WO2014194320A1

WO2014194320A1 - Chromatography mass spectrometry method and system

Info

Publication number: WO2014194320A1
Application number: PCT/US2014/040521
Authority: WO
Inventors: Michael R. Heaven; Archibald Leach COBBS
Original assignee: The Uab Research Foundation; Vulcan Analytical, Llc
Priority date: 2013-05-31
Filing date: 2014-06-02
Publication date: 2014-12-04

Abstract

A process for quantification of analytes by chromatography coupled to mass spectrometry is provided, as well as a system for the automated performance of the process. The methods include selecting and applying co-eluting endogenous reference analytes to normalize data for sources of technical variability in the analysis of complex analyte mixtures by chromatography mass spectrometry.

Description

CHROMATOGRAPHY MASS SPECTROMETRY METHOD AND SYSTEM

TECHNICAL FIELD

[0001] The field of this disclosure is analytical chemistry.

BACKGROUND

[0002] Thousands of proteins, peptides, and other analytes are now able to be identified within a day by liquid chromatography mass spectrometry. However, accurately measuring the relative abundance of these analytes across samples remains an unsolved problem in proteomics and metabolomics due to the reliance on isotope labeled internal standards for accurate relative abundance measurements. The field of chromatography mass spectrometry- based "omics" studies would benefit from an automated approach that provides accurate relative abundance measurements.

[0003] Stable isotope-labeled chemicals and peptides may be applied in a variety of ways in the field of mass spectrometry ("MS"). These applications take advantage of the principle that isotopically labeled chemical and biochemical analogs of many molecules consistently co-elute from chromatographic systems. This aspect of isotope incorporated standardization increases the confidence in matching signals from sample-to-sample comparisons and the precision and accuracy of quantitation. Generally, heavy labeled carbon- 13 and nitrogen- 15 isoptomers exactly co-elute with their unlabeled counterparts; however this is not true for deuterium incorporated compounds. For instance, peptide dimethylation labeling by formaldehyde with H₂ D₂ creates a shift in retention time between the light and heavy labeled peptide isoptomers. In the proteomics commercial isotope market numerous products are in use including isobaric tags for relative and absolute quantitation (iTRAQ), tandem mass tags (TMT), isotope coded affinity tags (ICAT), stable isotope labeled amino acids in cell culture (SILAC), stable isotope labeled amino acids in mammals (SILAM), mass tags for relative and absolute quantitation (mTRAQ), and heavy labeled peptide standards marketed by the name AQUA peptides (Sigma Aldrich, St. Louis, MO, USA).

[0004] Absolute quantitation of a single target peptide can be performed using a co-eluting internal standard created by making a single amino acid substitution to a peptide sequence. For example, the absolute level of cardiac troponin T can be quantified by assaying a peptide unique to cardiac troponin T with the sequence YEINVLR and using a synthetic co-eluting internal standard YEIQVLR with only a single amino acid substitution. This requires a priori knowledge of the peptides to be assayed, time to synthesize the co-eluting internal standard, and a high-degree of sequence homology.

[0005] SparseQuant is a software program to support the analysis of datasets where a limited number of isotope labeled peptides are added into samples as a more practical and affordable approach than purchasing an isotope labeled standard peptide for every endogenous peptide of interest to be assayed. This method uses all the available isotope standards that are added into samples to normalize data without any requirement that the isotope standards co-elute with the peptides that are being normalized.

[0006] A data post-processing statistical approach has been used for improving the precision of mass spectrometric assays without the addition of internal standards. An approach for normalizing LC-MS/MS data by the addition of a continuous series of eluting peptides that are matched to co-eluting analytes of interest and applied to normalize data has also been used, which requires the addition of internal standards.

[0007] The art would benefit from easier to use and more economical methods capable of accurately quantifying peptides and other analytes with similar accuracy to stable isotope labeled standards.

SUMMARY

[0008] The following presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview. It is not intended to identify key or critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0009] A method for determining the relative abundance of a plurality of analytes in a sample is provided, the method comprising: subjecting the sample to fractionation by chromatography to produce a time resolved eluate; subjecting the eluate to detection by a mass spectrometric technique to generate a mass spectrometric signal from each of the plurality of analytes; selecting an endogenous reference standard; matching at least one of the plurality of analytes to the endogenous reference standard; and comparing the mass spectrometric signal from each of the plurality of analytes to the mass spectrometric signal of the endogenous reference, wherein the endogenous reference co-elutes with and is non- identical to the analyte. The method benefits from the phenomenon that non-identical analytes reproducibly co-elute from chromatography mass spectrometry systems even in the presence of absolute differences in analyte retention time. [0010] A computing system is provided for performing the method above, comprising a processor, a memory, and a module for performing the method. The method can be performed on a local computer or accessed by the internet wherein the computation is performed by remote server(s).

