A method for analyzing the proteome of body fluids

Abstract

A method for analyzing a body fluid proteome, the method comprising: using affinity technology to remove high-abundance proteins in an initial sample A to obtain a test sample I enriched with low-abundance proteins; using a chemical precipitation method to remove high-abundance proteins in an initial sample B to obtain a test sample II enriched with low-abundance proteins, wherein the initial sample A and the initial sample B are from the same body fluid sample of the same subject; performing proteomic analysis on the test sample I to obtain a proteomic data set I; performing proteomic analysis on the test sample II to obtain a proteomic data set II; and determining a final quantitative proteomic data set in the body fluid based on the proteomic data set I and the proteomic data set II.

Description

Technical Field

[0001] The invention belongs to the field of biochemical detection, and particularly relates to a method for analyzing body fluid proteome. Background Art

[0002] Plasma and serum are body fluids that are abundant and easy to obtain, and are often used for clinical diagnosis and prognosis analysis. Plasma proteins can be used as indicators of individual health status and biomarkers for clinical testing, and more and more research is focused on plasma proteins. Plasma proteomics can identify and quantify a variety of proteins in clinical cohorts at a high throughput, and is a powerful tool for plasma protein research.

[0003] In clinical research, it is often necessary to perform quantitative proteomic analysis on hundreds or thousands of samples. At the same time, in order to develop more plasma biomarkers, it is also necessary to improve the coverage of the proteome. However, various technical difficulties in the prior art (such as the inability to efficiently remove high-abundance proteins that may cause interference) often limit the sample analysis throughput. The plasma proteome coverage reported in the literature is 500-1000 proteins, which limits the development of plasma protein markers. Therefore, it is necessary to develop new methods and systems to improve the throughput and efficiency of proteomic analysis. Summary of the invention

[0004] In some embodiments, a method for analyzing a body fluid proteome comprises:

[0005] Affinity technology is used to remove high-abundance proteins in the initial sample A to obtain a sample I to be tested that is enriched with low-abundance proteins;

[0006] A chemical precipitation method is used to remove high-abundance proteins in the initial sample B to obtain a test sample II enriched with low-abundance proteins, wherein the initial sample A and the initial sample B are from the same body fluid sample of the same subject;

[0007] Performing proteomic analysis on the sample I to be tested using an optimized data-independent acquisition technology to obtain a proteomic data set I;

[0008] Performing proteomic analysis on the sample II to be tested using an optimized data-independent acquisition technology to obtain a proteomic data set II;

[0009] Based on the proteomic dataset I and the proteomic dataset II, a final quantitative proteomic dataset of the body fluid sample is determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] This specification will be further described in the form of exemplary embodiments, which are described in detail in conjunction with the accompanying drawings. These embodiments are not restrictive, and include:

[0011] Figure 1 A flow chart showing a method for analyzing the proteome of a body fluid according to some embodiments of the present invention is shown.

[0012] Figure 2A-2F The results of plasma proteome analysis using data independent acquisition (DIA) technology according to some embodiments of the present invention are shown, wherein: Figure 2A and Figure 2B The number of peptides and proteins identified by DIA under the conditions of 15 cm, 25 cm, and 50 cm columns and 90 min gradient, respectively; Figure 2C and Figure 2D The number of peptides and proteins identified using a 25-cm column and gradients of 90 min, 120 min, and 150 min, respectively; Figure 2E and Figure 2F The number of peptides and proteins identified for a 50 cm column and gradients of 90 min, 120 min, and 150 min, respectively.

[0013] Figure 3A-3D The results of plasma proteome analysis using DIA technology according to some embodiments of the present invention are shown, wherein: Figure 3A and Figure 3B The numbers of peptides and proteins identified under 40, 50, and 60 isolation windows, respectively; Figure 3C and Figure 3D Coefficient of variation (CV) distribution of protein intensity and CV distribution of peptide intensity identified under 40, 50 and 60 isolation windows, respectively.

[0014] Figure 4A and Figure 4B A statistical diagram of the number of polypeptides obtained by proteomic analysis using optimized DIA technology according to some embodiments of the present invention is shown ( Figure 4A ) and protein quantity statistics ( Figure 4B ); undepleted represents the enzymatic peptides of the original plasma sample without removing the high-abundance proteins; sample ① represents the use of peptide set I; sample ② represents the use of peptide set II.

[0015] Figure 5A and Figure 5B A Venn diagram of proteins obtained by proteomic analysis using optimized DIA technology according to some embodiments of the present invention is shown ( Figure 5A ) and protein molecular weight distribution diagram ( Figure 5B); undepleted represents the enzymatic peptides of the original plasma sample without removing the high-abundance proteins; sample ① represents the use of peptide set I; sample ② represents the use of peptide set II.

