Patent Application
20150323545
The contents of the text file named “IDIA-004_C01US_ST25.txt”, which was created on Jun. 11, 2015 and is 395,000 bytes in size, are hereby incorporated by reference in their entireties.
Liquid Chromatography Selected Reaction Monitoring Mass Spectrometry (LC-SRM-MS) has emerged as an alternative technology to immunoassays for quantification of target proteins in biological samples. LC-SRM-MS methods are highly desirable because LC-SRM-MS methods provide both absolute structural specificity for the target protein and relative or absolute measurement of the target protein concentration when suitable internal standards are utilized. In contrast to immunoassays, LC-SRM-MS does not involve the manufacturing of biologics. LC-SRM-MS protein assays can be rapidly and inexpensively developed in contrast to the development of immunoassays. LC-SRM-MS are highly multiplexed, with simultaneous assays for hundreds of proteins performed in a single sample analysis. Using LC-SRM-MS in contrast to other proteomic technologies allows for complex assays for the identification diagnostic proteins in complex diseases such as cancer, autoimmune, and metabolic disease. In particular, the development of a highly multiplexed LC-SRM-MS assay that reproducibly identifies a specific set of proteins relevant to a clinical disease presents diagnostic advantages and efficiencies. To date, proteomic techniques have not enabled such inventions to exist where hundreds of proteins can be accurately quantified within a single sample. The present invention provides accurate measurement of hundreds of lung cancer associated proteins within a single sample using multiplexed techniques.
The present invention comprises a LC-SRM-MS assay for the measurement proteins in a single sample and in a single LC-SRM-MS assay. The assay was optimized for protein quantification and minimal interference among proteins in the assay. This LC-SRM-MS assay is novel because measurement of a large number of proteins in a single sample specifically associated with lung cancer has not been accomplished. Simultaneous measurement of such a large number of proteins without interference among the proteins requires specific techniques to distinguish among the proteins. The current invention provides clinical utility as this assay was used for development of lung cancer diagnostic tests for the early detection of lung cancer, managing disease treatment, as well as testing for disease recurrence.
The object of the present invention is to provide improved methods for the use of LC-SRM-MS in the development of assays. Accordingly, provided herein is a method for developing peptides and transitions for a plurality of at least 200 proteins for a single sample selected reaction monitoring mass spectrometry (LC-SRM-MS) assay, including the steps of providing a set of 200 or more proteins; generating transitions for each protein; assessing LC-SRM-MS data by Mascot score; performing collision energy optimization on the transitions; selecting peptides with transitions showing the greatest peak areas of their transitions; selecting a set of transitions for each peptide, wherein the transitions for each peptide have one of the four most intense b or y transition ions; the transitions for each peptide have m/z values of at least 30 m/z above or below those of the precursor ion; the transitions for each peptide do not interfere with transitions from other peptides; and the transitions represent transitions due to breakage of peptide bond at different sites of the protein.
In one embodiment of the method, each selected peptide in the set of peptides has a monoisotopic mass of 700-5000 Da; and does not contain a cysteine or a methionine; or may contain cysteine or methionine. In another embodiment, the transitions for each peptide have one of the four most intense b or y transition ions; have m/z values of at least 30 m/z above or below those of a precursor ion; do not interfere with transitions from other peptides; and represent transitions due to breakage of peptide bond at different sites of the protein.
In another embodiment of the method, the peptides do not include any peptide that is bounded by KK, KR, RK or RR (either upstream or downstream) in the corresponding protein sequence. Specifically, the amino acid is charged at pH 7.0. These amino acids include arginine and lysine. In another embodiment, each peptide of said set of peptides is unique to the corresponding protein. In yet another embodiment, the peptides do not include peptides which were observed in post-translational modified forms. In still another embodiment, each set of peptides is prioritized according to one or more of the following ordered set of criteria: unique peptides first, then non-unique; peptides with no observed post-translational modifications first, then those observed with post-translational modifications; peptides within the mass range 800-3500 Da first, then those outside of 800-3500 Da; and sorted by decreasing number of variant residues. In certain embodiments, The peptides are unique in that they only appear once among the peptides run in a single assay.
In one embodiment, each set of peptides is prioritized according to all of the ordered set of criteria. In another embodiment, each prioritized set of peptides contains 1-5 peptides.