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG. 1 is a schematic of the data normalization process of an embodiment of the method illustrating the data correction process for determining the relative abundance of analytes measured in sample mixtures by chromatography mass spectrometry.

[0012] FIG. 2 is an outline of the data normalization process for relative quantification, depicting steps of an embodiment of the data normalization procedure. Each peak in the various lines represents a different MS survey scan peptide.

[0013] FIG. 3 shows extracted ion chromatograms of a 5 member co-elution group from an LC-MS/MS analysis of 1,000 nanograms of an Alexander disease model mouse peptide sample and 200 nanograms of the isotope labeled and unlabeled mix with a 60 minute peptide elution gradient. Top shows ALAAELNQLR with m/z 549.81, next panel shows GLSSVPEVAEVEPTTK with m/z 821.93, next panel shows an unidentified analyte with m/z 524.59, next panel shows IGGHGAEYGAEALER acetylated at the I residue with m/z 786.38, and the bottom panel shows TPNVSVVDLTCR with carbamidomethylation at the C residue with m/z 680.84.

[0014] FIG. 4 shows the extracted ion chromatograms of the same four peptides and unknown analyte from FIG. 3 in an analysis of a second Alexander disease mouse tryptic peptide brain lysate.

[0015] FIG. 5 shows the same extracted ion chromatograms as FIG. 3 in a wild type mouse brain lysate tryptic peptide digest analyzed by LC-MS/MS following the analysis shown in FIGS. 3-4.

[0016] FIG. 6 shows the same extracted ion chromatograms as FIG. 3 in a second wild type mouse brain lysate tryptic digest analyzed by LC-MS/MS following the analysis shown in FIG. 5.

[0017] FIG. 7 shows the same extracted ion chromatograms as FIG. 3 in a third Alexander disease model mouse analyzed by LC-MS/MS following the analysis shown in FIG. 6.

[0018] FIG. 8 shows the same extracted ion chromatograms as FIG. 3 in a fourth Alexander disease model mouse analyzed by LC-MS/MS following the run displayed in FIG. 7. [0019] FIG. 9 shows the same extracted ion chromatograms as FIG. 3 in a third wild type mouse LC-MS/MS analysis following the data collected for FIG. 8.

[0020] FIG. 10 shows the same extracted ion chromatograms as FIG. 3 in a fourth wild type mouse LC-MS MS run after the run shown in FIG. 9.

[0021] FIG. 11 is a comparison of relative abundance values with a ¹⁵N labeled full-length glial fibrillary acidic protein standard to the Protalizer program in four wild type and Alexander disease mice showing the results obtained by applying an embodiment of the method to normalize three peptides from glial fibrillary acidic protein (GFAP) in mouse brain homogenates from wild type and Alexander disease model mice and compare the values obtained to the results from a ¹⁵N labeled GFAP standard.

[0022] FIGS. 12(a) and 12(b) show a comparison of the variability across four wild type and Alexander disease model brain tryptic digests for 189 unlabeled / labeled peptides of identical sequence, total-ion-current normalized data, the results from an embodiment of the method automated by the computer program Protalizer, and raw data. Histograms show the number of peptides per coefficient of variation for peptides detected in unlabeled and labeled form in

(a) wild type, and (b) Alexander disease model mice (n=4 per genotype).

[0023] FIGS. 13(a) and 13(b) show the average coefficient of variation in (a) wild type and

(b) Alexander disease mouse groups shown in FIG. 12. Error bars are standard deviation.

[0024] FIG. 14 is a schematic illustration of an embodiment of the computing system.

DETAILED DESCRIPTION

[0025] The term "MSI survey scan feature" in the art refers to the detection and intensity of m/z ratios eluting into the mass spectrometer over a period of time during which an analyte is eluted from a fractionation apparatus such as a high-pressure liquid chromatography system.

[0026] The term "co-eluting" is used to describe a minimum of two distinct analytes or molecules that partially or completely share a time interval during which they are released from a fractionation system (such as a chromatograph). For example, two co-eluting molecules share elution time during both molecules' maximum signal intensity.

