Patent Application
20220398733
The present disclosure relates generally to systems, devices, and methods of using detectable arrays. More specifically, the present disclosure relates to optimized systems, devices, and methods of detecting diverse types of analytes, and diagnosing disease states based on the detection of such analytes, using detectable arrays that comprise photoinitiators or photocleavage products thereof.
Detectable arrays can be used to bind or immobilize an analyte on a surface or feature for detecting presence of analytes and diagnosing disease states. Many existing detectable arrays are limited to selectively bind to a single analyte or single type of analyte, which limits their application to samples containing multiple analytes of interest. Methods of analyzing binding of analytes to detectable arrays for diagnosing disease states are also limited, as they typically require that analytes be either labeled before they are captured on a surface (such as an array), for example by addition of dye, stain, ligand, etc., or they must be bound by a labeled-probe (e.g., oligonucleotide, antibody, labeled antibody, etc.) after binding to the surface. These methods suffer from many limitations including label bias and added expense due to labeling reactions, background caused by non-specific labeling. Thus, there is a need in the art for improved methods for the unbiased capture and detection of unlabeled analytes.
The present disclosure provides, inter alia, optimized systems, devices, and methods of capturing on a substrate including a plurality of features (e.g., an array), and detecting with or without a label, a plurality of different types of analytes.
In an embodiment, an array includes a plurality of polymers disposed on a substrate, the polymers each comprising one or more polymerized monomers and one or more photoinitiators, or one or more photocleavage products thereof.
In an embodiment, a method for detecting an unlabeled analyte includes: contacting one or more samples including one or more analytes with an array of polymers comprised on a substrate, the polymers including a photoinitiator that was previously exposed to ultraviolet (UV) light for a first predetermined period of time; incubating the one or more samples disposed on the array of polymers for a second predetermined period of time; heating the array of polymers at a predetermined temperature for a third predetermined period of time; and measuring, using an imaging device, an amount of one or more colorimetric or luminescence signals produced in response to the heating.
In an embodiment, a method includes: preparing a photoinitiator solution including a photoinitiator; preparing a plurality of monomer stock solutions each including a monomer; adding a volume of the photoinitiator solution to a volume of each of the plurality of monomer stock solutions and each of a plurality of combinations of the plurality of monomer stock solutions to produce a plurality of spotting solutions; spotting a predetermined volume of each of the plurality of spotting solutions onto a surface; and exposing the array comprising the spotting solutions to ultraviolet (UV) light for a predetermined period of time; thereby polymerizing the predetermined volume of each of the plurality of spotting solutions spotted onto the surface to produce an array of polymers.
In an embodiment, a method includes: receiving, at a processor and for a plurality of subjects, image data associated with colorimetric or luminescence signal profiles of a plurality of array spots (1) contacted with one or more samples associated with that subject and (2) heated at a predetermined temperature for a predetermined period of time, each of the plurality of array spots including a different hydrogel composition with a photoinitiator that was previously exposed to ultraviolet (UV) light; processing, for each of the plurality of subjects, the image data associated with that subject to produce color intensity values for each of the plurality of array spots; training, for each of the plurality of subjects, a neural network to classify that subject by calibrating the neural network using the color intensity values associated with each subject other than that subject and of the plurality of subjects; and predicting, for each of the plurality of subjects, a diagnostic class for that subject using the neutral network trained for that subject.
In an embodiment, a method of determining disease, disorder, or condition state in a subject includes: contacting an array with one or more analytes from the subject; incubating the one or more analytes on the array for a first period of time, thereby allowing a portion of the one or more analytes to be captured on the array; heating the array at a predetermined temperature for a second period of time; and measuring, using an imaging device, an amount of one or more colorimetric or luminescence signals produced in response to the heating.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Systems, devices, and methods described herein relate inter alia to capturing and detecting different analytes using detectable arrays and detection methods. In some embodiments, the present disclosure provides detectable arrays comprising photoinitiators or photocleavage products thereof. In some embodiments, analytes (e.g. labeled or unlabeled analytes) are detected on the detectable arrays using non-enzymatic browning reaction. In some embodiments, the present disclosure provides methods of diagnosing diseases or conditions in a subject utilizing the detectable arrays to detect one or more analytes present in a sample obtained from the subject. In some embodiments, subjects diagnosed as having a disease or condition, according to such methods of diagnosing, are treated with a suitable treatment.
In embodiments described herein, systems, devices, and methods can employ, unless indicated specifically to the contrary, conventional methods of molecular biology, recombinant DNA techniques, protein expression, and protein/peptide/carbohydrate chemistry within the skill of the art, many of which are described below for the purpose of illustration. Examples of such techniques are described in Sambrook, et al., Molecular Cloning: A Laboratory Manual (3rd Edition, 2000); DNA Cloning: A Practical Approach, vol. I & II (D. Glover, ed.); Oligonucleotide Synthesis (N. Gait, ed., 1984); Oligonucleotide Synthesis: Methods and Applications (P. Herdewijn, ed., 2004); Nucleic Acid Hybridization (B. Hames & S. Higgins, eds., 1985); Nucleic Acid Hybridization: Modern Applications (Buzdin and Lukyanov, eds., 2009); Transcription and Translation (B. Hames & S. Higgins, eds., 1984); Animal Cell Culture (R. Freshney, ed., 1986); Freshney, R. I. (2005) Culture of Animal Cells, a Manual of Basic Technique, 5th Ed. Hoboken N.J., John Wiley & Sons; B. Perbal, A Practical Guide to Molecular Cloning (3rd Edition 2010); Farrell, R., RNA Methodologies: A Laboratory Guide for Isolation and Characterization (3rd Edition 2005). Poly(ethylene glycol), Chemistry and Biological Applications, ACS, Washington, 1997; Veronese, F., and J. M. Harris, Eds., Peptide and protein PEGylation, Advanced Drug Delivery Reviews, 54(4) 453-609 (2002); Zalipsky, S., et al., “Use of functionalized Poly(Ethylene Glycols) for modification of polypeptides” in Polyethylene Glycol Chemistry: Biotechnical and Biomedical Applications, the disclosures of each of which are incorporated herein.
The present disclosure can provide inter alia arrays for capturing diverse analytes, e.g., from complex biological mixtures, and methods for label-free detection of such analytes as well as a diagnostic platform based on the same.
The present disclosure is based in part on the surprising discovery that, in some embodiments, the inclusion of optimized concentrations of photoinitiator compounds (e.g., 2,2-dimethoxy 2-phenylacetophenone (DMPA), 1-hydroxy-cyclohexyl phenyl ketone (HCPK), and 2-hydroxy-2-methyl-1-phenyl-1-propanone (HMPP), and photocleavage products thereof) in hydrogel arrays prior to UV-based polymerization results in macromolecule polymers that are optimal for capturing analytes and detecting the same via non-enzymatic browning reactions (e.g., via caramelization or Maillard reactions). The present disclosure is also based in part on the surprising discovery that, in some embodiments, certain optimal lengths of exposure to UV light during hydrogel array polymerization results in hydrogel arrays that enable improved colorimetric detection of bound analytes via non-enzymatic browning reactions (e.g., via caramelization or Maillard reactions). The present disclosure is also based in part on the surprising discovery that, in some embodiments, heating such hydrogel arrays comprising photoinitiator compounds, or photocleavage products thereof, at certain optimal temperatures for certain optimal times after contacting the arrays with one or more analyte produces improved colorimetric signatures that are sufficiently differentiated and enable classification of samples including different types or amounts of analytes. Thus, the present disclosure provides inter alia improved and versatile detectable arrays and methods of using the same to capture and detect analytes without the need for analyte labeling.
