sequences was provided. Each has a common 5′ universal priming sequence, preferably 15-20 bases in length to facilitate amplification, and a 3′ label sequence, preferably 17-21 bases in length. Each type of primer in the collection has the same universal priming sequence but each type has a label sequence that is different from all of the other types in the collection. In one aspect there are about 4,000 to 5,000 different types of label sequences in the collection to be used. For testing the method, a set of 50 target sequences was synthesized. The target sequences each
was used in this experiment). Each of the 50 oligo target sequences that were generated has a different 21 base sequence from the GENFLEX array collection of sequences, for example,
The collection of label-tag oligos and the collection of target oligos was mixed. Various dilutions of the different targets were used in the mixture of targets to simulate a mixed population present at different levels, for example, different expression or copy number levels. T4 RNA ligase was added to ligate the label-tag oligos to the target oligos. There are 5000 different types of label oligos and 50 different types of target oligos so the majority of the target oligos of the same type will be labeled with a type of label oligo that is different from all of the other target oligos of that type. So target oligo type 1, occurrence 1 will be labeled with a label oligo type A (11A) and target oligo type 1, occurrence 2, will be labeled with a different label oligo, label oligo type B (12B). There is a finite and calculable probability that two or more occurrences of the same target type will be labeled with the same label oligo (11A and 12A), but that probability decreases as the number of different types of label oligos increases relative to the number of occurrences of any given type of target.
The ligated target/label oligos are then amplified using primers to the universal priming sites. Labels can be incorporated during amplification. The labeled amplification product is then hybridized to an array. For each different possible combination of target (50) and label (5000) there is a different probe on the array that targets that junction of the target ligated to the label. There will therefore be 50×5000 different probes on the array or 250,000 different probes.
Scanned images of the 48×3840 array were analyzed and compared to expected results. A total of 8 of the 48 targets were ligated to a pool of 3840 labels (counters). The assay was as shown in
Different ligation conditions were also tested by ligating either a single target or a pool of 48 targets to the 3,840 counters. The concentrations of the targets used in the experiment were high as in the previous experiment so most counters will be ligated to targets. In ligation 1 a single target was ligated to 3,840 labels. In ligation 2, 48 targets at 1:1 copy number were ligated to 3,840 labels. Ligation 3 is a negative control for PCR so no DNA was added. PCR with the pair of universal primers was performed using the ligation products as template and the products separated on a gel. As expected a band was observed from ligations 1 and 2, but not 3. The PCR products were labeled and hybridized to the array and the scan images after array hybridization were analyzed. As expected no signal was observed for ligation 3, all of the targets were observed for ligation 2 and the single expected target was observed for ligation 1. The single target lights up in the correct region of the chip, but background signal was also observed in unexpected locations. Increased stringency of hybridization conditions can be used to minimize hybridization to unexpected probes of the array.
In another example, conditions for optimization of hybridization to decrease cross hybridization were tested. The products used were as described above and hybridization was performed with formamide and with or without non-specific competitor (herring sperm DNA). The non-specific signal is significantly decreased in the presence of formamide, with and without non specific competitor. This demonstrates that even though the targets and counters alone have 15 bases of complementarity to probes on the array, the combination of target plus counter and the resulting increase to 30 bases of complementarity to the probes, results in specific hybridization. Within the block of 3,480 probes, there is heterogeneity in the hybridization intensity. Preliminary sequence analysis shows a strong correlation of GC content with high signals. To minimize this array probes may be selected to have similar melting temps for the counters or the target-counter combination may be optimized to obtain similar hybridization stabilities. For example, if two targets are to be analyzed the portions of each target that are to be part of the probe may be selected to have similar TMs.
To test the efficiency of T4 RNA ligase in the ligation of labels to targets, DNA ligase from E. coli was tested. This required a slight modification of the sample prep (as depicted in
The expected targets were observed, but some non-specific bands were also detected in the amplified DNA, even in the absence of the target. This suggests that the some of the 3,840 labels are combining with each other when this method is used. Selection of an optimized pool of labels may be used to mitigate such interference.
In another example random primed PCR was tested. Instead of a ligation step, the targets have a 3′ random region, that can be, for example, a degenerate region or an inosine region. The labels hybridize to the random region and the random region is used as a primer for extension through the label during the PCR step to append a copy of the label and the universal priming site at the 5′ end of the label oligo to the 3′ end of the target. The extended target has a copy of the label sequence and the universal priming sequence and can be amplified by PCR.
In another example, a purification method for removing excess un-ligated counters was tested. The method is shown schematically in
In
In another embodiment, illustrated schematically in
The probes of the array are complementary to the junction between the label and the restriction fragment. The sequences at the ends of the individual strands of the restriction fragments are predicted based on in silico digestion of the human genome. Also, fragments are targeted that are within the size range that is known to amplify efficiently by adaptor ligation PCR, for example, 200 bases to 2 kb. The adaptor 2201 had two segments, a constant priming region 2203 and a variable label region 2205. Together 2203 and 2205 form the label adaptor 2207. The primer 2209 has the same sequence 5′ to 3′ as the 2203. The schematic is drawn showing only one strand, but one of skill in the art would understand that in a preferred embodiment the genomic DNA is double stranded and the restriction fragments have two strands, which may be referred to as a top strand and a bottom strand. The convention is that the top strand is drawn 5′ to 3′ left to right and the bottom strand is the complement of the top strand and is drawn 3′ to 5′ left to right. Adaptors are preferably double stranded for at least a portion of the adaptor, they may have single stranded overhangs, for example to have “sticky ends” that facilitate hybridization and ligation to the overhang resulting from restriction digestion. In a preferred aspect, the same adaptor can be ligated to the two ends of a strand of a restriction fragment and may be ligated to one or both strands. The adaptor may be ligated to the ends of the top strand in opposite orientations as shown in
To test this method several adaptors were generated. The test adaptor has PCR002 (SEQ ID No. 1969) as top or sense strand and BamAdaAS (SEQ ID No. 1970) as bottom or antisense strand.
The single stranded region on the right is the BamHI single stranded overhang. Te adaptor also has a half Bgl II site. The “full length-label” adaptor has SEQ ID No. 1972 as top or sense strand and SEQ ID No. 1973 as bottom or antisense strand.
A 5′ phosphate may be added to one or both strands of the adaptor using, for example, T4 polynucleotide kinase. In some aspects a truncated adaptor may be used. An example of such an adaptor is shown in
In some aspects it is preferable to use shorter oligos. The full length adaptor in includes 87 bases. The truncated adaptor has 57 bases. Since 2 different oligos must be synthesized for each different label adaptor (e.g. 1,920 labels requires 3,840 different oligos) shorter adaptors are more economical. The separate oligos are preferably annealed together prior to being combined into a pool for ligation to fragments. The primer may be, for example, SEQ ID NO. 1969 or the 5′ 17 bases of SEQ ID No. 1974.
In another example, a truncated label adaptor was used (SEQ ID Nos. 1974 and 1975). The adaptor ligated fragments were extended to fill in the ends with polymerase prior to PCR. Hybridization was done in duplicate to either the CNV-type array or HG49 design C. Fragmented DNA and non-fragmented DNA were plotted. The intensity of the DNA that was not fragmented prior to hybridization is less than the intensity of the fragmented DNA. The peak of the intensity for both plots is at a fragment size of about 900 base pairs.
BamHI cuts the human genome into an estimated 360,679 fragments with a size distribution of 6 bp to 30,020,000 bp. The median size is 5142 bp and the mean is 8320 bp. There are 79,517 fragments in the size range of 150 bp to 2 kb. For testing it may be desirable to choose fragments that meet selected criteria, for example, within a selected size range, select fragments that have more than 1 probe on the HG49m array, exclude fragments that are in known CNV regions, or exclude fragments having a SNP in the first or last 20-30 bases.
The upper panel of
The array design for the experiment represented in
The lower panel shows the histogram of the intensity data corresponding to 960 specific labels. Also shown in the figure are the 2 fitted normal distributions, designated by red and green curves, respectively. The fitted distributions have the mean and standard deviation of 1447±680 and 12186±3580, respectively. The blue vertical line is the threshold, which has the same value as the blue horizontal line shown in the upper panel. Based on such threshold, 501 probes (i.e., labels) were counted as “used”.
PCR simulation. We defined n copies of a gene fragment T, each ligated to a single counter randomly selected from an infinite pool of m unique counters to generate a collection of k resulting counter-ligated gene target molecules T*={tli, . . . , i=1,2, . . . , k}. We assumed that each counter-ligated gene target molecule t1, replicates randomly and independently of other target molecules; and that the replication probability p (i.e., amplification efficiency) of different molecules, tli, remains constant throughout the PCR process. For each tli, we denote the number of molecules at PCR cycle c as Nci. When c=0, N0i) is the initial number of tli. The PCR process at cycle c+l can be modeled as a series of Nci independent trials that determine the replicability of each of the Nci molecules with replication probability p. Let ΔNci represent the number of molecules replicated at cycle c+l, then the number of molecule tli after cycle c+l is Nic+1=Nic+ΔNic, where the probability of ΔNci is
We determined the relative abundance of different counter-ligated gene target molecules tli, upon completion of the simulated PCR run for n=500, 50, or 5, and p=0.8, 0.7 or 0.6 (Table 5). In each case, we performed 1,000 independent runs to simulate 30 cycles of adaptor PCR, followed by 30 cycles of gene-specific PCR.
Focusing on the experiments with concentrations of 0.5 and 0.05 ng, (3rd and 4th in each group of 5), which provide the most accurate count of labels, there are 20 different opportunities for a given label to be observed (2 concentrations×5 amplicons χ 2 sides (left or right)). We observed 1,064 labels over the 20 opportunities.
To observe the distortion of the relative abundance of DNA molecules in the reaction resulting from the PCR process, dispersion in the quantitative distribution of PCR amplified DNA molecules was analyzed. A model of the PCR process was generated to understand the dispersion in the distribution of amplified molecules (
Example 1 of a method for selecting a collection of labels starting with all possible 14 mers (414 or ˜268 million possible labels). Step 1: clustering based on the last 7 bases: all sequences with the same last 7 bases are grouped together; within each cluster, randomly pick one sequence, this gives us 11,025 sequences, denoted by set A. Step 2: clustering based on the first 7 bases: all sequences with the same first 7 bases are grouped together; within each cluster, randomly pick one sequence, this gives us 13,377 sequences, denoted by set B. Step 3: get the union set of set A and B, the combined set has 24,073 sequences. Then do clustering based on the middle 6 bases, randomly pick one sequence out of every cluster, this gives us 3,084 sequences, denoted by set C. Step 4: calculate the all-against-all alignment score of set C, which gives us a 3,084×3,084 self-similarity score matrix, denoted by S. Step 5: filter based on the score matrix. If an element of the score matrix S(i,j) has a high value, that means, the corresponding
sequences i and j are very similar to each other. Starting from the elements with top self-similarity score, randomly pick one and discard the other; repeat this process until the number of retained sequences<2000. Until this step, 1,927 sequences were retained.
For the retained 1,927 sequences, an all-against-all complement score was calculated for each. This gave a 1,927×1,927 cross complement score matrix. A step similar to step 5 was performed, to avoid labels with maximal cross-complement with other labels. Starting from the pairs with top cross-complement score, one was randomly pick and the other discarded. This process was repeated until the number of retained sequences was 1920. Next the 1920 labels were split into 2 sets, with one set (denoted by set A) consisting of sequences that are maximum different from one-another; and the other set (denoted by set B) consisting of the remaining sequences. The procedure used to split sequences was as follows. Starting from the original 1920 by 1920 similarity score matrix, for each sequence, (1) sum up all its similarity scores with the rest of the sequences in the pool, that is, for each sequence, calculate a total similarity score. (2) Sort the total similarity scores of all sequences and select the sequence with the lowest total score, and move it to set A. (3) Remove the row and column corresponding to the selected sequence, i.e., both the number of rows and columns in the similarity score matrix are reduced by 1. Repeat steps 1-3, until the number of rows and columns in the similarity score matrix reaches 960 or half of the original. The selected sequences belong to set A and the remaining sequences belong to set B.
In another embodiment a collection of labels is selected using the following steps. Starting with all possible 14 mers (414 or ˜268 million possible labels) eliminate all that do not have 50% GC content. Eliminate those were each nucleotide does not occur at least twice. Eliminate those that have more than two G/C in tandem or more than three A/T in tandem. Eliminate those that contain a selected restriction site. That reduces the original set to ˜33 million or 12.43% of the original set. From that set select those that have a Tm within the range of 38.5° C. to 39.5° C. This step results in a set of ˜7 million or 2.73% of the original set. Remove those that have regions of self-complementarity. The resulting set in this example was now 521,291. A hierarchical clustering was performed to identify a set that has maximum sequence difference between one-another. The resulting set contained 1,927 labels. Labels were removed if the sequence had a tendency to bind to other labels in the set. This reduced the set to 1,920 labels. A final set of 960 labels was selected from the 1,920 as being maximally different for the “specific” labels and 192 additional counters to tile on the array as “non-specific” controls.
Selection of Targets and design of test array. Regions selected to assay as targets included Chr X, Chr Y, Chr 4 as a reference and Chr 21 for Trisomy. Locations on the chromosomes for assaying were selected to avoid centromeres and telomeres. Fragments were selected based on Bam HI fragments of between about 400 and 600 base pairs. Fragment intensity was checked using HG49 array hybridization. The first and the last 26 nucleotides of the fragments (from and including the Bam HI site) were tiled. Repeats were avoided and GC % was optimized.
The array format was 100/25. Feature size is Sum. There are 436×436 features or 190,096 features. Synthesis was NNPOC, 43 mer probes, 5′ up, no phosphate. The chip name is STCL-test2. The gridding probe used was the same as the HG49. No QC probes were included.
Aside from reducing whole chromosomes into 360,679 smaller molecular weight DNA pieces more suitable for ligation reactions, restriction digestion also serves to reduce the overall sequence complexity of the sample, as only an estimated 79,517 fragments reside in the 150 bp-2 kb size range that is effectively amplified by PCR. To detect and quantify counters that have been selected by the target molecules, the labeled genomic target fragments may be circularized and PCR amplified to prepare for analysis, for example, using microarray hybridization or DNA sequencing. A representative BamHI target fragment was sampled for each of the three test chromosomes. Simultaneous measurements of all three chromosomes serve as an internal control independent of dilution or other systematic errors. A suitable DNA array detector capable of distinguishing the set of counters bound to copies of the target molecules was constructed using photolithography (S. P. Fodor et al., Science 251, 767 (Feb. 15, 1991).). Each array element for a target to be evaluated consists of a target complementary sequence adjacent to one of the complements to the 960 counter sequences (
An equation to model the stochastic labeling of target molecules with a small library of 960 counters, and validate our equation model with numerical simulations is disclosed. In the model, the diversity of counters selected by, and ligated to target molecules in the reaction solution (simplified as ‘used’) is dictated by the number of copies of molecules present for each target fragment in the reaction (
On the other hand, when target copies exceed ˜100, detected labeling events appear to indicate fewer than actual molecules in solution (
To confirm the microarray results, a digital sequence counting of individual molecules in the DNA samples hybridized to microarrays was used as a means of validation, and to detect the presence of any false negatives that may have escaped microarray detection. Analysis of mapped sequence reads resulted in counts in agreement to the microarray observations. Furthermore, a second, independent sequencing run was repeated with similar findings (Table 3).
An additional feature of digital sequence counting is that unlike the microarray intensity data (
For the reverse scenario, high numbers of mapped sequence reads were always observed to correlate with high microarray intensities in these examples. No systematic or sequence correlations, or explanations were identified for the counters that are absent from any given sequencing experiment for which the microarray readout demonstrates a strong signal. While obviously underrepresented in some experiments, the same counters are sometimes present in high sequence counts in other experiments, suggesting that they are available for sequencing. PCR was used to resolve these isolated cases of disagreement and demonstrate these were false negatives in the sequencing experiments (Table 3). Despite their presence in the sequencing library, it is unclear why the counters were not observed or were underrepresented in the original sequencing run, and also in the subsequent replicate sequencing run.
Aside from the comparative analysis of absolute and relative counts of the numbers of target molecules and counter labels, additional ways to assess the stochasticity of the labeling process were evaluated. First, if the labeling process is random, the frequency of incorporation of identical counters in independent events across the paired left and right termini of target fragments should closely resemble outcomes from numerical simulation. Observed counts on microarrays do in fact match closely with numbers obtained from computer simulations (Table 4, columns 10-11). Second, if the target molecules are labeled randomly with an equal likelihood of incorporation for any member of the 960 counters in the library, we would expect the number of repeated observations of counters to follow a stochastic nature. For this analysis, we accumulated a total of 1,064 counter observations over several microarray experiments restricted to low target copy numbers. Exclusion of data from high copy targets was necessary to avoid undercounting labeling events from multiple incidences of identical counters attaching individually to numerous target copies. As a further and final demonstration of stochastic labeling, results show that the frequency of label usage follows a pattern consistent with outcomes from numerical simulation.
While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby. All cited references, including patent and non-patent literature, are incorporated herein by reference in their entireties for all purposes and particularly to disclose and describe the methods or materials in connection with which the publications are cited.
This application is a continuation of U.S. patent application Ser. No. 16/846,133, filed on Apr. 10, 2020, which is a continuation of U.S. patent application Ser. No. 15/217,886, filed on Jul. 22, 2016, now U.S. Pat. No. 10,619,203, which is a continuation of U.S. patent application Ser. No. 14/281,706, filed on May 19, 2014, now U.S. Pat. No. 9,816,137, which is a continuation of U.S. patent application Ser. No. 12/969,581, filed on Dec. 15, 2010, now U.S. Pat. No. 8,835,358, which claims priority to U.S. Provisional Application No. 61/286,768, filed on Dec. 15, 2009. The content of each of these related applications is incorporated herein by reference herein in its entirety.