[0027] The term "non-identical molecule" (or standard, or analyte) is defined as a charged molecule that differs in chemical structure from another. For the purposes of this disclosure, two molecules are not "non-identical" if they differ only in the isotopic identity of their constituent atoms. In some applications the molecules are not "non-identical" if they differ only in their stereochemistry, and the chromatographic step is not capable of separating the stereoisomers. In addition, if an analyte has a biological or chemical modification relative to one another they are non-identical. For example, the metabolites glucose and glucoses- phosphate are non-identical analytes. However, different isotopic forms of the same molecule are not considered to be non-identical analytes. For example, the unlabeled peptide sequence TEEGPTLSYGR and the peptide sequence TEEGPTLSYGR containing ^Relabelled and ¹⁵N-labelled arginine differ in mass by about 10.008 Daltons, but are still considered identical analytes to one another.

[0028] As used herein, the "fractionation system" is typically a chromatography system, including the use of high pressure liquid chromatography (HPLC) or ultra pressure liquid chromatography (UPLC) connected online to a mass spectrometer. Suitable resins include reverse phase C18, C12, C8, C4, as well as strong cation exchange (SCX) packed in columns or manufactured using the CHIP design. A compatible system could illustratively include a reverse phase CI 8 separation, or a MudPIT configuration utilizing SCX followed by a reverse phase CI 8 separation. Other fractionation systems known in the art are also applicable.

[0029] The present disclosure provides a process to select optimal co-eluting endogenous standards, which eliminates the need to synthesize and add an internal standard. The synthesis and addition of internal standards is not practical for proteomics and metabolomics experiments where thousands of analytes are being assayed. In addition, some embodiments of the process of the present disclosure locate as few as a single endogenous analyte that co- elutes with a sample analyte to provide highly accurate measurements.

[0030] The mass spectrometric technique may be any that is known in the art. Exemplary mass analyzers and detection systems that may be used include time-of-flight (TOF), triple quadrupole (QQQ), ion traps, Fourier transform (FT) and other types of mass spectrometers known in the art. Illustratively, an AB Sciex 5600 Triple-TOF (Q-TOF), Thermo-Scientific Q-Exactive (Q-Orbitrap), Waters Q-TOF Premiere, and a 4000 Q-trap triple quadrupole tandem mass spectrometer from Applied Systems are suitable. It is appreciated that other brands and types of mass spectrometers are similarly suitable.

[0031] Any type of mass spectrometry data acquisition known in the art may be used in the spectrometric technique, including data independent acquisition, data dependent acquisition, and selected reaction monitoring.

[0032] The sample may be any mixture of two or more molecules for which chemical analysis is desired. For example, the sample may be a material obtained from a biological organism, tissue, cell, cell culture medium, a medium mimicking a biological state, or the environment. Non-limiting examples of biological samples include saliva, gingival secretions, cerebrospinal fluid, gastrointestinal fluid, blood, plasma, mucous, urogenital secretions, synovial fluid, serum, or any other fluid or solid media. Additional examples include cell culture extracts and soil preparations. Samples may also be fresh, frozen, formalin-fixed, or preserved by other means. Samples may be derived from specimens that are healthy, or from organisms affected by a disease state, such as cancer or Alzheimer's disease. Suitable samples compatible with the disclosure may be obtained by any means known in the art, including surgical dissection, biopsy, and fine needle aspirate.

[0033] In some embodiments of the method, the selecting step comprises grouping non- identical endogenous reference standard candidates that co-elute, and comparing the relative intensity or abundance ratio between each member in the co-eluting group. For example, to determine whether analyte A is suitable for use as an endogenous reference analyte, and whether analytes A, B, C, and D co-elute by chromatography, the intensity ratio of A/B, A/C, and A D are recorded in a sample. Then the same intensity ratios are determined in another sample and the average or median difference in the ratios is used to identify analytes as endogenous references with the smallest variability.

[0034] In some embodiments, the selection of endogenous reference standards involves eliminating analytes that have variable relative abundance as potential endogenous reference analytes for data normalization. One example of such an embodiment of the process involves six steps that correspond to step 2 in FIG 1. As shown in FIG. 1, the method involves identifying analytes that are present in the samples being analyzed; analytes with an absolute signal difference in the samples above a threshold are eliminated; the remaining candidate reference analytes are then clustered by elution time to the most similar elution time analytes in a single file; the same matches are then found in all other files; a comparison of the signal intensity between each candidate reference analyte and the other analytes in the retention time cluster is documented and compared across all the files in a dataset; and the difference in the relative ratio is used to apply an iterative exclusion process to generate new analyte retention time clusters with progressively more consistent relative abundance for each endogenous reference standard candidate. This is done by applying a filtering step that removes a prospective analyte reference candidate with a maximum average ratio difference.