In an embodiment, a plurality of monomer, polymer, or monomer and polymer stock solutions are prepared, mixed with a photoinitiator compound, and placed in contact with a substrate. In an embodiment, the plurality of monomer, polymer, or monomer and polymer stock solutions are prepared, mixed with a photoinitiator compound, and spotted onto a substrate to form arrays. The monomer, polymer, or monomer and polymer stock solutions may be mixed together by means of the spotting on the arrays, or they may be mixed together prior to spotting on the arrays. In some aspects, the substrate comprises a substrate coating. In some aspects, the substrate coating comprises one or more appropriate chemical groups for fixing one or more macromolecules to the surface of the substrate. For example, in particular embodiments, the substrate coating comprises chemical groups that can be polymerized along with monomers or polymers spotted on the substrate. In some aspects, the arrays including a plurality of monomer, polymer, or monomer and polymer stock solution mixed with a photoinitiator, are exposed to UV light for a period of time to enable curing of any monomers and polymers spotted thereon with the photoinitiator and the substrate coating, e.g., to facilitate covalent attachment of the monomers and polymers to the substrate and/or to facilitate photocleavage of the photoinitiator into free radicals that react with other free radicals to form new molecular species which are trapped within macromolecules. After polymerization, the arrays can be exposed to different samples comprising one or more analytes (e.g., from samples obtained from a subject) thereby capturing one or more analytes from the samples on the array. The arrays comprising the captured analytes may subsequently be heated at a certain temperature to produce colorimetric signatures, which can be captured using an imaging device. In some aspects, colorimetric signatures produced by the arrays and captured using an imaging device are processed and analyzed using a computational model (e.g., neural network) to classify the samples (e.g., to diagnose a disease or condition).
In some embodiments, systems, devices, and methods described herein are used to diagnose different types of diseases or conditions and/or different stages of disease (e.g., different stages of cancer) or a condition. Conventional approaches to diagnosis typically require identification and validation of new biomarkers for detecting and classifying disease. Such identification and validation of biomarkers is a rate limiting step for medical diagnostics. Complex signatures integrating multiple analytes can be one approach to address this limitation. Current signature platforms, however, tend to be focused on specific analyte classes such as, for example, nucleic acids, antibodies, proteins or volatile organic compounds. Such sample-based specificity requires sample processing steps to isolate and label the target analyte class. Specialized equipment (e.g., DNA sequencers, microarray scanners, and mass spectrometers), therefore, are often required to isolate the analytes and/or enable detection of the results. Therefore, such signature platforms can be limited in the analytes they can analyze and require significant sample processing and specialized knowledge of equipment that makes such platforms difficult to use.
Systems, devices, and methods described herein provide signature platforms that address a chemical space that covers multiple analyte classes, such as, for example, nucleic acids, antibodies, proteins or volatile organic compounds. In some embodiments, use of such signature platforms comprise sample contact with hydrogel arrays, as described herein, and heating of such arrays. Such signature platforms can therefore reduce costs, be easy to use, and enable simultaneous screening of multiple conditions.
In some embodiments, systems, devices, and methods can utilize urine samples, which is an under-utilized source of diagnostic information. Despite the advantages of urine, the market for hematologic testing is greater than urinalysis. Urine contains defined analytes that can arrive in a collection vessel pre-filtered and ready for analysis without requiring further processing. Collection of urine is non-invasive and less prone to sample degradation, unlike blood samples, which can suffer from hemolysis. In addition, analyses based on the detection of ctDNA and other circulating nucleic acids can be highly specific and lacking in sensitivity. Such liquid biopsy approaches can require additional layers of information, such as epigenetics and proteomics, to increase sensitivity. Accordingly, systems, devices, and methods described herein that make use of urine profiles with hydrogel arrays can be a useful complement to liquid biopsy approaches.
In some embodiments, systems, devices, and methods described herein can be customized for certain contexts. For example, analyte signatures can be used as a general screen for diseases, to localize a disease site (e.g., a cancer site), to detect recurrence of disease, or as a screening test for a single disease. In particular, urine signatures can be used to screen for diseases such as, for example, lung cancer, for which no generally available screening test currently exists. In the U.S., for example, a computerized tomography (CT) scan can be recommended for individuals at high risk of lung cancer because of the harm that results from radiation and the high false positive rate or such scans.
In some embodiments, systems, devices, and methods described herein can be used to detect different stages of a disease, e.g., different stages of cancer. The present disclosure is based in part on the surprising discovery that cancer at different stages can have distinct colorimetric signatures that can be distinguished using computational methods described herein. In some embodiments, the distinct signatures can be used to distinguish indolent from lethal cancers. Such data can be useful in minimizing the over-diagnosis and over-treatment of slow growing tumors that have no effect on a patient's life expectancy.
Systems, devices, and methods can reduce the need for human interpretation and therefore human training and/or knowledge. In some embodiments, signature platforms as described herein can be based on a data-driven approach. Such signature platforms can capture complex dependencies and correlations within colorimetric data using computational systems and methods. Such complex dependencies and correlations can be lost when a single or few biomarkers are focused on. In some embodiments, such platforms can be used to identify biomarkers to illuminate the molecular mechanisms underlying a disease.
It should be noted that embodiments and features described in the context of one of the aspects or embodiments of the present invention also apply to the other aspects and embodiments of the invention.
A detectable array may comprise a substrate with a plurality of features or binding sites for binding one or more analytes.
The one or more macromolecules (e.g., 104 on exemplary detectable array 100) can include any macromolecules. In some particular embodiments, the macromolecules provide one or more unbiased binding sites. Such macromolecules with unbiased binding sites can, in some embodiments, have one or more free functional groups for binding analytes, such as carbonyls, amines, amides, carboxylic acids, esters, alcohols, and the like. For example, some polymer-based macromolecules used in the present invention have one or more free functional groups for binding analytes, such as carbonyls, amines, amides, carboxylic acids, esters, alcohols, and the like. Additionally, polymers with or without free functional groups can bind one or more analytes based not only on the nature of the functional groups (if present), but also based on the shape of the polymers and their interaction with the analytes in three dimensions, e.g., by virtue of their size and shape. Similarly, macromolecules comprising nanotubes may, or may not contain any free functional groups, but can still bind one or more analytes by virtue of their size and shape.