Number | Name | Date | Kind |
---|---|---|---|
4510244 | Parks et al. | Apr 1985 | A |
4725536 | Fritsch et al. | Feb 1988 | A |
5124246 | Urdea et al. | Jun 1992 | A |
5137809 | Loken et al. | Aug 1992 | A |
5149625 | Church et al. | Sep 1992 | A |
5200314 | Urdea | Apr 1993 | A |
5308990 | Takahashi et al. | May 1994 | A |
5424186 | Fodor et al. | Jun 1995 | A |
5424413 | Hogan et al. | Jun 1995 | A |
5445934 | Fodor et al. | Aug 1995 | A |
5470570 | Taylor et al. | Nov 1995 | A |
5604097 | Brenner | Feb 1997 | A |
5635352 | Urdea et al. | Jun 1997 | A |
5635400 | Brenner | Jun 1997 | A |
5648245 | Fire et al. | Jul 1997 | A |
5654413 | Brenner | Aug 1997 | A |
5656731 | Urdea | Aug 1997 | A |
5658737 | Nelson et al. | Aug 1997 | A |
5714330 | Brenner et al. | Feb 1998 | A |
5744305 | Fodor et al. | Apr 1998 | A |
5759778 | Li et al. | Jun 1998 | A |
5763175 | Brenner | Jun 1998 | A |
5800992 | Fodor et al. | Sep 1998 | A |
5830712 | Rampersad et al. | Nov 1998 | A |
5846719 | Brenner et al. | Dec 1998 | A |
5854033 | Lizardi | Dec 1998 | A |
5871928 | Fodor et al. | Feb 1999 | A |
5925525 | Fodor et al. | Jul 1999 | A |
5935793 | Wong | Aug 1999 | A |
5962271 | Chenchik et al. | Oct 1999 | A |
5962272 | Chenchik et al. | Oct 1999 | A |
5968740 | Fodor et al. | Oct 1999 | A |
5981176 | Wallace | Nov 1999 | A |
5981179 | Lorinez et al. | Nov 1999 | A |
6013445 | Albrecht et al. | Jan 2000 | A |
6040138 | Lockhart et al. | Mar 2000 | A |
6046005 | Ju et al. | Apr 2000 | A |
6060596 | Lerner et al. | May 2000 | A |
6064755 | Some | May 2000 | A |
6114149 | Fry et al. | Sep 2000 | A |
6117631 | Nilsen | Sep 2000 | A |
6124092 | O'neill et al. | Sep 2000 | A |
6138077 | Brenner | Oct 2000 | A |
6140489 | Brenner | Oct 2000 | A |
6172214 | Brenner | Jan 2001 | B1 |
6197506 | Fodor et al. | Mar 2001 | B1 |
6197554 | Lin et al. | Mar 2001 | B1 |
6214558 | Shuber et al. | Apr 2001 | B1 |
6235475 | Brenner et al. | May 2001 | B1 |
6235483 | Wolber et al. | May 2001 | B1 |
6265163 | Albrecht et al. | Jul 2001 | B1 |
6268152 | Fodor et al. | Jul 2001 | B1 |
6284460 | Fodor et al. | Sep 2001 | B1 |
6284485 | Boyle et al. | Sep 2001 | B1 |
6309822 | Fodor et al. | Oct 2001 | B1 |
6309823 | Cronin et al. | Oct 2001 | B1 |
6326148 | Pauletti et al. | Dec 2001 | B1 |
6355431 | Chee et al. | Mar 2002 | B1 |
6355432 | Fodor et al. | Mar 2002 | B1 |
6372813 | Johnson et al. | Apr 2002 | B1 |
6395491 | Fodor et al. | May 2002 | B1 |
6406848 | Bridgham et al. | Jun 2002 | B1 |
6436675 | Welch et al. | Aug 2002 | B1 |
6440667 | Fodor et al. | Aug 2002 | B1 |
6440706 | Vogelstein et al. | Aug 2002 | B1 |
6451536 | Fodor et al. | Sep 2002 | B1 |
6458530 | Morris et al. | Oct 2002 | B1 |
6468744 | Cronin et al. | Oct 2002 | B1 |
6480791 | Strathmann | Nov 2002 | B1 |
6489114 | Laayoun et al. | Dec 2002 | B2 |
6489116 | Wagner | Dec 2002 | B2 |
6492121 | Kurane et al. | Dec 2002 | B2 |
6500620 | Yu et al. | Dec 2002 | B2 |
6512105 | Hogan et al. | Jan 2003 | B1 |
6514699 | O'neill et al. | Feb 2003 | B1 |
6544739 | Fodor et al. | Apr 2003 | B1 |
6551784 | Fodor et al. | Apr 2003 | B2 |
6576424 | Fodor et al. | Jun 2003 | B2 |
6600996 | Webster et al. | Jul 2003 | B2 |
6629040 | Goodlett et al. | Sep 2003 | B1 |
6653077 | Brenner | Nov 2003 | B1 |
6753147 | Vogelstein et al. | Jun 2004 | B2 |
6787308 | Balasubramanian et al. | Sep 2004 | B2 |
6808906 | Shen et al. | Oct 2004 | B2 |
6849404 | Park et al. | Feb 2005 | B2 |
6852488 | Fodor et al. | Feb 2005 | B2 |
6858412 | Willis et al. | Feb 2005 | B2 |
6890741 | Fan et al. | May 2005 | B2 |
6946251 | Kurn | Sep 2005 | B2 |
6974669 | Mirkin et al. | Dec 2005 | B2 |
7022479 | Wagner | Apr 2006 | B2 |
7034145 | Shen et al. | Apr 2006 | B2 |
7155050 | Sloge | Dec 2006 | B1 |
7294466 | McMillan | Nov 2007 | B2 |
7323309 | Mirkin et al. | Jan 2008 | B2 |
7393665 | Brenner | Jul 2008 | B2 |
7407757 | Brenner | Aug 2008 | B2 |
7424368 | Huang et al. | Sep 2008 | B2 |
7432055 | Pemov et al. | Oct 2008 | B2 |
7470515 | Rashtchian et al. | Dec 2008 | B2 |
7473767 | Dimitrov | Jan 2009 | B2 |
7476786 | Chan et al. | Jan 2009 | B2 |
7537897 | Brenner et al. | May 2009 | B2 |
7544473 | Brenner | Jun 2009 | B2 |
7635566 | Brenner | Dec 2009 | B2 |
7638612 | Rashtchian et al. | Dec 2009 | B2 |
7718403 | Kamberov et al. | May 2010 | B2 |
7771946 | Kurn | Aug 2010 | B2 |
7822555 | Huang et al. | Oct 2010 | B2 |
7824856 | Monforte | Nov 2010 | B2 |
7824889 | Vogelstein et al. | Nov 2010 | B2 |
7915015 | Vogelstein et al. | Mar 2011 | B2 |
7985546 | Church et al. | Jul 2011 | B2 |
8071311 | Kurn | Dec 2011 | B2 |
8110351 | Bosnes | Feb 2012 | B2 |
8114681 | Martin et al. | Feb 2012 | B2 |
8148068 | Brenner | Apr 2012 | B2 |
8168385 | Brenner | May 2012 | B2 |
8206913 | Kamberov et al. | Jun 2012 | B1 |
8241850 | Brenner | Aug 2012 | B2 |
8298767 | Brenner et al. | Oct 2012 | B2 |
8318433 | Brenner | Nov 2012 | B2 |
8367051 | Matyjaszewski et al. | Feb 2013 | B2 |
8420324 | Rashtchian et al. | Apr 2013 | B2 |
8445205 | Brenner | May 2013 | B2 |
8470996 | Brenner | Jun 2013 | B2 |
8476018 | Brenner | Jul 2013 | B2 |
8481292 | Casbon et al. | Jul 2013 | B2 |
8535889 | Larson et al. | Sep 2013 | B2 |
8563274 | Brenner et al. | Oct 2013 | B2 |
8603749 | Gillevet et al. | Dec 2013 | B2 |
8679756 | Brenner et al. | Mar 2014 | B1 |
8685678 | Casbon et al. | Apr 2014 | B2 |
8685753 | Martin et al. | Apr 2014 | B2 |
8715967 | Casbon et al. | May 2014 | B2 |
8722368 | Casbon et al. | May 2014 | B2 |
8728766 | Casbon et al. | May 2014 | B2 |
8741606 | Casbon et al. | Jun 2014 | B2 |
8835110 | Wang et al. | Sep 2014 | B2 |
8835358 | Fodor et al. | Sep 2014 | B2 |
8841071 | Link | Sep 2014 | B2 |
8856410 | Park | Oct 2014 | B2 |
8865470 | Yan et al. | Oct 2014 | B2 |
9150852 | Samuels et al. | Oct 2015 | B2 |
9181582 | Kurn | Nov 2015 | B2 |
9188586 | Fan et al. | Nov 2015 | B2 |
9228229 | Olson et al. | Jan 2016 | B2 |
9262376 | Tsuto | Feb 2016 | B2 |
9290808 | Fodor et al. | Mar 2016 | B2 |
9290809 | Fodor et al. | Mar 2016 | B2 |
9297047 | Furchak et al. | Mar 2016 | B2 |
9315857 | Fu et al. | Apr 2016 | B2 |
9371598 | Chee | Jun 2016 | B2 |
9567645 | Fan et al. | Feb 2017 | B2 |
9567646 | Fan et al. | Feb 2017 | B2 |
9582877 | Fu et al. | Feb 2017 | B2 |
9593365 | Frisen et al. | Mar 2017 | B2 |
9598736 | Fan et al. | Mar 2017 | B2 |
9637799 | Fan et al. | May 2017 | B2 |
9644204 | Hindson et al. | May 2017 | B2 |
9677131 | Fredriksson et al. | Jun 2017 | B2 |
9689024 | Hindson et al. | Jun 2017 | B2 |
9695468 | Hindson et al. | Jul 2017 | B2 |
9708659 | Fodor et al. | Jul 2017 | B2 |
9727810 | Fodor et al. | Aug 2017 | B2 |
9787810 | Chiang | Oct 2017 | B1 |
9816137 | Fodor et al. | Nov 2017 | B2 |
9845502 | Fodor et al. | Dec 2017 | B2 |
9850515 | McCoy et al. | Dec 2017 | B2 |
9856530 | Hindson et al. | Jan 2018 | B2 |
9868979 | Chee et al. | Jan 2018 | B2 |
9879313 | Chee et al. | Jan 2018 | B2 |
9905005 | Fu et al. | Feb 2018 | B2 |
9938523 | LaBaer | Apr 2018 | B2 |
9951386 | Hindson et al. | Apr 2018 | B2 |
9988660 | Rashtchian et al. | Jun 2018 | B2 |
10002316 | Fodor et al. | Jun 2018 | B2 |
10011872 | Belgrader et al. | Jul 2018 | B1 |
10017761 | Weissman et al. | Jul 2018 | B2 |
10023910 | Drmanac et al. | Jul 2018 | B2 |
10030267 | Hindson et al. | Jul 2018 | B2 |
10041116 | Hindson et al. | Aug 2018 | B2 |
10047394 | Fodor | Aug 2018 | B2 |
10059991 | Fodor | Aug 2018 | B2 |
10131958 | Fan et al. | Nov 2018 | B1 |
10138518 | Chun | Nov 2018 | B2 |
10151003 | Fan et al. | Dec 2018 | B2 |
10202641 | Shum | Feb 2019 | B2 |
10202646 | Fodor | Feb 2019 | B2 |
10208343 | Hindson et al. | Feb 2019 | B2 |
10208356 | Fan et al. | Feb 2019 | B1 |
10227648 | Hindson et al. | Mar 2019 | B2 |
10246703 | Church et al. | Apr 2019 | B2 |
10253364 | Hindson et al. | Apr 2019 | B2 |
10253375 | Fan et al. | Apr 2019 | B1 |
10266874 | Weissleder et al. | Apr 2019 | B2 |
10273541 | Hindson et al. | Apr 2019 | B2 |
10288608 | Kozlov et al. | May 2019 | B2 |
10294511 | Sanches-Kuiper et al. | May 2019 | B2 |
10301677 | Shum et al. | May 2019 | B2 |
10308982 | Chee | Jun 2019 | B2 |
10323278 | Belgrader et al. | Jun 2019 | B2 |
10337061 | Hindson et al. | Jul 2019 | B2 |
10338066 | Fan et al. | Jul 2019 | B2 |
10344329 | Hindson et al. | Jul 2019 | B2 |
10392661 | Fodor | Aug 2019 | B2 |
10450607 | Hindson et al. | Oct 2019 | B2 |
10550429 | Harada et al. | Feb 2020 | B2 |
10619186 | Betts et al. | Apr 2020 | B2 |
10619203 | Fodor | Apr 2020 | B2 |
RE47983 | Gao et al. | May 2020 | E |
11092607 | Gaublomme et al. | Aug 2021 | B2 |
11460468 | Fan et al. | Oct 2022 | B2 |
11467157 | Fan et al. | Oct 2022 | B2 |
11535882 | Fu et al. | Dec 2022 | B2 |
20010024784 | Wagner | Sep 2001 | A1 |
20010036632 | Yu et al. | Nov 2001 | A1 |
20020019005 | Kamb | Feb 2002 | A1 |
20020051986 | Baez et al. | May 2002 | A1 |
20020065609 | Ashby | May 2002 | A1 |
20020072058 | Voelker et al. | Jun 2002 | A1 |
20020094116 | Forst et al. | Jul 2002 | A1 |
20020132241 | Fan et al. | Sep 2002 | A1 |
20020168665 | Okawa | Nov 2002 | A1 |
20020172953 | Mirkin et al. | Nov 2002 | A1 |
20020187480 | Brandon | Dec 2002 | A1 |
20020192687 | Mirkin et al. | Dec 2002 | A1 |
20030003490 | Fan et al. | Jan 2003 | A1 |
20030013091 | Dimitrov | Jan 2003 | A1 |
20030032049 | Wagner | Feb 2003 | A1 |
20030049616 | Brenner et al. | Mar 2003 | A1 |
20030077611 | Slepnev | Apr 2003 | A1 |
20030082818 | Bahnson et al. | May 2003 | A1 |
20030087242 | Mirkin et al. | May 2003 | A1 |
20030104436 | Morris et al. | Jun 2003 | A1 |
20030165935 | Vann et al. | Sep 2003 | A1 |
20030175908 | Linnarsson | Sep 2003 | A1 |
20030186251 | Dunn et al. | Oct 2003 | A1 |
20030207296 | Park et al. | Nov 2003 | A1 |
20030207300 | Matray et al. | Nov 2003 | A1 |
20040047769 | Tanaami | Mar 2004 | A1 |
20040091864 | French et al. | May 2004 | A1 |
20040096368 | Davis et al. | May 2004 | A1 |
20040096892 | Wang et al. | May 2004 | A1 |
20040121342 | McKeown | Jun 2004 | A1 |
20040146901 | Morris et al. | Jul 2004 | A1 |
20040147435 | Hawiger et al. | Jul 2004 | A1 |
20040157243 | Huang et al. | Aug 2004 | A1 |
20040209298 | Kamberov et al. | Oct 2004 | A1 |
20040224325 | Knapp et al. | Nov 2004 | A1 |
20040259118 | Macevicz | Dec 2004 | A1 |
20050019776 | Callow et al. | Jan 2005 | A1 |
20050032110 | Shen et al. | Feb 2005 | A1 |
20050048500 | Lawton | Mar 2005 | A1 |
20050053952 | Hong et al. | Mar 2005 | A1 |
20050105077 | Padmanabhan et al. | May 2005 | A1 |
20050170373 | Monforte | Aug 2005 | A1 |
20050175993 | Wei | Aug 2005 | A1 |
20050196760 | Pemov et al. | Sep 2005 | A1 |
20050214825 | Stuelpnagel | Sep 2005 | A1 |
20050250146 | McMillan | Nov 2005 | A1 |
20050250147 | Macevicz | Nov 2005 | A1 |
20060002824 | Chang et al. | Jan 2006 | A1 |
20060035258 | Tadakamalla et al. | Feb 2006 | A1 |
20060040297 | Leamon et al. | Feb 2006 | A1 |
20060041385 | Bauer | Feb 2006 | A1 |
20060057634 | Rye | Mar 2006 | A1 |
20060073506 | Christians et al. | Apr 2006 | A1 |
20060211030 | Brenner | Sep 2006 | A1 |
20060257902 | Mendoza et al. | Nov 2006 | A1 |
20060263709 | Matsumura et al. | Nov 2006 | A1 |
20060263789 | Kincaid | Nov 2006 | A1 |
20060280352 | Muschler et al. | Dec 2006 | A1 |
20060281092 | Wille et al. | Dec 2006 | A1 |
20060286570 | Rowlen et al. | Dec 2006 | A1 |
20070020640 | Mccloskey et al. | Jan 2007 | A1 |
20070031829 | Yasuno et al. | Feb 2007 | A1 |
20070042400 | Choi et al. | Feb 2007 | A1 |
20070042419 | Barany et al. | Feb 2007 | A1 |
20070065823 | Dressman et al. | Mar 2007 | A1 |
20070065844 | Golub et al. | Mar 2007 | A1 |
20070105090 | Cassidy et al. | May 2007 | A1 |
20070117121 | Hutchison et al. | May 2007 | A1 |
20070117134 | Kou | May 2007 | A1 |
20070117177 | Luo et al. | May 2007 | A1 |
20070133856 | Dutta-Choudhury | Jun 2007 | A1 |
20070172873 | Brenner et al. | Jul 2007 | A1 |
20070178478 | Dhallan et al. | Aug 2007 | A1 |
20070202523 | Becker et al. | Aug 2007 | A1 |
20070281317 | Becker et al. | Dec 2007 | A1 |
20080038727 | Spier | Feb 2008 | A1 |
20080070303 | West et al. | Mar 2008 | A1 |
20080119736 | Dentinger | May 2008 | A1 |
20080194414 | Albert et al. | Aug 2008 | A1 |
20080261204 | Lexow | Oct 2008 | A1 |
20080268508 | Sowlay | Oct 2008 | A1 |
20080269068 | Church et al. | Oct 2008 | A1 |
20080274458 | Latham et al. | Nov 2008 | A1 |
20080299609 | Kwon et al. | Dec 2008 | A1 |
20080318802 | Brenner | Dec 2008 | A1 |
20090053669 | Liu et al. | Feb 2009 | A1 |
20090061513 | Andersson Svahn et al. | Mar 2009 | A1 |
20090105959 | Braverman et al. | Apr 2009 | A1 |
20090131269 | Martin et al. | May 2009 | A1 |
20090137407 | Church et al. | May 2009 | A1 |
20090208936 | Tan et al. | Aug 2009 | A1 |
20090220385 | Brown et al. | Sep 2009 | A1 |
20090226891 | Nova et al. | Sep 2009 | A2 |
20090252414 | Suzuki | Oct 2009 | A1 |
20090253586 | Nelson et al. | Oct 2009 | A1 |
20090283676 | Skoglund | Nov 2009 | A1 |
20090290151 | Agrawal et al. | Nov 2009 | A1 |
20090298709 | Ma | Dec 2009 | A1 |
20090311694 | Gallagher et al. | Dec 2009 | A1 |
20100069250 | White, III | Mar 2010 | A1 |
20100105049 | Ehrich et al. | Apr 2010 | A1 |
20100105112 | Holtze et al. | Apr 2010 | A1 |
20100105886 | Woudenberg et al. | Apr 2010 | A1 |
20100120098 | Grunenwald et al. | May 2010 | A1 |
20100120630 | Huang et al. | May 2010 | A1 |
20100136544 | Agresti et al. | Jun 2010 | A1 |
20100159533 | Lipson et al. | Jun 2010 | A1 |
20100167354 | Kurn | Jul 2010 | A1 |
20100184076 | Lawton | Jul 2010 | A1 |
20100255471 | Clarke | Oct 2010 | A1 |
20100267028 | Pasche | Oct 2010 | A1 |
20100291666 | Collier et al. | Nov 2010 | A1 |
20100300895 | Nobile et al. | Dec 2010 | A1 |
20100323348 | Hamady et al. | Dec 2010 | A1 |
20100330574 | Whitman | Dec 2010 | A1 |
20110038507 | Hager | Feb 2011 | A1 |
20110059436 | Hardin et al. | Mar 2011 | A1 |
20110059556 | Strey | Mar 2011 | A1 |
20110070584 | Wohlgemuth et al. | Mar 2011 | A1 |
20110072889 | Albitar et al. | Mar 2011 | A1 |
20110160078 | Fodor et al. | Jun 2011 | A1 |
20110201507 | Rava et al. | Aug 2011 | A1 |
20110230358 | Rava | Sep 2011 | A1 |
20110244455 | Larson et al. | Oct 2011 | A1 |
20110245111 | Chee | Oct 2011 | A1 |
20110263457 | Krutzik et al. | Oct 2011 | A1 |
20110294689 | Namsaraev | Dec 2011 | A1 |
20110312511 | Winquist et al. | Dec 2011 | A1 |
20110319289 | Libutti | Dec 2011 | A1 |
20120004132 | Zhang et al. | Jan 2012 | A1 |
20120010091 | Linnarson | Jan 2012 | A1 |
20120014977 | Furihata et al. | Jan 2012 | A1 |
20120034607 | Rothberg et al. | Feb 2012 | A1 |
20120040843 | Ducree et al. | Feb 2012 | A1 |
20120045844 | Rothberg et al. | Feb 2012 | A1 |
20120058520 | Hayashida | Mar 2012 | A1 |
20120058902 | Livingston et al. | Mar 2012 | A1 |
20120065081 | Chee | Mar 2012 | A1 |
20120071331 | Casbon | Mar 2012 | A1 |
20120087862 | Hood et al. | Apr 2012 | A1 |
20120142018 | Jiang | Jun 2012 | A1 |
20120149603 | Cooney et al. | Jun 2012 | A1 |
20120156675 | Lueerssen et al. | Jun 2012 | A1 |
20120163681 | Lohse | Jun 2012 | A1 |
20120165219 | Van Der Zaag et al. | Jun 2012 | A1 |
20120173159 | Davey et al. | Jul 2012 | A1 |
20120190020 | Oliphant et al. | Jul 2012 | A1 |
20120202293 | Martin et al. | Aug 2012 | A1 |
20120220022 | Ehrlich et al. | Aug 2012 | A1 |
20120220494 | Samuels et al. | Aug 2012 | A1 |
20120231972 | Golyshin et al. | Sep 2012 | A1 |
20120252012 | Armougom et al. | Oct 2012 | A1 |
20120253689 | Rogan | Oct 2012 | A1 |
20120316074 | Saxonov | Dec 2012 | A1 |
20120322681 | Kung et al. | Dec 2012 | A1 |
20130005585 | Anderson et al. | Jan 2013 | A1 |
20130022977 | Lapidus et al. | Jan 2013 | A1 |
20130045994 | Shinozuka et al. | Feb 2013 | A1 |
20130116130 | Fu et al. | May 2013 | A1 |
20130190206 | Leonard | Jul 2013 | A1 |
20130203047 | Casbon et al. | Aug 2013 | A1 |
20130210643 | Casbon et al. | Aug 2013 | A1 |
20130210659 | Watson et al. | Aug 2013 | A1 |
20130224743 | Casbon et al. | Aug 2013 | A1 |
20130225418 | Watson | Aug 2013 | A1 |
20130225623 | Buxbaum et al. | Aug 2013 | A1 |
20130237458 | Casbon et al. | Sep 2013 | A1 |
20130267424 | Casbon et al. | Oct 2013 | A1 |
20130274117 | Church | Oct 2013 | A1 |
20130323732 | Anderson et al. | Dec 2013 | A1 |
20140004569 | Lambowitz et al. | Jan 2014 | A1 |
20140057799 | Johnson et al. | Feb 2014 | A1 |
20140066318 | Frisen et al. | Mar 2014 | A1 |
20140147860 | Kaduchak et al. | May 2014 | A1 |
20140155274 | Xie et al. | Jun 2014 | A1 |
20140155295 | Hindson et al. | Jun 2014 | A1 |
20140178438 | Sahin et al. | Jun 2014 | A1 |
20140194324 | Gormley et al. | Jul 2014 | A1 |
20140206079 | Malinoski et al. | Jul 2014 | A1 |
20140206547 | Wang | Jul 2014 | A1 |
20140216128 | Trotter et al. | Aug 2014 | A1 |
20140227684 | Hindson et al. | Aug 2014 | A1 |
20140227705 | Vogelstein et al. | Aug 2014 | A1 |
20140228239 | McCoy et al. | Aug 2014 | A1 |
20140228255 | Hindson et al. | Aug 2014 | A1 |
20140235506 | Hindson et al. | Aug 2014 | A1 |
20140243242 | Nicol et al. | Aug 2014 | A1 |
20140244742 | Yu et al. | Aug 2014 | A1 |
20140272952 | May et al. | Sep 2014 | A1 |
20140274811 | Arnold | Sep 2014 | A1 |
20140287963 | Hindson et al. | Sep 2014 | A1 |
20140303005 | Samuels et al. | Oct 2014 | A1 |
20140309945 | Park et al. | Oct 2014 | A1 |
20140315211 | Sugino et al. | Oct 2014 | A1 |
20140322716 | Robins | Oct 2014 | A1 |
20140357500 | Vigneault et al. | Dec 2014 | A1 |
20140378322 | Hindson et al. | Dec 2014 | A1 |
20140378345 | Hindson et al. | Dec 2014 | A1 |
20140378349 | Hindson et al. | Dec 2014 | A1 |
20140378350 | Hindson et al. | Dec 2014 | A1 |
20150005185 | Fodor et al. | Jan 2015 | A1 |
20150005199 | Hindson et al. | Jan 2015 | A1 |
20150005200 | Hindson et al. | Jan 2015 | A1 |
20150011396 | Schroeder et al. | Jan 2015 | A1 |
20150017654 | Gorfinkel et al. | Jan 2015 | A1 |
20150066385 | Schnall-levin et al. | Mar 2015 | A1 |
20150072867 | Soldatov et al. | Mar 2015 | A1 |
20150072873 | Heinz et al. | Mar 2015 | A1 |
20150080266 | Volkmuth et al. | Mar 2015 | A1 |
20150099661 | Fodor et al. | Apr 2015 | A1 |
20150099673 | Fodor et al. | Apr 2015 | A1 |
20150111256 | Church et al. | Apr 2015 | A1 |
20150118680 | Fodor et al. | Apr 2015 | A1 |
20150119255 | Fodor et al. | Apr 2015 | A1 |
20150119256 | Fodor et al. | Apr 2015 | A1 |
20150119257 | Fodor et al. | Apr 2015 | A1 |
20150119258 | Fodor et al. | Apr 2015 | A1 |
20150119290 | Fodor et al. | Apr 2015 | A1 |
20150133319 | Fu et al. | May 2015 | A1 |
20150141292 | Fodor et al. | May 2015 | A1 |
20150152409 | Seitz et al. | Jun 2015 | A1 |
20150203897 | Robons et al. | Jul 2015 | A1 |
20150211050 | Iafrate et al. | Jul 2015 | A1 |
20150218620 | Behlke et al. | Aug 2015 | A1 |
20150225777 | Hindson et al. | Aug 2015 | A1 |
20150225778 | Hindson et al. | Aug 2015 | A1 |
20150247182 | Faham et al. | Sep 2015 | A1 |
20150253237 | Castellarnau et al. | Sep 2015 | A1 |
20150259734 | Asbury et al. | Sep 2015 | A1 |
20150275295 | Wang et al. | Oct 2015 | A1 |
20150298091 | Weitz | Oct 2015 | A1 |
20150299784 | Fan et al. | Oct 2015 | A1 |
20150307874 | Jaitin | Oct 2015 | A1 |
20150329852 | Nolan | Nov 2015 | A1 |
20150360193 | Fan et al. | Dec 2015 | A1 |