[0035] For instance, in working example 1, the first maximum difference relative intensity ratio applied was 1.7. Then reference analyte candidates were eliminated from consideration that did not have an average relative intensity ratio difference of 1.7 or less and the remaining pool of candidate analytes were regrouped by similar retention time in a single file and the relative ratio for each potential internal standard reference recalculated in all the other files being analyzed. A smaller allowable relative difference was then applied to filter out more analyte candidates such as those with a 1.5-fold relative ratio difference. The remaining endogenous analyte references were then successively processed until the relative ratio difference was 0.9, indicating almost no change in relative abundance for the analyte reference across the data files being compared. The maximum relative difference threshold filter at which each analyte reference candidate was filtered out is then used to prioritize which analyte references were used to normalize the relative abundance of each analyte.

[0036] In some embodiments, the endogenous reference analytes identified by the process described are applied by matching each sample analyte to all the co-eluting analyte references available as outlined in step 3 of FIG. 1 step 3. Sample analyte relative abundance may be normalized by dividing the sample analyte signal intensity by the endogenous references that were eliminated by the smallest maximum difference relative ratio threshold as summarized in step 4 of FIG. 1.

[0037] For example, if two co-eluting endogenous reference analytes are found, but one was eliminated by the 1.6-fold maximum relative difference threshold, and the other endogenous reference was not eliminated until the 1.1 -fold maximum relative difference threshold, the reference that was not filtered out until the 1.1 -fold difference maximum relative ratio difference is applied to normalize the sample analyte in all the files being compared. Thus, in many situations more than one co-eluting endogenous reference can be used to normalize data. The corrected values for relative analyte abundance are used to identify analytes that change in a dataset such as a protein differentially regulated in a disease state as shown in FIG. 11, or in response to a drug treatment.

[0038] This disclosure also provides a computing system for the analysis of mass spectrometry data and conducting the processes set forth above. The system has a processor, memory read by the processor, and a user interface run by the processor to allow samples to be selected for analysis by the user. Furthermore, the user interface allows information to be collected from the user on the origin and preparation of one or more samples. The system also includes a tandem MS search algorithm for the identification of analytes. The system and process generate a sample report showing a relative abundance of analytes in a disease state, drug treatment, or other model system.

[0039] The computer system includes a bus or other communication mechanism for communicating information, in which the processor is coupled with bus for processing information. The memory may be any form of non-transient data storage, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus for storing information and instructions to be executed by the processor (the term "memory" as used herein cannot be interpreted to include any aspect of a human mind, in whole or in part). The memory also may be used for storing temporary variable or other intermediate information during execution of instructions to be executed by the processor. The computer system may further include a read only memory (ROM) or other static storage device coupled to the bus for storing static information and instructions for the processor. A storage device, such as a magnetic disk or optical disk, is provided and coupled to bus for storing information and instructions.

[0040] The computer system may be coupled via bus to a display, such as a cathode ray tube (CRT), for displaying information to a computer user. An input device, including alphanumeric and other keys, is coupled to the bus for communicating information and command selections to the processor. Another type of user input device is cursor control, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor and for controlling cursor movement on the display. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.

[0041] The disclosure is related to the use of the computer system for performing the method. According to one embodiment, the method is performed by the computer system in response to the processor executing one or more sequences of one or more instructions contained in main memory. Such instructions may be read into the main memory from another computer- readable medium, such as a storage device. Execution of the sequences of instructions contained in the main memory causes the processor to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

[0042] The term "computer-readable medium" as used herein refers to any medium that participates in providing instructions to the processor for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as the storage device. Volatile media include dynamic memory, such as the main memory. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise the bus. Common forms of computer-readable media include, for example, floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD- ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASHEPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

[0043] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to the processor for execution. For example, the instructions may initially be borne on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the computer system can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus can receive the data carried in the infrared signal and place the data on the bus. The bus carries the data to the main memory, from which the processor retrieves and executes the instructions. The instructions received by the main memory may optionally be stored on the storage device either before or after execution by the processor. The computer system also includes a communication interface coupled to the bus. The communication interface provides a two-way data communication coupling to a network link that is connected to a local network. For example, the communication interface may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the communication interface sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various type of information. The network link typically provides data communication through one or more networks to other data devices.