The one or more macromolecules (e.g., 104 on exemplary detectable array 100) can be, for example, at least one of polymer, surfactant, nanosphere, nanotube, dendrimer, microsphere, and polymerized microsphere. When the macromolecules include polymer, the polymer can be a homopolymer or copolymer. The macromolecules 104 can comprise one or more of (meth)acrylamides, (meth)acrylates, and N,N′-(alkylene)bisacrylamide. For example, in some embodiments, the macromolecules 104 comprise one or more of 2-carboxyethyl acrylate, acrylic acid, acrylamide, histamine acrylate, N-[tris(hydroxymethyl)methyl]acrylamide, hydroxypropyl acrylates, 4-hydroybutyl acrylate, N-hydroxyethyl acrylamide, N,N,-dimethylacrylamide, N-(1,1-dimethyl-3-oxobutyl)acrylamide, N-isopropylacrylamide, ethylene glycol phenyl ether acrylate, N,N′-methylenebisacrylamide, 1,1,3,3,3-hexafluoroisopropyl acrylate, and N-tert-octylacrylamide. In some embodiments, the macromolecules comprise two or more of 2-carboxyethyl acrylate, acrylic acid, acrylamide, histamine acrylate, N-[tris(hydroxymethyl)methyl]acrylamide, hydroxypropyl acrylates, 4-hydroybutyl acrylate, N-hydroxyethyl acrylamide, N,N,-dimethylacrylamide, N-(1,1-dimethyl-3-oxobutyl)acrylamide, N-isopropylacrylamide, ethylene glycol phenyl ether acrylate, N,N′-methylenebisacrylamide, 1,1,3,3,3-hexafluoroisopropyl acrylate, and N-tert-octylacrylamide.
In some embodiments, the one or more macromolecules (e.g., 104 on exemplary detectable array 100) can also comprise at least one cross-linker. The cross-linker can be any cross-linker known in the art. Cross-linkers can include, for example, one or more molecules containing two, three, four, or more olefins or acrylic functional groups, such as one or more of bis-acrylamide, trimethylolpropane triacrylate, bisphenol A-bis(2-hydroxypropyl)acrylate, and 1-(acryloyloxy)-3-(methacryloyloxy)-2-propanol.
The one or more macromolecules (e.g., 104 on exemplary detectable array 100) can be arranged on the substrate in a predetermined shape or pattern. For example, as shown on detectable array 100, a plurality of macromolecules may be deposed evenly along the x and y axis of the detectable array creating an ordered plurality of macromolecules each separated from the nearest neighboring macromolecule by the same distance. In some embodiments, it is desirable to optimize the spacing between the macromolecules, e.g., to ensure that during detection, signals from any analytes captured by one macromolecule to not interfere with the signals from analytes captured by a nearby macromolecule. In some embodiments, the spacing of the macromolecules on the detectable array is not even, but varies along one or more portions of the detectable array. In some embodiments, each of the plurality features (e.g., the plurality of features 102 on exemplary detectable array 100) comprises a different macromolecule. In some embodiments, several of the plurality of features (e.g., the plurality of features 102 on exemplary detectable array 100) comprise similarly or identical macromolecules. In some embodiments, such redundancy of macromolecules presented on the detectable array can be beneficial, as it enables replicate capture and detection of analytes on a single array, which can increase the reliability and reproducibility of analyte detection using such arrays. Thus, each substrate coating 106 of each of the plurality of features can contain macromolecules that are unique in either chemical identity, three-dimensional shape, pattern of physical disposition, or a combination thereof, with respect to the other features of the substrate. Or, the detectable array may comprise a plurality of each of such unique macromolecules, thereby providing redundancy on the array to enable replicate detection of analytes on a single array. These variables can be accomplished in a number of ways. For example, lithographic techniques can be used to create different patterns of macromolecules on the different features. As another example, when the substrate is a plate, the macromolecules fixed to each well of the plate can have different chemical identities. As a further example, when the substrate 120 is a slide as depicted in
In some embodiments, as further described below, the macromolecules 104 can include hydrogels cured with photoinitiators. The hydrogels can be formed by combining one or more monomer stock solutions and a photoinitiator solution, and then curing the combined solutions with ultraviolet (UV) light. For example, a macromolecule 104 can be formed by combining a single monomer with a photoinitiator. Alternatively, different macromolecules 104 can be formed by polymerizing two monomers, e.g., A and B, together at different ratios (e.g., 50:50, 10:90). In some instances, having different ratios of monomers can lead to differences in structure and differential binding of analytes.
Examples of suitable monomers include: acrylamide, 2-carboxyethyl acrylate, acrylic acid, 2-cyanoethyl acrylate, N-[tris(hydroxymethyl)methyl] acrylamide, hydroxypropyl acrylate isomers, 4-hydroxybutyl acrylate, N-hydroxyethyl acrylamide, N,N-dimethylacrylamide, N-isopropylacrylamide, N-(1,1-dimethyl-3-oxobutyl) acrylamide, 2-methacryloxyethyl phenyl urethane, 1-acryloyloxy-3-(methacryloyloxy)-2-propanol and ethylene glycol phenyl ether acrylate. The monomer stock solutions can be prepared using methods known in the art. For example, in some embodiments, monomer stock solutions are prepared by dissolving a monomer (e.g., one of the suitable monomers listed above) in a suitable solvent. For example, in some embodiments, dimethyl sulfoxide (DMSO) is used to dissolve one or more of such monomers. In some embodiments, monomer stock solutions are prepared by dissolving a monomer in a monomer solvent solution disclosed herein (e.g., Monomer Solvent 1 or Monomer Solvent 2) either directly or after first dissolving the monomer in dimethyl sulfoxide (DMSO). For example, in some embodiments, a monomer is dissolved in DMSO and then further mixed with a monomer solvent solution containing acrylamide, N,N′-methylenebisacrylamide, distilled water, and DMSO, and then additional acrylic acid or acrylamide is added as required to achieve desired concentrations of monomer. The photoinitiator solution can be formulated by dissolving the photoinitiator in any suitable solvent. In particular embodiments, the photoinitiator is dissolved in DMSO. Examples of suitable photoinitiators DMPA, HCPK, and HMPP. The hydrogel solutions can be placed (e.g., spotted) onto a substrate (e.g., substrate 120) and subsequently cured using UV light. Further details regarding the specific parameters of certain hydrogel preparations and UV curing parameters are described with reference to
In some embodiments, when a detectable array such as the detectable array 100 is placed in contact with a sample containing analytes, one or more macromolecules 104 on the detectable array 100 bind to one or more analytes 110. In some embodiments, the analytes bind to a surface 104a of the one or more macromolecules 104. In some embodiments, the analytes are able to diffuse inside the macromolecules via pores created during the polymerization process, and the analytes then bind inside the macromolecules. The one or more analytes 110 can comprise one or more of small molecules, proteins, peptides, nucleotides, nucleosides, bacteria, viruses, fungi cells, yeast cells, and animal cells bound to at least one of the one or more macromolecules. In particular, a plurality of different analytes 110 can be bound to the one or more macromolecules. For example, if the detectable array 100 is contacted with the blood or urine of a subject, the one or more macromolecules 104 can bind to cells, such as red blood cells, white blood cells, t-cells, and the like, and also bind to proteins and small molecules that are present in the blood. In particular, when each of the plurality of features 102 has a different affixed macromolecules, different types and quantities of analytes 110 can bind to each of the plurality of features, thereby creating a detectable pattern of bound analytes.