In certain embodiments of the preceding methods, the two best peptides per protein and the two best transitions per peptide are selected based on experimental data resulting from LC-SRM-MS analysis of one or more of the following experimental samples: a biological disease sample, a biological control sample, and a mixture of synthetic peptides of interest. In a particular embodiment, the biological disease and biological control samples are processed using an immunodepletion method prior to LC-SRM-MS analysis. In another embodiment, the experimental samples contain internal standard peptides. In yet another embodiment, the LC-SRM-MS analysis method specifies a maximum of 7000 transitions, including transitions of the internal standard peptides and transitions. In other embodiments the method specifies a maximum of between 1000-7000, 2000-6000, 3000-5000 and about 3500 transitions.
In one embodiment of the method, the top two transitions per peptide are selected according to one or more of the following criteria the transitions exhibit the largest peak areas measured in either of the two biological experimental samples; the transitions are not interfered with by other ions; the transitions do not exhibit an elution profile that visually differs from those of other transitions of the same peptide; or the transitions are not beyond the detection limit of both of the two biological experimental samples.
In another embodiment of the method, the top two peptides per protein are selected according to one or more of the following criteria: one or more peptides exhibit two transitions and represent the largest combined peak areas of the two transitions; or one or more peptides exhibit one transition and represent the largest combined peak areas of the two transitions.
In another aspect, provided herein is an assay developed according to the foregoing method, and embodiments thereof.
In yet another aspect provided herein is the use of an assay developed according to the foregoing method, and embodiments thereof, to detect a plurality of at least 200 proteins in a single biological sample.
In another aspect, provided herein is an assay developed according to the foregoing method, and embodiments thereof.
The disclosure provides a composition comprising at least five transition ions selected from the listing of transition ions in Table 2. In one embodiment of the assay each transition ion independently corresponds to a unique protein. The five transition ions corresponded to proteins selected from the group consisting of LRP1, BGH3, COIA1, TETN, TSP1, ALDOA, GRP78, ISLR, FRIL, LG3BP, PRDX1, FIBA, and GSLG1. (see: U.S. application Ser. No. 13/306,823 PCT/US11/62461). The composition can further include an additional five transition ions selected from the listing of transition ions in Table 2. The additional five transition ions can corresponded to the proteins APOE, BASP1, CD14, FOXA2 and HSPB1.
The disclosure provides a composition comprising at least five synthetic peptides selected from the listing of peptides and proteins in Table 2. In one embodiment, each peptide can independently correspond to a unique protein. At least one of the peptides was isotopically labeled. The amount of each of the at least five synthetic peptides is known. In another embodiment, the composition included one or more polar solvents. The five synthetic peptides can correspond to the proteins LRP1, BGH3, COIA1, TETN, TSP1, ALDOA, GRP78, ISLR, FRIL, LG3BP, PRDX1, FIBA, and GSLG1. The composition can also include an additional five synthetic peptides selected from the listing of peptides and proteins in Table 2. The additional five synthetic peptides can correspond to the proteins APOE, BASP1, CD14, FOXA2 and HSPB1.
The disclosure provides a use of a composition, as described above, for the development of an assay to detect a disease, disorder or condition in a mammal.
The disclosure provides a method comprising analyzing a composition, as described above, using mass spectrometry. The method can use selected reaction monitoring mass spectrometry.
The present disclosure relates to methods for developing peptides and transitions for a single sample selected reaction monitoring mass spectrometry (LC-SRM-MS) assay, generally comprising the steps of providing a set of proteins; identifying representative proteolytic peptides for each protein according to a set of criteria; identifying representative transitions for each peptide according to another set of criteria; and selecting the optimum peptides per protein and the optimum transitions per peptide.
Selected reaction monitoring mass spectrometry is capable of highly sensitive and accurate protein quantification based on the quantification of proteolytic peptides. In terms of clinical utility, mass spectrometry-based assays are often compared to immunoassays (e.g., Enzyme-Linked Immunosorbent Assay, or ELISA), which have the ability to quantify specific analytes in large sample sets (e.g., 96 or 384 samples in parallel microtitre plate-based format). Until recently, mass spectrometry-based protein assays were not able to match these sample sizes or quantitative accuracy. Considerable time and expense is required to generate and characterize antibodies required for immunoassays. Increasingly efficient LC-SRM-MS assays, therefore, may surpass immunoassays such as ELISA in the rapid development of clinically useful, multiplexed protein assays.