[0044] For example, the network link may provide a connection through the local network to the host computer or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the worldwide packet data communication network, now commonly referred to as the "Internet." The local network and the Internet both use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link and through the communication interface, which carry the digital data to and from the computer system, are exemplary forms of carrier waves transporting the information. The computer system can send messages and receive data, including program codes, through the network(s), the network link, and the communication interface. In the Internet example, a server might transmit a requested code for an application program through the Internet, the ISP, the local network, and the communication interface. In accordance with one aspect of the disclosure, one such downloaded application provides for performing the steps as described herein. The received code may be executed by the processor as it is received, and/or stored in the storage device, or other non-volatile storage for later execution. In this manner, the computer system may obtain an application code in the form of a carrier wave.

[0045] The computing system may comprise one or more modules. In this context a module is one or more interacting pieces of software or firmware containing instructions that are executable by the processor. A given module may reside in the memory, or on additional memory storage devices (a given module may reside on multiple memory storage devices).

[0046] Some embodiments of the computing system comprise one or both of a chromatograph and a mass spectrometer. The chromatograph and spectrometer may be any that are disclosed as suitable for use in the method above. In some embodiments of the system the spectrometer is positioned to receive an eluate from the chromatograph. In further embodiments one or both of the spectrometer and the chromatograph are configured to receive instructions from the processor. In still further embodiments one or both of the spectrometer and the chromatograph are configured to transmit data to the processor.

[0047] Various aspects of the method and system are illustrated by the following non- limiting examples. The examples are for illustrative purposes and are not limiting. It will be understood that variations and modifications can be made without departing from the spirit and scope of what is disclosed.

[0048] Working Example 1.

[0049] An example of using the endogenous co-eluting analyte selection and application process is described in the following example. Whole brains were harvested from four 25- day old Alexander disease model mice carrying an R236H+/- heterozygous mutation in the astrocyte specific cell marker, glial fibrillary acidic protein (GFAP), as well as a human GFAP transgene on a mixed C57B6/FVB background (Hagemann, TL, et al. Hum Mol Genet, 2009; 18: 1190-1199), and four wild type littermates. Each brain was Dounce homogenized in ice cold deionized water with 20 strokes using pestles A and B. The resulting lysate was immediately added into a 9 M urea / 111 mM Tris-HCl pH 8.3 buffer making a final concentration of 8 M urea / 100 mM Tris-HCl. The mouse samples were then allowed to solubilize overnight at 4°C. The following morning a bicinchoninic acid assay assay (BCA) was performed to determine the total protein concentration. Then 70 micrograms of brain protein in 50 microliters of total volume from each mouse was placed in a Fisherbrand 1.5 mL tube and 2.5 uL of 200 mM dithiothreitol (DTT)/ 100 mM Tris-HCl was added and the tube vortexed. The samples were allowed to reduce at room temperature for one hour followed by the addition of 10 microliters of 200 mM iodoactetamide/ 100 mM Tris-HCl and mixed by vortexing. Samples were incubated for one hour at room temperature in the dark. Then 10 microliters of 200 mM DTT/ 100 mM Tris-HCl was added and mixed by vortexing to consume unreacted iodoacetamide by incubation for one hour at room temperature. The urea concentration was then reduced by the addition of 387.5 microliters of HPLC grade water and 6 microliters of trypsin in 100 mM Tris-HCl was added at a stock concentration of 200 nanograms/microliter to make the protein to trypsin ratio 1 :50. The trypsin digestion was carried out overnight at 37°C. The next day the trypsin reaction was quenched by addition of 30 microliters of 20% formic acid until the pH was < 6 determined by placing 1 microliter aliquots on pH paper. The theoretical final concentration of the mouse digests was 140 nanograms/microliter. Then 75 microliters or 10.5 micrograms of peptide digest was vacuum centrifuged to dryness and resuspended in 21 microliters of 0.1% formic acid making the peptide concentration 500 nanograms/microliter. The Alexander disease model and wild type mouse samples were Ziptipped using 50% acetonitrile (v/v)/0.1% formic acid for elution. The Ziptipped peptides were then vacuum centrifuged until dry and resuspended in 0.1% formic acid. A BCA assay was performed with undigested BSA as a standard to determine the final peptide concentrations.