20150376609 | Hindson et al. | Dec 2015 | A1 |
20160010151 | Fan et al. | Jan 2016 | A1 |
20160017320 | Wang et al. | Jan 2016 | A1 |
20160026758 | Jabara et al. | Jan 2016 | A1 |
20160053253 | Salathia et al. | Feb 2016 | A1 |
20160055632 | Fu et al. | Feb 2016 | A1 |
20160060621 | Agresti et al. | Mar 2016 | A1 |
20160060682 | Pregibon et al. | Mar 2016 | A1 |
20160068889 | Gole et al. | Mar 2016 | A1 |
20160122751 | LaBaer | May 2016 | A1 |
20160122753 | Mikkelsen et al. | May 2016 | A1 |
20160138091 | Chee et al. | May 2016 | A1 |
20160145677 | Chee et al. | May 2016 | A1 |
20160145683 | Fan et al. | May 2016 | A1 |
20160153973 | Smith | Jun 2016 | A1 |
20160201125 | Samuels et al. | Jul 2016 | A1 |
20160208322 | Anderson et al. | Jul 2016 | A1 |
20160222378 | Fodor et al. | Aug 2016 | A1 |
20160232291 | Kyriazopoulou-Panagiotopoulou et al. | Aug 2016 | A1 |
20160244742 | Linnarsson et al. | Aug 2016 | A1 |
20160244828 | Mason | Aug 2016 | A1 |
20160253584 | Fodor et al. | Sep 2016 | A1 |
20160257993 | Fu et al. | Sep 2016 | A1 |
20160258012 | Fodor et al. | Sep 2016 | A2 |
20160265027 | Sanches-Kuiper et al. | Sep 2016 | A1 |
20160265069 | Fan et al. | Sep 2016 | A1 |
20160266094 | Ankrum et al. | Sep 2016 | A1 |
20160289669 | Fan et al. | Oct 2016 | A1 |
20160289670 | Samuels et al. | Oct 2016 | A1 |
20160289740 | Fu et al. | Oct 2016 | A1 |
20160298180 | Chee | Oct 2016 | A1 |
20160312276 | Fu et al. | Oct 2016 | A1 |
20160320720 | Murata et al. | Nov 2016 | A1 |
20160326584 | Fodor et al. | Nov 2016 | A1 |
20160355879 | Kamberov et al. | Dec 2016 | A1 |
20160362730 | Alexander et al. | Dec 2016 | A1 |
20160376583 | Fodor et al. | Dec 2016 | A1 |
20160376648 | Fodor et al. | Dec 2016 | A1 |
20170044525 | Kaper et al. | Feb 2017 | A1 |
20170073730 | Betts et al. | Mar 2017 | A1 |
20170136458 | Dunne et al. | May 2017 | A1 |
20170154421 | Fu et al. | Jun 2017 | A1 |
20170192013 | Agresti et al. | Jul 2017 | A1 |
20170260584 | Zheng et al. | Sep 2017 | A1 |
20170275669 | Weissleder et al. | Sep 2017 | A1 |
20170314067 | Shum et al. | Nov 2017 | A1 |
20170337459 | Fodor et al. | Nov 2017 | A1 |
20170342405 | Fu et al. | Nov 2017 | A1 |
20170342465 | Shum et al. | Nov 2017 | A1 |
20170342484 | Shum et al. | Nov 2017 | A1 |
20170344866 | Fan et al. | Nov 2017 | A1 |
20180002764 | Fan et al. | Jan 2018 | A1 |
20180016634 | Hindson et al. | Jan 2018 | A1 |
20180024139 | Peikon et al. | Jan 2018 | A1 |
20180030522 | Kamberov et al. | Feb 2018 | A1 |
20180037942 | Fu et al. | Feb 2018 | A1 |
20180057873 | Zhou et al. | Mar 2018 | A1 |
20180088112 | Fan et al. | Mar 2018 | A1 |
20180094312 | Hindson et al. | Apr 2018 | A1 |
20180094314 | Hindson et al. | Apr 2018 | A1 |
20180094315 | Hindson et al. | Apr 2018 | A1 |
20180105808 | Mikkelsen et al. | Apr 2018 | A1 |
20180112266 | Hindson et al. | Apr 2018 | A1 |
20180127743 | Vigneault et al. | May 2018 | A1 |
20180142292 | Hindson et al. | May 2018 | A1 |
20180163201 | Larson | Jun 2018 | A1 |
20180179590 | Belgrader et al. | Jun 2018 | A1 |
20180179591 | Belgrader et al. | Jun 2018 | A1 |
20180201923 | LaBaer | Jul 2018 | A1 |
20180201980 | Chee et al. | Jul 2018 | A1 |
20180208975 | Peterson et al. | Jul 2018 | A1 |
20180216174 | Shum et al. | Aug 2018 | A1 |
20180230527 | Fang et al. | Aug 2018 | A1 |
20180251825 | Stoeckius et al. | Sep 2018 | A1 |
20180258482 | Hindson et al. | Sep 2018 | A1 |
20180258500 | Fan et al. | Sep 2018 | A1 |
20180267036 | Fan et al. | Sep 2018 | A1 |
20180276332 | Fan et al. | Sep 2018 | A1 |
20180282803 | Belgrader et al. | Oct 2018 | A1 |
20180291470 | Fan et al. | Oct 2018 | A1 |
20180002738 | Wang et al. | Nov 2018 | A1 |
20180320241 | Nolan et al. | Nov 2018 | A1 |
20180327835 | Fodor et al. | Nov 2018 | A1 |
20180327836 | Fodor et al. | Nov 2018 | A1 |
20180327866 | Fan et al. | Nov 2018 | A1 |
20180327867 | Fan et al. | Nov 2018 | A1 |
20180340170 | Belhocine et al. | Nov 2018 | A1 |
20180346969 | Chang et al. | Dec 2018 | A1 |
20180346970 | Chang et al. | Dec 2018 | A1 |
20180371536 | Fu et al. | Dec 2018 | A1 |
20190010552 | Xu et al. | Jan 2019 | A1 |
20190025304 | Vigneault et al. | Jan 2019 | A1 |
20190032129 | Hindson et al. | Jan 2019 | A1 |
20190040474 | Fan et al. | Feb 2019 | A1 |
20190085412 | Fan et al. | Mar 2019 | A1 |
20190095578 | Shum et al. | Mar 2019 | A1 |
20190100798 | Fodor et al. | Apr 2019 | A1 |
20190119726 | Shum et al. | Apr 2019 | A1 |
20190136316 | Hindson et al. | May 2019 | A1 |
20190136317 | Hindson et al. | May 2019 | A1 |
20190136319 | Hindson et al. | May 2019 | A1 |
20190161743 | Church et al. | May 2019 | A1 |
20190177788 | Hindson et al. | Jun 2019 | A1 |
20190177800 | Boutet et al. | Jun 2019 | A1 |
20190203270 | Amit et al. | Jul 2019 | A1 |
20190203291 | Hindson et al. | Jul 2019 | A1 |
20190211395 | Tsao et al. | Jul 2019 | A1 |
20190218276 | Regev et al. | Jul 2019 | A1 |
20190218607 | Love et al. | Jul 2019 | A1 |
20190221287 | Tsujimoto | Jul 2019 | A1 |
20190221292 | Tsujimoto | Jul 2019 | A1 |
20190256888 | Weissleder et al. | Aug 2019 | A1 |
20190256907 | Ryan et al. | Aug 2019 | A1 |
20190292592 | Shum et al. | Sep 2019 | A1 |
20190338278 | Shum et al. | Nov 2019 | A1 |
20190338353 | Belgrader et al. | Nov 2019 | A1 |
20190338357 | Fan et al. | Nov 2019 | A1 |
20190390253 | Kennedy et al. | Dec 2019 | A1 |
20200102598 | Xie et al. | Apr 2020 | A1 |
20200109437 | Chang et al. | Apr 2020 | A1 |
20200115753 | Shalek et al. | Apr 2020 | A1 |
20200124601 | Fan et al. | Apr 2020 | A1 |
20210039582 | Patton et al. | Feb 2021 | A1 |
20210123044 | Zhang et al. | Apr 2021 | A1 |
20210132078 | Peikon et al. | May 2021 | A1 |
20210198754 | Fan et al. | Jul 2021 | A1 |
20210213413 | Saligrama et al. | Jul 2021 | A1 |
20210222163 | Wu et al. | Jul 2021 | A1 |
20210371914 | Stoeckius et al. | Dec 2021 | A1 |
20220010361 | Song et al. | Jan 2022 | A1 |
20220010362 | Campbell | Jan 2022 | A1 |
20220033810 | Song et al. | Feb 2022 | A1 |
20220154288 | Mortimer | May 2022 | A1 |
20220162695 | Sakofsky et al. | May 2022 | A1 |
20220162773 | Sakofsky et al. | May 2022 | A1 |
20220178909 | Huang et al. | Jun 2022 | A1 |
20220214356 | Henikoff et al. | Jul 2022 | A1 |
20220219170 | Khurana et al. | Jul 2022 | A1 |
20220220549 | Shum et al. | Jul 2022 | A1 |
20220267759 | Sanjana et al. | Aug 2022 | A1 |
20220333185 | Fu et al. | Oct 2022 | A1 |
20220348904 | Shum et al. | Nov 2022 | A1 |
20230083422 | Fu et al. | Mar 2023 | A1 |
20230109336 | Shum et al. | Apr 2023 | A1 |
20230125113 | Fan et al. | Apr 2023 | A1 |
20230193372 | Shum et al. | Jun 2023 | A1 |
Number | Date | Country |
---|---|---|
2474509 | Feb 2003 | CA |
2961210 | Mar 2016 | CA |
106460033 | Feb 2017 | CN |
107208158 | Sep 2017 | CN |
110498858 | Nov 2019 | CN |
102008025656 | Dec 2009 | DE |
1473080 | Nov 2004 | EP |
1647600 | Apr 2006 | EP |
1845160 | Oct 2007 | EP |
2036989 | Mar 2009 | EP |
1379693 | May 2009 | EP |
2204456 | Jul 2010 | EP |
2431465 | Mar 2012 | EP |
2203749 | Aug 2012 | EP |
2511708 | Oct 2012 | EP |
2538220 | Dec 2012 | EP |
2623613 | Aug 2013 | EP |
2702146 | Mar 2014 | EP |
1745155 | Oct 2014 | EP |
2805769 | Nov 2014 | EP |
2556171 | Sep 2015 | EP |
2989215 | Mar 2016 | EP |
2970958 | Dec 2017 | EP |
3263715 | Jan 2018 | EP |
3286326 | Feb 2018 | EP |
2670863 | Jun 2018 | EP |
3136103 | Aug 2018 | EP |
3256606 | Aug 2018 | EP |
2954102 | Dec 2018 | EP |
3428290 | Jan 2019 | EP |
2970957 | Apr 2019 | EP |
3058092 | May 2019 | EP |
3327123 | Aug 2019 | EP |
2293238 | Mar 1996 | GB |
2001078768 | Mar 2001 | JP |
2005233974 | Sep 2005 | JP |
2007504831 | Mar 2007 | JP |
2008256428 | Oct 2008 | JP |
2013039275 | Feb 2013 | JP |
2018509896 | Apr 2018 | JP |
2019522268 | Aug 2019 | JP |
WO1989001050 | Feb 1989 | WO |
WO1996024061 | Aug 1996 | WO |
WO1997010365 | Mar 1997 | WO |
WO1999015702 | Apr 1999 | WO |
WO1999028505 | Jun 1999 | WO |
WO2000058516 | Oct 2000 | WO |
WO2001020035 | Mar 2001 | WO |
WO2001048242 | Jul 2001 | WO |
WO2001053539 | Jul 2001 | WO |
WO2002018643 | Mar 2002 | WO |
WO2002046472 | Jun 2002 | WO |
WO2002056014 | Jul 2002 | WO |
WO2002059355 | Aug 2002 | WO |
WO2002070684 | Sep 2002 | WO |
WO2002072772 | Sep 2002 | WO |
WO2002079490 | Oct 2002 | WO |
WO2002083922 | Oct 2002 | WO |
WO2002101358 | Dec 2002 | WO |
WO2003031591 | Apr 2003 | WO |
WO2003035829 | May 2003 | WO |
WO2004017374 | Feb 2004 | WO |
WO2004021986 | Mar 2004 | WO |
WO2004033669 | Apr 2004 | WO |
WO2004066185 | Aug 2004 | WO |
WO2004081225 | Sep 2004 | WO |
WO2005017206 | Feb 2005 | WO |
WO2005021731 | Mar 2005 | WO |
WO2005042759 | May 2005 | WO |
WO2005071110 | Aug 2005 | WO |
WO2005080604 | Sep 2005 | WO |
WO2005111242 | Nov 2005 | WO |
WO2005111243 | Nov 2005 | WO |
WO2006026828 | Mar 2006 | WO |
WO2006071776 | Jul 2006 | WO |
WO2006102264 | Sep 2006 | WO |
WO2006137932 | Dec 2006 | WO |
WO2007087310 | Aug 2007 | WO |
WO2007087312 | Aug 2007 | WO |
WO2007147079 | Dec 2007 | WO |
WO2008047428 | Apr 2008 | WO |
WO2008051928 | May 2008 | WO |
WO2008057163 | May 2008 | WO |
WO2008096318 | Aug 2008 | WO |
WO2008104380 | Sep 2008 | WO |
WO2008147428 | Dec 2008 | WO |
WO2008150432 | Dec 2008 | WO |
WO2009048530 | Apr 2009 | WO |
WO2009148560 | Dec 2009 | WO |
WO2009152928 | Dec 2009 | WO |
WO2010048605 | Apr 2010 | WO |
WO2010059820 | May 2010 | WO |
WO2010117620 | Oct 2010 | WO |
WO2010131645 | Nov 2010 | WO |
WO2011091393 | Jul 2011 | WO |
WO2011106738 | Sep 2011 | WO |
WO2011123246 | Oct 2011 | WO |
WO2011127099 | Oct 2011 | WO |
WO2011143659 | Nov 2011 | WO |
WO2011155833 | Dec 2011 | WO |
WO2012038839 | Mar 2012 | WO |
WO2012042374 | Apr 2012 | WO |
WO2012047297 | Apr 2012 | WO |
WO2012048341 | Apr 2012 | WO |
WO2012041802 | May 2012 | WO |
WO2012083225 | Jun 2012 | WO |
WO2012099896 | Jul 2012 | WO |
WO2012103154 | Aug 2012 | WO |
WO2012106385 | Aug 2012 | WO |
WO2012106546 | Aug 2012 | WO |
WO2012108864 | Aug 2012 | WO |
WO2012112804 | Aug 2012 | WO |
WO2012129363 | Sep 2012 | WO |
WO2012140224 | Oct 2012 | WO |
WO2012142213 | Oct 2012 | WO |
WO2012148477 | Nov 2012 | WO |
WO2012149042 | Nov 2012 | WO |
WO2012156744 | Nov 2012 | WO |
WO2012162267 | Nov 2012 | WO |
WO2012177639 | Dec 2012 | WO |
WO2013019075 | Feb 2013 | WO |
WO2013070990 | May 2013 | WO |
WO2013096802 | Jun 2013 | WO |
WO2013117595 | Aug 2013 | WO |
WO2013130674 | Sep 2013 | WO |
WO2013148525 | Oct 2013 | WO |
WO2013173394 | Nov 2013 | WO |
WO2013176767 | Nov 2013 | WO |
WO2013177206 | Nov 2013 | WO |
WO2013188831 | Dec 2013 | WO |
WO2013188872 | Dec 2013 | WO |
WO2013191775 | Dec 2013 | WO |
WO2014015084 | Jan 2014 | WO |
WO2014015098 | Jan 2014 | WO |
WO2014018093 | Jan 2014 | WO |
WO2014018460 | Jan 2014 | WO |
WO2014028537 | Feb 2014 | WO |
WO2014031997 | Feb 2014 | WO |
WO2014065756 | May 2014 | WO |
WO2014093676 | Jun 2014 | WO |
WO2014108850 | Jul 2014 | WO |
WO2014124046 | Aug 2014 | WO |
WO2014124336 | Aug 2014 | WO |
WO2014124338 | Aug 2014 | WO |
WO2014126937 | Aug 2014 | WO |
WO2014144495 | Sep 2014 | WO |
WO2014145458 | Sep 2014 | WO |
WO2014200767 | Dec 2014 | WO |
WO2014201273 | Dec 2014 | WO |
WO2014204939 | Dec 2014 | WO |
WO2014210223 | Dec 2014 | WO |
WO2014210225 | Dec 2014 | WO |
WO2014210353 | Dec 2014 | WO |
WO2015002908 | Jan 2015 | WO |
WO2018015365 | Jan 2015 | WO |
WO2015017586 | Feb 2015 | WO |
WO2015031691 | Mar 2015 | WO |
WO2015035087 | Mar 2015 | WO |
WO2015044428 | Apr 2015 | WO |
WO2015047186 | Apr 2015 | WO |
WO2015057985 | Apr 2015 | WO |
WO2014071361 | May 2015 | WO |
WO2015061844 | May 2015 | WO |
WO2015103339 | Jul 2015 | WO |
WO2015117163 | Aug 2015 | WO |
WO2015134787 | Sep 2015 | WO |
WO2015160439 | Oct 2015 | WO |
WO2015168161 | Nov 2015 | WO |
WO2015179339 | Nov 2015 | WO |
WO2015200869 | Dec 2015 | WO |
WO2015200893 | Dec 2015 | WO |
WO2016044227 | Mar 2016 | WO |
WO2016049418 | Mar 2016 | WO |
WO2016061517 | Apr 2016 | WO |
WO2016100976 | Jun 2016 | WO |
WO2016118915 | Jul 2016 | WO |
WO2016126871 | Aug 2016 | WO |
WO2016130578 | Aug 2016 | WO |
WO2016160965 | Aug 2016 | WO |
WO2016138496 | Sep 2016 | WO |
WO2016138500 | Sep 2016 | WO |
WO2016145409 | Sep 2016 | WO |
WO2016149418 | Sep 2016 | WO |
WO2016160844 | Oct 2016 | WO |
WO2016168825 | Oct 2016 | WO |
WO2016176091 | Nov 2016 | WO |
WO2016190795 | Dec 2016 | WO |
WO2016191272 | Dec 2016 | WO |
WO2017032808 | Mar 2017 | WO |
WO2017040306 | Mar 2017 | WO |
WO2017044574 | Mar 2017 | WO |
WO2017053905 | Mar 2017 | WO |
WO2017079593 | May 2017 | WO |
WO2017087873 | May 2017 | WO |
WO2017096239 | Jun 2017 | WO |
WO2017097939 | Jun 2017 | WO |
WO2017117358 | Jul 2017 | WO |
WO2017125508 | Jul 2017 | WO |
WO2017139690 | Aug 2017 | WO |
WO2017164936 | Sep 2017 | WO |
WO2017173328 | Oct 2017 | WO |
WO2017205691 | Nov 2017 | WO |
WO2018017949 | Jan 2018 | WO |
WO2018018008 | Jan 2018 | WO |
WO2018020489 | Feb 2018 | WO |
WO2018031631 | Feb 2018 | WO |
WO2018058073 | Mar 2018 | WO |
WO2018064640 | Apr 2018 | WO |
WO2018075693 | Apr 2018 | WO |
WO2018111872 | Jun 2018 | WO |
WO2018115852 | Jun 2018 | WO |
WO2018119447 | Jun 2018 | WO |
WO2018132635 | Jul 2018 | WO |
WO2018140966 | Aug 2018 | WO |
WO2018144240 | Aug 2018 | WO |
WO2018144813 | Aug 2018 | WO |
WO2018174827 | Sep 2018 | WO |
WO2018217862 | Nov 2018 | WO |
WO2018218222 | Nov 2018 | WO |
WO2018222548 | Dec 2018 | WO |
WO2018226293 | Dec 2018 | WO |
WO2019055852 | Mar 2019 | WO |
WO2019076768 | Apr 2019 | WO |
WO2019084046 | May 2019 | WO |
WO2019099906 | May 2019 | WO |
WO2019113457 | Jun 2019 | WO |
WO2019113499 | Jun 2019 | WO |
WO2019113506 | Jun 2019 | WO |
WO2019113533 | Jun 2019 | WO |
WO2019118355 | Jun 2019 | WO |
WO2019126789 | Jun 2019 | WO |
WO2019157529 | Aug 2019 | WO |
WO2013137737 | Sep 2019 | WO |
WO2019178164 | Sep 2019 | WO |
WO2019213237 | Nov 2019 | WO |
WO2019213294 | Nov 2019 | WO |
WO2019218101 | Nov 2019 | WO |
WO2020028266 | Feb 2020 | WO |
WO2020033164 | Feb 2020 | WO |
WO2020037065 | Feb 2020 | WO |
WO2020046833 | Mar 2020 | WO |
WO2020072380 | Apr 2020 | WO |
WO2020097315 | May 2020 | WO |
WO2020123384 | Jun 2020 | WO |
WO2020131699 | Jun 2020 | WO |
WO2020154247 | Jul 2020 | WO |
WO2020159757 | Aug 2020 | WO |
WO2020167920 | Aug 2020 | WO |
WO2020214642 | Oct 2020 | WO |
WO2020219721 | Oct 2020 | WO |
WO2020242377 | Dec 2020 | WO |
WO2021092386 | May 2021 | WO |
WO2021142233 | Jul 2021 | WO |
WO2021146207 | Jul 2021 | WO |
WO2021146219 | Jul 2021 | WO |
WO2021146636 | Jul 2021 | WO |
WO2021155057 | Aug 2021 | WO |
WO2021155284 | Aug 2021 | WO |
WO2021163374 | Aug 2021 | WO |
WO2021168015 | Aug 2021 | WO |
WO2021168261 | Aug 2021 | WO |
WO2021178199 | Sep 2021 | WO |
WO2021247593 | Dec 2021 | WO |
WO2021257795 | Dec 2021 | WO |
WO2022015667 | Jan 2022 | WO |
WO2022026909 | Feb 2022 | WO |
WO2022040453 | Feb 2022 | WO |
WO2022115608 | Feb 2022 | WO |
WO2022115608 | Feb 2022 | WO |
WO2022076912 | Apr 2022 | WO |
WO2022132206 | Jun 2022 | WO |
WO2022143221 | Jul 2022 | WO |
WO2022256324 | Dec 2022 | WO |
WO2023034739 | Mar 2023 | WO |
WO2023034789 | Mar 2023 | WO |
WO2023034790 | Mar 2023 | WO |
WO2023034794 | Mar 2023 | WO |
WO2023034872 | Mar 2023 | WO |
WO2023039433 | Mar 2023 | WO |
Entry |
---|
Hug et al. Measurement of the number of molecules of a single mRNA species in a complex mRNA preparation. 2003. Journal of Theoretical Biology. vol. 221, No. 4, pp. 615-623. (Year: 2003). |
10X Genomics, Inc., 2019, User Guide: Visium Spatial Gene Expression Reagent Kits, www.10xGenomics.com, 76 pp. |
2018 Top 10 Innovations, The Scientist Magazine® (2018). Available at: https://www.thescientist.com/features/2018-top-10-innovations-65140, 16 pp. |
10X Genomics, Inc., 2022, “Chromium Fixed RNA Profiling Reagent Kits,” 10xGenomics.com, User Guide, in 95 pages. |
10x_LIT099_Product-Sheet_Chromium-Single-Cell-Multiome-ATAC-Gene-Expression_Letter_digital. |
Achim et al., “High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin,” Nature Biotechnology 2015, 33(5), 503-511. |
Adey et al., “Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition,” Genome Biology 2010, 11(R19), in 17 pages. |
Advisory Action dated Dec. 2, 2019 in U.S. Appl. No. 15/055,407. |
Advisory Action dated Nov. 29, 2019 in U.S. Appl. No. 15/084,307. |
Advisory Action dated Aug. 25, 2020 in U.S. Appl. No. 15/084,307. |
Advisory Action dated May 31, 2023 in U.S. Appl. No. 16/789,311. |
Agasti et al., “Photocleavable DNA barcode-antibody conjugates allow sensitive and multiplexed protein analysis in single cell,” J Am Chem Soc. 2012, 134(45), 18499-18502. |
Ahern, “Biochemical, Reagent Kits Offer Scientists Good Return on Investment,” The Scientist 1995, 9(15), in 5 pages. |
Alexandra M. Ewing of Richards, Layton and Finger, P.A., Entry of Appearance dated Jan. 18, 2019 in the USDC District of Delaware, C.A. No. 18-1800-RGA, 1 pp. |
Alkan et al., “Personalized copy number and segmental duplication maps using next-generation sequencing,” Nat Genet. 2009, 41(10):1061-1067. |
Anderson, “Study Describes RNA Sequencing Applications for Molecular Indexing Methods,” GenomeWeb 2014, 5 pp. |
Ansorge, “Next-generation DNA sequencing techniques,” New Biotechnology 2009, 25(4), 195-203. |
Applied Biosystems, Apr. 2008, SOLiD™ System Barcoding, Application Note, 4 pp. |
Argrawal et al., “Counting Single Native Biomolecules and Intact Viruses with Color-Coded Nanoparticles,” Analytical Chemistry 2006, 78, 1061-1070. |
Arguel et al., “A cost effective 5′ selective single cell transcriptome profiling approach with improved UMI design,” Nucleic Acids Research 2017, 45(7), e48, in 11 pages. |
Arslan et al., “An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays,” BMC Bioinformatics 2009, 10(411), 1-17. |
Armbrecht, et al. “Single-cell protein profiling in microchambers with barcoded beads”, Microsystems & Nanoengineering, 2019, 5:55. |
Atanur et al., “The genome sequence of the spontaneously hypertensive rat: Analysis and functional significance.” Genome Res. 2010, 20(6), 791-803. |
Audic et al., “The Significance of Digital Gene Expression Profiles,” Genome Res. 1997, 7, 986-995. |
Baek et al., “Development of Hydrogel TentaGel Shell-Core Beads for Ultra-high Throughput Solution Phase Screening of Encoded OBOC Combinatorial Small Molecule Libraries,” J. Comb Chem. 2009, 11(1), 91-102. |
BD Life Sciences, 2018, BD AbSeq antibody-oligo conjugates, www.bd.com/genomics, 2 pp. |
BD Life Sciences, 2018, BD AbSeq on the BD Rhapsody system: Exploration of single-cell gene regulation by simultaneous digital mRNA and protein quantification, www.bd.com/genomics, 7 pp. |
Bendall et al., “Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum,” Science 2011, 332(6030), 687-696. |
Bionumbers, “Useful fundamental numbers in molecular biology,” http://bionumbers.hms.harvard.edu/KeyNumbers/aspx, 1-4. |
Biosciences Product Catalogue, Dynal® Catalog 1999, Oslo, Norway, 49-51. |
Bioscribe “Massively parallel sequencing technology for single-cell gene expression published” (press release), PhysOrg 2015, 1-2. |
Blainey, “The future is now: single-cell genomics of bacteria and archaea,” FEMS Microbiol Rev. 2013, 37(3), 407-427. |
Bogdanova et al., “Normalization of full-length enriched cDNA,” Molecular Biosystems 2008, 4(3), 205-212. |