[0016] Figure 6 The coefficient of variation distribution diagram of protein intensity obtained by proteomic analysis using optimized DIA technology according to some embodiments of the present invention is shown; wherein, "sample ① all" represents the coefficient of variation distribution of protein intensity of all proteins identified by peptide set I-optimized DIA; "sample ② all" represents the coefficient of variation distribution of protein intensity of all proteins identified by peptide set II-optimized DIA; "sample ① between" represents the protein intensity of proteins jointly identified by peptide set I-optimized DIA and peptide set II-optimized DIA in peptide set I-optimized DIA "Sample ② between" represents the distribution of coefficient of variation of protein intensity of proteins identified by peptide set I-optimized DIA and peptide set II-optimized DIA in peptide set II-optimized DIA; "Sample ② unique" represents the distribution of coefficient of variation of protein intensity of unique proteins identified by peptide set II-optimized DIA; "Sample ① all + sample ② unique" represents the distribution of coefficient of variation of protein intensity of all proteins identified by peptide set I-optimized DIA plus unique proteins identified by peptide set II-optimized DIA.

[0017] Figure 7A-7C The figure shows the coverage of the proteomic data set finally quantified according to the method for analyzing the body fluid proteome in some embodiments of the present invention; wherein, Fig. 7A The FDA-approved biomarkers covered by the final quantitative proteomic dataset are shown; Figure 7B Brain tissue-specific proteins covered by the final quantitative proteomic dataset are shown; Figure 7C Liver-specific proteins covered by the final quantitative proteomic dataset are shown. DETAILED DESCRIPTION

[0018] Typical body fluids such as serum, plasma, and cerebrospinal fluid are commonly used samples in the field of proteomics research based on mass spectrometry platforms. These samples contain a wide variety of proteins, among which high-abundant proteins (HAP) account for 97% to 99%, including albumin, IgG, IgA, fibrinogen, transferrin, haptoglobin, antitrypsin, etc. Low-abundant proteins (LAP) are often specific biomarkers of diseases or target protein molecules for drug action. Therefore, removing high-abundant proteins that interfere with detection and enriching as many low-abundant proteins as possible has become one of the key factors in the number of mass spectrometry identifications.

[0019] To address this problem, currently commonly used methods for removing high-abundance proteins include affinity technology, precipitation method, ultrafiltration centrifugation method, gold magnetic particle method, isoelectric capture method, liquid chromatography, etc. Affinity technology is to separate or remove the target protein from the sample by specific affinity between the immobilized ligand and the target protein. Chemical precipitation method is to remove HAP in the sample by adding a precipitant to precipitate the protein. The precipitant can be an organic solvent or an ammonium salt. Liquid chromatography is more used for protein separation. Commonly used ion exchange chromatography and size exclusion chromatography use the different sizes of protein molecules and the different charges they carry to remove proteins.

[0020] Data independent acquisition (DIA) technology is a new mass spectrometry technology developed in recent years and belongs to the label-free proteomics method. It adopts data-independent scanning mode: the entire full scan range of the mass spectrometer is divided into several windows, and then all ions in each window are detected and fragmented, so as to obtain information on all ions in the sample without omission or difference. DIA technology can reduce the missing values ​​of sample detection, while improving quantitative accuracy and repeatability, and realizing highly stable and highly accurate proteome quantitative analysis in large sample cohorts. The above advantages of DIA technology have made it increasingly used in plasma proteomics.

[0021] One of the objects of the present invention is to provide a method for analyzing the proteome of body fluids.

[0022] In some embodiments, Figure 1 As shown, the method for analyzing the proteome of body fluids includes:

[0023] Affinity technology is used to remove high-abundance proteins in the initial sample A to obtain a sample I to be tested that is enriched with low-abundance proteins;

[0024] A chemical precipitation method is used to remove high-abundance proteins in the initial sample B to obtain a test sample II enriched with low-abundance proteins, wherein the initial sample A and the initial sample B are from the same body fluid sample of the same subject;

[0025] Performing proteomic analysis on the sample I to be tested to obtain a proteomic data set I; performing proteomic analysis on the sample II to be tested to obtain a proteomic data set II;

[0026] Based on the proteomic dataset I and the proteomic dataset II, a final quantitative proteomic dataset of the body fluid sample is determined.

[0027] In some embodiments, performing proteomic analysis on the sample I includes: using DIA technology to perform quantitative proteomic analysis on the sample I. In some embodiments, performing proteomic analysis on the sample II includes: using DIA technology to perform quantitative proteomic analysis on the sample II.