LC-SRM-MS is a highly selective method of tandem mass spectrometry which has the potential to effectively filter out all molecules and contaminants except the desired analyte(s). This is particularly beneficial if the analysis sample is a complex mixture which may comprise several isobaric species within a defined analytical window. LC-SRM-MS methods may utilize a triple quadrupole mass spectrometer which, as is known in the art, includes three quadrupole rod sets. A first stage of mass selection is performed in the first quadrupole rod set, and the selectively transmitted ions are fragmented in the second quadrupole rod set. The resultant transition (product) ions are conveyed to the third quadrupole rod set, which performs a second stage of mass selection. The product ions transmitted through the third quadrupole rod set are measured by a detector, which generates a signal representative of the numbers of selectively transmitted product ions. The RF and DC potentials applied to the first and third quadrupoles are tuned to select (respectively) precursor and product ions that have m/z values lying within narrow specified ranges. By specifying the appropriate transitions (m/z values of precursor and product ions), a peptide corresponding to a targeted protein may be measured with high degrees of sensitivity and selectivity. Signal-to-noise ratio in LC-SRM-MS is often superior to conventional tandem mass spectrometry (MS/MS) experiments that do not selectively target (filter) particular analytes but rather aim to survey all analytes in the sample.
Accordingly, provided herein is a method for developing peptides and transitions for a plurality of proteins for use in selected reaction monitoring mass spectrometry (LC-SRM-MS) assay. In a preferred embodiment, the assay involves the analysis of a single sample containing all analytes of interest (e.g., a proteolytic digest of plasma proteins). As to the selection of the protease(s) used, trypsin, which cleaves exclusively C-terminal to arginine and lysine residues, is a preferred choice to generate peptides because the masses of generated peptides are compatible with the detection ability of most mass spectrometers (up to 2000 m/z), the number and average length of generated peptides, and also the availability of efficient algorithms for the generation of databases of theoretical trypsin-generated peptides. High cleavage specificity, availability, and cost are other advantages of trypsin. Other suitable proteases will be known to those of skill in the art. Miscleavage is a factor for failure or ambiguous protein identification. A miscleavage can be defined as partial enzymatic protein cleavages generating peptides with internal missed cleavage sites reflecting the allowed number of sites (targeted amino acids) per peptide that were not cut. The presence of post-translational modifications (PTMs) is also a potential contributor to the problem of miscleavages.
LC-SRM-MS mass spectrometry involves the fragmentation of gas phase ions and occurs between the different stages of mass analysis. There are many methods used to fragment the ions and these can result in different types of fragmentation and thus different information about the structure and composition of the molecule. The transition ions observed in an LC-SRM-MS spectrum result from several different factors, which include, but are not limited to, the primary sequence, the amount of internal energy, the means of introducing the energy, and charge state. Transitions must carry at least one charge to be detected. An ion is categorized as either a, b or c if the charge is on a transition comprising the original N terminus of the peptide, whereas the ion is categorized as either x, y or z if the charge is on a transition comprising the original C terminus of the peptide. A subscript indicates the number of residues in the transition (e.g., one peptide residue in x1, two peptide residues in y2, and three peptide residues in z3, etc.).
In a generic peptide repeat unit represented —N—C(O)—C—, an x ion and an a ion resulting from cleavage of the carbonyl-carbon bond (i.e., C(O)—C). The x ion is an acylium ion, and the a ion is an iminium ion. A y ion and a b ion result from cleavage of the carbonyl-nitrogen bond (i.e., C(O)—N, also known as the amide bond). In this case, the y ion is an ammonium ion and the b ion is an acylium ion. Finally, a z ion and a c ion result from cleavage of the nitrogen-carbon (i.e., C—N) bond. The z ion is a carbocation and the c ion is an ammonium ion.
Superscripts are sometimes used to indicate neutral losses in addition to the backbone fragmentation, for example, * for loss of ammonia and ∘ for loss of water. In addition to protons, c ions and y ions may abstract an additional proton from the precursor peptide. In electrospray ionization, tryptic peptides may carry more than one charge.
Internal transitions arise from double backbone cleavage. These may be formed by a combination of b-type and y-type cleavage (i.e., cleavage producing b and y ions). Internal cleavage ions may also be formed by a combination of a-type and y-type cleavage. An internal transition with a single side chain formed by a combination of a-type and y-type cleavage is called an iminium ion (sometimes also referred to as an imonium or immonium ion). These ions are labeled with the one letter code for the corresponding amino acid.