Bolivar et al., “Targeted next-generation sequencing of endometrial cancer and matched circulating tumor DNA: identification of plasma-based, tumor-associated mutations in early stage patients,” Modern Pathology 2019, 32(3), 405-414. |
Bonaldo et al., “Normalization and Subtraction: Two Approaches to facilitate Gene Discovery,” Genome Res. 1996, 6, 791-806. |
Bontoux et al., “Integrating whole transcriptome assays on a lab-on-a-chip for single cell gene profiling”, Lab on a Chip 2008, 8(3), 443-450. |
Bose et al., “Scalable microfluidics for single-cell RNA printing and sequencing,” Genome Biology 2015, 16(120), 1-16. |
Brady et al., “Construction of cDNA libraries form single cells”, Methods in Enzymology 1993, (225), 611-623. |
Braha et al., “Simultaneous stochastic sensing of divalent metal ions,” Nature Biotechnology 2000, 18, 1005-1007. |
Bratke et al., “Differential expression of human granzymes A, B, and K in natural killer cells and during CD8+ T cell differentiation in peripheral blood,” Eur J Immunol. 2005, 35, 2608-2616. |
Brenner et al., “Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays,” Nature Biotechnology 2000, 18, 630-634. |
Brenner et al., “In vitro cloning of complex mixtures of DNA on microbeads: Physical separation of differentially expressed cDNAs,” PNAS 2000, 97(4), 1665-1670. |
Brinza et al., “Detection of somatic mutations at 0.1% frequency from cfDNA in peripheral blood with a multiplex next-generation sequencing assay,” Conference Poster, AACR 107th Annual Meeting, Apr. 16-20, 2016, 1 p. |
Brisco et al., “Quantification of RNA integrity and its use for measurement of transcript number,” Nucleic Acids Research 2012, 40(18), e144, 1-9. |
Brodin et al., “Challenges with Using Primer IDs to Improve Accuracy of Next Generation Sequencing,” PLoS One 2015, 19(3), 1-12. |
Brouilette et al., “A Simple and Novel Method for RNA-seq Library Preparation of Single Cell cDNA Analysis by Hyperactive Tn5 Transposase,” Developmental Dynamics 2012, 241, 1584-1590. |
Buenrostro et al., “Transposition of native chromatin for multimodal regulatory analysis and personal epigenomics,” Nat Methods 2013, 10(12), 1213-1218. |
Buenrostro et al., “ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide,” Curr Protoc Mol Biol 2016, 109, 1-21. |
Buggenum et al., “A covalent and cleavable antibody DNA conjugation strategy for sensitive protein detection via immunoPCR,” Scientific Reports 2016, 6(22675), 1-12. |
Buschmann et al., Enhancing the detection of barcoded reads in high throughput DNA sequencing DNA by controlling the false discovery rate, BMC Bioinformatics, 15(1), 264, 1-16. |
Bustin, “Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays,” Journal of Molecular Endocrinology 2000, 25, 169-193. |
Butkus, “Cellular research set to launch first gene expression platform using ‘molecular indexing’ technology,” GenomeWeb 2014, 1-5. |
Cai, “Turning single cells in microarrays by super-resolution bar-coding,” Briefings in Functional Genomics 2012, 12(2), 75-80. |
Cao et al., “Comprehensive single-cell transcriptional profiling of a multicellular organism,” Science 2017, 357, 661-667. |
Carr et al., “Inferring relative proportions of DNA variants from sequencing electropherograms,” Bioinformatics 2009, 25(24), 3244-3250. |
Caruccio et al., “Nextera (TM) Technology for NGS DNA Library Preparation: Simultaneous Fragmentation and Tagging by in Vitro Transposition,” EpiBio 2009, 16(3), 4-6. |
Casbon et al., “A method for counting PCR template molecules with application to next-generation sequencing,” Nucleic Acids Res. 2011, 39(12), e81, 1-8. |
Castellarnau et al., “Stochastic particle barcoding for single-cell tracking and multiparametric analysis,” Small 2015, 11(4), 489-498. |
Castle et al., “DNA copy number, including telomeres and mitochondria, assayed using next-generation sequencing,” BMC Genomics 2010, 11(244), 1-11. |
CG000209_Chromium_NextGEM_SingleCell_ATAC_ReagentKits_v1.1_UserGuide_RevG. |
CG000496_Chromium_NextGEM_SingleCell_ATAC_ReagentKits_v2_UserGuide_RevB. |
CG000505_Chromium_Nuclei_Isolation_Kit_UG_RevA. |
Chamberlain et al., “Deletion screening of the Duchenne muscular dystrophy locus via multiplex DNA amplification,” Nucleic Acids Res. 1988, 16(23), 11141-11156. |
Chang et al., “Detection of Allelic Imbalance in Ascitic Supernatant by Digital Single Nucleotide Polymorphism Analysis,” Clinical Cancer Research, 8, 2580-2585. |
Chang et al., “Single-cell protein and gene expression profiling of stem memory T cells by BD Ab-seq,” Annual Joint Meeting of the American Society for Cell Biology and the European Molecular Biology Organization 2017, 28(26), p. 1896. |
Chapin et al., “Rapid microRNA Profiling on Encoded Gel Microparticles,” Angew Chem Int Ed Engl. 2011, 50(10), 2289-2293. |
Chee et al., “Accessing genetic information with high-density DNA arrays,” Science 1996, 274, 610-614. |
Chee, “Enzymatic multiplex DNA sequencing,” Nucleic Acids Research 1991, 19(12), 3301-3305. |
Chen et al., “Spatially resolved, highly multiplexed RNA profiling in single cells,” Science Express 2015, 348(6233), aaa6090, 1-36. |
Chen et al., “High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell,” Nature Biotechnology 2019, 37, 1452-1457. |
Chen et al., “Single-Cell Protein Secretion Detection and Profiling”, Annual Reviews, Anal. Chem, 2019, 12, 431-449. |
Church et al., “Multiplex DNA sequencing,” Science 1988, 240(4849), 185-188. |
Civil Cover Sheet filed Nov. 15, 2018 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 1 pp. |
Clontech Laboratories, Inc., “SMART™ PCR cDNA Synthesis Kit User Manual,” Clontech 2007, 1-39. |
Cloonan et al., “Stem cell transcriptome profiling via massive-scale mRNA sequencing”, Nature Methods 2008, 5(7), 613-619. |
Combined Search and Examination Report dated Aug. 6, 2014 in UK Patent Application No. 1408829.8. |
Combined Search and Examination Report dated Feb. 21, 2017 in UK Patent Application No. 1609740.4. |
Communication of a Notice of Opposition dated Jul. 27, 2016 in European Patent Application No. EP 10762102.1. |
Complaint filed in Becton, Dickinson and Company and Cellular Research Inc. v. 10X Genomics, Inc. dated Nov. 15, 2018 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 141 pp. |
Costa et al., “Single-Tube Nested Real-Time PCR as a New Highly Sensitive Approach to Trace Hazelnut,” Journal of Agricultural and Food Chemistry 2012, 60, 8103-8110. |
Costello et al., “Discovery and characterization of artefactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation,” Nucleic Acids Res 2013, 41(6), e67, 1-12. |
Cotten et al., “Selection of proteins with desired properties from natural proteome libraries using mRNA display,” Nature Protocols 2011, 6, 1163-1182. |
Cox, “Bar coding objects with DNA,” Analyst 2001, 126, 545-547. |
Craig et al., “Identification of genetic variants using bar-coded multiplexed sequencing,” Nat Methods 2008, 5(10), 887-893. |
Cusanovich et al., “Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing,” Science 2015, 348(6237), 910-914. |
Custom Antibody Services, Precision Antibody, accessed Apr. 16, 2014, 2 pp. |
Daines et al., “High-throughput multiplex sequencing to discover copy number variants in Drosophila,” Genetics 2009, 182(4), 182, 935-941. |
Dalerba et al., “Single-cell dissection of transcriptional heterogeneity in human colon tumors,” Nat Biotechnol. 2011, 29(12), 1120-1127. |
D'Antoni et al., “Rapid quantitative analysis using a single molecule counting approach,” Anal Biochem. 2006, 352, 97-109. |
Daser et al., “Interrogation of genomes by molecular copy-number counting (MCC),” Nature Methods 2006, 3(6), 447-453. |
Day et al., “Immobilization of polynucleotides on magnetic particles,” Biochem. J. 1991, 278, 735-740. |
De Saizieu et al., “Bacterial transcript imaging by hybridization of total RNA to oligonucleotide arrays,” Nature Biotechnology 1988, 16, 45-48. |
Decision of Grant dated Aug. 21, 2023 in Japanese Patent Application 2020-561800. |
Decision of Refusal dated Aug. 21, 2017 in Japanese Patent Application No. 2014-558975. |
Defendant 10X Genomic Inc.'s Notice of Service for Initial Requests for Production and Interrogatories Served to Becton, Dickinson, and Company and Cellular Research, Inc., dated May 31, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 2 pp. |
Defendant 10X Genomics Inc's, Notice of Service of Technical Documents, dated Jul. 8, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 2 pp. |
Defendant 10X Genomic's Motion for Admission Pro Hac Vice of Paul Ehrlich, Azra Hadzimehmedovic and Aaron Nathan, Pursuant to Local Rule 83.5, dated May 1, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 5 pp. |
Defendant 10X Genomic's Notice of Service for Initial Disclosures served to Opposing Counsel, dated Jun. 7, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 2 pp. |
Defendant 10X Genomic's Request for Oral Argument Under D. Del. LR 7.1.4, dated Apr. 18, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA 2 pp. |
Defendant 10X Genomic's Response Letter to Judge Richard G. Andrews re Request for a Rule 16, dated Apr. 16, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 2 pp. |
Defendant 10X Genomics, Inc.'s [Proposed] Order for Partial Dismissal Pursuant to Federal Rules of Civil Procedure 12(b)(6), dated Jan. 18, 2019 in the USDC District of Delaware, C.A. No. 18-1800-RGA, 1 pp. |
Defendant 10X Genomics, Inc.'s Letter to Judge Andrews in Response to Plaintiff's Letter of Supplemental Authority, dated Jul. 11, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 2 pp. |
Defendant 10X Genomics, Inc.'s Motion for Admission Pro Hac Vice Pursuant to Local Rule 83.5, dated Jan. 18, 2019 in the USDC District of Delaware, C.A. No. 18-1800-RGA, 5 pp. |
Defendant 10X Genomics, Inc.'s Motion to Dismiss Pursuant to Federal Rule of Civil Procedure 12(b)(6), dated Jan. 18, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 1 pp. |
Defendant 10X Genomics, Inc.'s Motion to Dismiss the First Amended Complaint Pursuant to Federal Rule of Civil Procedure 12(b)(6), dated Mar. 1, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 1 pp. |
Defendant 10X Genomics, Inc.'s Opening Brief in Support of Its Motion to Dismiss Pursuant to Federal Rule of Civil Procedure 12(b)(6), dated Jan. 18, 2019 in the USDC District of Delaware, C.A. No. 1:18-cv-01800-RGA, 25 pp. |
Defendant 10X Genomics, Inc.'s Opening Brief in Support of Its Motion to Dismiss Pursuant to Federal Rule of Civil Procedure 12(b)(6), dated Mar. 1, 2019 in the USDC District of Delaware, C.A. No. 18-1800 RGA, 26 pp. |
Defendant 10X Genomics, Inc.'s Rule 7.1 Disclosure Statement, dated Jan. 18, 2019 in the USDC District of Delaware, C.A. No. 18-1800-RGA, 1 pp. 1. |
Defendant 10X Genomics, Inc's Proposed Order for Dismissal pursuant to Federal Rules of Civil Procedure 12(b)(6), filed Mar. 1, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 1 pp. |
Defendant 10X Genomics's Reply Brief in support of its Motion to Dismiss Pursuant to Federal Rule of Civil Procedure 12(b)(6), dated Apr. 12, 2019 in USDC District of Delaware, C.A. No. 18-1800 RGA, 15 pp. |
Delebecque et al. “Designing and using RNA scaffolds to assemble proteins in vivo”. Nature protocols, 2012, 7(10), 1797-1807. |
Dengl et al., “Engineered hapten-binding antibody derivatives for modulation of pharmacokinetic properties of small molecules and targeted payload delivery,” Immunol Rev. 2016, 270, 165-177. |
Delley et al., “Combined aptamer and transcriptome sequencing of single cells,” bioRxiv 2017, 1-10. |
De Simone et al., “Single Cell T Cell Receptor Sequencing: Techniques and Future Challenges,” Frontiers in Immunology 2018, 9(1638), 1-7. |
Di Carlo et al., “Dynamic single-cell analysis for quantitative biology,” Analytical Chemistry 2006, 78(23), 7918-7925. |
Dickey and Giangrande. “Oligonucleotide Aptamers: A Next-Generation Technology for the Capture and Detection of Circulating Tumor Cells.” Methods, 2016 97:94-103. |
Dirks et al., Triggered amplification by hybridization chain reaction., Proc Natl Acad Sci 2014, 101(43), 15275-15278. |
Dovgan et al., “Antibody-Oligonucleotide Conjugates as Therapeutic, Imaging, and Detection Agents,” Bioconjugate Chem. 2019, 30, 2483-2501. |
Dua, et al. “Patents on SELEX and therapeutic aptamers. Recent patents on DNA & gene sequences,” 2008, 2( 3), 172-186. |
Dube et al., “Mathematical Analysis of Copy Number Variation in a DNA Sample Using Digital PCR on a Nanofluidic Device,” PLoS One 2008, 3(8) e2876. |
Eberwine et al., “Analysis of gene expression in single live neurons,” Proc. Natl. Acad. Sci. 1992, 89, 3010-3014. |
Erickson et al., “AbSeq Protocol Using the Nano-Well Cartridge-Based Rhapsody Platform toGenerate Protein and Transcript Expression Data on the Single-Cell Level,” STAR Protocols 2020, in 31 pages. |
Eulberg, et al. “Development of an automated in vitro selection protocol to obtain RNA-based aptamers: identification of a biostable substance P antagonist,” Nucleic acids research, 2005, 33(4), e45. https://doi.org/ 10.1093/nar/gni044. |
Evanko et al., “Hybridization chain reaction,” Nature Methods 2004, 1(3), 186-187. |
Examination Report dated Apr. 10, 2017 in European Patent Application No. 14761937.3. |
Examination Report dated Apr. 26, 2019 in European Patent Application No. 16710357.1. |
Examination Report dated Aug. 2, 2019 in European Patent Application No. 17202409.3. |
Examination Report dated Dec. 12, 2018 in European Patent Application No. 16719706.0. |
Examination Report dated Dec. 4, 2019 in European Patent Application No. 16719706.0. |
Examination Report dated Feb. 19, 2016 in United Kingdom Patent Application No. GB1511591.8. |
Examination Report dated Feb. 6, 2019 in European Patent Application No. 13754428.4. |
Examination Report dated Jan. 2, 2019 in European Patent Application No. 16757986.1. |
Examination Report dated Jan. 27, 2016 in United Kingdom Patent Application No. 1408829.8. |
Examination Report dated Jan. 3, 2018 in UK Patent Application No. 1609740.4. |
Examination Report dated Jul. 12, 2016 in European Patent Application No. 13755319.4. |
Examination Report dated Jul. 20, 2018 in Australian Patent Application No. 2014312208. |
Examination Report dated May 12, 2020 in Australian Patent Application No. 2018220004. |
Examination Report dated Jul. 24, 2019 in European Patent Application No. 16714081.3. |
Examination Report dated Jun. 15, 2016 in United Kingdom Patent Application No. GB1511591.8. |
Examination Report dated Jun. 18, 2019 in European Patent Application No. 16710551.9. |
Examination Report dated Jun. 8, 2016 in United Kingdom Patent Application No. 1408829.8. |
Examination Report dated Mar. 16, 2018 in European Patent Application No. 13754428.4. |
Examination Report dated Mar. 18, 2019 in Singapore Patent Application No. 11201405274W. |
Examination Report dated Oct. 10, 2017 in European Patent Application No. 14761937.3. |
Examination Report dated Oct. 11, 2019 in European Patent Application No. 16757986.1. |
Examination Report dated Oct. 24, 2017 in Australian Patent Application No. 2013226081. |
Examination Report dated Sep. 26, 2018 in European Patent Application No. 16714081.3. |
Examination Report dated Sep. 5, 2018 in European Patent Application No. 16710357.1. |
Examination Report dated Feb. 19, 2020 in European Patent Application No. 16710551.9. |
Examination Report dated Mar. 18, 2020 in European Patent Application No. 17202409.3. |
Examination Report dated Jul. 6, 2020 in European Patent Application No. 17781265.8. |
Examination Report dated Sep. 21, 2020 in European Patent Application No. 18703156.2. |
Examination Report dated Sep. 21, 2023 in Canadian Patent Application 3,034,924. |
Examination Report dated Nov. 12, 2020 in European Patent Application No. 18716877.8. |
Examination Report dated Dec. 3, 2020 in European Patent Application No. 16719706.0. |
Examination Report dated Mar. 25, 2021 in European Patent Application No. 17781265.8. |
Examination Report dated Oct. 8, 2021 in European Patent Application No. 18716877.8. |
Examination Report dated Nov. 18, 2021 in European Patent Application No. 19724003.9. |
Examination Report dated Nov. 24, 2021 in European Patent Application No. 19762517.1. |
Examination Report dated Dec. 6, 2021 in European Patent Application No. 18703156.2. |
Examination Report dated Dec. 9, 2021 in European Patent Application No. 19723988.2. |
Examination Report dated Apr. 7, 2022 in Singapore Patent Application No. 10201806890V. |
Examination Report dated Apr. 8, 2022 in Australian Patent Application No. 2018281745. |
Examination Report dated Oct. 31, 2023 in European Patent Application 20753616.0. |
Examination Report dated Nov. 9, 2023 in European Patent Application 20711394.5. |
Examination Report Dated Nov. 24, 2023 in European Patent Application 20209777.0. |
Extended European Search Report dated May 6, 2021 in European Patent Application No. 20207621.2. |
Extended European Search Report dated May 28, 2021 in European Patent Application No. 20209777.0. |
Extended European Search Report Dated Oct. 4, 2023 in European Patent Application No. 23166582.9. |
Exhibit A filed Jul. 10, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 25 pp. |
Exhibits 12-32 filed Feb. 8, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 795 pp. |
Exhibits A-D filed Jan. 18, 2019 in the USDC District of Delaware, C.A. No. 1:18-cv-01800-RGA, 47 pp. |
Exhibits A-E filed Mar. 1, 2019 in the USDC District of Delaware, C.A. No. 18-1800 RGA, 75 pp. |
Extended European Search Report dated Dec. 14, 2015 in European Patent Application No. 13754428.4. |
Extended European Search Report dated Feb. 8, 2018 in European Patent Application No. 17202409.3. |
Extended European Search Report dated Jul. 17, 2015 in European Patent Application No. 13755319.4. |
Extended European Search Report dated Jun. 11, 2018 in European Patent Application No. 16740872.3. |
Extended European Search Report dated Mar. 22, 2019 in European Patent Application No. 18195513.9. |
Fan et al., “Combinatorial labeling of single cells for gene expression cytometry,” Science 2015, 347(6222), 1258366-1258369. |
Fan et al., “Microfluidic digital PCR enables rapid prenatal diagnosis of fetal aneuploidy,” Am Obstet Gynecol. 2009, 200, 543e1-543e7. |
Fan et al., “Non-invasive Prenatal Measurement of the Fetal Genome,” Nature 2012, 487(7407), 320-324. |
Fan et al., “Parallel Genotyping of Human SNPs Using Generic High-density Oligonucleotide Tag Arrays,” Genome Research 2000, 10, 853-860. |
Fan, “Molecular counting: from noninvasive prenatal diagnostics to whole-genome haplotyping,” Doctoral Dissertation, Stanford University 2010, 1-185. |
Fathi,, P. Design and Characterization of SSDNA Aptamer Candidates to Bind Bacteroides Fragilis Toxin Subtypes BFT-1 and BFT-2 (Doctoral dissertation, Johns Hopkins University).2017. |
Feldhaus et al., “Oligonucleotide-conjugated beads for transdominant genetic experiments,” Nucleic Acids Res. 2000, 28(2), 534-543. |
Final Office Action dated Apr. 11, 2016 for U.S. Appl. No. 14/800,526. |
Final Office Action dated Aug. 12, 2016 in U.S. Appl. No. 14/381,488. |