[0028] In some embodiments, the test sample I is reduced, alkylated, enzymatically digested, and desalted to obtain a peptide set I for proteomic analysis using the DIA technique. In some embodiments, the test sample II is desalted, reduced, alkylated, and enzymatically digested to obtain a peptide set II for proteomic analysis using the DIA technique.

[0029] In some embodiments, the DIA technique is an optimized DIA technique; the optimized DIA technique uses a chromatographic column length of 50 cm and a chromatographic column gradient of 90 min. In some embodiments, the optimized DIA technique uses a chromatographic column length of 50 cm and a chromatographic column gradient of 150 min. In some embodiments, the optimized DIA technique uses a chromatographic column length of 50 cm and a chromatographic column gradient of 120 min. In some embodiments, in the optimized DIA technique, the MS1 resolution is set to 60K, the MS2 resolution is set to 30K, and the parent ion scan range is set to m / z 350-1200 and divided into 50 windows.

[0030] In some embodiments, the use of affinity technology to remove high-abundance proteins in the initial sample A includes: using antibodies to the high-abundance proteins as affinity ligands to remove the high-abundance proteins. In some embodiments, the high-abundance proteins include albumin, IgA, IgD, IgE, IgG, IgM, α1-acid glycoprotein, α1-antitrypsin, α2-macroglobulin, apolipoprotein A1, fibrinogen, haptoglobin, transferrin, complement C3, apolipoprotein A-II, α-2-HS-glycoprotein, apolipoprotein C-III, α-1-antichymotrypsin, vitamin D binding protein, ceruloplasmin, complement C4-A, complement C1q, hemagglutinin, kininogen-1, synaptotagmin 5, histidine-rich glycoprotein, vitronectin, complement factor H, plasma proteinase C1 inhibitor, C4b binding protein and fibronectin. One or more.

[0031] In some embodiments, the antibody to the high abundance protein is immobilized on a solid phase carrier. In some embodiments, the solid phase carrier comprises one or more of cellulose, polyacrylamide, polystyrene, polyethylene, polypropylene, cross-linked dextran, glass, silicone rubber, agarose gel and gel resin. In some embodiments, the solid phase carrier is a gel resin. In some embodiments, by immobilizing a solid phase carrier with antibodies to multiple high abundance proteins, multiple high abundance proteins can be removed at one time. In some embodiments, by immobilizing a solid phase carrier with antibodies to 31 kinds of high abundance proteins, 31 kinds of high abundance proteins can be removed at one time, which not only improves efficiency but also reduces cost.

[0032] In some embodiments, the removal of high-abundance proteins in the initial sample A using affinity technology is performed in a multi-chamber container. In some embodiments, the removal of high-abundance proteins in the initial sample B using chemical precipitation is performed in a multi-chamber container. In some embodiments, the multi-chamber container is a multi-well plate, such as a 96-well, 48-well or 24-well plate. The method for removing high-abundance proteins based on a multi-well plate can process multiple samples at a time, such as 2×96 samples, thereby increasing the throughput of analyzing the proteome.

[0033] In some embodiments, the body fluid comprises one or more of plasma, serum, urine, interstitial fluid, intrapleural fluid, intraperitoneal fluid, cerebrospinal fluid, semen, and vaginal fluid.

[0034] In some embodiments, the chemical precipitation method for removing the high-abundance proteins in the initial sample B comprises: using an organic solvent as a precipitant to precipitate the high-abundance proteins. In some embodiments, the organic solvent comprises one or more of methanol, ethanol, isopropanol, acetonitrile, chloroform, trichloroacetic acid, and trifluoroacetic acid.

[0035] In some embodiments, the chemical precipitation method for removing the high-abundance proteins in the initial sample B further comprises: before the high-abundance proteins are precipitated by the precipitant, denaturing the high-abundance proteins by using a denaturant. In some embodiments, the denaturant comprises one or more of guanidine hydrochloride and urea.

[0036] In some embodiments, based on the proteome dataset I and the proteome dataset II, determining the final quantitative proteome dataset of the body fluid sample comprises removing the data overlapping between the proteome dataset II and the proteome dataset I from the proteome dataset II to obtain a proteome dataset III; and using the proteome dataset I and the proteome dataset III as the final quantitative proteome dataset in the body fluid sample. In some embodiments, removing the data overlapping between the proteome dataset II and the proteome dataset I from the proteome dataset II comprises: comparing the proteome dataset I with the proteome dataset II through a Venn diagram, removing the data overlapping between the proteome dataset II and the proteome dataset I from the proteome dataset II, and obtaining a proteome dataset III. In some embodiments, the data overlapping between the proteome dataset II and the proteome dataset I is the data overlapping between the proteins in the proteome dataset II and the proteome dataset I.