Low energy CID (i.e., collision induced dissociation in a triple quadrupole or an ion trap) involves the fragmentation of a peptide carrying a positive charge, primarily along its backbone, to generate primarily a, b and y ions.
In one aspect, provided herein is a method for developing peptides and transitions for a plurality of proteins for a single sample selected reaction monitoring mass spectrometry (LC-SRM-MS) assay, by: (a) providing a panel of a plurality of proteins; (b) identifying a set of peptides for each protein, wherein (i) each peptide in the set of peptides corresponds to a transition of said protein; (ii) the peptides have a monoisotopic mass of 700-5000 Da; and (iii) the peptides do not contain a cysteine or a methionine; or may contain cysteine or methionine; (c) identifying a set of transitions for each peptide, wherein (i) the transitions for each peptide have one of the four most intense b or y transition ions; (ii) the transitions for each peptide have m/z values of at least 30 m/z above or below those of the precursor ion; (iii) the transitions for each peptide do not interfere with transitions from other peptides; and (iv) the transitions represent transitions due to breakage of peptide bond at different sites of the protein; and (d) selecting the peptides for each protein that best fit the criteria of step (b) and the transitions per peptide that best fit the criteria of step (c); thereby developing peptides and transitions for a LC-SRM-MS assay.
By plurality of proteins it is meant at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500 or more In certain embodiments, the plurality of proteins can encompass between 2 and 10. 10 and 20, 20 and 50, 50 and 100, 100 and 200, or 200 and 500 proteins. In other embodiments, the plurality of proteins can encompass between 250 and 450; or 300 and 400 proteins.
Trypsin-like proteases cleave peptide bonds following a positively charged amino acid (e.g., lysine (K) or arginine (R)). This specificity is driven by the residue which lies at the base of the enzyme's S1 pocket (generally a negatively charged aspartic acid or glutamic acid). Accordingly, in one embodiment of the method, the peptides do not include any peptide that is bounded by KK, KR, RK or RR, either upstream of downstream in the corresponding protein sequence. In another embodiment, each peptide of said set of peptides is unique to the corresponding protein.
Post-translational modification (PTM) is the chemical modification of a protein after its translation. It can include any modification following translation, including cleavage. It is one of the later steps in protein biosynthesis, and thus gene expression, for many proteins. It is desirable to avoid such peptides for the purpose of protein identification. Thus, in another embodiment, the peptides do not include peptides which were observed in post-translational modified forms.
In still another embodiment, each set of peptides is prioritized according to one or more of the following ordered set of criteria: (a) unique peptides first, then non-unique; (b) peptides with no observed post-translational modifications first, then those observed with post-translational modifications; (c) peptides within the mass range 800-3500 Da first, then those outside of 800-3500 Da; and (d) sorted by decreasing number of variant residues. In one embodiment, each set of peptides is prioritized according to all of the ordered set of criteria. In another embodiment, each prioritized set of peptides contains 1-5 peptides.
In certain embodiments, one or more liquid chromatography (LC) purification steps are performed prior to a subsequent LC-SRM-MS analysis step. Traditional LC analysis relies on the chemical interactions between sample components and column packing materials, where laminar flow of the sample through the column is the basis for separation of the analyte of interest from the test sample. The skilled artisan will understand that separation in such columns is a diffusional process. A variety of column packing materials are available for chromatographic separation of samples, and selection of an appropriate separation protocol is an empirical process that depends on the sample characteristics, the analyte of interest, the interfering substances present and their characteristics, etc. Various packing chemistries can be used depending on the needs (e.g., structure, polarity, and solubility of compounds being purified). In various embodiments the columns are polar, ion exchange (both cation and anion), hydrophobic interaction, phenyl, C-2, C-8, C-18 columns, polar coating on porous polymer, or others that are commercially available. During chromatography, the separation of materials is effected by variables such as choice of eluant (also known as a “mobile phase”), choice of gradient elution and the gradient conditions, temperature, etc. In certain embodiments, an analyte may be purified by applying a sample to a column under conditions where the analyte of interest is reversibly retained by the column packing material, while one or more other materials are not retained. In these embodiments, a first mobile phase condition can be employed where the analyte of interest is retained by the column, and a second mobile phase condition can subsequently be employed to remove retained material from the column, once the non-retained materials are washed through. Alternatively, an analyte may be purified by applying a sample to a column under mobile phase conditions where the analyte of interest elutes at a differential rate in comparison to one or more other materials. As discussed above, such procedures may enrich the amount of one or more analytes of interest relative to one or more other components of the sample.