Final Office Action dated Apr. 22, 2019 in U.S. Appl. No. 15/987,851. |
Final Office Action dated Dec. 4, 2019 in U.S. Appl. No. 15/596,364. |
Final Office Action dated Feb. 13, 2017 in U.S. Appl. No. 14/381,488. |
Final Office Action dated Feb. 19, 2019 in U.S. Appl. No. 14/381,526. |
Final Office Action dated Feb. 4, 2020 in U.S. Appl. No. 15/715,028. |
Final Office Action dated Jan. 16, 2020 in U.S. Appl. No. 16/012,584. |
Final Office Action dated Jan. 25, 2018 in U.S. Appl. No. 14/381,526. |
Final Office Action dated Jan. 29, 2020 in U.S. Appl. No. 14/381,488. |
Final Office Action dated Jan. 8, 2020 in U.S. Appl. No. 15/459,977. |
Final Office Action dated Jul. 20, 2016 for U.S. Appl. No. 14/281,706. |
Final Office Action dated Jul. 20, 2018 in U.S. Appl. No. 15/217,886. |
Final Office Action dated Jul. 5, 2018 in U.S. Appl. No. 15/004,618. |
Final Office Action dated Mar. 1, 2019 in U.S. Appl. No. 16/012,584. |
Final Office Action dated May 10, 2018 in U.S. Appl. No. 14/381,488. |
Final Office Action dated May 2, 2019 in U.S. Appl. No. 16/012,635. |
Final Office Action dated May 3, 2018 in U.S. Appl. No. 15/046,225. |
Final Office Action dated May 3, 2019 in U.S. Appl. No. 15/937,713. |
Final Office Action dated May 8, 2017 in U.S. Appl. No. 15/224,460. |
Final Office Action dated Nov. 16, 2017 in U.S. Appl. No. 14/381,488. |
Final Office Action dated Nov. 16, 2018 in U.S. Appl. No. 15/134,967. |
Final Office Action dated Oct. 16, 2017 in U.S. Appl. No. 15/409,355. |
Final Office Action dated Oct. 2, 2019 in U.S. Appl. No. 15/084,307. |
Final Office Action dated Oct. 6, 2015 in U.S. Appl. No. 14/540,018. |
Final Office Action dated Sep. 1, 2015 for U.S. Appl. No. 14/540,029. |
Final Office Action dated Sep. 18, 2019 in U.S. Appl. No. 15/055,407. |
Final Office Action dated Sep. 24, 2015 for U.S. Appl. No. 14/540,007. |
Final Office Action dated Mar. 9, 2020 in U.S. Appl. No. 15/987,851. |
Final Office Action dated Apr. 28, 2020 in U.S. Appl. No. 15/134,967. |
Final Office Action dated Jun. 5, 2020 in U.S. Appl. No. 15/084,307. |
Final Office Action dated Aug. 19, 2020 in U.S. Appl. No. 15/875,816. |
Final Office Action dated Sep. 14, 2020 in U.S. Appl. No. 16/789,358. |
Final Office Action dated Sep. 22, 2020 in U.S. Appl. No. 16/789,311. |
Final Office Action dated Sep. 25, 2020 in U.S. Appl. No. 15/055,407. |
Final Office Action dated Dec. 7, 2020 in U.S. Appl. No. 16/012,584. |
Final Office Action dated Feb. 11, 2021 in U.S. Appl. No. 15/134,967. |
Final Office Action dated Mar. 16, 2021 in U.S. Appl. No. 15/715,028. |
Final Office Action dated Mar. 25, 2021 in U.S. Appl. No. 16/374,626. |
Final Office Action dated Jun. 15, 2021 in U.S. Appl. No. 15/084,307. |
Final Office Action dated Jul. 15, 2021 in U.S. Appl. No. 16/836,750. |
Final Office Action dated Aug. 10, 2021 in U.S. Appl. No. 16/012,584. |
Final Office Action dated Aug. 27, 2021 in U.S. Appl. No. 15/055,407. |
Final Office Action dated Sep. 24, 2021 in U.S. Appl. No. 16/788,743. |
Final Office Action dated Oct. 1, 2021 in U.S. Appl. No. 16/677,012. |
Final Office Action dated Nov. 2, 2021 in U.S. Appl. No. 16/789,311. |
Final Office Action dated Jan. 18, 2022 in U.S. Appl. No. 16/588,405. |
Final Office Action dated Feb. 23, 2022 in U.S. Appl. No. 16/707,780. |
Final Office Action dated Mar. 15, 2022 in U.S. Appl. No. 16/374,626. |
Final Office Action dated Mar. 25, 2022 in U.S. Appl. No. 16/551,620. |
Final Office Action dated Apr. 12, 2022 in U.S. Appl. No. 15/084,307. |
Final Office Action dated May 26, 2022 in U.S. Appl. No. 16/747,737. |
Final Office Action dated Jun. 14, 2022 in U.S. Appl. No. 15/055,407. |
Final Office Action dated Aug. 23, 2022 in U.S. Appl. No. 16/012,584. |
Final Office Action dated Nov. 15, 2022 in U.S. Appl. No. 16/525,054. |
Final Office Action dated Nov. 16, 2022 in U.S. Appl. No. 16/588,405. |
Final Office Action dated Jan. 25, 2023 in U.S. Appl. No. 16/789,311. |
Final Office Action dated Jan. 26, 2023 in U.S. Appl. No. 16/459,444. |
Final Office Action dated Feb. 21, 2023 in U.S. Appl. No. 16/551,620. |
Final Office Action dated Apr. 25, 2023 in U.S. Appl. No. 16/525,054. |
Final Office Action dated May 15, 2023 in U.S. Appl. No. 16/551,638. |
Final Office Action dated May 19, 2023 in U.S. Appl. No. 17/163,177. |
Final Office Action dated May 31, 2023 in U.S. Appl. No. 16/934,530. |
Final Office Action dated Jun. 8, 2023 in U.S. Appl. No. 17/147,283. |
Final Office Action dated Oct. 5, 2023 in U.S. Appl. No. 17/151,050. |
Final Office Action dated Oct. 13, 2023 in U.S. Appl. No. 15/055,407. |
Final Office Action dated Oct. 23, 2023 in U.S. Appl. No. 16/540,971. |
Final Office Action dated Dec. 27, 2023 in U.S. Appl. No. 17/174,249. |
Fitzgerald and Grivel, “A Universal Nanoparticle Cell Secretion Capture Assay,” Cytometry Part A 2012, 83A(2), 205-211. |
First Action Interview Office Action Summary dated Jan. 25, 2019 in U.S. Appl. No. 15/987,851. |
First Action Interview Pilot Program Pre-Interview Communication dated Oct. 15, 2018 in U.S. Appl. No. 15/987,851. |
Flanigon et al., “Multiplex protein detection with DNA readout via mass spectrometry,” N Biotechnol. 2013, 30(2), 153-158. |
Forster et al., “A human gut bacterial genome and culture collection for improved metagenomic analyses,” Nature Biotechnology 2019, 37, 186-192. |
Fox-Walsh et al., “A multiplex RNA-seq strategy to profile poly(A+) RNA: application to analysis of transcription response and 3′ end formation,” Genomics 2011, 98, 266-721. |
Fu et al., “Counting individual DNA molecules by the stochastic attachment of diverse labels,” Proc Natl Acad Sci 2011, 108(22), 9026-9031. |
Fu et al., Digital Encoding of Cellular mRNAs Enabling Precise and Absolute Gene Expression Measurement by Single-Molecule Counting. Anal Chem. 2014, 86, 2867-2870. |
Fu et al., “Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparation,” PNAS 2014, 111(5), 1891-1896. |
Gerlach, et al., “Combined quantification of intracellular (phospho-) proteins and transcriptomics from fixed single cells”, Scientific Reports, 2019 vol. 9:1469, pp. 1-10. |
Gertz et al., “Transposase mediated construction of RNA-seq libraries,” Genome Research 2012, 22, 134-141. |
Gerry et al., “Universal DNA Microarray Method for Multiplex Detection of Low Abundance Point Mutations,” Journal of Molecular Biology 1999, 292, 251-262. |
Gillespie, “Exact Stochastic Simulation of Coupled Chemical Reactions,” Journal of Physical Chemistry 1977, 81(25), 2340-2361. |
Gong et al., “Massively parallel detection of gene expression in single cells using subnanolitre wells,” Lab Chip 2010, 10, 2334-2337. |
Gong et al., “Simple Method Prepare Oligonucleotide-Conjugated Antibodies and Its Application in Multiplex Protein Detection in Single Cells,” Bioconjugate Chem. 2016, 27, 217-225. |
Goodridge et al., “Synthesis of Albumin and Malic Enzyme in Wheat-Germ Lysates and Xenopus laevis Oocytes Programmed with Chicken-Liver Messenger RNA,” Eur. J. Biochem. 1979, 96, 1-8. |
Gratton et al., “Cell-permeable peptides improve cellular uptake and therapeutic gene delivery of replication-deficient viruses in cells and in vivo,” Nature Medicine 2003, 9(3), 357-362. |
Grant et al., “SNP genotyping on a genome-wide amplified DOP-PCR template,” Nucleic Acids Res 2002, 30(22), e25, 1-6. |
Gu et al., “Complete workflow for detection of low frequency somatic mutations from cell-free DNA using Ion Torrent™ platforms,” Conference Poster, AACR 107th Annual Meeting, Apr. 16-20, 2016, 1 p. |
Gu et al., “Depletion of abundant sequences by hybridization (DSH): using Cas9 to remove unwantedhigh-abundance species in sequencing libraries and molecular counting applications,” Genome Biology 2016, 17(41) 1-13. |
Gunderson et al., “Decoding Randomly Ordered DNA Arrays,” Genome Research 2004, 14, 870-877. |
Gundry et al., “Direct mutation analysis by high-throughput sequencing: from germline to low-abundant, somatic variants,” Mutat Res. 2012, 729(1-2), 1-15. |
Gundry et al., “Direct, genome-wide assessment of DNA mutations in single cells,” Nucleic Acids Research 2011, 40(5), 2032-2040. |
Hacia et al., “Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays,” Nature Genetics 1999, 22, 164-167. |
Haff, “Improved Quantitative PCR Using Nested Primers,” PCR Methods and Applications 1994, 3, 332-337. |
Hamady et al., “Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex,” Nat Methods 2008, 5(3), 235-237. |
Han et al., “An approach to multiplexing an immunosorbent assay with antibody-oligonucleotide conjugates,” Bioconjug Chem. 2010, 21(12), 2190-2196. |
Harbers, “The current status of cDNA cloning,” Genomics 2008, 91, 232-242. |
Harrington et al., Cross-sectional characterization of HIV-1 env compartmentalization in cerebrospinal fluid over the full disease course, AIDS 2009, 23(8), 907-915. |
Hartmann, “Gene expression profiling of single cells on large-scale oligonucleotide arrays”, Nucleic Acids Research, (Oct. 2006) vol. 34, No. 21, p. e143, 1-12. |
Hashimshony et al., “CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification,” Cell Rep. 2012, 2(3), 666-673. |
Hensel et al., “Simultaneous identification of bacterial virulence genes by negative selection,” Science 1995, 269(5222), 400-403. |
Hiatt et al., “Parallel, tag-directed assembly of locally derived short sequence reads,” Nat Methods 2010, 7(2), 119-122. |
Hiatt et al., “Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation,” Genome Res. 2013, 23(5), 843-854. |
Hoinka and Przytycka. “AptaPLEX-A Dedicated, Multithreaded Demultiplexer for HT-SE LEX Data.” Methods, 2016, 106:82-85. |
Holcomb et al., “Abstract 1853: Single-cell multiplexed profiling of protein-level changes induced by EGFR inhibitor gefitinib,” Cancer Res 2016, 76(14 Suppl), Abstract 1853. |
Hollas et al., “A stochastic approach to count RNA molecules using DNA sequencing methods,” Algorithms in Bioinformatics. WABI 2003, Lecture Notes in Computer Science, 2812, 55-62. |
How many species of bacteria are there? Wisegeek.org, accessed Jan. 21, 2014, 2 pp. |
Hu et al., “Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq,” Molecular Cell 2017, 68, 1006-1015. |
Hu et al., “Single Cell Multi-Omics Technology: Methodology and Application,” Frontiers in Cell and Developmental Biology 2018, 6(28), 1-13. |
Hug et al., Measure of the Number of Molecular of a Single mRNA Species in a Complex mRNA Preparation, Journal of Theoretical Biology 2003, 221, 615-624. |
Illumina, “Data Processing of Nextera Mate Pair Reads on Illumina Sequencing Platforms”, Data Processing Technical Note from 2012. |
Illumina, “Estimating Sequencing Coverage” Technical Note: Sequencing from 2014. |
Illumina, “Optimizing Cluster Density on Illumina Sequencing Systems”, Publication No. 770-2014-031, 2016. |
Ingolia et al., Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling, Science 2009, 324(5924), 218-223. |
International Preliminary Report on Patentability dated Aug. 15, 2019 in PCT Application No. PCT/US2018/014385. |
International Preliminary Report on Patentability dated Mar. 26, 2019 in PCT Application No. PCT/US2017/053331. |
International Search Report and Written Opinion dated Aug. 16, 2013 for PCT Application No. PCT/US2013/027891. |
International Search Report and Written Opinion dated Sep. 28, 2016 in PCT Application No. PCT/US2016/028694. |
International Search Report and Written Opinion dated Aug. 7, 2017 in PCT Application No. PCT/US2017/034576. |
International Search Report and Written Opinion dated Aug. 9, 2016 in PCT Application No. PCT/US2016/019971. |
International Search Report and Written Opinion dated Dec. 19, 2014 in PCT Application No. PCT/US2014/059542. |
International Search Report and Written Opinion dated Dec. 4, 2019 in PCT Application No. PCT/US2019/053868. |
International Search Report and Written Opinion dated Dec. 5, 2016 in PCT Application No. PCT/US2016/024783. |
International Search Report and Written Opinion dated Feb. 3, 2015 in PCT Application No. PCT/US2014/053301. |
International Search Report and Written Opinion dated Jan. 27, 2020 in PCT Application No. PCT/US2019/048179. |
International Search Report and Written Opinion dated Jan. 31, 2017 in PCT Application No. PCT/US2016/050694. |
International Search Report and Written Opinion dated Jul. 16, 2018 in PCT Application No. PCT/US2018/024602. |
International Search Report and Written Opinion dated Jun. 14, 2013 in PCT Application No. PCT/US2013/028103. |
International Search Report and Written Opinion dated Jun. 17, 2016 in PCT Application No. PCT/US2016/019962. |
International Search Report and Written Opinion dated Jun. 20, 2016 in PCT Application No. PCT/US2016/014612. |
International Search Report and Written Opinion dated Jun. 24, 2019 in PCT Application No. PCT/US2019/030175. |
International Search Report and Written Opinion dated Jun. 6, 2012 in PCT Application No. PCT/US2011/065291. |
International Search Report and Written Opinion dated Jun. 9, 2016 in PCT Application No. PCT/US2016/022712. |
International Search Report and Written Opinion dated Mar. 20, 2018 in PCT Application No. PCT/US2017/053331. |
International Search Report and Written Opinion dated Mar. 28, 2018 in PCT Application No. PCT/US2018/014385. |
International Search Report and Written Opinion dated May 3, 2016 in PCT Application No. PCT/US2016/018354. |
International Search Report and Written Opinion dated May 7, 2012 for PCT Application No. PCT/IB2011/003160. |
International Search Report and Written Opinion dated Nov. 27, 2019 in PCT Application No. PCT/US2019/046549. |
International Search Report and Written Opinion dated Oct. 16, 2019 in PCT Application No. PCT/US2019/030245. |
International Search Report and Written Opinion dated Oct. 8, 2019 in PCT Application No. PCT/US2019/043949. |
International Search Report and Written Opinion dated Sep. 27, 2016 in PCT Application No. PCT/US2016/034473. |
International Search Report and Written Opinion dated Sep. 8, 2017 in PCT Application No. PCT/US2017/030097. |
International Search Report and Written Opinion dated Mar. 30, 2020 in PCT Application No. PCT/US2019/060243. |
International Search Report and Written Opinion dated Mar. 30, 2020 in PCT Application No. PCT/US2019/065237. |
International Search Report and Written Opinion dated May 18, 2020 in PCT Application No. PCT/US2020/014339. |
International Search Report and Written Opinion dated Nov. 12, 2020 in PCT Application No. PCT/US2020/042880. |
International Search Report and Written Opinion dated Jan. 19, 2021 in PCT Application No. PCT/US2020/059419. |
International Search Report and Written Opinion dated Apr. 9, 2021 in PCT Application No. PCT/US2021/013137. |
International Search Report and Written Opinion dated Apr. 21, 2021 in PCT Application No. PCT/US2021/015571. |
International Search Report and Written Opinion dated May 4, 2021 in PCT Application No. PCT/US2021/013109. |
International Search Report and Written Opinion dated May 11, 2021 in PCT Application No. PCT/US2021/013748. |
International Search Report and Written Opinion dated Jul. 15, 2021 in PCT Application No. PCT/US2021/019475. |
International Search Report and Written Opinion dated Jul. 20, 2021 in PCT Application No. PCT/US2021/015898. |
International Search Report and Written Opinion dated Aug. 31, 2021 in PCT Application No. PCT/US2021/035270. |
International Search Report and Written Opinion dated Sep. 22, 2021, in PCT Application No. PCT/US2021/013747. |
International Search Report and Written Opinion dated Sep. 27, 2021, in PCT Application No. PCT/US2021/017719. |
International Search Report and Written Opinion dated Oct. 12, 2021, in PCT Application No. PCT/US2021/041327. |
International Search Report and Written Opinion dated Oct. 29, 2021, in PCT Application No. PCT/US2021/032319. |
International Search Report and Written Opinion dated Dec. 6, 2021, in PCT Application No. PCT/US2021/046750. |
International Search Report and Written Opinion dated Nov. 12, 2021, in PCT Application No. PCT/US2021/044036. |
International Search Report and Written Opinion dated Mar. 10, 2022, in PCT Application No. PCT/US2021/060206. |
International Search Report and Written Opinion dated Apr. 12, 2022, in PCT Application No. PCT/US2021/059573. |
International Search Report and Written Opinion dated Mar. 11, 2022, in PCT Application No. PCT/US2021/060197. |
International Search Report and Written Opinion dated Apr. 5, 2022, in PCT Application No. PCT/US2021/062473. |
International Search Report and Written Opinion dated Jun. 8, 2022, in PCT Application No. PCT/US2022/021015. |
International Search Report and Written Opinion dated Jul. 29, 2022, in PCT Application No. PCT/US2022/029023. |
International Search Report and Written Opinion dated Jul. 29, 2022, in PCT Application No. PCT/US2022/029057. |
International Search Report and Written Opinion dated Dec. 5, 2022, in PCT Application No. PCT/US2022/075774. |
International Search Report and Written Opinion dated Dec. 15, 2022, in PCT Application No. PCT/US2022/075655. |
International Search Report and Written Opinion dated Dec. 20, 2022, in PCT Application No. PCT/US2022/075661. |
International Search Report and Written Opinion dated Jan. 9, 2023, in PCT Application No. PCT/US2022/076366. |
International Search Report and Written Opinion dated Jan. 17, 2023, in PCT Application No. PCT/US2022/076056. |
International Search Report and Written Opinion dated Feb. 13, 2023, in PCT Application No. PCT/US2022/075656. |
International Search Report and Written Opinion dated Jun. 5, 2023, in PCT Application No. PCT/US2023/061980. |
International Search Report and Written Opinion dated Jun. 23, 2023 in PCT Application No. PCT/US2023/062070. |
International Search Report and Written Opinion dated Jan. 12, 2024 in PCT Application PCT/US2023/078302. |
International Search Report and Written Opinion dated Feb. 27, 2024 in PCT Application PCT/US2023/036545. |
Invitation to Pay Fees dated May 25, 2021 in PCT Application No. PCT/US2021/01598. |
Invitation to Pay Additional Search Fees dated Sep. 8, 2021 in PCT Application No. PCT/US2021/032319. |
Invitation to Provide Informal Clarification dated Jun. 9, 2021 in PCT Application No. PCT/US2021/019475. |
Invitation to Pay Fees dated Mar. 16, 2016 in PCT Application No. PCT/US2016/019971. |
Invitation to Pay Fees dated May 16, 2018 in PCT Application No. PCT/US2018/024602. |
Invitation to Pay Fees dated Nov. 26, 2019 in PCT Application No. PCT/US2019/048179. |
Invitation to Pay Fees dated May 7, 2020 in PCT Application No. PCT/US2020/017890. |
Invitation to Respond to Written Opinion dated May 26, 2017 in Singapore Patent Application No. 11201405274W. |
Invitrogen, “The attraction is simply magnetisk, Dynabeads® Streptavidin products and applications” Invitrogen, 2010, 1-8. |
Islam et al., “Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq,” Genome Research 2011, 21, 1160-1167. |
Islam et al., “Highly multiplexed and strand specific single-cell RNA 5′ end sequencing,” Nature Protocols 2012, 7(5), 813-828. |
Islam et al., “Quantitative single-cell RNA-seq with unique molecular identifiers,” Nature Methods 2014, 11(2), 163-168. |