[0037] In some embodiments, the methods of the present invention are used only for non-diagnostic applications.

[0038] In some embodiments, the subject includes at least one of a human and a non-human mammal.

[0039] In some embodiments, the method for analyzing body fluid proteomes has good reproducibility and high coverage of plasma proteomes, with more than 1,700 proteins quantified.

[0040] Example

[0041] Plasma collection

[0042] Blood samples were collected from healthy subjects, and the citrate / blood mixture (1:9, v / v) was centrifuged (3000 rpm) at 10°C for 10 min and stored at -80°C until use.

[0043] Antibody-based affinity removal of abundant proteins

[0044] (1) Preparation of gel resin

[0045] 150 mg dry Pierce TMNHS-Activated gel resin (purchased from Thermo Fisher Scientific, USA) was placed in an empty centrifugal column, and 2 mL of each of the following protein antibody solutions with an antibody content of 1 mg / mL were added to the centrifugal column: albumin, IgA, IgD, IgE, IgG, IgM, α1-acid glycoprotein, α1-antitrypsin, α2-macroglobulin, apolipoprotein A1, fibrinogen, haptoglobin, transferrin, complement C3, apolipoprotein A-II, α-2-HS-glycoprotein, apolipoprotein C-III, α-1-antichymotrypsin, vitamin D binding protein, ceruloplasmin, complement C4-A, complement C1q, hemagglutinin, kininogen-1, synaptotagmin 5, histidine-rich glycoprotein, vitronectin, complement factor H, plasma proteinase C1 inhibitor, C4b binding protein and fibronectin; invert and mix for 2 hours; place the centrifugal column in a collection tube and centrifuge at 1000 g for 1 min; add pH Add 2mL of 0.1M sodium phosphate and 0.15M NaCl at pH 7.2 to the centrifuge column; centrifuge at 1000g for 1min, repeat once; add 1mL of 1M Tris, pH 7.4 stop buffer, mix by inversion at room temperature for 15-20min; centrifuge at 1000g for 1min; add 2mL of 0.1M sodium phosphate, 0.15M NaCl, pH 7.2 to the centrifuge column; centrifuge at 1000g for 1min; add 500μL of 0.1M sodium phosphate, 0.15M NaCl, 0.05wt% sodium azide, pH 7.2 to preserve the gel resin.

[0046] (2) Removal of high-abundance plasma proteins

[0047] Mix the prepared gel resin and add 40 μL to each well of a 0.45 μm 96-well plate; add 6 μL of 6-fold diluted plasma sample to each well; incubate with shaking for 30 min; centrifuge at 4000 g for 2 min, and collect the eluate from each well.

[0048] (3) Preparation of peptide set I for DIA

[0049] Add 70 μl of protein lysis solution containing 1wt% SDC, 100mM Tris-HCl, pH 8.5 to the eluate of each well; add 2 μl of 0.5M tris(2-carboxyethyl)phosphine (TCEP) and 8 μL of 0.5M trichloroacetic acid (TCA) solution, react at 70°C for 10 min; cool to room temperature; then add 1 μg of Lys C / Trypsin mixed enzyme reagent, react at 37°C for 2 hours to obtain enzyme digestion solution. Add 100 μL of 1% trifluoroacetic acid (TFA) solution of isopropanol (IPA) to the enzyme digestion solution of each well for SCX desalting. 200 μl of enzyme digestion solution was added to each well of the 96-well SCX solid phase extraction plate, and the plate was centrifuged at 1000g for 2 min; 400 μL of 1% TFA solution of IPA was added for elution, and the plate was centrifuged at 1000g for 2 min, and the eluent was discarded; 400 μL of 0.2% TFA was added, and the plate was centrifuged at 1000g for 2 min for elution, and the eluent was discarded; 200 μL of a mixture of 1% ammonia and 80% acetonitrile (ACN) was added, and the plate was centrifuged at 1000g for 2 min, and the eluent from each well was collected. The eluent was concentrated and dried in a freeze concentrator, and 20 μL of 0.1% formic acid (FA) solution was added for re-dissolution to obtain peptide set I for DIA.

[0050] Chemical precipitation to remove high-abundance proteins

[0051] (1) Removal of high-abundance plasma proteins

[0052] 50 μL of plasma and 50 μL of lysis solution (8 M urea, 100 mM Tris, pH 8.0) were added to each well of a 96-well plate and incubated at room temperature for 5 min. 100 μL of 20% TCA solution was added and shaken at 4°C, 1500 RPM for 60 min to precipitate protein. Centrifuge at 4000 g and take 150 μL of supernatant from each well.