The following parameters are used to specify an LC-SRM-MS assay of a protein under a particular LC-SRM-MS system: (1) a tryptic peptide of the protein; (2) the retention time (RT) of the peptide; (3) the m/z value of the peptide precursor ion; (4) the declustering potential used to ionize the precursor ion; (5) the m/z value of a fragment ion generated from the peptide precursor ion; and (6) the collision energy (CE) used to fragment the peptide precursor ion that is optimized for the particular peptide.
In certain embodiments of the preceding methods, the two best peptides per protein and the two best transitions per peptide are selected based on experimental data resulting from LC-SRM-MS analysis of one or more of the following experimental samples: a biological disease sample, a biological control sample, and a mixture of synthetic peptides of interest. Biological samples include body fluids, tissue samples and cell samples. Body fluid samples can include blood, serum, sputum, genital secretions, cerebrospinal fluid, sweat or excreta such as urine. Body tissue samples can include lung, skin, brain, spine, bone, muscle, epithelial, liver, kidney, pancreas, gastrointestinal tract, cardiovascular tissue, heart or nervous tissue. Biological disease samples can include cancer, benign tumors, infected tissue and tissue subject to trauma. In a particular embodiment, the biological disease and biological control samples are processed using an immunodepletion method prior to LC-SRM-MS analysis. Immunodepletion involves removal of one or more proteins through the use of antibodies. Numerous immunodepletion techniques are known to those of skill in the art. In another embodiment, the biological disease and biological control samples are processed using an immunocapture method prior to LC-SRM-MS analysis. Immunocapture involves selection of one or more proteins through the use of antibodies. Numerous immunocapture techniques are known to those of skill in the art.
To facilitate accurate quantification of the peptide transitions by the methods disclosed herein, a set of isotopically-labeled synthetic versions of the peptides of interest may be added in known amounts to the sample for use as internal standards. Since the isotopically-labeled peptides have physical and chemical properties identical to the corresponding surrogate peptide, they co-elute from the chromatographic column and are easily identifiable on the resultant mass spectrum. The addition of the labeled standards may occur before or after proteolytic digestion. Methods of synthesizing isotopically-labeled peptides will be known to those of skill in the art. Thus, in another embodiment, the experimental samples contain internal standard peptides. Other embodiments may utilize external standards or other expedients for peptide quantification.
In yet another embodiment, the LC-SRM-MS analysis method specifies a maximum of 7000 transitions, including transitions of the internal standard peptides and transitions. As used herein, the term “transition” refers to the specific pair of m/z (mass-to-charge) values associated with the precursor and transition ions corresponding to a specific peptide and, therefore, to a specific protein.
In one embodiment of the method, the top two transitions per peptide are selected according to one or more of the following criteria (A): (1) the transitions exhibit the largest peak areas measured in either of the two biological experimental samples; (2) the transitions are not interfered with by other ions; (3) the transitions do not exhibit an elution profile that visually differs from those of other transitions of the same peptide; (4) the transitions are not beyond the detection limit of both of the two biological experimental samples; (5) the transitions do not exhibit interferences.
For the mass spectrometric analysis of a particular peptide, the quantities of the peptide transitions in the sample may be determined by integration of the relevant mass spectral peak areas, as known in the prior art. When isotopically-labeled internal standards are used, as described above, the quantities of the peptide transitions of interest are established via an empirically-derived or predicted relationship between peptide transition quantity (which may be expressed as concentration) and the area ratio of the peptide transition and internal standard peaks at specified transitions.
In another embodiment of the method, the top two peptides per protein are selected according to one or more of the following criteria (B): (1) one or more peptides exhibit two transitions according to criteria (A) and represent the largest combined peak areas of the two transitions according to criteria (A); and (2) one or more peptides exhibit one transition according to criteria (A) and represent the largest combined peak areas of the two transitions according to criteria (A).
The methods of the present disclosure allow the quantification of high abundance and low abundance plasma proteins that serve as detectable markers for various health states (including diseases and disorders), thus forming the basis for assays that can be used to determine the differences between normal levels of detectable markers and changes of such detectable markers that are indicative of changes in health status. In one aspect of the invention, provided herein is an assay developed according to the foregoing method, and embodiments thereof. In another aspect, provided herein is the use of an assay developed according to the foregoing method, and embodiments thereof, to detect a plurality of at least 200, 300, or more proteins in a single sample. In a merely illustration embodiment, 388 proteins in the following table 1 are detected utilizing the method of present invention.