Jabara et al., “Accurate sampling and deep sequencing of the HIV-1 protease gene using a Primer ID,” PNAS 2011, 108(50), 20166-20171. |
Jabara, “Capturing the cloud: High throughput sequencing of multiple individual genomes from aretroviral population,” Biology Lunch Bunch Series, Training Initiatives in Biomedical & Biological Sciences of the University of North Carolina at Chapel Hill 2010. |
Jacobsen et al., “33rd Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer,” Journal for Immunotherapy of Cancer 2018, 6(S1), 7-11. |
Janeway et al., “Structural variation in immunoglobulin constant regions,” Immunology: The Immune System in Health and Disease 1999, 101-103. |
Jason J. Rawnsley of Richards, Layton and Finger, P.A., Entry of Appearance dated Jan. 18, 2019 in the USDC District of Delaware, C.A. No. 18-1800-RGA, 1 pp. |
Jiang et al., “Synthetic spike-in standards for RNA-seq experiments,” Genome Res. 2011, 21, 1543-1551. |
Joint Stipulation and Order to Extend Time to Respond to Plaintiff's First Amended Complaint, dated Feb. 21, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 2 pp. |
Joint Stipulation and Order to Extended Time to Submit Agreed Document Production Protocol, filed Jun. 28, 2019 in the USDC for the District of Delaware, C.A. 18-1800 (RGA), 1 pp. |
Joint Stipulation and Order to Request Extended Time to File Opposition to Defendant's Motion to Dismiss dated, Mar. 8, 2019 in the USDC District of Delaware, C.A. No. 18-1800 RGA, 2 pp. |
Joint Stipulation and Order to Request Extended Time to Submit a proposed Protective Order, dated Jun. 7, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 1 pp. |
Joint Stipulation and Order to Request Extended Time to Submit Agreed Document Production Protocol, dated Jul. 11, 2019 in the USDC for the District of Delaware, C.A. 18-1800 (RGA), 1 pp. |
Junker et al., “Single-Cell Transcriptomics Enters the Age of Mass Production,” Molecular Cell 2015, 58, 563-564. |
Kanagawa, “Bias and artifacts in multi-template polymerase chain reactions (PCR),” Journal of Bioscience and Bioengineering 2003, 96(4), 317-323. |
Kang et al., “Application of multi-omics in single cells,” Ann Biotechnol. 2018, 2(1007), 1-8. |
Kang et al., “Targeted sequencing with enrichment PCR: a novel diagnostic method for the detection of EGFR mutations,” Oncotarget 2015, 6(15), 13742-13749. |
Karrer et al., “In situ isolation of mRNA from individual plant cells: creation of cell-specific cDNA libraries,” Proc. Natl. Acad. Sci. USA 1995, 92, 3814-3818. |
Kausch et al., “Organelle Isolation by Magnetic Immunoabsorption,” BioTechniques 1999, 26(2), 336-343. |
Kebschull et al., “Sources of PCR-induced distortions in high-throughput sequencing data sets,” Nucleic Acids Research 2015, 1-15. |
Keys et al., Primer ID Informs Next-Generation Sequencing Platforms and Reveals Preexisting DrugResistance Mutations in the HIV-1 Reverse Transcriptase Coding Domain, AIDS Research and Human Retroviruses 2015, 31(6), 658-668. |
Kim et al., Polony Multiplex Analysis of Gene Expression (PMAGE) in Mouse Hypertrophic Cardiomyopathy, Science 2007, 316(5830), 1481-1484. |
Kinde et al., “Detection and quantification of rare mutations with massively parallel sequencing,” Proc. Natl Acad Sci 2011, 108(23), 9530-0535. |
Kirsebom et al., “Stimuli-Responsive Polymers in the 21st Century: Elaborated Architecture to Achieve High Sensitivity, Fast Response, and Robust Behavior,” Journal of Polymer Science: Part B: Polymer Physics 2011, 49, 173-178. |
Kivioja et al., “Counting absolute numbers of molecules using unique molecular identifiers,” Nature Proceedings 2011, 1-18. |
Klein et al., Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells, Cell 2015, 161, 1187-1201. |
Ko et al., “RNA-conjugated template-switching RT-PCR method for generating an Escherichia coli cDNA library for small RNAs,” Journal of Microbiological Methods 2006, 64, 297-304. |
Ko, “An ‘equalized cDNA library’ by the reassociation of short double-stranded cDNAs,” Nucleic Acids Res. 1990, 18(19), 5705-5711. |
Koboldt et al., VarScan: variant detection in massively parallel sequencing of individual and pooled samples, Bioinformatics 2009, 25(17), 2283-2285. |
Kolodziejczyk et al., The Technology and Biology of Single-Cell RNA Sequencing, Molecular Cell 2015, 58, 610-620. |
Konig et al., iCLIP reveals the function of hnRNAP particles in splicing at individual nucleotide resolution, Nature Structural & Molecular Biology 2010, 17(7), 909-916. |
Kotake et al., “A simple nested RT-PCR method for quantitation of the relative amounts of multiple cytokine mRNAs in small tissue samples,” Journal of Immunological Methods 1996, 199, 193-203. |
Kozarewa & Turner, “96-Plex Molecular Barcoding for the Illumina Genome Analyzer,” High-Throughput Next Generation Sequencing. Methods in Molecular Biology (Methods and Applications) 2011, 733, 24 pp. DOI: 10.1007/978-1-61779-089-8_20. |
Kozlov et al., “A high-complexity, multiplexed solution-phase assay for profiling protease activity on microarrays,” Comb Chem High Throughput Screen 2008, 11(1), 24-35. |
Ku, et al. “Nucleic Acid Aptamers: An Emerging Tool for Biotechnology and Biomedical Sensing.” Sensors, 2015, 15, 16281-16313. |
Kurimoto et al., “An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis,” Nucleic Acids Res. 2006, 34(5), e42, 1-17. |
Kurimoto et al., “Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis,” Nature Protocols 2007, 2(3), 739-752. |
Lake et al., “Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain,” Nature Biotechnology 2018, 36(1), 70-80. |
Lan et al., “Droplet barcoding for massively parallel single-molecule deep sequencing,” Nature Communications 2016, 7(11784), in 10 pages. |
Lamble et al., “Improved workflows for high throughput library preparation using the transposome-based nextera system,” BMC Biotechnology 2013, 13, 104, 1-10. |
Larson et al., “A single molecule view of gene expression,” Trends Cell Biol. 2009, 19(11), 630-637. |
Lass-Napiorkowska et al., “Detection methodology based on target molecule-induced sequence-specific binding to a single-stranded oligonucleotide,” Anal Chem. 2012, 84(7), 3382-3389. |
Lebl et al. “A High-Complexity, Multiplexed Solution-Phase Assay for Profiling Protease Activity oin Microarrays”, Combinatorial Chemistry and High Throughput Screening, 2008, 11(1), 24-35. |
Leamon et al., A massively parallel PicoTiterPlate based platform for discrete picoliter-scale polymerase chain reactions, Electrophoresis 2003, 24, 3769-3777. |
Lee et al., “Highly Multiplexed Subcellular RNA Sequencing in Situ,” Science 2014, 343, 1360-1363. |
Lee et al., “Large-scale arrays of picolitre chambers for single-cell analysis of large cell populations,” Lab Chip 2010, 10, 2952-2958. |
Lee et al., “Universal process-inert encoding architecture for polymer microparticles,” Nature Materials 2014, 13(5), 524-529. |
Letter regarding the opposition procedure dated Jul. 22, 2015 for European Patent Application No. 11810645.9. |
Letter to Judge Andrews regarding Agreement on Proposed Scheduling Order, dated May 7, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 1 pp. |
Letter to Judge Andrews regarding Notice of Supplemental Authority, dated Jul. 10, 2019 in the USDC for the District of Delaware, C.A. 18-1800(RGA), 2pp. |
Letter to Judge Richard G. Andrews Requesting a Rule 16 Conference, dated Apr. 15, 2019 in the USDC for the District of Delaware, C.A. 18-1800 (RGA), 1 pp. |
Lin et al., “Self-Assembled Combinatorial Encoding Nanoarrays for Multiplexed Biosensin,” Nano Lett. 2007, 7 (2), 507-512. |
Liu et al., “Single-cell transcriptome sequencing: recent advances and remaining challenges,” F1000Research 2016, 5(F1000 Faculty Rev)(182), 1-9. |
Livingstone, “rRNA depletion, poly(A) enrichment, or exonuclease treatment?” Tebu-Bio Blog 2015,. 5 pgs. |
Lizardi et al., “Mutation detection and single-molecule counting using isothermal rolling-circle amplification,” Nat Genet. 1998, 19, 225-232. |
Lockhart et al., “Expression monitoring by hybridization to high-density oligonucleotide arrays,” Nature Biotechnology 1996, 14, 1675-1680. |
Lovatt et al., “Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue,” Nat Methods 2014, 11(2), 190-196. |
Loy et al., “A rapid library preparation method with custom assay designs for detection of variants at 0.1% allelic frequency in liquid biopsy samples,” Oct. 2, 2018, 1 p. |
Lucito et al., “Representational Oligonucleotide Microarray Analysis: A High-Resolution Method to Detect Genome Copy Number Variation,” Genome Research 2003, 13, 2291-2305. |
Lundberg et al., “Practical innovations for high-throughput amplicon sequencing,” Nature Methods 2013, 10(10), 999-1007. |
Lundberg et al., “Supplementary Information for: Practical innovations for high-throughput amplicon sequencing,” Nature Methods 2013, 1-24. |
Lutz et al., “Isolation and analysis of high quality nuclear DNA with reduced organellar DNA for plant genome sequencing and resequencing,” BMC Biotechnology 2011, 11(54), in 9 pages. |
Maamar et al., “Noise in Gene Expression Determines Cell Fate in Bacillus subtilis,” Science 2007, 317, 526-529. |
Macaulay et al., “G&T-seq: parallel sequencing of single-cell genomes and transcriptomes,” Nature Methods 2015, 1-7. |
Macaulay et al., “Single Cell Genomics: Advances and Future Perspectives,” PLoS Genetics 2014, 10(1), 1-9. |
Macosko et al., “Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets,” Cell 2015, 161, 1202-1214. |
Maeda et al., “Development of a DNA barcode tagging method for monitoring dynamic changes in gene expression by using an ultra high-throughput sequencer,” BioTechniques 2008, 45(1), 95-97. |
Mair et al., “A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level”, Cell Reports 2020, 31(1), 107499, in 20 pages. |
Mairal et al. “Aptamers: Molecular Tools for Analytical Applications.” Analytical and bioanalytical chemistry 2008,390: 989-1007. |
Mayer et al., “Obtaining deeper insights into microbiome diversity using a simple method to block host and nontargets in amplicon sequencing,” Molecular Ecology Resources 2021, 21(6), 1952-1965. |
Makrigiorgos et al., “A PCR-Based amplification method retaining quantities difference between two complex genomes,” Nature Biotech 2002, 20(9), 936-939. |
Marcus et al., 2006, “Microfluidic single-cell mRNA isolation and analysis,” Anal Chem. 2006, 78, 3084-3089. |
Mardis, “Next-generation DNA sequencing methods”, Annu. Rev. Genomics Hum. Genet. 2008, 9, 387-402. |
Marguerat et al., “Next-generation sequencing: applications beyond genomes,” Biochem. Soc. Trans. 2008, 36(5), 1091-1096. |
Marguiles et al., Genome sequencing in microfabricated high-density picolitre reactors, Nature 2005, 437, 376-380. |
Martinez et al., “A microfluidic approach to encapsulate living cells in uniform alginate hydrogel microparticles,” Macromol. Biosci 2012, 12, 946-951. |
Massachusetts General Hospital, Overview of Illumina Chemistry, http://nextgen.mgh.harvard.edu/IlluminaChemistry.html, downloaded Jan. 28, 2020, 2 pp. |
McCloskey et al., “Encoding PCR products with batch-stamps and barcodes,” Biochem Genet. 2007, 45(11-12), 761-767. |
Medvedev et al., “Detecting copy number variation with mated short reads,” Genome Res. 2010, 20, 1613-1622. |
Mei et al., “Identification of recurrent regions of Copy-Number Variants across multiple individuals,” BMC Bioinformatics 2010, 11, 147, 1-14. |
Merriam-Webster, definition of associate: http://www.merriam-webster.com/dictionary/associate, accessed Apr. 5, 2016. |
Meyer et al., “Parallel tagged sequencing on the 454 platform,” Nature Protocols 2008, 3(2), 267-278. |
Miller et al., Directed evolution by in vitro compartmentalization, Nature Methods 2006, 3(7), 561-570. |
Miner et al., “Molecular barcodes detect redundancy and contamination in hairpin-bisulfite PCR,” Nucleic Acids Research 2004, 32(17), e135, 1-4. |
Minnoye et al., “Chromatin accessibility profiling methods,” Nature Reviews Method Primers 2021, 1-24. |
Monneron, “One-step Isolation and Characterization of Nuclear Membranes, 1974 Electron Microscopy and Composition of Biological Membranes and Envelops,” The Royal Publishing Society 1974, 268, 101-108. |
Mortazavi et al., “Mapping and quantifying mammalian transcriptomes by RNA-Seq,” Nat. Methods 2008, 5(7), 621-628. |
Motion and Order for Admission Pro Hac Vice Pursuant to Local Rule 83.5, dated Jan. 24, 2019 in the USDC District of Delaware, C.A. No. 18-1800-RGA, 7 pp. |
Nadai et al., Protocol for nearly full-length sequencing of HIV-1 RNA from plasma, PLoS One 2008, 3(1), e1420, 1-6. |
Nagai et al., “Development of a microchamber array for picoleter PCR,” Anal. Chem. 2001, 73, 1043-1047. |
Navin et al., “The first five years of single-cell cancer genomics and beyond,” Genome Research 2015, 25, 1499-1507. |
Newell et al., Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 2012, 36(1), 142-152. |
Non-Final Office Action dated Apr. 11, 2016 in U.S. Appl. No. 14/472,363. |
Non-Final Office Action dated Apr. 6, 2018 in U.S. Appl. No. 15/603,239. |
Non-Final Office Action dated Aug. 17, 2016 for U.S. Appl. No. 14/800,526. |
Non-Final Office Action dated Aug. 20, 2019 in U.S. Appl. No. 15/715,028. |
Non-Final Office Action dated Dec. 3, 2015 for U.S. Appl. No. 14/281,706. |
Non-Final Office Action dated Dec. 31, 2015 for U.S. Appl. No. 14/800,526. |
Non-Final Office Action dated Feb. 18, 2015 for U.S. Appl. No. 14/540,007. |
Non-Final Office Action dated Feb. 19, 2019 in U.S. Appl. No. 14/381,526. |
Non-Final Office Action dated Feb. 26, 2015 for U.S. Appl. No. 14/540,029. |
Non-Final Office Action dated Feb. 5, 2020 in U.S. Appl. No. 15/875,816. |
Non-Final Office Action dated Jan. 12, 2018 in U.S. Appl. No. 15/217,886. |
Non-Final Office Action dated Jan. 14, 2019 in U.S. Appl. No. 16/219,553. |
Non-Final Office Action dated Jan. 17, 2020 in U.S. Appl. No. 15/084,307. |
Non-Final Office Action dated Jan. 19, 2017 in U.S. Appl. No. 15/055,445. |
Non-Final Office Action dated Jan. 7, 2019 in U.S. Appl. No. 15/055,407. |
Non-Final Office Action dated Jan. 9, 2018 in U.S. Appl. No. 15/217,896. |
Non-Final Office Action dated Jul. 25, 2018 in U.S. Appl. No. 15/108,268. |
Non-Final Office Action dated Jul. 28, 2017 in U.S. Appl. No. 14/975,441. |
Non-Final Office Action dated Jul. 9, 2019 in U.S. Appl. No. 15/596,364. |
Non-Final Office Action dated Jun. 17, 2019 in U.S. Appl. No. 14/381,488. |
Non-Final Office Action dated Jun. 2, 2017 in U.S. Appl. No. 14/381,526. |
Non-Final Office Action dated Jun. 7, 2017 in U.S. Appl. No. 14/381,488. |
Non-Final Office Action dated Mar. 19, 2015 in U.S. Appl. No. 14/540,018. |
Non-Final Office Action dated Mar. 19, 2019 in U.S. Appl. No. 15/046,225. |
Non-Final Office Action dated Mar. 24, 2017 in U.S. Appl. No. 15/409,355. |
Non-Final Office Action dated Mar. 8, 2018 in U.S. Appl. No. 15/608,780. |
Non-Final Office Action dated May 10, 2016 in U.S. Appl. No. 14/381,488. |
Non-Final Office Action dated May 13, 2016 in U.S. Appl. No. 14/508,911. |
Non-Final Office Action dated May 15, 2019 in U.S. Appl. No. 15/084,307. |
Non-Final Office Action dated May 23, 2019 in U.S. Appl. No. 15/459,977. |
Non-Final Office Action dated May 7, 2015 for U.S. Appl. No. 13/327,526. |
Non-Final Office Action dated Nov. 1, 2017 in U.S. Appl. No. 15/667,125. |
Non-Final Office Action dated Nov. 26, 2018 in U.S. Appl. No. 15/937,713. |
Non-Final Office Action dated Nov. 29, 2019 in U.S. Appl. No. 15/937,713. |
Non-Final Office Action dated Nov. 5, 2018 in U.S. Appl. No. 16/038,790. |
Non-Final Office Action dated Nov. 9, 2017 in U.S. Appl. No. 15/004,618. |
Non-Final Office Action dated Oct. 11, 2016 in U.S. Appl. No. 15/224,460. |
Non-Final Office Action dated Oct. 25, 2018 in U.S. Appl. No. 16/012,584. |
Non-Final Office Action dated Oct. 3, 2013 in U.S. Appl. No. 12/969,581. |
Non-Final Office Action dated Oct. 4, 2018 in U.S. Appl. No. 15/260,106. |
Non-Final Office Action dated Sep. 18, 2019 in U.S. Appl. No. 16/194,819. |
Non-Final Office Action dated Sep. 26, 2016 in U.S. Appl. No. 15/167,807. |
Non-Final Office Action dated Sep. 8, 2017 in U.S. Appl. No. 15/046,225. |
Non-Final Office Action dated Sep. 8, 2017 in U.S. Appl. No. 15/134,967. |
Non-Final Office Action dated Mar. 17, 2020 in U.S. Appl. No. 15/055,407. |
Non-Final Office Action dated Mar. 12, 2020 in U.S. Appl. No. 16/789,358. |
Non-Final Office Action dated Mar. 26, 2020 in U.S. Appl. No. 16/789,311. |
Non-Final Office Action dated Mar. 26, 2020 in U.S. Appl. No. 16/012,635. |
Non-Final Office Action dated Jun. 8, 2020 in U.S. Appl. No. 15/715,028. |
Non-Final Office Action dated Aug. 4, 2020 in U.S. Appl. No. 15/459,977. |
Non-Final Office Action dated Aug. 19, 2020 in U.S. Appl. No. 16/374,626. |
Non-Final Office Action dated Aug. 25, 2020 in U.S. Appl. No. 14/381,488. |
Non-Final Office Action dated Dec. 4, 2020 in U.S. Appl. No. 16/677,012. |
Non-Final Office Action dated Dec. 9, 2020 in U.S. Appl. No. 16/788,743. |
Non-Final Office Action dated Jan. 19, 2021 in U.S. Appl. No. 16/836,750. |
Non-Final Office Action dated Feb. 2, 2021 in U.S. Appl. No. 16/535,080. |
Non-Final Office Action dated Feb. 25, 2021 in U.S. Appl. No. 15/055,407. |
Non-Final Office Action dated Feb. 25, 2021 in U.S. Appl. No. 15/084,307. |
Non-Final Office Action dated Mar. 29, 2021 in U.S. Appl. No. 16/789,358. |
Non-Final Office Action dated Apr. 14, 2021 in U.S. Appl. No. 16/789,311. |
Non-Final Office Action dated Apr. 20, 2021 in U.S. Appl. No. 15/875,816. |
Non-Final Office Action dated May 18, 2021 in U.S. Appl. No. 16/535,080. |
Non-Final Office Action dated Jun. 9, 2021 in U.S. Appl. No. 16/588,405. |
Non-Final Office Action dated Aug. 17, 2021 in U.S. Appl. No. 16/551,620. |
Non-Final Office Action dated Aug. 19, 2021 in U.S. Appl. No. 16/781,814. |
Non-Final Office Action dated Aug. 31, 2021 in U.S. Appl. No. 15/715,028. |
Non-Final Office Action dated Sep. 1, 2021 in U.S. Appl. No. 16/789,358. |
Non-Final Office Action dated Sep. 14, 2021 in U.S. Appl. No. 16/707,780. |
Non-Final Office Action dated Sep. 28, 2021 in U.S. Appl. No. 16/400,885. |
Non-Final Office Action dated Sep. 30, 2021 in U.S. Appl. No. 16/374,626. |
Non-Final Office Action dated Oct. 1, 2021 in U.S. Appl. No. 16/677,012. |
Non-Final Office Action dated Oct. 8, 2021 in U.S. Appl. No. 16/400,866. |
Non-Final Office Action dated Dec. 15, 2021 in U.S. Appl. No. 15/875,816. |
Non-Final Office Action dated Dec. 21, 2021 in U.S. Appl. No. 15/055,407. |
Non-Final Office Action dated Jan. 6, 2022 in U.S. Appl. No. 15/084,307. |
Non-Final Office Action dated Feb. 3, 2022 in U.S. Appl. No. 16/747,737. |
Non-Final Office Action dated Feb. 9, 2022 in U.S. Appl. No. 16/525,054. |
Non-Final Office Action dated Apr. 5, 2022 in U.S. Appl. No. 16/400,885. |
Non-Final Office Action dated Apr. 8, 2022 in U.S. Appl. No. 16/232,287. |
Non-Final Office Action dated May 3, 2022 in U.S. Appl. No. 16/012,584. |
Non-Final Office Action dated May 11, 2022 in U.S. Appl. No. 16/588,405. |
Non-Final Office Action dated May 19, 2022 in U.S. Appl. No. 16/459,444. |
Non-Final Office Action dated Jul. 7, 2022 in U.S. Appl. No. 16/788,743. |