[0053] (2) Preparation of peptide set II for DIA

[0054] The supernatant was added to each well of a 96-well HLB solid phase extraction plate, centrifuged at 1000g for 2min, and the supernatant was discarded; 400μl of 0.2% TFA solution was added to each well, centrifuged at 1000g for 2min, and the supernatant was discarded; 400μL of a mixture of 0.2% TFA and 80% ACN solution was added to each well, and the eluate was collected by centrifugation at 1000g for 2min. The eluate was concentrated and drained in a freeze concentrator, and 100μL of 50mM NH4CO3 was added for re-dissolution; 2μl of 0.5M TCEP and 8μl of 0.5M TCA were added, and the reaction was carried out at 70℃ for 10min; 1μg of Lys C / Trypsin mixed enzyme reagent was added, and the reaction was carried out at 37℃ for 2 hours. The enzyme digestion solution was concentrated and drained in a freeze concentrator, and 20μL of 0.1% FA solution was added for re-dissolution to obtain peptide set II for DIA.

[0055] DIA Technology

[0056] DIA is a method of MS / MS fragmentation of all peptide precursor ions in a specific mass-to-charge ratio (m / z) range indiscriminately after a high-resolution full scan of the primary mass spectrometer. In DIA data acquisition, high-resolution MS2 spectra are used for peptide identification, and high-resolution MS1 ​​and MS2 can be used for peptide / protein quantification. The following instrument parameters generally need to be considered: (i) the number of DIA isolation windows and the size of each isolation window. DIA co-isolates and co-fragment all precursor ions within a given precursor ion isolation window. Therefore, the size of the isolation window directly affects the selectivity, dynamic range, and sensitivity of DIA analysis. Using a wide isolation window can increase the accumulation time of the secondary spectrum and improve the sensitivity of the analysis, and is used for the analysis of very low-abundance samples; using a narrow isolation window can reduce the number of co-fragmented precursor ions and reduce interference, and is used for samples with higher complexity; (ii) DIA cycle time and chromatographic peak width. In DIA, the cycle time corresponds to the sum of the MS1 and MS2 scan times. Accurate quantitative analysis by the average sum of chromatographic peak widths requires an average of 7-10 collected data points to fit the extracted ion current chromatogram and calculate the optimal DIA cycle time. For example, when the average peak width is 30s, the cycle time can be set to 3-4s. In DIA data analysis, it is necessary to establish a DIA quantitative spectrum library, which contains information on proteins and their peptides, such as retention time, parent ion m / z, fragment ion m / z, relative abundance of fragment ions, etc. Peak extraction of DIA data is performed based on the peptide information in the spectrum library, and the sum of the fragment ion peak areas is used to characterize the intensity of the peptide.

[0057] In order to obtain a good balance between proteome depth and throughput and between protein qualitative and quantitative performance, the parameters of DIA technology were optimized. Undepleted peptides of original plasma samples without removal of high-abundance proteins were used as samples for DIA technology optimization.

[0058] In nanoliter chromatography using DIA technology, column length and gradient length affect peak capacity and thus the proteome depth of single-shot DIA. Figure 2A , Figure 2B As shown, Figure 2A and Figure 2B The number of peptides and proteins identified by DIA under the conditions of 15 cm, 25 cm and 50 cm columns and 90 min gradient, respectively. The number of peptides and proteins identified is the largest when the column length is 50 cm, which has the smallest peak width and the largest peak capacity. Figure 2C , Figure 2D As shown, Figure 2C and Figure 2D The number of peptides and proteins identified by the 25 cm column and the gradients of 90 min, 120 min and 150 min were comparable when the 150 min gradient was used as compared to the 120 min or 90 min gradient. In order to increase the detection throughput, the 90 min gradient was selected for plasma proteome analysis. Figure 2E , Figure 2F As shown, Figure 2E and Figure 2F The number of peptides and proteins identified by a 50 cm column and gradients of 90 min, 120 min, and 150 min, respectively. Therefore, a 50 cm column and a 90 min gradient are the best combination for plasma proteomics, showing a good balance between proteomic depth and throughput.

[0059] In the mass spectrometry of DIA technology, a narrower DIA isolation window can improve sensitivity, but the extended cycle time will lead to fewer peaks and poor quantitative reproducibility; on the contrary, a wider isolation window shortens the cycle time, resulting in more peaks and better reproducibility, but reduces sensitivity. Therefore, the appropriate number of DIA isolation windows balances qualitative and quantitative performance. The MS1 resolution is fixed at 60K, the MS2 resolution is set to 30K, and the parent ion scanning range is set to m / z 350-1200. In view of the average peak width of the optimized nanoliter chromatography is 0.21min (a combination of a 50cm column and a 90min gradient), the number of DIA windows for one DIA cycle time is set to 40, 50, or 60. Figure 3A-D, the DIA method using 40 DIA isolation windows and the shortest cycle time gave the fewest number of peptide and protein identifications, but the best quantitative reproducibility; the method using 60 DIA isolation windows with the longest cycle time gave the most number of peptide and protein identifications, but the worst quantitative reproducibility. Therefore, the method using 50 DIA isolation windows achieved a balance between the number of protein identifications and quantitative reproducibility.