Of the 388 proteins, the 36 most cooperative proteins are listed in Table 2.
SRM assays for the 388 proteins were developed using standard synthetic peptide techniques. Of the 388 candidates, SRM assays were successfully developed for 371 candidates. The 371 SRM assays were applied to benign and lung cancer plasma samples to evaluate detection rate in blood. The summary of the SRM assay for these 371 proteins is listed in table 3 (see also Example III).
As used herein, “transition” refers to a pair of m/z values associated with a peptide. Normally, labeled synthetic peptides are used as quality controls in SRM assays. However, for very large SRM assays such as the 371 protein lung cancer assay, labeled peptides are not feasible. However, correlation techniques (Kearney, Butler et al. 2008) were used to confirm the identity of protein transitions with high confidence. In
As used herein, a “tryptic peptide” refers to the peptide that is formed by the treatment of a protein with trypsin.
As used herein, “RT” refers to “retention time”, the elapsed time between injection and elution of an analyte.
As used herein, “m/z” indicates the mass-to-charge ratio of an ion.
As used herein, “DP” refers to “declustering potential”, a voltage potential to dissolvate and dissociate ion clusters. It is also known as “fragmentor voltage” or “ion transfer capillary offset voltage” depending on the manufacture.
As used herein, “CE” refers to “collision energy”, the amount of energy precursor ions receive as they are accelerated into the collision cell.
As used herein, “LC-SRM-MS” is an acronym for “selected reaction monitoring” and may be used interchangeably with “LC-MRM-MS”.
As used herein, “MS/MS” represents tandem mass spectrometry, which is a type of mass spectrometry involving multiple stages of mass analysis with some form of fragmentation occurring in between the stages.
As used herein, “ISP” refers to “internal standard peptides”.
As used herein, “HGS” refers to “human gold standard”, which is comprised of a pool of plasma from healthy individuals.
As used herein, “MGF” refers to “Mascot generic file”. Mascot is a search engine that uses mass spectrometry data to identify proteins from primary sequence databases. A Mascot generic file is a plain text (ASCII) file containing peak list information and, optionally, search parameters.
Mascot is a web-based tool for deriving protein sequences from mass spectrometry data. This data can be acquired from any mass spectrometry technique including MALDI-TOF and MS/MS (including LC-SRM-MS) data. Mascot uses a ‘probability-based MOWSE’ algorithm to estimate the significance of a match (i.e., that the observed transitions correspond to a particular protein). The total score is the absolute probability that the observed match is a random event. They are reported as −10×LOG 10(P), where P is the absolute probability. Lower probabilities, therefore, are reported as higher scores. For example, if the absolute probability that an observed match is random is 1×10−12, Mascot reports it as 120.
The disclosure also provides compositions. These compositions can include any of the transition ions described in Table 2. These transition ions exist while peptides derived from the proteins in Table 2 are undergoing analysis with LC-SRM-MS. In one embodiment, the composition includes any of the transition ions described in Table 2. In another embodiment, the composition includes any two transition ions described in Table 2. In other embodiments, the composition includes, any 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or 331 transition ions described in Table 2.
In another embodiment, the transition ions correspond with human proteins including LRP1, BGH3, COIA1, TETN, TSP1, ALDOA, GRP78, ISLR, FRIL, LG3BP, PRDX1, FIBA, and GSLG1. In another embodiment, the transition ions are derived from human proteins including LRP1, BGH3, COIA1, TETN, TSP1, ALDOA, GRP78, ISLR, FRIL, LG3BP, PRDX1, FIBA, and GSLG1. These proteins can further include transition ions corresponding with and/or derived from any number of additional proteins from Table 2. Thus, the composition can include, any additional 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or 331 transition ions described in Table 2.
In another embodiment, each of the transition ions in the composition corresponds and/or is derived from a different protein. In another embodiment, 90% of the transition ions in the composition correspond with and/or are derived from a protein that no other transition ion in the composition corresponds. In other embodiments, 80, 70, 60, 50, 40, 30, 20, 10 or 0% of the transition ions in the composition correspond and/or are derived from a protein that no other transition ion in the composition corresponds.