[0050] Hundreds of isotope labeled and unlabeled peptides were added into the mouse brain protein digests to facilitate a comparison between the results obtained with the embodiment of the method. A database of all unlabeled and lys-8 or arg-10 labeled peptides added to samples were identified by having a match within +/- .01 m/z to the unlabeled and labeled MS survey scan m/z's and a retention time maximum difference of 2 seconds for the unlabeled and labeled m/z's and identical charge (note that of the 398 unlabeled and labeled peptide standards were added into samples, only 189 heavy and light forms were consistently detected). All peptides selected for the mix did not have methionine or cysteine residues, lacked post-translational or chemical modifications, and had no trypsin miscleavages. The labeled and unlabeled peptides were synthesized as PepScreen crude peptides by Sigma Aldrich. Peptides were solubilized in 100% acetonitrile and mixed together to produce a mix containing the labeled and unlabeled counterparts. The mix was diluted 50-fold in 0.1% formic acid and BCA assayed to determine the peptide concentration.

[0051] In addition to the comparison above using isotope labeled PepScreen peptides the results with using the method were also compared to a ¹⁵N labeled glial fibrillary acidic protein (GFAP) standard. Wild type human GFAP sequence standard was produced similarly to methods described by Der Perng et al. (Der Pemg, M, et al. Am J Hum Genet, 2006; 79: 197-213), except the expression was performed in 99% pure ¹⁵N complete medium (Cambridge Isotope Laboratories, Cambridge, MA) and BCA assayed with BSA as a reference standard after DEAE column purification to determine final protein concentration. Five nanograms of this standard was spiked into one microgram of mouse peptide samples with 200 nanograms of the labeled and unlabeled lysine and arginine mix prior to LC-MS/MS analysis.

[0052] Mouse peptides in the presence of 200 nanograms of the labeled and unlabeled peptide mix were injected onto an in-house packed 11 cm x 150 μηι inner diameter CI 8 Jupiter 5μηι 300 A reverse phase resin (Phenomenex, Torrance, CA) analytical column using a nanoflow high-performance liquid chromatography system (HPLC) with a flow rate of 650 nanoliters/minute and peptide elution gradient of 0-33% acetonitrile/0.1% formic acid from 0-60 minutes. Blank runs were performed between all sample runs to reduce carryover effects. All mass spectra were acquired in positive ion mode by data-dependent acquisition on an LTQ-Orbitrap Velos with a 'top 20' setup with an Orbitrap MS survey scan from 300- 1800 m/z followed by MS/MS of the 20 most abundant ions in the LTQ. Automatic gain control was set to 1,000,000 for MS survey scans at a resolution of 30,000 at m/z 400. LTQ MS/MS fragmentation was performed by collision-induced dissociation with a target value of 5,000 ions. Ion selection threshold for MS/MS selection was 1,000 counts and a repeat count of 1 was used with dynamic exclusion for 45 seconds.

[0053] Raw data files were analyzed by a computer program that implements an embodiment of the method named "Protalizer" version 1.1. Raw files were converted to centroided mzML format using the Proteo Wizard MS convert tool (Chambers, MC, et al. Nat Biotechnol, 2012; 30: 918-920) and MS survey scan feature maps were created for 2-3⁺ charge state features consistent with peptide isotope envelopes according to the averagine model using the OpenMS Feature Finder Centroid program (Sturm, M, et al. BMC Bioinformatics, 2008; 9: 163) with a .005 m/z tolerance. Peptide and protein identifications were made using the X! Tandem search engine (Craig, R, Bioinformatics, 2004; 20: 1466-1467) against the forward and reverse M s musc lus UniProtKB database with a 20 ppm parent mass tolerance and 0.6 Da fragment mass tolerance that included variable oxidation of methionine residues, deamidation, N-terminal acetylation, phosphorylation, and fixed carbamidomethylation of cysteine. Peptides with up to three trypsin miscleavages were included in the analysis and protein and peptide false discovery rates were set to 0.01. Only 'top hit' proteins were used and in rare situations where identified peptides were assigned to two or more proteins in different files the ambiguous peptides were eliminated from being analyzed further by the program. All lysine and arginine labeled and unlabeled peptides in the synthetic peptide mix were removed from being used as internal standards in Protalizer data normalization.