Non-Final Office Action dated Jul. 7, 2022 in U.S. Appl. No. 16/677,012. |
Non-Final Office Action dated Jul. 18, 2022 in U.S. Appl. No. 16/551,620. |
Non-Final Office Action dated Jul. 27, 2022 in U.S. Appl. No. 16/747,737. |
Non-Final Office Action dated Oct. 13, 2022 in U.S. Appl. No. 17/147,272. |
Non-Final Office Action dated Nov. 17, 2022 in U.S. Appl. No. 16/551,638. |
Non-Final Office Action dated Dec. 8, 2022 in U.S. Appl. No. 16/934,530. |
Non-Final Office Action dated Dec. 21, 2022 in U.S. Appl. No. 15/055,407. |
Non-Final Office Action dated Jan. 10, 2023 in U.S. Appl. No. 17/163,177. |
Non-Final Office Action dated Jan. 19, 2023 in U.S. Appl. No. 17/091,639. |
Non-Final Office Action dated Jan. 23, 2023 in U.S. Appl. No. 17/183,840. |
Non-Final Office Action dated Jan. 24, 2023 in U.S. Appl. No. 17/157,872. |
Non-Final Office Action dated Feb. 10, 2023 in U.S. Appl. No. 17/390,640. |
Non-Final Office Action dated Feb. 23, 2023 in U.S. Appl. No. 17/408,374. |
Non-Final Office Action dated Mar. 13, 2023 in U.S. Appl. No. 17/151,050. |
Non-Final Office Action dated Apr. 26, 2023 in U.S. Appl. No. 16/540,971. |
Non-Final Office Action dated Apr. 26, 2023 in U.S. Appl. No. 16/374,626. |
Non-Final Office Action dated Jun. 14, 2023 In U.S. Appl. No. 17/174,249. |
Non-Final Office Action dated Jun. 30, 2023 In U.S. Appl. No. 17/684,289. |
Non-Final Office Action dated Jul. 27, 2023 in U.S. Appl. No. 17/373,519. |
Non-Final Office Action dated Sep. 21, 2023 in Canadian Patent Application No. 3,034,924. |
Non-Final Office Action dated Sep. 28, 2023 in U.S. Appl. No. 16/789,311. |
Non-Final Office Action Dated Sep. 28, 2023 in U.S. Appl. No. 17/184,405. |
Non-Final Office Action Dated Oct. 5, 2023 in U.S. Appl. No. 16/848,241. |
Non-Final Office Action Dated Nov. 7, 2023 in U.S. Appl. No. 17/528,104. |
Non-Final Office Action Dated Dec. 28, 2023 in U.S. Appl. No. 17/157,872. |
Non-Final Office Action Dated Jan. 2, 2024 in U.S. Appl. No. 17/373,653. |
Non-Final Office Action Dated Jan. 19, 2024 in U.S. Appl. No. 17/336,055. |
Notice of Allowability dated Jun. 19, 2014 for U.S. Appl. No. 12/969,581. |
Notice of Allowance dated Aug. 22, 2014 for U.S. Appl. No. 12/969,581. |
Notice of Allowance dated Dec. 15, 2015 for U.S. Appl. No. 14/540,007. |
Notice of Allowance dated Dec. 21, 2015 in U.S. Appl. No. 14/540,018. |
Notice of Allowance dated Dec. 27, 2019 in U.S. Appl. No. 15/260,106. |
Notice of Allowance dated Jan. 21, 2016 for U.S. Appl. No. 13/327,526. |
Notice of Allowance dated Jan. 9, 2019 in U.S. Appl. No. 15/603,239. |
Notice of Allowance dated Mar. 21, 2014 for U.S. Appl. No. 12/969,581. |
Notice of Allowance dated Mar. 21, 2019 in U.S. Appl. No. 15/993,468. |
Notice of Allowance dated May 28, 2019 in U.S. Appl. No. 16/219,553. |
Notice of Allowance dated Mar. 20, 2019 in U.S. Appl. No. 16/219,553. |
Notice of Allowance dated Nov. 11, 2019 in Japanese Patent Application No. 2017-245295. |
Notice of Allowance dated Nov. 29, 2019 in U.S. Appl. No. 16/012,635. |
Notice of Allowance dated Sep. 24, 2019 in U.S. Appl. No. 15/217,886. |
Notice of Allowance dated Mar. 5, 2020 in U.S. Appl. No. 15/217,886. |
Notice of Allowance dated Mar. 27, 2020 in U.S. Appl. No. 15/596,364. |
Notice of Allowance dated Mar. 30, 2020 in U.S. Appl. No. 15/937,713. |
Notice of Allowance dated Apr. 15, 2020 in U.S. Appl. No. 16/012,635. |
Notice of Allowance dated Sep. 23, 2020 in Korean Patent Application No. 10-2016-7008144. |
Notice of Allowance dated Oct. 29, 2020 in U.S. Appl. No. 15/987,851. |
Notice of Allowance dated Jan. 13, 2021 in U.S. Appl. No. 14/381,488. |
Notice of Allowance dated Jan. 13, 2021 in U.S. Appl. No. 15/459,977. |
Notice of Allowance dated Apr. 26, 2021 in Japanese Patent Application No. 2019-014564. |
Notice of Allowance dated Aug. 16, 2021 in Japanese Patent Application No. 2018-512152. |
Notice of Allowance dated Nov. 16, 2021 in U.S. Appl. No. 16/836,750. |
Notice of Allowance dated Jan. 24, 2022 in Korean U.S. Appl. No. 16/836,750. |
Notice of Allowance dated Feb. 9, 2022 in U.S. Appl. No. 16/781,814. |
Notice of Allowance dated Feb. 11, 2022 in Chinese Patent Application No. 201680007351.2. |
Notice of Allowance dated Feb. 16, 2022 in U.S. Appl. No. 15/875,816. |
Notice of Allowance dated Feb. 21, 2022 in Korean Patent Application No. 10-2020-7033213. |
Notice of Allowance dated Apr. 11, 2022 in U.S. Appl. No. 15/134,967. |
Notice of Allowance dated Apr. 25, 2022 in Korean Patent Application No. 10-2018-7008560. |
Notice of Allowance dated Apr. 26, 2022 in Chinese Patent Application No. 201780058799.1. |
Notice of Allowance dated Apr. 27, 2022 in U.S. Appl. No. 16/400,886. |
Notice of Allowance dated May 9, 2022 in Australian Patent Application No. 2018281745. |
Notice of Allowance dated May 15, 2022 in Japanese Patent Application No. 2019-540515. |
Notice of Allowance dated May 23, 2022 in U.S. Appl. No. 15/715,028. |
Notice of Allowance dated May 26, 2022 in Korean Patent Application No. 10-2019-7038794. |
Notice of Allowance dated Jun. 6, 2022 in U.S. Appl. No. 16/789,358. |
Notice of Allowance dated Jul. 20, 2022 in U.S. Appl. No. 16/707,780. |
Notice of Allowance dated Aug. 9, 2022 in U.S. Appl. No. 16/232,287. |
Notice of Allowance dated Sep. 26, 2022, 2022 in U.S. Appl. No. 16/232,287. |
Notice of Allowance dated Oct. 17, 2022, 2022 in U.S. Appl. No. 16/400,885. |
Notice of Allowance dated Oct. 20, 2022 in Australian Patent Application No. 2019204928. |
Notice of Allowance dated Oct. 21, 2022 in European Patent Application No. 19762517.1. |
Notice of Allowance dated Oct. 24, 2022 in European Patent Application No. 20708266.0. |
Notice of Allowance dated Oct. 25, 2022 in European Patent Application No. 19724003.9. |
Notice of Allowance dated Nov. 7, 2022 in U.S. Appl. No. 16/012,584. |
Notice of Allowance dated Jan. 10, 2023 in U.S. Appl. No. 16/588,405. |
Notice of Allowance dated Jan. 19, 2023 in Korean Patent Application No. 10-2022-7004715. |
Notice of Allowance dated Jan. 31, 2023 in U.S. Appl. No. 16/747,737. |
Notice of Allowance dated Feb. 1, 2023 in U.S. Appl. No. 17/147,272. |
Notice of Allowance dated Feb. 21, 2023 in Korean Patent Application No. 10-2022-7017261. |
Notice of Allowance dated Mar. 1, 2023 in U.S. Appl. No. 17/192,814. |
Notice of Allowance dated Mar. 10, 2023 in European Patent Application No. 19762517.1. |
Notice of Allowance dated Mar. 10, 2023 in European Patent Application No. 20708266.0. |
Notice of Allowance dated Mar. 10, 2023 in European Patent Application No. 19724003.9. |
Notice of Allowance dated Mar. 13, 2023 in European Patent Application No. 17781265.8. |
Notice of Allowance dated Apr. 4, 2023 in Australian Patent Application No. 2017331459. |
Notice of Allowance dated Jun. 8, 2023 in U.S. Appl. No. 16/459,444. |
Notice of Allowance dated Aug. 23, 2023 in Canadian Patent Application No. 2,865,575. |
Notice of Allowance dated Aug. 25, 2023 in European Patent Application No. 22 200 785.8. |
Notice of Allowance dated Aug. 28, 2023 in U.S. Appl. No. 16/374,626. |
Notice of Allowance dated Sep. 14, 2023 in Canada Application No. 2982467. |
Notice of Allowance dated Sep. 29, 2023 in European Application No. 22165594.7. |
Notice of Allowance dated Oct. 2, 2023 in European Application 21735067.8. |
Notice of Allowance dated Oct. 25, 2023 in European Application No. 20816802.1. |
Notice of Opposition dated Jul. 9, 2015 for European Patent Application No. 11810645.9. |
Notice of Reason for Refusal dated Nov. 21, 2019 in Korean Patent Application No. 10-2016-7008144. |
Notice of Reasons for Rejection dated Apr. 2, 2018 in Japanese Patent Application No. 2014-558975. |
Notice of Reasons for Rejection dated Aug. 31, 2018 in Japanese Patent Application No. 2016-520632. |
Notice of Reasons for Rejection dated Dec. 28, 2016 in Japanese Patent Application No. 2014-558975. |
Notice of Reasons for Rejection dated Dec. 5, 2018 in Japanese Patent Application No. 2017-245295. |
Notice of Reasons for Rejection dated Jul. 30, 2018 in Japanese Patent Application No. 2016-537867. |
Notice of Reasons for Rejection dated Feb. 25, 2020 in Japanese Patent Application No. 2019-014564. |
Notice of Reasons for Refusal dated May 11, 2020 in Japanese Patent Application No. 2017-549390. |
Notice of Service of Disclosures to Opposing Counsel, dated Jun. 10, 2019 in the USDC for the District of Delaware, C.A. 18-1800 (RGA), 3 pp. |
Notice of Service of Interrogatories and First Request of Documents and Things to Defendant 10X Genomics, Inc., dated Jul. 5, 2019 in the USDC for the District of Delaware, C.A. 18-1800 (RGA), 3 pp. |
Notice, Consent, and Reference of a Civil Action to a Magistrate Judge (Rule 73.1), filed Nov. 15, 2018 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 3 pp. |
Notification Prior to Examination dated Nov. 27, 2019 in Israeli Patent Application No. 265478. |
Novak et al., “Single-Cell Multiplex Gene Detection and Sequencing with Microfluidically Generated Agarose Emulsions,” Angew. Chem. Int. Ed. 2011, 50, 390-395. |
Novus Biologicals, “Fixation and Permeability in ICC IF,” Novus Biologicals 2021, 1-3. |
Nowak et al., “Does the KIR2DS5 gene protect from some human diseases?,” PLoS One 2010, 5(8), in 6 pages. |
Office Action dated Dec. 13, 2018 in Canadian Patent Application No. 2,865,575. |
Office Action dated Dec. 19, 2017 in Chinese Patent Application No. 201480061859.1. |
Office Action dated Dec. 27, 2016 in Chinese Patent Application No. 201380022187.9. |
Office Action dated Feb. 15, 2018 in Canadian Patent Application No. 2,865,575. |
Office Action dated Feb. 17, 2017 in Canadian Patent Application No. 2,865,575. |
Office Action dated Jan. 2, 2019 in Chinese Patent Application No. 201480059505.3. |
Office Action dated Jul. 14, 2017 in Chinese Patent Application No. 201380022187.9. |
Office Action dated Jun. 6, 2016 in Chinese Patent Application No. 201380022187.9. |
Office Action dated Sep. 7, 2018 in Chinese Patent Application No. 201480061859.1. |
Office Action dated Mar. 4, 2020 in Canadian Patent Application No. 2,865,575. |
Office Action dated Oct. 29, 2020 in Chinese Patent Application No. 2018800377201. |
Office Action dated Jan. 4, 2021 in Japanese Patent Application No. 2017-549390. |
Office Action dated Jan. 6, 2021 in Chinese Patent Application No. 201680052330.2. |
Office Action dated Jan. 14, 2021 in Japanese Patent Application No. 2019-014564. |
Office Action dated Jan. 15, 2021 in Korean Patent Application No. 10-2020-7033213. |
Office Action dated Jan. 26, 2021 in Chinese Patent Application No. 201680007351.2. |
Office Action dated Feb. 4, 2021 in Canadian Patent Application No. 2,865,575. |
Office Action dated Feb. 20, 2021 in Chinese Patent Application No. 201680022865.5. |
Office Action dated Mar. 1, 2021 in Chinese Patent Application No. 201680007652.5. |
Office Action dated Mar. 2, 2021 in Chinese Patent Application No. 2016800157452. |
Office Action dated Mar. 8, 2021 in Japanese Patent Application No. 2018-512152. |
Office Action dated Mar. 16, 2021 in Chinese Patent Application No. 2018800377201. |
Office Action dated May 10, 2021 in Japanese Patent Application No. 2019-566787. |
Office Action dated May 21, 2021 in Chinese Patent Application No. 201680007351.2. |
Office Action dated Jul. 26, 2021 in Korean Patent Application No. 10-2019-7011635. |
Office Action dated Jul. 28, 2021 in Korean Patent Application No. 10-2020-7033213. |
Office Action dated Aug. 13, 2021 in Chinese Patent Application No. 2017800587991. |
Office Action dated Aug. 27, 2021 in Chinese Patent Application No. 2016800076525. |
Office Action dated Aug. 30, 2021 in Japanese Patent Application No. 2019-540515. |
Office Action dated Aug. 31, 2021 in Chinese Patent Application No. 2016800157452. |
Office Action dated Aug. 31, 2021, in Korean Patent Application No. 10-2019-7038794. |
Office Action dated Sep. 14, 2021, in Chinese Patent Application No. 2016800523302. |
Office Action dated Oct. 21, 2021, in Chinese Patent Application No. 2016800073512. |
Office Action dated Nov. 2, 2021, in Japanese Patent Application No. 2017-549390. |
Office Action dated Dec. 23, 2021, in Japanese Patent Application No. 2019-566787. |
Office Action dated Dec. 17, 2021 in Korean Patent Application No. 10-2018-7008560. |
Office Action dated Jan. 13, 2022 in Chinese Patent Application No. 2017800587991. |
Office Action dated Feb. 9, 2022 in Japanese Patent Application No. 2019-540515. |
Office Action dated Feb. 23, 2022 in Chinese Patent Application No. 2016800523302. |
Office Action dated Mar. 7, 2022 in Korean Patent Application No. 10-2022-7004715. |
Office Action dated May 2, 2022 in European Patent Application No. 19787547.9. |
Office Action dated May 17, 2022 in Australian Patent Application No. 2019204928. |
Office Action dated May 24, 2022 in European Patent Application No. 20708266.0. |
Office Action dated Jun. 28, 2022 in European Patent Application No. 16719706.0. |
Office Action dated Aug. 2, 2022 in European Patent Application No. 19765601.0. |
Office Action dated Aug. 1, 2022 in Korean Patent Application No. 10-2022-7017261. |
Office Action dated Sep. 21, 2022 in Israel Patent Application No. 265478. |
Office Action dated Jan. 30, 2023 in European Patent Application No. 19752792.2. |
Office Action dated Feb. 8, 2023 in Australian Patent Application No. 2017331459. |
Office Action dated Feb. 20, 2023 in European Patent Application No. 19723988.2. |
Office Action dated Feb. 23, 2023 in European Patent Application No. 20816802.1. |
Office Action dated Feb. 28, 2023 in Chinese Patent Application No. 2019111653930. |
Office Action dated Nov. 24, 2022 in Chinese Patent Application No. 2018800147939. |
Office Action dated Mar. 15, 2023 in European Patent Application No. 19787547.9. |
Office Action dated Mar. 27, 2023 in European Patent Application No. 19836036.4. |
Office Action dated Mar. 29, 2023 in Chinese Patent Application No. 2020800144092. |
Office Action dated Apr. 10, 2023 in Japanese Patent Application No. 2022-030956. |
Office Action dated Apr. 14, 2023 in Chinese Patent Application No. 201980082680.7. |
Office Action dated Apr. 24, 2023 in Japanese Patent Application No. 2020-561800. |
Office Action dated Apr. 24, 2023 in European Patent Application No. 21714995.4. |
Office Action dated Apr. 26, 2023 in European Patent Application No. 18703156.2. |
Office Action dated May 16, 2023 in European Patent Application No. 21707112.5. |
Office Action dated May 26, 2023 in Chinese Patent Application No. 2019800373421. |
Office Action dated May 27, 2023 in Chinese Patent Application No. 2019800656859. |
Office Action dated May 30, 2023 in Chinese Patent Application No. 2019800653102. |
Office Action dated Jun. 1, 2023 in Japanese Patent Application No. 2020-561807. |
Office Action dated Jun. 16, 2023 in Chinese Patent Application No. 2019800708938. |
Office Action dated Jun. 22, 2023 in Japanese Patent Application No. 2022-071002. |
Office Action dated Jun. 28, 2023 in European Patent Application 19836239.4. |
Office Action dated Jul. 12, 2023 in Chinese Patent Application No. 2020800212600. |
Office Action dated Jul. 12, 2023 in Canadian Patent Application No. 3,059,559. |
Office Action Dated Jul. 13, 2023 in Chinese Patent Application No. 202080077712.7. |
Office Action dated Jul. 28, 2023 in Chinese Patent Application No. 201880014793.9. |
Office Action dated Jul. 29, 2023 in Chinese Patent Application No. 201980073850.5. |
Office Action dated Jul. 31, 2023 in Chinese Patent Application No. 201980068704.3. |
Office Action dated Jul. 31, 2023 in Chinese Patent Application No. 201980037175.0. |
Office Action dated Aug. 11, 2023 in European Patent Application 19752792.2. |
Office Action dated Aug. 21, 2023 in Japanese Patent Application No. 2021-507836. |
Office Action dated Aug. 30, 2023 in Chinese Patent Application 2019111653930. |
Office Action dated Aug. 31, 2023 in Chinese Patent Application 2020800483617. |
Office Action dated Sep. 21, 2023 in Japanese Patent Application 2022-030956. |
Office Action dated Sep. 21, 2023 in Israel Patent Application 265478. |
Office Action dated Oct. 10, 2023 in European Patent Application 16719706.0. |
Office Action dated Oct. 13, 2023 in Chinese Patent Application 202080014409.2. |
Office Action dated Oct. 19, 2023 in Japanese Patent Application 2019-566787. |
Office Action dated Oct. 23, 2023 in Japanese Patent Application 2021-517856. |
Office Action dated Oct. 26, 2023 In Japanese Patent Application 2022-525692. |
Office Action Dated Oct. 30, 2023 in Japanese Patent Application 2021-523956. |
Office Action Dated Nov. 9, 2023 in Japanese Patent Application 2017-549390. |
Office Action Dated Feb. 1, 2024 in Japanese Patent Application 2022-071002. |
Ogawa, T. et al., “The Efficacy and further functional advantages of random-base molecular barcodes for absolute and digital quantification of nucleic acid molecules”, Sci Rep 7, 2017 12576. |
Ogino et al., “Quantification of PCR bias caused by a single nucleotide polymorphism in SMN gene dosage analysis,” J Mol Diagn. 2002, 4(4), 185-190. |
Opposition to Defendant's Motion to Dismiss Pursuant to Federal Rule of Civil Procedure 12(b)(6) dated Feb. 15, 2019, in the USDC for the District of Delaware, C.A. 18-800-RGA, 3 pp. |
Oral Order by Judge Andrews Canceling Scheduling Conference set for May 8, 2019. |
Order Scheduling ADR Mediation Teleconference, filed May 13, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 4pp. |
Order Setting Rule 16(b) Conference as Ordered by Judge Andrews Pursuant to Fed. R. Civ. P. 16(b), ruling dated Apr. 17, 2019 in the USDC District of Delaware, C.A. 18-1800-RGA, 1 pp. |
O'Shea et al., “Analysis of T Cell Receptor Beta Chain CDR3 Size Using RNA Extracted from Formalin Fixed Paraffin Wax Embedded Tissue,” Journal of Clinical Pathology 1997, 50(10), 811-814. |
Ozkumur et al., “Inertial Focusing for Tumor Antigen-Dependent and -Independent Sorting of Rare Circulating Tumor Cells,” Science Translational Medicine 2013, 5(179), 1-20. |
Parameswaran et al., “A pyrosequencing-tailored nucleotide barcode design unveils opportunities for large-scale sample multiplexing,” Nucleic Acids Res. 2007, 35(19), e130, 1-9. |
Park et al., “Discovery of common Asian copy No. variants using integrated high-resolution array CGH and massively parallel DNA sequencing,” Nat Genet. 2010, 42(5), 400-405. |
Patanjali et al., “Construction of a uniform-abundance (normalized) CNDA library,” Proceedings of the National Academy of Sciences 1991, 88(5), 1943-1947. |
Peng et al., “Reducing amplification artifacts in high multiplex amplicon sequencing by using molecular barcodes,” BMC Genomics 2015, 16(589), 1-12. |
Pérez-Rentero et al., “Synthesis of Oligonucleotides Carrying Thiol Groups Using a Simple Reagent Derived from Threoninol,” Molecules 2012, 17, 10026-10045. |
Peterson et al., “Multiplexed quantification of proteins and transcripts in single cells,” Nature Biotechnology 2017, 35, 936-939. |
Pfaffl et al., “Determination of stable housekeeping genes, differentially regulated target genes andsample integrity: BestKeeper—Excel-based tool using pair-wise correlations,” Biotechnology Letters, 26(6), 505-515. |