[0060] Peptide set I and peptide set II were analyzed by mass spectrometry using optimized DIA technology to obtain proteome dataset I and proteome dataset II, respectively.

[0061] DIA chromatography: 4 μL of the reconstituted peptide was loaded onto a nanocolumn with an inner diameter of 75 μm and a length of 50 cm, and filled with 1.9 μm Reprosil-Pur C18. The mobile phase A of the Ultimate 3000RSLC nano was 0.1% formic acid / H2O, and the mobile phase B was 80% ACN / 0.1% formic acid. The gradient was 0-4 min, 3-6% B; 4-83 min, 6-30% B; 83-87 min, 30%-90%; 87-90 min, 90%-90%; total gradient 90 min. Adjust the time of the 6-30% B segment and set the total gradient to 120 min or 150 min.

[0062] DIA mass spectra were acquired using an Orbitrap mass spectrometer. Each mass spectrometer cycle included a full scan MS (R 60,000@m / z 200, AGC 2e5, maximum ion injection time 20ms, and mass range 350-1,200) and 50 DIA scans (R 30,000@m / z 200, AGC 5E5, maximum ion injection time 55ms, normalized energy NCE 32, and mass range 200-2,000), with a cycle time of 3.4s.

[0063] Establishment of deep coverage plasma spectral library: 200 μg of peptide set I and peptide set II were dissolved in 50 μL 10 mM NH3·H2O, and then loaded onto XbridgeBEH300 C18 column and chromatographically separated at a flow rate of 100 μL / min using Ultimate 3000HPLC. Buffer A was 10 mM NH3·H2O; buffer B was 10 mM NH3·H2O in 90% ACN. The gradient was 0-4 min, 2-2% B; 4-50 min, 2-30% B; 50-58 min, 30%-90% B; 58-60 min, 90%-90% B; 60-65 min, 2%-2% B. Fractions from 4 to 58 min were manually collected at 1 min intervals. Lyophilized in a vacuum freeze concentrator and dissolved in 10 μL 0.1% FA for LC-MS / MS analysis. 5 μL of peptide was loaded onto a laboratory-made chromatographic column with an inner diameter of 75 μm, a length of 30 cm, and a filler of 3 μm Reprosil-Pur C18. Mobile phase A was 0.1% formic acid / H2O, and mobile phase B was 80% ACN / 0.1% formic acid. The gradient was 0-4 min, 3-6% B; 4-83 min, 6-30% B; 83-87 min, 30%-90%; 87-90 min, 90%-90%; total gradient 90 min. Mass spectrometry data were collected using the Orbitrap Fusion Lumos Tribrid MS in data-dependent scanning mode DDA with the following parameters: spray voltage 2 kV; S-lens RF 30; capillary temperature 300 °C; full scan resolution 60 000 @ m / z 200 and automatic gain control AGC 4e5, maximum ion injection time 30 ms; mass range 350-1500; scan resolution 15000 @ m / z 200; maximum ion injection time 30 ms and AGC 5e4 for secondary scan; starting m / z 110 for secondary scan; HCD fragmentation normalized collision energy NCE 30; MIPS "peptide"; parent ions with charge numbers 2-7 were selected for secondary mass spectrometry acquisition; dynamic exclusion time 40 s; DDA cycle time 3 s. DDA database searches were performed using the MaxQuant software package (V.1.5.6.0), and the FDR for proteins and peptides was set to 1%.For peptide identification, a minimum length of 6 amino acids and a maximum mass of 10 000Da were required; the Andromeda search engine was used for retrieval; the database was the Swiss-Prot human database (V.201502; 20,534 protein sequences) and 262 common contaminating protein sequences; the enzyme specificity was set to C-terminal cleavage of arginine and lysine, with a maximum of 2 missed cleavage sites; carbamidomethylation (C) was set as a fixed modification, and oxidation (M) was set as a variable modification; the "match between run (MBR)" and "second peptide" functions were enabled. The DDA results of MaxQuant were imported into Spectronaut V13 of Biognosys, and for each parent ion, a minimum of 3 and a maximum of 6 fragment ions were selected; the m / z was set to 350-1800.

[0064] The deep coverage plasma spectra constructed by peptide set I and peptide set II were analyzed respectively, which contained information of proteins and their peptides, such as retention time, parent ion mass-to-nuclear ratio, fragment ion mass-to-nuclear ratio, and relative abundance of fragment ions.