The compositions described herein included synthetic peptides. Synthetic peptides can be used as controls for the abundance of proteins they are derived from and/or correspond. In certain embodiments, the abundance of the synthetic peptides is defined and the results are compared to LC-SRM-MS results from a peptide found in a sample to the LC-SRM-MS results in the corresponding synthetic peptide. This allows for the calculation of the abundance of the peptide in the sample. In certain embodiments, by knowing the abundance of a peptide in a sample, the abundance of the protein it corresponded to is determined.
Synthetic peptides can be generated using any method known in the art. These methods can include recombinant expression techniques such as expression in bacteria or in vitro expression in eukaryotic cell lysate. These methods can also include solid phase synthesis.
In one embodiment, the composition includes synthetic peptides selected from any of the peptides described in Table 2. In another embodiment, the composition included any two peptides described in Table 2. In other embodiments, the composition included, any 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more peptides described in Table 2.
In another embodiment, the peptides corresponded with human genes including LRP1, BGH3, COIA1, TETN, TSP1, ALDOA, GRP78, ISLR, FRIL, LG3BP, PRDX1, FIBA, and GSLG1 as described in corresponding patent application. These genes can further include peptides corresponding with any number of additional genes from Table 2. Thus, the composition can include, any additional 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more peptides described in Table 2.
In another embodiment, each of the peptides in the composition each corresponds with a different protein. In another embodiment, 90% of the peptides in the composition correspond with a protein that no other peptide in the composition corresponds with. In other embodiments, 80, 70, 60, 50, 40, 30, 20, 10 or 0% of the peptides in the composition correspond with from a protein that no other peptide in the composition corresponds with.
The peptides can be isotopically labeled. The isotopes with which they can be labeled include 13C, 2H, 15N and 18O. The peptides can also include a polar solvent. Polar solvents can include water and mixtures of ethanol and water.
In certain embodiments, the samples described herein are taken from mammals. These mammals include rats, mice, rabbits, dogs, non-human primates and humans. Samples can be isolated from any tissue or organ or from any bodily fluid. Organs from which samples can be taken include skin, heart, lung, brain, kidney, liver, pancreas, spleen, testes, ovaries, gall bladder, thymus, thyroid, eye, ear, nose, mouth, tongue, penis, vagina, bladder or larynx. Tissues include nervous tissue, vascular tissue, muscle, bone, gastrointestinal tract, epithelial tissue, fibroblastic tissue, mucous membranes, hair, skin, reproductive tissue and connective tissue. Body fluids and excretions include, blood, serum, saliva, urine, semen, vaginal secretions, excrement, bile, tears, lymph, ear wax, mucous, shed skin, finger nails, toe nails, skin oils, sweat and dandruff.
The relative abundance of one or more of the proteins represented by the transition ions and synthetic peptides described above can be used to diagnose, determine likelihood of the presence of, develop prognoses for and/or stage various diseases and pathologies. Often the organ, tissue or bodily fluid or excretion from which the sample is taken is distinct from the organ, tissue or bodily fluid or excretion involved with the disease or pathology. For example, the presence of lung cancer can be determined from a sample taken from blood. Any type of body fluid may be used in the assays.
Diseases and pathologies that status, diagnosis, presence or prognosis can be found using the transition ions and/or synthetic peptides described herein include cancer, metabolic diseases, neurological disorders, infectious diseases and cardiovascular disorders.
Proteins known to be over-expressed on the cell surface of lung cancer tumors were obtained (through literature searching, experimental data or proprietary databases). This was referred to as set ‘A’. Proteins known to be over-secreted by lung cancer tumor cells were obtained (through literature searching, experimental data or proprietary databases). This was referred to as set ‘B’. Proteins associated with lung cancer in the literature were mined. This was referred to as set ‘C’. Proteins of interest (sets A, B and C are merged resulting in over 700 proteins) were assembled. The set of proteins was reduced to a set of 388 proteins (see Table 4) by prioritizing those proteins that have been previously detected my LC-MS/MS in blood (serum or plasma).
Selected proteins were then identified by their UniProt protein name and accession, their Entrez gene symbol and gene name, the isoform accession and their amino acid sequence. The canonical isoform in UniProt was selected if a protein has more than one isoform.
The five best peptides per protein for LC-SRM-MS assay were selected for as follows. Fully tryptic peptides having a monoisotopic mass of 800-3500 mass units, without miscleavages, not containing a cysteine (C) or a methionine (M), without having high miscleavage probability were selected. Further, any peptide that was bounded by KK, KR, RK or RR (either upstream or downstream) in the corresponding protein sequence was not selected.