The presence or absence of one or more analytes 110 bound to the one or more macromolecules 104 can be detectable by a plurality of detection methods, as further described herein. Exemplary detection methods include one or more of Maillard reaction, caramelizing, reaction with one or more amine reactive dyes, reaction with one or more thiol reactive dyes, reaction with one or more cellular dyes, reaction with one or more solvatochromic dyes, reaction with one or more acid indicators, reaction with one or more base indicators, reaction with one or more labeled antibodies, luminescence, surface texture analysis, photo-scanning, microscopy, photo-scanning with reflectance or transmittance illumination, photography with reflectance or transmittance illumination, mass spectrometry and spectroscopy. Further details with respect to detecting using one or more detection methods, e.g., Maillard reactions and caramelizing, are described below with reference to the examples below and
Systems, devices, and methods described herein can provide an analyte-agnostic approach using arrays including hydrogels cured with a photoinitiator. Suitable examples of photoinitiators for hydrogel arrays as described herein include DMPA, HCPK, and HMPP.
Spray reagents provide a way to visualize organic compounds on Thin Layer Chromatography (TLC) plates. Spray reagents can include photocleavage products of photopolymerization initiators or photoinitiators. By incorporating the chemical reactivity of photoinitiators into a combinatorially-diverse array of hydrogels, such arrays can provide colorimetric signatures that can enable the classification of samples without prior knowledge of specific anlaytes bound to the array. Such an approach can involve heating the array to produce a visual colorimetric output, but can avoid the need for more costly modes of data acquisition such as, for example, mass spectrometry.
In an embodiment, DMPA can be used for photo-polymerizing acrylamide and acrylate-based materials. Photocleavage of DMPA results in the production of a benzoyl and a ketal fragment, which can further dimerize or cross-react to create multiple photolysis products. Two such products are benzil and benzoin (i.e., a reduced form of benzil), both of which can be used as TLC spray visualization reagents.
Experiments were conducted to evaluate the effect of UV cleavage on DMPA and the colorimetric signals produced by resulting molecules.
In a second experiment, a DMPA solution was prepared by dissolving 1.5 g of DMPA in 6 ml of DMSO. Two glass vials of 700 uL DMPA were prepared and one was exposed to UV light (390 nm) for one hour while the other was not exposed to UV light. Various ratios of the non-UV-exposed DMPA solution (UV−) and UV-exposed DMPA solution (UV+) were prepared according to Table 1:
Multiple factors can affect the properties of the colorimetric products produced using hydrogels cured with DMPA. These factors can include, for example, UV exposure time, DMPA concentration, and heating temperature. These factors were tested using a prototype spot array of fourteen different hydrogels mounted on a microscope slide and photocured with DMPA under different conditions.
The fourteen hydrogels A1 to A14 were prepared using the following working solutions, monomer stock solutions, and photoinitiator solutions.
Working Solutions:
The hydrogels A1 to A14 comprised between one and three monomers selected from a group of fourteen monomers including: acrylamide, 2-carboxyethyl acrylate, acrylic acid, 2-cyanoethyl acrylate, N-[tris(hydroxymethyl)methyl] acrylamide, hydroxypropyl acrylate isomers, 4-hydroxybutyl acrylate, N-hydroxyethyl acrylamide, N,N-dimethylacrylamide, N-isopropylacrylamide, N-(1,1-dimethyl-3-oxobutyl) acrylamide, 2-methacryloxyethyl phenyl urethane, 1-acryloyloxy-3-(methacryloyloxy)-2-propanol and ethylene glycol phenyl ether acrylate.
Monomer stock solutions M1 and M15 were formulated by dissolving the monomer first in DMSO and either MS-1 or MS-2, and then adding additional acrylic acid or acrylamide as required to the final concentrations stated in Table 2 below. MS-1 was used as the solvent for M1 to M14, and MS-2 was used for M15.
The photoinitiator solutions were made by first dissolving the photoinitiator in DMSO to make a stock solution and then constituting the relevant photoinitiator solutions as described below.
Table 3 provides the proportion by volume of each monomer stock solution for each polymer used in the hydrogels A1 to A14.
For the arrays, borosilicate glass slides were initially washed three times with distilled G-9T water, followed by sonication in absolute ethanol for five minutes to remove surface contaminants. Slides were blow dried then dried in an oven at 120° C. for five minutes followed by coating with 3-methacryloxypropyltrimethoxylsilane through vapor deposition overnight at 93° C. and 580 mmHg. Thereafter, slides were dried in an oven at 120° C. for one hour.
For each polymer solution A1 to A14, 10 uL of DMPA PI solution was added to 10 uL of each solution A1 to A14 and mixed with a pipet. 1.5 uL of each solution was spotted onto a glass slide, as appropriately silanized according to the above procedures, and polymerized with a 302 nm UV transilluminator (e.g., Biorad 2000 UV transilluminator). UV light exposure catalyzes both the curing of the polymer and also the covalent attachment of the polymers onto the silanized glass surface.
The arrays were digitally scanned for image analyses. Specifically, slides were scanned with a digital scanner (e.g., EPSON Perfection v500) at 1200 dpi and stored as images. Each image was converted to 8-bit grayscale and inverted. For each spot associated with a hydrogel, a central circular region of standardized area was selected suing an area selection tool and analyzed. Mean intensity values were measured for the central circular region of each spot. Background mean intensity values were also measured for each array. The spot mean intensity values were then adjusted based on the background mean intensity values and plotted against the factors of interest, i.e., UV exposure time, DMPA concentration, heating temperature, and/or photoinitiator species (i.e., DMPA, HCPK, HMPP). Each data point was generated and analyzed in triplicate.
As depicted in graph 500, each of the hydrogel spots with the exception of A4 demonstrates an increase in color intensity with increasing UV exposure time. The signal change between 5 and 60 minutes of UV curing was statistically significant for each of the hydrogel spots with the exception of A4, as depicted in
The arrays were digitally scanned for image analyses, as described above. In graph 510, the first bar (i.e., left most bar) above each hydrogel label A1 through A14 corresponds to the relative signal of the hydrogel with 0.5×DMPA, the second bar (i.e., left middle bar) above each hydrogel label A1 through A14 corresponds to the relative signal of the hydrogel with 1×DMPA, the third bar (i.e., right middle bar) above each hydrogel label A1 through A14 corresponds to the relative signal of the hydrogel with 2×DMPA, and the fourth bar (i.e., right most bar) above each hydrogel label A1 through A14 corresponds to the relative signal of the hydrogel with 4×DMPA. Graph 510 depicts each of the hydrogel spots with the exception of A8 demonstrating an increase in color intensity with increasing relative DMPA concentrations. The signal change with increasing DMPA concentrations from 0.5× to 4× DMPA was statistically significant for each of the hydrogel spots with the exception of A6 and A7, as depicted in
To further test the effect of heating temperature on the relative colorimetric signal production of hydrogels cured with a photoinitiator, an experiment was conducted that heated hydrogel arrays including different photoinitiators at temperatures ranging from 100° C. to 400° C. (i.e., at 100° C., 150° C., 200° C., 250° C., 300° C., 350° C. or 400° C.). The arrays were digitally scanned for image analyses, as described above.