[0054] Selection of Endogenous Reference Peptides

[0055] To correct data for sample preparation and instrument variability, Protalizer normalized relative abundances by identifying co-eluting, non-identical peptides as effective reference standards as shown in FIG 2. Only peptides detected in every file were used to normalize data. To demonstrate that non-identical peptides can reproducibly co-elute from an LC-MS/MS chromatograph, FIGS. 3-10 show a group of five analytes across eight different mouse brain tryptic peptide digests in the presence of the unlabeled and labeled peptide mix. Peptides were disqualified from being considered endogenous reference standards that have an MS precursor abundance difference across the files being compared of 3-fold or greater. In a comparison of the brain homogenate from wild type and Alexander disease model mice (n=4 analyzed per genotype), 11% of the total peptides with an MS survey scan feature appearing in every file were rejected for use as internal standards due to absolute intensity differences at this step. The remaining peptide standard candidates were then paired to ten other candidates with the most similar retention time in a single file, and then the same pairs were identified in all other files in a dataset. An average relative abundance ratio was calculated and standard candidates with a relative ratio deviating by more than a maximal threshold were recorded. This step was performed several times beginning with the largest relative ratio of 1.7 and gradually reducing the ratio by increments of 0.2 to a minimum of 0.9. Standards that have the most consistent relative ratios and similar elution time were used to normalize sample peptides. In the wild type and Alexander disease mice study, 1,379 endogenous reference standards remained at the end of the internal reference selection process.

[0056] Pairing of Sample Peptides to Endogenous Reference Standards

[0057] Each sample peptide analyte was matched to the same co-eluting endogenous reference standard(s) in every file in a dataset. The retention time difference between analyte peptides and internal reference standards was set to allow matching between peptides that share co-elution at or above half maximum intensity and are co-detected by mass spectrometry. Since peptide elution band widths are generally related to LC gradient length, the retention time difference in the matching step was based on the average difference in seconds between the first and last peptide sequenced in the files compared. Endogenous reference standard matches were not used in situations where the sample peptide was also an internal standard. In the Alexander disease and wild type mice comparison, 78% of all the peptides quantified were able to be matched to at least one internal standard. The remaining 22% were matched to the ten endogenous reference standards with similar elution time and consistent relative abundance.

[0058] Calculation of Peptide Relative Abundance

[0059] Peptide relative abundance was calculated in every file by dividing each MS survey scan area by the matched internal standard(s). Each peptide / internal standard ratio was expressed in a relative abundance scale with 1 being the smallest relative value for a peptide with an MS survey scan feature located in the files being compared. This was done to be able to express data in relative fold change values, and to equally consider the values for peptides with multiple internal standards.

[0060] To test the effectiveness of the Protalizer approach for identifying endogenous co- eluting peptides, the Protalizer results were compared to a full-length ¹⁵N labeled glial fibrillary acidic protein (GFAP) standard added into brain lysates from wild type and Alexander disease model mice. The results shown in FIG. 11 indicated a 42-fold GFAP increase in the Alexander disease mice that is consistent with published results from enzyme linked immunosorbent assays (Hagemann, TL, et al. Hum Mol Genet, 2009; 18: 1190-1199). Overall, there was a three percent difference in the relative abundance calculated by the ¹⁵N standard compared to the Protalizer values. These results indicate the Protalizer can accurately quantify large differences in relative protein levels.

[0061] The Protalizer' s ability to normalize data was then tested with small changes of less than 1.7-fold in the wild type and Alexander disease mice by comparing the coefficient of variation for unlabeled peptides in raw data, total-ion-current (TIC) normalized data, Protalizer corrected data, and data obtained using spiked-in isotope labeled peptide standards. Histogram plots of the coefficient of variation normalized by various methods for the 189 peptides are shown in FIGS. 12(a) and (b). The average percent coefficient of variation (CV) for the wild type and Alexander disease mouse groups shown in FIGS. 13(a) and (b) indicates that use of the labeled arginine and lysine peptide standards yields ratios with the lowest overall variability. However, the Protalizer analysis produced 67% less variability in the wild type mice and 23% less in the Alexander disease mice than results from TIC normalized data. Overall these results indicate Protalizer substantially reduces the variability in data without the requirement to synthesize isotope labeled standards.

[0062] Various modifications, in addition to those shown and described herein, will be apparent to those skilled in the art of the above description. Such modifications are also intended to fall within the scope of the appended claims.