[0065] Table 1. Plasma proteome spectral libraries of peptide set I and peptide set II

[0066] sample Number of parent ions Peptides protein Peptide Set I 77,840 52,986 5,106 Peptide Collection II 19,436 15,708 1,325

[0067] The DIA data of peptide set I and peptide set II were extracted by Spectronaut V13 of Biognosys Company. The plasma proteome spectrum library with deep coverage constructed by peptide set I and peptide set II was used respectively. Carbamidomethylation was set as fixed modification, methionine oxidation was set as variable modification, trypsin was used for enzyme cleavage and up to 2 missed cleavage sites were allowed, and the FDR of peptide and protein levels was controlled to 1%. The average of the three peptides with the highest intensity was taken to calculate the protein intensity.

[0068] Data analysis

[0069] 1) Comparison of identification results

[0070] Comparison of the numbers of peptides and proteins identified using the results of peptide set I-optimized DIA (proteome dataset I), the results of peptide set II-optimized DIA (proteome dataset II), and the results of undepleted-optimized DIA (triplicates).

[0071] The identification results are as follows Figure 4A and Figure 4BAs shown, proteomic dataset I identified the most peptides and proteins, with an average of 14,000 peptides and 1,400 proteins identified per replicate. Proteomic dataset II identified 700 peptides and 770 proteins. The Undepleted-optimized DIA results identified 7,700 peptides and 740 proteins.

[0072] like Figure 5A As shown in the figure, proteome dataset I and proteome dataset II basically contain all the proteins in the results of undepleted-optimized DIA, indicating that affinity removal and chemical precipitation removal are highly complementary. Therefore, considering the simplicity of the method and the throughput of the analysis, the results of undepleted-optimized DIA were discarded in the subsequent analysis. Figure 5B As shown, the chemical precipitation method (proteome dataset II) covers many low molecular weight proteins between 1-20 kD, further demonstrating the complementarity of affinity removal and chemical precipitation removal.

[0073] 2) Quantitative reproducibility of high-abundance protein removal and proteome coverage analysis

[0074] The reproducibility of removing high-abundance proteins is an important factor in evaluating the feasibility of the method for analyzing body fluid proteomes of the present application for deep coverage of plasma proteomes. Five technical replicates were performed to examine the reproducibility of the method for analyzing body fluid proteomes of the present application.

[0075] like Figure 6 As shown, 5 technical replicates were performed, and 1,510 proteins were quantified in proteomic dataset I, with a median RSD of 13% for the quantitative protein intensity, indicating that the affinity removal method has good reproducibility. 802 proteins were quantified in proteomic dataset II, with a median RSD of 20% for the quantitative protein intensity, indicating that the chemical precipitation removal method also has good quantitative reproducibility. 992 proteins were uniquely quantified in proteomic dataset I, which can only be attributed to the affinity removal method; 284 proteins were uniquely quantified in proteomic dataset II, which can only be attributed to the chemical precipitation removal method.

[0076] Next, the reproducibility of the 518 proteins quantified in both proteome datasets I and II was compared. The reproducibility of the 518 proteins in the affinity removal method was better than that in the chemical precipitation removal method. Therefore, these 518 proteins were quantified in proteome dataset I.

[0077] Therefore, the dataset of all proteins quantified in proteome dataset I and the dataset of proteins quantified exclusively in proteome dataset II are combined as the final quantitative proteome dataset obtained by the method for analyzing body fluid proteome of the present application. The dataset contains a total of 1,794 proteins, and the median RSD of the protein intensity is 14%, indicating that the method for analyzing body fluid proteome of the present application has good reproducibility.

[0078] 3) Perform coverage analysis on the final quantitative proteomic dataset.

[0079] The 1794 proteins quantified achieved deep coverage of the plasma proteome. These 1794 proteins spanned a dynamic range of 8 orders of magnitude and included many clinically significant proteins: covering 114 of the 222 biomarkers approved by the FDA (FDA-approved biomarkers can be downloaded from http: / / mrmassaydb.proteincentre.com / ) Fig. 7A , Table 2); 16 proteins are brain tissue-specific proteins in the Human Tissue Proteome Atlas (https: / / www.proteinatlas.org / humanproteome / tissue) ( Figure 7B , Table 3); 124 proteins are liver tissue-specific proteins in Human TissueProteome Atlas (https: / / www.proteinatlas.org / humanproteome / tissue) ( Figure 7C , Table 4).