In some embodiments, an array includes a plurality of spotted solutions, each including one or more macromolecules mixed with a photoinitiator. In some embodiments, the one or more macromolecules can include one or more monomers selected from the group consisting of: acrylamide, 2-carboxyethyl acrylate, acrylic acid, 2-cyanoethyl acrylate, N-[tris(hydroxymethyl)methyl] acrylamide, hydroxypropyl acrylate isomers, 4-hydroxybutyl acrylate, N-hydroxyethyl acrylamide, N,N-dimethylacrylamide, N-isopropylacrylamide, N-(1,1-dimethyl-3-oxobutyl) acrylamide, 2-methacryloxyethyl phenyl urethane, 1-acryloyloxy-3-(methacryloyloxy)-2-propanol and ethylene glycol phenyl ether acrylate. In some embodiments, the one or more macromolecules can include one or more monomers selected from the group consisting of: 2-carboxyethyl acrylate, acrylic acid, acrylamide, histamine acrylate, N-[tris(hydroxymethyl)methyl]acrylamide, hydroxypropyl acrylate isomers, 4-hydroybutyl acrylate, N-hydroxyethyl acrylamide, N,N,-dimethylacrylamide, N-(1,1-dimethyl-3-oxobutyl)acrylamide, N-isopropylacrylamide, ethylene glycol phenyl ether acrylate, N,N′-methylenebisacrylamide, 1,1,3,3,3-Hexafluoroisopropyl acrylate, and N-tert-octylacrylamide. In some embodiments, the plurality of spotted solutions can include a homopolymer including one monomer, a heteropolymer including at least two monomers, a heteropolymer including at least three monomers, or any number or combinations thereof.
In some embodiments, the substrate includes a surface that is functionalized. In some embodiments, the substrate includes a surface that is functionalized with a silane or siloxane coating. In some embodiments, the substrate includes a surface that is functionalized with acrylosiloxane, e.g., selected from methacryloxypropyl trimethoxy silane, 3-acryloxypropyl trimethoxy silane, N-(3-acryloxy-2-hydroxypropyl-3-aminopropyltriethoxysilane, 3-methacryloxy propyldimethylchlorosilane, and any combination thereof.
Examples of suitable photoinitiators include DMPA, HCPK, and HMPP. In some embodiments, the concentration of photoinitiator can be consistent across the plurality of solutions. In alternative embodiments, individual or subsets of solutions can have the photoinitiator concentrations that differ from other individual or subsets of solutions. In some embodiments, each of the plurality of solutions can have a concentration of photoinitiator between about 0.5× (6.25 nM) and about 4× (50 nM), including all ranges and values therebetween. In some embodiments, each of the plurality of solutions can have a concentration of photoinitiator of about 1× (12.5 nM).
In some embodiments, the array can include about fourteen spots, about seventy spots, including all ranges and values therebetween. In some embodiments, each spot can include about 1 uL, about 1.3 uL, about 1.5 uL, about 2 uL, including all ranges and values therebetween.
In some embodiments, the array including the plurality of spotted solutions can be polymerized using UV light for a predetermined period of time. In some embodiments, the UV light can have a wavelength of between about 250 nm to about 400 nm. In some embodiments, the UV light can have a wavelength of between about 300 nm to about 350 nm. In some embodiments, the UV light can have a wavelength of about 250 nm, about 302 nm, about 350 nm, about 390 nm, about 400 nm, inclusive of all ranges therebetween. In some embodiments, the predetermined period of time for UV light exposure can be about 5 minutes, about 20 minutes, about one hour, about two hours, about several hours, inclusive of all ranges and values therebetween.
In some embodiments, the polymerized array can be exposed to a sample including one or more analytes (labeled or unlabeled) and incubated for a predetermined period of time. In some embodiments, the arrays can be incubated with the sample for about 5 minutes, about 10 minutes, about 15 minutes, including all ranges and values therebetween. After incubating with the sample, the polymerized array can be heated at a predetermined temperature for a predetermined period of time. In some embodiments, the array can be heated at about 100° C., about 150° C., about 200° C., about 250° C., about 300° C., about 350° C., about 400° C., including all ranges and values therebetween. In some embodiments, the array can be heated at a single temperature, while in other embodiments, the array can be heated at multiple temperatures for equal or different periods of time. In some embodiments, the array can be heated at the predetermined temperature for about 1 minute, about 5 minutes, about 10 minutes, about 15 minutes, including all ranges and values therebetween. In some embodiments, the array can be heated to a temperature from about 200° C. to about 400° C., such as about 275° C. to about 325° C. or about 300° C., for sufficient time to induce a Maillard reaction, such as from about 1 min to about 15 min, or about 5 min. In some embodiments, the array can be heated to a temperature from about 100° C. to about 210° C., such as about 130° C. to about 210° C., or about 160° C. to about 200° C., for a sufficient time to cause caramelization. In some embodiments, the arrays can be heated via a hot plate.
In an embodiment, a hydrogel array suitable for disease detection applications as described herein can include a plurality of hydrogel spots each polymerized with about 1×DMPA (12.5 mM) with about 20 minutes of UV light exposure and capable of being heated at a temperature of as high as about 250° C.
One or more monomer and polymer solutions can be prepared, at 714. In some embodiments, preparation of such solutions can include preparing a plurality of monomer stock solutions, e.g., similar to the preparation of monomer stock solutions M1 through M15, as described herein. For example, each monomer stock solution can be prepared by dissolving a monomer in a solution including one or more of DMSO, acrylamide, N,N′-methylenebisacrylamide, or distilled water, and optionally adding additional acrylic acid or acrylamide as necessary to achieve desired concentrations of the monomer. Examples of suitable monomer stock solutions (e.g., monomer stock solutions M1 to M15) are depicted in Table 2 above. One or more polymer solutions can then be prepared using the monomer stock solutions, e.g., by combining different amounts of monomer stock solutions, as depicted, for example, in Tables 3-12. The photoinitiator solution can be added to each monomer and polymer solutions to produce spotting solutions, at 716. In some embodiments, the monomer and polymer solutions can be mixed with the photoinitiator solutions using a pipet. In some embodiments, the final concentrations of the photoinitiator can be between about 0.5× (6.25 mM) and about 4× (50 mM).