Picelli et al., “Single-cell RNA-sequencing: The future of genome biology is now,” RNA Biology 2017, 14(5), 637-650. |
Picelli et al., “Tn5 transposase and tagmentation procedures for massively scaled sequencing projects,” Genome Research 2014, 24(12), 2033-2040. |
Pihlak et al., “Rapid genome sequencing with short universal tiling probes,” Nature Biotechnology 2008, 26, 1-9. |
Pinkel et al., “Comparative Genomic Hybridization,” Annual Review of Genomics and Human Genetics 2005, 6, 331-354. |
Plaintiff's Brief in Opposition to Defendant's Motion to Dismiss Pursuant to Fed. R. Civ. P. 12(b)(6), filed Mar. 29, 2019 in the USDC District of Delaware, C.A. No. 18-1800 (RGA), 27 pp. |
Plaintiff's First Amended Complaint filed on Feb. 8, 2019, in the USDC for the District of Delaware, C.A. 18-1800-RGA, 178 pp. |
Pleasance et al., “A small-cell lung cancer genome with complex signatures of tobacco exposure,” Nature 2010, 463(7278), 184-190. |
Plessy et al., “Population transcriptomics with single-cell resolution: a new field made possible by microfluidics: a technology for high throughput transcript counting and data-driven definition of cell types,” Bioessays 2012, 35, 131-140. |
Pre-interview communication dated Nov. 27, 2018 in U.S. Appl. No. 16/012,635. |
Preissl et al., “Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation,” Nature Neuroscience 2018, 21(3), 432-439. |
Prevette et al., “Polycation-Induced Cell Membrane Permeability Does Not Enhance Cellular Uptake or Expression Efficiency of Delivered DNA,” Molecular Pharmaceutics 2010, 7(3), 870-883. |
Pringle et al., “In Situ Hybridization Demonstration of Poly-Adenylated RNA Sequences in Formalin-Fixed Parafin Sections Using a Biotinylated Oligonucleotide Poly d(T) Probe,” Journal of Pathology 1989, 158, 279-286. |
Proposed Stipulated Protective Order Pursuant to Rule 26(c) of the Federal Rules of Civil Procedure, filed Jun. 20, 2019 in the USDC for the District of Delaware, C.A. 18-1800 (RGA), 26 pp. |
Qiu et al., “DNA Sequence-Based “Bar Codes” for Tracking the Origins of Expressed Sequence Tags from a Maize cDNA Library Constructed Using Multiple mRNA Sources,” Plant Physiol. 2003, 133, 475-481. |
Quail et al., “SASI-Seq: sample assurance Spike-Ins, and highly differentiating 384 barcoding for Illumina sequencing,” BMC Genomics 2014, 15(110), in 13 pages. |
Raj et al., “Imaging individual mRNA molecules using multiple singly labeled probes,” Nature Methods 2008, 5(10), 877-879. |
Raj et al., “Single-Molecule Approaches to Stochastic Gene Expression,” Annu Rev Biophys 2009, 38, 255-270. |
Raj et al., “Stochastic mRNA synthesis in mammalian cells,” PLoS Biol. 2006, 4(10) 1707-1719. |
Rajeevan et al., “Global amplification of sense RNA: a novel method to replicate and archive mRNA for gene expression analysis,” Genomics 2003, 82, 491-497. |
Report on the Filing or Determination of an Action Regarding a Patent or Trademark filed Nov. 15, 2018 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 2 pp. |
Restriction Requirement dated Jun. 19, 2019 in U.S. Appl. No. 15/596,364. |
Restriction Requirement dated Mar. 15, 2016 in U.S. Appl. No. 14/381,488. |
Restriction Requirement dated Mar. 17, 2016 in U.S. Appl. No. 14/472,363. |
Restriction Requirement dated Mar. 29, 2019 in U.S. Appl. No. 15/715,028. |
Restriction Requirement dated Sep. 20, 2019 in U.S. Appl. No. 15/875,816. |
Restriction Requirement dated Jun. 4, 2021 in U.S. Appl. No. 16/551,620. |
Restriction Requirement dated Aug. 8, 2022 in U.S. Appl. No. 17/163,177. |
Restriction Requirement dated Aug. 11, 2022 in U.S. Appl. No. 17/091,639. |
Restriction Requirement dated Aug. 19, 2022 in U.S. Appl. No. 17/147,283. |
Restriction Requirement dated Sep. 16, 2022 in U.S. Appl. No. 17/151,050. |
Restriction Requirement dated Sep. 19, 2022 in U.S. Appl. No. 16/934,530. |
Restriction Requirement dated Oct. 21, 2022 in U.S. Appl. No. 17/320,052. |
Restriction Requirement dated Nov. 8, 2022 in U.S. Appl. No. 17/157,872. |
Restriction Requirement dated Dec. 23, 2022 in U.S. Appl. No. 17/531,618. |
Restriction Requirement dated Jan. 20, 2023 in U.S. Appl. No. 17/373,519. |
Restriction Requirement dated Feb. 27, 2023 in U.S. Appl. No. 17/151,058. |
Restriction Requirement dated Apr. 3, 2023 in U.S. Appl. No. 17/161,558. |
Restriction Requirement dated Jun. 28, 2023 in U.S. Appl. No. 17/336,055. |
Restriction Requirement dated Oct. 5, 2023 in U.S. Appl. No. 17/373,653. |
Restriction Requirement dated Oct. 11, 2023 in U.S. Appl. No. 17/531,555. |
Rhee et al., “Simultaneous detection of mRNA and protein stem cell markers in live cells,” BMC Biotechnology 2009, 9(30), 1-10. |
Roche Diagnostics GmbH, “Genome Sequencer 20 System: First to the Finish,” 2006, 1-40. |
Rule 7.1 Disclosure Statement dated Nov. 15, 2018 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 1 pp. |
Sah et al., “Complete Genome Sequence of a 2019 Novel Coronavirus (SARS-CoV-2) Strain Isolated in Nepal,” Microbiol Resour Announc. 2020, 9(11), e00169-20, 3 pp. |
Sano et al., “Immuno-PCR: Very Sensitive Antigen Detection by Means of Specific Antibody-DNA Conjugates,” Science 1992, 258, 120-122. |
Sasagawa et al., “Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity,” Genome Biology 2013, 14, R31. |
Sasuga et al., Single-cell chemical lysis method for analyses of intracellular molecules using an array of picoliter-scale microwells, Anal Chem 2008, 80(23), 9141-9149. |
Satija et al., Spatial reconstruction of single-cell gene expression data, Nature Biotechnology 2015, 33(5), 495-508. |
Scheduling Order pursuant to Local Rule 16.1(b), filed May 7, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 10 pp. |
Scheduling Order Signed by Judge Andrews, dated May 8, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 10 pp. |
Schmitt et al., “Detection of ultra-rare mutations by next-generation sequencing,” Proc Natl Acad Sci 2012, 109(36), 1-6. |
Schouten et al., “Relative quantification of 40 nucleic acid sequences by multiplex ligation-dependent probe amplification,” Nucleic Acids Research 2002, 30(12), e57. |
Shapiro et al., “Single-cell sequencing-based technologies will revolutionize whole-organism science,” Nature Reviews Genetics 2013, 14, 618-629. |
Search and Examination Report dated Aug. 26, 2015 in United Kingdom Patent Application No. 1511591.8. |
Search Report and Written Opinion dated Jan. 26, 2016 in Singapore Patent Application No. 1120140527W. |
Search Report and Written Opinion dated Aug. 26, 2020 in Singapore Patent Application No. 10201806890V. |
Sebat et al., “Large-Scale Copy Number Polymorphism in the Human Genome,” Science 2004, 305, 525-528. |
Shahi et al., “Abseq: ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding,” Scientific Reports 2017, 7(44447), 1-10. |
Shalek et al., “Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells,” Nature 2013, 498(7453), 236-240. |
Shendure et al., “Next-generation DNA sequencing,” Nature Biotechnology 2008, 26(10), 1135-1145. |
Shiroguchi et al., “Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes,” Proc Natl Acad Sci 2012, 109(4):1347-1352. |
Shoemaker et al., “Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy,” Nature Genetics 1996, 14, 450-456. |
Shortreed et al., “A thermodynamic approach to designing structure-free combinatorial DNA word sets,” Nucleic Acids Res. 2005, 33(15), 4965-4977. |
Shum et al., “Quantitation of mRNA Transcripts and Proteins Using the BD Rhapsody™ Single-Cell Analysis System,” Adv Exp Med Biol. 2019, 1129, 63-79. |
Simpson et al., “Copy number variant detection in inbred strains from short read sequence data,” Bioinformatics 2010, 26(4), 565-567. |
Smith et al., “Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples,” Nucleic Acids Research 2010, 38(13), e142, 1-7. |
Soares et al., “Construction and characterization of a normalized cDNA library,” Proc. Natl., Acad. Sci. 1994, 91, 9228-9232. |
Sogin et al., “Microbial diversity in the deep sea and the underexplored “rare biosphere”,” PNAS 2008, 103(32), 12115-12120. |
Sommer et al., “Minimal homology requirements for PCR primers,” Nucleic Acids Research 1989, 17(16), 6749. |
Song et al., “Design rules for size-based cell sorting and sheathless cell focusing by hydrophoresis,” Journal of Chromatography A 2013, 1302, 191-196. |
Song et al., DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells, Cold Spring Harb Protoc 2010, 2, in 13 pages. |
Sos et al., “Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing (THS-seq) assay,” Genome Biology 2016, 17(20), in 15 pages. |
Spanova et al., “Magnetic hydrophilic methacrylate-based polymer microspheres designed for polymerase chain reaction applications”, Journal of Chromatography vol. 800, 2004, 27-32. |
Soumillon et al., “Characterization of directed differentiation by high-throughput single-cell RNA-Seq,” bioRxiv 2014, 1-13. |
Speicher et al., “The new cytogenetics: blurring the boundaries with molecular biology,” Nature Reviews Genetics 2005, 6(10), 782-792. |
Statement of Opposition dated Jul. 21, 2016 filed against European Patent No. EP2414548B1. |
Statement of Opposition filed against European Patent No. EP2414548B1 on Jul. 26, 2016. |
Statement of Opposition of Strawman Limited filed against European Patent No. EP2414548B1 on Jul. 19, 2016. |
Statement regarding Third-Party Submission filed on Jun. 6, 2018 for U.S. Appl. No. 15/847,752. |
Stipulated Protective Order Pursuant to Rule 26(c) of the Federal Rules of Civil Procedure, dated Jun. 21, 2019 in the USDC for the District of Delaware, C.A. 18-1800 (RGA), 26 pp. |
Stipulation and Order to Extend Time to File Opposition to Motion to Dismiss, and Reply in Support of the Motion, dated Jan. 28, 2019 in the USDC for the District of Delaware, C.A. 18-1800-RGA, 2 pp. |
Stoeckius et al., “Large-scale simultaneous measurement of epitopes and transcriptomes in single cells,” Nature Methods 2017, 14(9), 865-868. |
Stoeckius et al., “Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics,” Genome Biology 2018, 19(224), 1-12. |
Stratagene 1988 Catalog, Gene Characterization Kits, 39. |
Subkhankulova et al., “Comparative evaluation of linear and exponential amplification techniques for expression profiling at the single cell level,” Genome Biology 2006, 7(3), 1-16. |
Submission dated Jan. 15, 2018 in preparation for upcoming oral proceedings in opposition against European Patent No. EP2414548B1. |
Summons in a Civil Action to Defendant 10X Genomics, Inc. filed Nov. 16, 2018 in the USDC for the District of Delaware, Civil Action No. 18-1800, 2 pp. |
Summons to Attend Oral Proceedings dated Nov. 16, 2020 in European Patent Application No. 17202409.3. |
Summons to Attend Oral Proceedings Dated Aug. 8, 2023 in European Patent Application No. 14749671.5. |
Sun et al., “Ultra-deep profiling of alternatively spliced Drosophila Dscam isoforms by circularization-assisted multi-segment sequencing,” EMBO J. 2013, 32(14), 2029-2038. |
Takahashi et al., “Novel technique of quantitative nested real-time PCR assay for Mycobacterium tuberculosis DNA,” Journal of Clinical Microbiology 2006, 44, 1029-1039. |
Takara Bio, “SMARTer Human BCR IgG IgM H/K/L Profiling Kit User Manual,” Takara Bio USA Inc. 2019, 1-22. |
Tan et al., “Genome-wide comparison of DNA hydroxymethylation in mouse embryonic stem cells andneural progenitor cells by a new comparative hMeDIP-seq method,” Nucleic Acids Res. 2013, 41(7), e84, 1-12. |
Tang et al., “RNA-Seq analysis to capture the transcriptome landscape of a single cell,” Nature Protocols 2010, 5(3), 516-535. |
Taudien et al., “Haplotyping and copy number estimation of the highly polymorphic human beta-defensin locus on 8p23 by 454 amplicon sequencing,” BMC Genomics 2010, 11, 252, 1-14. |
The Tibbs Times, UNC bioscience newsletter, Apr. 2010, 1-17. |
Third-Party Submission filed on May 21, 2018 for U.S. Appl. No. 15/847,752. |
Tomaz et al., “Differential methylation as a cause of allele dropout at the imprinted GNAS locus,” Genet Test Mol Biomarkers 2010, 14(4), 455-460. |
TotalSeq™—A0251 anti-human Hashtag 1 Antibody, BioLegend®, Jul. 2018, 1-10. |
Trzupek et al., “Discovery of CD8O and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis”, Genome Medicine 2020, 12(1), in 22 pages. |
Treutlein et al., Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq, Nature 2014, 509, 371-375. |
Ullal et al., “Cancer cell profiling by barcoding allows multiplexed protein analysis in fine needle aspirates,” Sci Transl Med. 2014, 6(219), 22 pp. |
Uellendahl-Werth et al., “A benchmark of hemoglobin blocking during library preparation for mRNA Sequencing of human blood samples,” Scientific Reports 2020, 10(1), 5630. |
Unopposed Motion to Extend Time for Defendant's Response, dated Dec. 4, 2018 in the USDC for the District of Delaware, C.A. 18-1800-(RGA), 2 pp. |
Vandesompele et al., “Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes,” Genome Biology 2002, 3(7), 1-12. |
Velculescu et al., “Characterization of the Yeast Transcriptome,” Cell 1997, 88, 243-251. |
Velculescu et al., “Serial Analysis of Gene Expression,” Science 1995, 270(5235), 484-487. |
Vestheim et al., “Application of Blocking Oligonucleotides to Improve Signal-to-Noise Ratio in a PCR,” Methods in Molecular Biology 2011, 687, 265-274. |
Vogelstein et al., “Digital PCR,” Proc. Natl. Acad. Sci. 1999, 96, 9236-9241. |
Vollbrecht et al., “Validation and comparison of two NGS assays for the detection of EGFR T790M resistance mutation in liquid biopsies of NSCLC patients,” Oncotarget 2018, 9(26), 18529-18539. |
Walker et al., “Isothermal in vitro amplification of DNA by a restriction enzyme/DNA polymerase system,” Proc Natl Acad Sci 1992, 89, 392-396. |
Walsh et al., “Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing,” Proc Natl Acad Sci 2010, 107(28), 12629-12633. |
Wang et al., “Advances and applications of single-cell sequencing technologies,” Molecular Cell 2015, 58, 598-609. |
Wang et al., “Combining Gold Nanoparticles with Real-Time Immuno-PCR for Analysis of HIV p24 Antigens,” Proceedings of ICBBE 2007, 1198-1201. |
Wang et al., “iCLIP predicts the dual splicing effects of TIA-RNA interactions,” PLoS Biol 2010, 8(10), e1000530, 1-16. |
Wang et al., “RNA-Seq: a revolutionary tool for transcriptomics,” Nature Reviews Genetics 2009, 10(1), 57-63. |
Wang et al., “Tagmentation-based whole-genome bisulfite sequencing,” Nature Protocols 2013, 8(10), 2022-2032. |
Wangsanuwat et al., “Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion,” BMC Genomics 2020, 21(1), 1-12. |
Warren et al., “Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR,” PNAS 2006, 103(47), 17807-17812. |
Weber et al., “A real-time polymerase chain reaction assay for quantification of allele ratios and correction of amplification bias,” Anal Biochem. 2003, 320, 252-258. |
Weibrecht et al., “Proximity ligation assays: a recent addition to the proteomics toolbox,” Expert Rev. Proteomics 2010, 7(3), 401-409. |
Weiner et al., “Kits and their unique role in molecular biology: a brief retrospective,” BioTechniques 2008, 44(5), 701-704. |
White et al., “High-throughput microfluidic single-cell RT-qPCR,” PNAS 2011, 108(34), 13999-14004. |
Wittes et al., “Searching for Evidence of Altered Gene Expression: a Comment on Statistical Analysis of Microarray Data,” Journal of the National Cancer Institute 1999, 91(5), 400-401. |
Wodicka et al., “Genome-wide expression monitoring in Saccharomyces cerevisiae,” Nature Biotechnology 1997, 15, 1359-1367. |
Wojdacz et al., “Primer design versus PCR bias in methylation independent PCR amplifications,” Epigenetics 2009, 4(4), 231-234. |
Wood et al., “Using next-generation sequencing for high resolution multiplex analysis of copy number variation from nanogram quantities of DNA from formalin-fixed paraffin-embedded specimens,” Nucleic Acids Res. 2010, 38(14), 1-14. |
Written Submission of Publications dated Jun. 14, 2018 in Japanese Patent Application No. 2016-537867. |
Wu et al., “Quantitative assessment of single-cell RNA-sequencing methods,” Nat Methods 2014, 11(1), 41-46. |
Wu, et al., “Time-resolved assessment of single-cell protein secretion by sequencing”, bioRxiv, Dec. 21, 2021. |
Yang & Zhao, “Quantitative Analysis of Nonoxynol-9 in Blood,” Contraception 1991, 43(2), 161-166. |
Yandell et al., “A probabilistic disease-gene finder for personal genomes,” Genome Res. 2011, 21(9), 1529-1542. |
Ye et al., Fluorescent microsphere-based readout technology for multiplexed human single nucleotide polymorphism analysis and bacterial identification, Human Mutation 2001, 17(4), 305-316. |
Yoon et al., Sensitive and accurate detection of copy number variants using read depth of coverage, Genome Res. 2009, 19, 1586-1592. |
Zagordi et al., “Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies,” Nucleic Acids Research 2010, 38(21), 7400-7409. |
Zeberg et al., “The major genetic risk factor for severe COVID-19 is inherited from Neanderthals,” Nature 2020, 587(7835), 1-13. |
Zhang et al., “Immunoaffinity Purification of Plasma Membrane with Secondary Antibody Superparamagnetic Beads,” Journal of Proteome 2006, 6, 34-43. |
Zhang et al., “DNA-based hybridization chain reaction for amplified bioelectronic signal and ultrasensitive detection of proteins,” Anal Chem. 2012, 84, 5392-5399. |
Zhang et al., “The impact of next-generation sequencing on genomics,” J Genet Genomics 2011, 38(3), 95-109. |
Zhao et al., “Homozygous Deletions and Chromosome Amplifications in Human Lung Carcinomas Revealed by Single Nucleotide Polymorphism Array Analysis,” Cancer Research 2005, 65(13), 5561-5570. |
Zheng et al., “Haplotyping germline and cancer genomes with high-throughput linked-read sequencing,” Nature Biotechnology 2016, 34(3), 303-311. |
Zhou et al., “Counting alleles reveals a connection between chromosome 18q loss and vascular invasion,” Nature Biotechnology 2001, 19, 78-81. |
Zhou et al., “Photocleavable Peptide-Oligonucleotide Conjugates for Protein Kinase Assays by MALDI-TOF MS,” Mol. BioSyst. 2012, 8, 2395-2404. |
Zhu et al., “Reverse Transcriptase Template Switching: A SMART Approach for Full-Length cDNA Library Construction,” BioTechniques 2001, 30(4), 892-897. |
Zheng, et al. “Aptamer-Functionalized Barcode Particles for the Capture and Detection of Multiple Types of Circulating Tumor Cells.” Advanced materials (Weinheim), 2014, 26, 7333-7338. |
Zhou and Rossi. “Aptamers as Targeted Therapeutics: Current Potential and Challenges.” Nature reviews. Drug discovery, 2017, 16:181-202. |
Zhulidov et al., “Simple cDNA normalization using kamchatka crab duplex-specific nuclease,” Nucleic Acids Research. 2004, 32(3)e37. |
Number | Date | Country | |
---|---|---|---|
20230366022 A1 | Nov 2023 | US |
Number | Date | Country | |
---|---|---|---|
61286768 | Dec 2009 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 16846133 | Apr 2020 | US |
Child | 18190884 | US | |
Parent | 15217886 | Jul 2016 | US |
Child | 16846133 | US | |
Parent | 14281706 | May 2014 | US |
Child | 15217886 | US | |
Parent | 12969581 | Dec 2010 | US |
Child | 14281706 | US |