Peptides were selected that were unique to the protein of interest. Peptides were only selected that match only one protein or protein family including analogues of the one protein, when searched in protein databases. Further, peptides which were observed in post-translational modified forms were not selected. Databases were assessed that showed expression of the proteins from which the peptides were isolated in human blood. Also databases of good quality MS peptides were searched. Peptides that appeared in human blood and were good quality MS peptides were favored. If these methods did not result in a sufficient number of peptides, rules were relaxed in a step wise manner to allow a greater number of peptides until a sufficient number was reached. The purity of the synthesized peptides was >75% and the amount of material was ≧25 μg. Peptides did not need to be desalted.
The four best transitions per peptide are then selected and optimized based on experimental results from a mixture of synthetic peptides. LC-SRM-MS-triggered MS/MS spectra were acquired for each synthetic peptide, using a QTRAP 5500 instrument. One spectrum for the doubly- and one for the triply-charged precursor ion was collected for each peptide For the identified peptides (Mascot score≧15), retention time was recorded for the four most intense b or y transition ions. The selected transition ions possessed m/z values were at least 30 m/z above or below those of the precursor ions; they did not interfere with other synthetic peptides; and they were transition ions due to breakage of peptide bond at different sites.
If an insufficient percentage of the synthetic peptides were acquired, the steps were repeated. In some cases, the second transition with first with theoretical y+ ions with m/z values at least 30 m/z above those of the doubly charged precursor ion was selected if an insufficient percentage was acquired. Peptides that failed to trigger the acquisition of MS/MS spectrum were discarded.
Collision energy (CE) for each selected transition (See Table 4) was optimized.
The abundance of the following proteins can be assessed substantially simultaneously using the MS-LC-SRM-MS system described herein. Transitions from these proteins can be used to diagnose diseases including lung cancer when their abundance is measured in a biological specimen from a subject to be diagnosed for lung cancer. In one embodiment, the abundances of these proteins are measured in the blood serum of the subject.
M. tuberculosis
SRM assays for 388 targeted proteins were developed based on synthetic peptides, using a protocol similar to those described in the literature (Lange, Picotti et al. 2008, Picotti, Rinner et al. 2010, Huttenhain, Soste et al. 2012). Up to five SRM suitable peptides per protein were identified from public sources such as the PeptideAtlas, Human Plasma Proteome Database or by proteotypic prediction tools (Mallick, Schirle et al. 2007) and synthesized. SRM triggered MS/MS spectra were collected on an ABSciex 5500 QTrap for both doubly and triply charged precursor ions. The obtained MS/MS spectra were assigned to individual peptides using MASCOT (cutoff score≧15) (Perkins, Pappin et al. 1999). Up to four transitions per precursor ion were selected for optimization. The resulting corresponding optimal retention time, declustering potential and collision energy were assembled for all transitions. Optimal transitions were measured on a mixture of all synthetic peptides, a pooled sample of benign patients and a pooled sample of cancer patients. Transitions were analyzed in batches, each containing up to 1750 transitions. Both biological samples were immuno-depleted and digested by trypsin. All three samples were analyzed on an ABSciex 5500 QTrap coupled with a reversed-phase (RP) high-performance liquid chromatography (HPLC) system. The obtained SRM data were manually reviewed to select the two best peptides per protein and the two best transitions per peptide. Transitions having interference with other transitions were not selected. Ratios between intensities of the two best transitions of peptides in the synthetic peptide mixture were also used to assess the specificity of the transitions in the biological samples. The intensity ratio was considered as an important metric defining the SRM assays. The complete transition table is shown below in Table 2.
The following 164 proteins and their peptides were detected simultaneously in a large-scale experiment of 158 samples using the MS-LC-SRM-MS system described herein.
Exemplary Biomarker Protein
The following 36 proteins were identified as biomarker candidates in a large-scale experiment of 72 lung cancer samples and 71 benign lung nodule samples using the MS-LC-SRM-MS system described herein.
This application is a continuation of U.S. application Ser. No. 13/725,098 filed Dec. 21, 2012, which in turn claims priority and benefit of U.S. Provisional Application No. 61/578,718 filed Dec. 21, 2011, and U.S. Provisional Application No. 61/614,818 filed Mar. 23, 2012, the contents of each of which are incorporated herein by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| 61578718 | Dec 2011 | US | |
| 61614818 | Mar 2012 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13725098 | Dec 2012 | US |
| Child | 14738146 | US |