[0080] Table 2 Final quantitative proteomic datasets in body fluids covering FDA-approved biomarkers

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Table 3 Final quantitative proteome datasets in body fluids covering brain tissue-specific proteins in the Human Tissue Proteome Atlas

[0088]

[0089] Table 4 Final quantitative proteome dataset in body fluids covers liver tissue-specific proteins in Human Tissue Proteome Atlas

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[0095] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0096] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0097] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for analyzing a body fluid proteome, comprising: Affinity technology is used to remove high-abundance proteins in the initial sample A to obtain a sample I enriched with low-abundance proteins, wherein: The method of removing the high-abundance protein in the initial sample A by using affinity technology includes: using the antibody of the high-abundance protein as an affinity ligand to remove the high-abundance protein; Chemical precipitation is used to remove high-abundance proteins in the initial sample B to obtain a sample II enriched with low-abundance proteins, wherein: The method of removing the high-abundance proteins in the initial sample B by chemical precipitation includes: using an organic solvent as a precipitant to precipitate the high-abundance proteins; before using the precipitant to precipitate the high-abundance proteins, using a denaturant to denature the high-abundance proteins, wherein the denaturant includes one or more of guanidine hydrochloride and urea; The initial sample A and the initial sample B are from the same body fluid sample of the same subject, wherein the high-abundance proteins consist of albumin, IgA, IgD, IgE, IgG, IgM, α1-acid glycoprotein, α1-antitrypsin, α2-macroglobulin, apolipoprotein A1, fibrinogen, haptoglobin, transferrin, complement C3, apolipoprotein A-II, α-2-HS-glycoprotein, apolipoprotein C-III, α-1-antichymotrypsin, vitamin D binding protein, ceruloplasmin, complement C4-A, complement C1q, hemagglutinin, kininogen-1, synaptotagmin 5, histidine-rich glycoprotein, vitronectin, complement factor H, plasma proteinase C1 inhibitor, C4b binding protein and fibronectin; Performing proteomic analysis on the sample I to be tested using a data-independent acquisition technique to obtain a proteomic data set I; Performing proteomic analysis on the sample II to be tested using a data-independent acquisition technology to obtain a proteomic data set II, wherein the data-independent acquisition technology is an optimized data-independent acquisition technology; the optimized data-independent acquisition technology is completed by nano-liquid chromatography and Orbitrap mass spectrometry, in which a C18 column with a chromatographic column length of 50 cm is used, and a total gradient time is 90 min. In the optimized data-independent acquisition technology, the MS1 resolution is set to 60 K, the MS2 resolution is set to 30 K, and the parent ion scanning range is set to m / z 350-1200 and divided into 50 isolation windows; Based on the proteomic dataset I and the proteomic dataset II, a final quantitative proteomic dataset of the body fluid sample is determined, which includes: Removing the data overlapping between the proteome dataset II and the proteome dataset I from the proteome dataset II to obtain a proteome dataset III; and The proteomic dataset I and the proteomic dataset III are used as the final quantitative proteomic datasets in the body fluid sample.

2. The method according to claim 1, wherein: The body fluid includes one or more of plasma, serum, urine, tissue fluid, pleural fluid, peritoneal fluid, cerebrospinal fluid, semen and vaginal fluid.

3. The method according to claim 1, wherein: The antibody against the high-abundance protein is immobilized on a solid support.

4. The method according to claim 3, wherein: The solid phase carrier includes one or more of cellulose, polyacrylamide, polystyrene, polyethylene, polypropylene, cross-linked dextran, glass, silicone rubber, agarose gel and gel resin.

5. The method according to any one of claims 1 to 2, wherein: The removal of high-abundance proteins in the initial sample A by using affinity technology and the removal of high-abundance proteins in the initial sample B by using chemical precipitation are performed in a multi-chamber container.

6. The method according to claim 1, wherein: The organic solvent includes one or more of methanol, ethanol, isopropanol, acetonitrile, chloroform, trichloroacetic acid, and trifluoroacetic acid.

7. The method according to claim 1, wherein: After reduction, alkylation, enzyme digestion and desalting, the sample I to be tested is subjected to a peptide set I for proteomics analysis using a data-independent acquisition technique.

8. The method according to claim 1, wherein: After desalting, reduction, alkylation and enzyme digestion, the sample II to be tested is subjected to a peptide set II for proteomics analysis using a data-independent acquisition technique.

9. The method according to claim 1, wherein: Removing the data overlapping between the proteome dataset II and the proteome dataset I from the proteome dataset II comprises: comparing the proteome dataset I and the proteome dataset II through a Venn diagram, removing the data overlapping between the proteome dataset II and the proteome dataset I from the proteome dataset II, and obtaining the proteome dataset III.

10. The method according to claim 1, wherein: The subject includes at least one of a human and a non-human mammal.

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