Each spotting solution can be spotted onto a surface of a substrate, at 718, and exposed to electromagnetic energy in the form of UV light, at 720. In some embodiments, the substrate can be, for example, borosilicate glass. In some embodiments, the borosilicate glass can be prepared by washing each slide with distilled water, followed by sonication in absolute ethanol to remove surface contaminants. The slides can then be air dried and/or dried with heat (e.g., in an oven), coated with a substrate coating (e.g., 3-methacryloxypropyltrimethoxylsilane), and dried again. The UV light can be at a wavelength of about 250 nm to about 400 nm. In some embodiments, the array can be exposed to UV light for the first predetermined period of time, e.g., between about 5 minutes and about 60 minutes. Exposure of the spotting solution can result in several outcomes for the photoinitiator. In some instances, the photoinitiator is photocleaved by the UV into free radicals and these free radicals participate in polymerizing the monomers or polymers and can become incorporated into terminal ends of the monomers or polymers in the process. In some instances, the photoinitiator is photocleaved by UV into free radicals that do not participate in polymerization but react with other free radicals to form new molecular species, which in turn are able to react with analytes to produce a colorimetric signal. And in some instances, residual photoinitiator can escape photocleavage. Systems, devices, and methods described herein predominately rely on the photocleaved products of the photoinitiators to produce the colometric signals described herein for detection and/or diagnostic purposes. After the UV exposure, the method 700 can then proceed to 702, i.e., where a sample (e.g., including analytes) is contacted with the array.
The processor 1702 can be any suitable processing device configured to run and/or execute a set of instructions or code and may include one or more data processors, image processors, graphics processing units, physics processing units, digital signal processors, and/or central processing units. In embodiments described herein, the processor 1702 can be any suitable processing device configured to run and/or execute functions associated with processing and/or analyzing image data, including applying one or more trained models, to classify a sample. The processor 1702 can be, for example, a general purpose processor, Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), and/or the like. The processor 1702 can be configured to run and/or execute application processes and/or other modules, processes and/or functions associated with the system and/or a network associated therewith. The underlying device technologies may be provided in a variety of component types (e.g., metal-oxide semiconductor field-effect transistor (MOSFET) technologies like complementary metal-oxide semiconductor (CMOS), bipolar technologies like emitter-coupled logic (ECL), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures), mixed analog and digital, and the like.
The memory 1704 can include a database (not shown) and may be, for example, a random access memory (RAM), a memory buffer, a hard drive, an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), a read-only memory (ROM), Flash memory, and/or the like. The memory 1704 can store instructions to cause the processor 1702 to execute modules, processes, and/or functions associated with processing and/or analyzing image data. Some embodiments described herein relate to a computer storage product with a non-transitory computer-readable medium (also may be referred to as a non-transitory processor-readable medium) having instructions or computer code thereon for performing various computer-implemented operations. The computer-readable medium (or processor-readable medium) is non-transitory in the sense that it does not include transitory propagating signals per se (e.g., a propagating electromagnetic wave carrying information on a transmission medium such as space or a cable). The media and computer code (also may be referred to as code or algorithm) may be those designed and constructed for the specific purpose or purposes.
Examples of non-transitory computer-readable media include, but are not limited to, magnetic storage media such as hard disks, floppy disks, and magnetic tape; optical storage media such as Compact Disc/Digital Video Discs (CD/DVDs); Compact Disc-Read Only Memories (CD-ROMs), and holographic devices; magneto-optical storage media such as optical disks; solid state storage devices such as a solid state drive (SSD) and a solid state hybrid drive (SSHD); carrier wave signal processing modules; and hardware devices that are specially configured to store and execute program code, such as Application-Specific Integrated Circuits (ASICs), Programmable Logic Devices (PLDs), Read-Only Memory (ROM), and Random-Access Memory (RAM) devices. Other embodiments described herein relate to a computer program product, which may include, for example, the instructions and/or computer code disclosed herein.
The input/output device 1706 can include an input device (e.g., keyboard, touch screen, audio device) and/or an output device (e.g., display device, audio device). The input/output device 1706 can include a communication interface that enables compute device 1700 to interface with one or more components of a signature platform or system, as described herein. For example, the communication interface can be configured to receive data from an imaging device and/or send signals (e.g., generated by processor 1702) to one or more of an imaging device, a heating device, a light source (e.g., a UV light or illuminator), etc. to control operation of those devices. In some embodiments, the processor 1702 via the communication interface of input/output device 1706 can send signals to activate a UV light (e.g., to apply UV light to polymerize an array), to activate an imaging device to capture image data or an array, and/or to activate a heating device to heat an array.
In some embodiments, the compute device 1700 can be configured to perform method 700 as depicted in
A trained computational model (e.g., a neural network) can be applied to the data associated with the various color channels (e.g., color intensity data or RGB data), at 728. In some embodiments, the model can include fully connected neural networks, which can output a probability of the sample belonging to a certain class from a plurality of classes. An example of a suitable neural network is further described in Example 3 below. In some embodiments, the model can be used to classify the sample into one or more diagnostic classes (e.g., healthy, disease 1, disease 2, etc.). For example, as further described in Example 3 below, the model can be used to classify the sample into diagnostic classes including healthy and one or more types of cancer or disease. Optionally, a clinical recommendation can be provided (e.g., by the compute device based on preset algorithms or rules) based on the class. For example, the compute device can indicate that a patient who provided a sample (e.g., a urine sample) should conduct additional testing to identify and/or treat a disease and/or cancer.
Systems, devices, and methods described herein can be used to distinguish between different analytes. In some embodiments, systems, devices, and methods can use transmittance and colorimetric profiles to distinguish between different analytes.
Hydrogel arrays cured with DMPA were tested with the following 39 analytes:
The hydrogel arrays included with 70 spots including varying constituents of hydrogels that provided different combinations of charge, polarity, hydrophobicity, and size exclusion. To prepare the arrays, an equal volume of DMPA photoinitiator solution was added to and mixed with each of the monomer stock solutions A1 to A14 (Table 3), as well as additional stock solutions B1 to B14, C1 to C14, D1 to D14, and E1 to E14, as detailed in Tables 4-7 below.
1.3 uL of each solution was then spoiled onto a silanized glass slide in the following configuration. Arrays with this configuration can be referred to herein as α-arrays.
The spotting was performed using a Biomek 2000 fluid handling robot, but other suitable methods and devices can be used. The glass slides used to create each array were laser etched with unique identifiers (IDs). After spotting the solutions, the spots were photopolymerized under a 365 nm gel-curing UV lamp (e.g., Jiadi JD818) for twenty minutes.
A total of six α-arrays were incubated with each of the 39 analytes for 10 minutes at room temperature. The arrays were then removed and air dried at room temperature for 20 minutes and further dried on a heated plate at 100° C. for 20 minutes. Subsequently, the arrays were heated on a heated plate at 250° C. for 5 minutes to develop their colorimetric signal profiles. The arrays were scanned with a digital scanner (e.g., EPSON Perfection v500) at 1200 dpi and saved as red, green, and blue (“RGB” or “R, G, and B”) JPEG images. Each array spot was cropped using a fixed window size and then split into R, G, and B channels for further analysis. The R, G and B intensities of each spot were obtained by summing the respective R, G and B pixel intensities for each spot window.