[0063] It is to be understood that any given elements of the disclosed embodiments may be embodied in a single structure, a single step, a single substance, or the like. Similarly, a given element of the disclosed embodiment may be embodied in multiple structures, steps, substances, or the like. The foregoing description illustrates and describes the processes, machines, manufactures, compositions of matter, and other teachings of the present disclosure. Additionally, the disclosure shows and describes only certain embodiments of the processes, machines, manufactures, compositions of matter, and other teachings disclosed, but, as mentioned above, it is to be understood that the teachings of the present disclosure are capable of use in various other combinations, modifications, and environments and is capable of changes or modifications within the scope of the teachings as expressed herein, commensurate with the skill and/or knowledge of a person having ordinary skill in the relevant art. The embodiments described hereinabove are further intended to explain certain best modes known of practicing the processes, machines, manufactures, compositions of matter, and other teachings of the present disclosure and to enable others skilled in the art to utilize the teachings of the present disclosure in such, or other, embodiments and with the various modifications required by the particular applications or uses. Accordingly, the processes, machines, manufactures, compositions of matter, and other teachings of the present disclosure are not intended to limit the exact embodiments and examples disclosed herein. Any section headings herein are provided only for consistency with the suggestions of 37 C.F.R. § 1.77 or otherwise to provide organizational queues. These headings shall not limit or characterize what is set forth herein.

Claims

CLAIMS What is claimed:

1. A method for determining the relative abundance of a plurality of analytes in a sample, said method comprising:

(a) subjecting the sample to fractionation by chromatography to produce a time resolved eluate;

(b) subjecting the eluate to detection by a mass spectrometric technique to generate a mass spectrometric signal from each of the plurality of analytes contained in the mixture;

(c) selecting an endogenous reference standard;

(d) matching at least one of the plurality of analytes to the endogenous reference standard; and

(e) comparing the mass spectrometric signal from each of the plurality of analytes to the mass spectrometric signal of the endogenous reference, wherein the endogenous reference co-elutes with and is non-identical to the analyte.

2. The method of claim 1, comprising selecting a plurality of endogenous reference standards, matching at least one of the plurality of analytes to each of the plurality of endogenous reference standards, and comparing the mass spectrometric signal from each of the plurality of analytes to the mass spectrometric signal of at least one of the plurality of endogenous references, wherein the endogenous reference co-elutes with and is non-identical to the analyte.

3. The method of claim 1, wherein the selecting step comprises eliminating endogenous reference standards with an absolute intensity or abundance difference greater than a threshold value.

4. The method of claim 1, wherein the selecting step comprises grouping non-identical endogenous reference standard candidates that co-elute and comparing the relative intensity or abundance ratio between each member in the co-eluting group.

5. The method of claim 4, wherein the difference in the ratios used to identify analytes as endogenous references is an average, median, largest, or smallest ratio difference.

6. The method of claim 4, wherein an iterative exclusion process is used to selectively create analyte co-elution groups with reduced variability by iteratively eliminating endogenous reference candidates from being placed in co-elution groups that have a ratio difference above a threshold.

7. The method of claim 4, wherein only an endogenous reference standard with the least variability across the samples is matched to co-eluting sample analytes.

8. The method of claim 1, wherein the fractionation is liquid chromatography.

9. The method of claim 1, wherein the fractionation is gas chromatography.

10. The method of claim 1, wherein the plurality of analytes are peptides.

11. The method of claim 1, wherein the plurality of analytes are metabolites.

12. The method of claim 1 , wherein the plurality of analytes are organic compounds.

13. The method of claim 1, wherein the plurality of analytes are lipids.

14. The method of claim 1, wherein the plurality of analytes are pharmaceutical compounds.

15. The method of claim 1, wherein the process is used to compare two or more samples from two or more distinct biological states.

16. The method of claim 15, wherein the two or more distinct biological states are a disease state and a non-disease state.

17. The method of claim 15, wherein the two or more distinct biological states are at least in part caused by a drug or other therapeutic treatment.

18. A computing system for the analysis of mass spectrometry data, the system comprising:

(a) a processor;

(b) a memory configured to be read by the processor;

(c) a user interface configured to be run by the processor to allow samples to be selected for analysis by a user, and configured to allow information to be collected from the user on the origin and preparation of one or more samples;

(d) an algorithm for the identification of analytes based on tandem mass spectrometry data;

(e) a module for performing the method of claim 1 with the processor; and

(f) a module for generating a sample report showing a relative abundance of analytes in a disease state, drug treatment, or other model system.

19. The system of claim 18, further comprising a chromatograph that produces eluate from a sample; and a mass spectrometer positioned to receive eluate from the chromatograph.