A vector of 210 spot intensities (70 spots multiplied three color channels) was obtained from each array data point. Each vector was L1 normalized. Unsupervised hierarchical clustering analysis was performed using hierarchical clustering software “hierarchical_clustering.py” authored by Nathan Salomonis (J. David Gladstone Institutes, San Francisco Calif.).
L2 normalization was used for both the row-wise and column-wise metrics. A heatmap was generated using the color gradient “gist_ncar.” To score the clustering results, misclassified arrays were identified in a two-step process: (1) for each class of six arrays, identify the largest contiguous cluster, and (2) any array which was not part of this largest contiguous cluster was identified as misclassified. The clustering accuracy was calculated as
As depicted in
Unsupervised hierarchical clustering was performed on 234 arrays (i.e., 39 analytes with 6 arrays per analyte). Arrays and array spots were both clustered using the Euclidean distance measure. Such hierarchical clustering was performed using systems, devices, and methods described herein, e.g., as described with reference to
Two-dimensional (2D) and three-dimensional (3D) linear discriminant analyses were performed with the image data using the classifier “sklearn.lda.LDA” from the machine learning library “scikit-learn,” version 0.16.1. The plotted output of the 2D and 3D linear discriminant analyses are depicted in graphs 840 and 850, respectively (see
Color distribution analysis was performed on the 234 arrays (i.e., 39 analytes with 6 arrays per analyte). Specifically, the general color profile of the scanned array images was obtained using the Color Inspector 3D plug-in software in ImageJ.
The five misclassified arrays included one of glucose, one or carrageenan (lambda), two of phenol, and one or lysine. Of the misclassified arrays, four were mis-clustered with other samples that had similar chemical structures and properties. In particular, one glucose array was clustered with sucrose, one carrageenan (lambda) array was clustered with carrageenan (kappa), and two phenol arrays were clustered with cresol. The remaining mis-clustered array for lysine was mistakenly clustered with sodium chloride due to a fainter signature.
This 39-analyte study demonstrates that system, devices, and methods as described herein provide an array platform that is able to profile multiple sample types with high accuracy, even when many the samples are chemically similar. For example, the lambda, kappa, and iota forms of carrageenan were clustered separately with the exception of one array. Complex mixtures, such as, for example, coffee, curry, green tea, LB media, milk, and nutrient broth, were distinguished with no errors. Different salts including, for example, potassium sorbate, sodium citrate, sodium carbonate, ammonium sulfate, and sodium chloride were also clustered with no errors. Consistent with the clustering data, the linear discriminant analysis showed that samples clustered tightly with two to three principal components (graphs 840 and 850).
In some embodiments, systems, devices, and methods can use fluorescence or luminescence profiles to distinguish between different analytes.
α-arrays as described above (i.e., hydrogel arrays cured with DMPA) were tested with the following 29 analytes:
A total of three α-arrays were incubated with each of 29 analytes for 10 minutes at room temperature, and three α-arrays were incubated with no analyte to establish a control. The arrays were then removed and air dried at room temperature for 20 minutes and further dried on a heated plate at 100° C. for 20 minutes. Subsequently, the arrays were heated on a heated plate at 250° C. for 5 minutes to develop their colorimetric signal profiles. Fluorescence images were captured by illuminating the arrays with UV light at 390 nm, and Apple® iPhone® night camera (version 3) software application to capture RGB JPEG images using the following manual configuration: Shutter speed: 1/15s; Auto ISO; Exposure −3.3 (range between −8.0 to +8.0).
Systems, devices, and methods described herein can be used to detect multiple disease classes simultaneously without priority knowledge of analytes within tested samples. In an embodiment, hydrogel arrays as described herein can be used to detect disease classes from biofluid samples such as, for example, urine samples. The ability of such arrays to handle arbitrary sample types without or with minimal sample processing and labeling enables analysis using biofluids other than blood. The reduced sample processing required by the hydrogels also results in less analyte losses and degradation prior to analysis, which can limit detection of circulating tumor deoxyribonucleic acid (DNA) isolated from plasma.
In a study, hydrogel arrays cured with DMPA were used to detect multiple disease classes using urine samples. Urine was selected because the collection of such samples are relatively simple and non-invasive and do not require pre-processing. Urine, however, is a relatively underutilized sample type despite being informationally rich in small molecule analytes. Accordingly, systems, devices, and methods described herein can produce non-redundant information to complement other clinical lab tests as well as imaging modalities, or be used as a stand-alone test. Tests using urine can be easier to implement, more cost-effective, less invasive, and safer, and therefore can be particularly suited for disease screening or surveillance of relapse in patients.
For the study, α-arrays as described above were used along with a second 70-spot hydrogel array, referred to as β-arrays. β-arrays include spot diversities different from the α-arrays so as to increase sensitivity of the disease class detection.
β-arrays were created using the stock solutions F1 to F14, G1 to G9, H1 to H15, I1 to I18, J1 to J11 and K1 to K3, shown in Tables 8-12.
The β-arrays have the following configuration:
Urinalysis was performed on 592 study participants with the α-arrays and β-arrays. The study participants included healthy individuals (197), patients who were Hepatitis B virus-infected (93), and patients diagnosed with various stages of biopsy-proven colorectal (78), liver (144), prostate (24), and lung (56) cancer. These five diseases (Hepatitis B, colorectal cancer, liver cancer, prostate cancer, and lung cancer) were selected because they have a high prevalence in the general population and/or suffer from a lack of simple screening options despite favorable therapeutic outcomes if detected and treated early.
Healthy individuals had a mean age of 47.3 (with standard deviation of 12.52) and patients had a mean age of 54.4 (with standard deviation of 14.2). The demographics of the patient population are summarized in
Sample collection was conducted according to set policies and procedures. Healthy individuals were enrolled during their routine physical examinations. Urine was collected before any clinical testing or procedures (e.g., colonoscopy) to avoid changes arising from the clinical examination itself. With some individuals, blood work, liver and renal function, tumor markers, chest x-rays and ultrasound for liver, gallbladder, pancreas, spleen and kidneys were performed. Other healthy individuals were males greater than 40 years old, without known medical conditions, and with normal prostate ultrasound and normal cancer biomarkers. Individuals were enrolled as healthy when they did not exhibit any disease and did not have any pre-existing medical conditions (e.g., tumors, hypertension, diabetes, and kidney disease). Similar procedures were used for enrolling patients with clinically validated Hepatitis B, colorectal cancer, liver cancer, prostate cancer, and lung cancer. For these, urine was collected prior to any surgery (e.g., biopsy) and were classified under a particular disease after pathology results became available.
Each study participant sample was tested with six arrays comprising three α-arrays and three β-arrays. Undiluted urine samples were mixed 1:1 with a fixative solution containing 50% methanol and stored at room temperature until tested. Fixed urine, 9 mL/chamber, was then added into two disposable plastic slide chambers, one containing the α-arrays and the other containing the β-arrays. After incubation at room temperature for fifteen minutes, the arrays were removed from the chamber and excess urine removed by tapping the arrays on paper towels. The arrays were then heat cycled for about 10 minutes in an infrared oven (SMTHouse Reflow Oven T962A) with a maximum temperature of 250° C. to develop their colorimetric signal profiles.
Arrays were scanned with a digital scanner (EPSON Perfection v500) at 1200 dpi and saved as RGB JPEG images. Each array spot was cropped using a fixed window size and then split into R, G and B channels for further analysis. The R, G and B intensities of each spot were obtained by summing the respective R, G and B pixel intensities for each spot window. A vector of 210 spot intensities (70 spots multiplied by three color channels) was obtained from each array data point. Each vector was L1 normalized.
The resulting vectors were used to train and validate a neural network implemented as a multilayer perceptron (MLP) with a single fully connected hidden layer. While further analysis is described herein with reference to such a classification algorithm, one of ordinary skill can appreciate that other classification algorithms can be used.
The MLP was implemented using the skleam.neural_network.MLPClassifier model, version 0.18. The architecture of the model included 210 input nodes and a single 235-node hidden layer. A ReLU or rectified linear unit was used as the activation function, and logistic regression was performed using Limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) optimization algorithm. The MLP was trained and evaluated using a leave-one-out cross-validation framework, which isolated each patient sample data in turn, trained the MLP on the remaining patients, and then applied the trained MLP to the isolated patient sample to predict the class of that isolated patient sample.
The analysis was repeated with colorectal, liver, lung and prostate cancer being combined into a single “cancer” class. Charts 1420 and 1430 depict the performance metrics of when evaluated with this lesser number of classes. In chart 1430, shaded boxes identify where an individual with a certain condition was classified as having that condition. As shown in charts 1420 and 1430, 539 out of 592 individuals were correctly classified, giving a raw accuracy of 91.1% and a normalized accuracy of 90.1%. In this combined “cancer” scenario, the sources of misclassification was the 9.0% healthy individuals and 8.6% HBV patients who were classified as “cancer.” “Cancer” sensitivity (92.3%) and positive predictive value (91.4%) was higher than the sensitivity and positive predictive value of each cancer when evaluated as separate classes. This outcome is consistent with more cancer patients being correctly identified and explains why the normalized accuracy for the combined “cancer” analysis is 90.1% compared to 75.6% when the cancers were differentiated. The increase in “cancer” sensitivity, however, imposes a cost in increased false positives. This trend is reflected in the lower specificity (with the exception of liver cancer) and negative predictive value of the combined “cancer” analysis compared to the differentiated cancer analysis.
The results depicted in
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. Although any methods, compositions, reagents, cells, similar or equivalent to those described herein can be used in the practice or testing of the invention, the preferred methods and materials are described herein. All publications and references, including but not limited to patents and patent applications, cited in this specification are herein incorporated by reference in their entirety as if each individual publication or reference were specifically and individually indicated to be incorporated by reference herein as being fully set forth. Any patent application to which this application claims priority is also incorporated by reference herein in its entirety in the manner described above for publications and references.
“Analytes” are entities of interest that can bind to (by covalent, ionic, physical, or any other means) and be detected on an array (such as the arrays disclosed herein). In some embodiments, analytes may include, but are not limited to, small molecules, such as vitamins and minerals, cells, proteins, peptides, nucleotides, and the like.
A “subject,” as used herein, includes any animal that exhibits a symptom, or is at risk for exhibiting a symptom, which can be treated or diagnosed (e.g., with a system, device, composition or method disclosed herein or known in the art). Suitable subjects (patients) include non-human primates and, preferably, human patients, as well as laboratory animals (such as mouse, rat, rabbit, or guinea pig), farm animals, and domestic animals or pets (such as a cat or dog).
“Treatment” or “treating,” as used herein, includes any desirable effect on the symptoms or pathology of a disease or condition, and may include even minimal changes or improvements in one or more measurable markers of the disease or condition being treated. “Treatment” or “treating” does not necessarily indicate complete eradication or cure of the disease or condition, or associated symptoms thereof. The subject receiving this treatment is any subject in need thereof. Exemplary markers of clinical improvement will be apparent to persons skilled in the art.
It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiment(s) without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.
Also, various concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
As used herein, the terms “about” and/or “approximately” when used in conjunction with numerical values and/or ranges generally refer to those numerical values and/or ranges near to a recited numerical value and/or range. In some instances, the terms “about” and “approximately” may mean within ±10% of the recited value. For example, in some instances, “about 100 [units]” may mean within ±10% of 100 (e.g., from 90 to 110). The terms “about” and “approximately” may be used interchangeably.
Some embodiments described herein relate to a computer storage product with a non-transitory computer-readable medium (also may be referred to as a non-transitory processor-readable medium) having instructions or computer code thereon for performing various computer-implemented operations. The computer-readable medium (or processor-readable medium) is non-transitory in the sense that it does not include transitory propagating signals per se (e.g., a propagating electromagnetic wave carrying information on a transmission medium such as space or a cable). The media and computer code (also may be referred to as code or algorithm) may be those designed and constructed for the specific purpose or purposes. Examples of non-transitory computer-readable media include, but are not limited to, magnetic storage media such as hard disks, floppy disks, and magnetic tape; optical storage media such as Compact Disc/Digital Video Discs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), and holographic devices; magneto-optical storage media such as optical disks; carrier wave signal processing modules; and hardware devices that are specially configured to store and execute program code, such as Application-Specific Integrated Circuits (ASICs), Programmable Logic Devices (PLDs), Read-Only Memory (ROM) and Random-Access Memory (RAM) devices. Other embodiments described herein relate to a computer program product, which may include, for example, the instructions and/or computer code disclosed herein.
The systems, devices, and/or methods described herein may be performed by software (executed on hardware), hardware, or a combination thereof. Hardware modules may include, for example, a general-purpose processor (or microprocessor or microcontroller), a field programmable gate array (FPGA), and/or an application specific integrated circuit (ASIC). Software modules (executed on hardware) may be expressed in a variety of software languages (e.g., computer code), including C, C++, Java®, Ruby, Visual Basic®, and/or other object-oriented, procedural, or other programming language and development tools. Examples of computer code include, but are not limited to, micro-code or micro-instructions, machine instructions, such as produced by a compiler, code used to produce a web service, and files containing higher-level instructions that are executed by a computer using an interpreter. Additional examples of computer code include, but are not limited to, control signals, encrypted code, and compressed code.
This application is a continuation of International Application PCT/US2020/048826, filed Aug. 31, 2020, titled “DETECTABLE ARRAYS FOR DISTINGUISHING ANALYTES AND DIAGNOSIS, AND METHODS AND SYSTEMS RELATED THERETO,” which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 62/893,703, filed Aug. 29, 2019, titled “BIOMARKER-AGNOSTIC COLORIMETRIC SIGNATURE ARRAY DISTINGUISHES BETWEEN MULTIPLE ANALYTES AND DISEASE STATES,” the disclosure of each of which are incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62893703 | Aug 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2020/048826 | Aug 2020 | US |
| Child | 17679888 | US |
