High throughput detection of metabolites based on direct injection high resolution mass spectrometry

Abstract

The application discloses a new method for high-throughput detection of metabolites based on direct injection high-resolution mass spectrometry, and relates to the fields of detection of small molecule metabolites and high-resolution mass spectrometry. The application is based on direct injection high-resolution mass spectrometry technology, and first, a high-coverage and high-quality-accuracy serum metabolite molecular formula database is constructed by using direct injection Fourier transform ion cyclotron resonance ultra-high-resolution mass spectrometry; second, according to the distribution of the number of molecular formulas in different mass-to-charge ratio ranges in the self-built database, a high-throughput metabolome detection method based on direct injection nanoliter spray ionization high-resolution mass spectrometry is established; finally, non-targeted metabolite data obtained by the high-throughput detection method is searched in the self-built database, and high-throughput molecular annotation of metabolites is realized. The application adopts a two-step acquisition strategy, takes a high-reliability and high-coverage metabolome molecular library as a bridge, and takes into account the detection throughput, coverage and reliability. The method has higher reliability, wider metabolome coverage range and is suitable for large-scale metabolome data analysis.

Description

Technical Field

[0001] This relates to the detection of small molecule metabolites and the field of high-resolution mass spectrometry, specifically to a novel high-throughput method for metabolite detection based on direct injection high-resolution mass spectrometry. Background Technology

[0002] Metabolomics studies small molecule metabolites (molecular weight less than 1500) in organisms, utilizing various analytical techniques such as mass spectrometry (MS), nuclear magnetic resonance (NMR), and chromatography-mass spectrometry to investigate the composition of intracellular metabolites and their physiological and pathological changes at the holistic level. Given the substantial differences in individual phenotypes, large-scale metabolomics research is becoming increasingly common, particularly in molecular epidemiology, precision medicine, and genome-wide metabolomics association studies, where it has become a crucial supporting technology. It plays a vital role in understanding the physiological mechanisms of complex diseases (pathologous diseases) and in their diagnosis, prevention, and treatment. Developing high-throughput metabolomics analysis methods is essential for large-scale metabolomics and has become a primary task in large-scale sample-based metabolomics research.

[0003] With the development of mass spectrometry (MS), MS-based analytical techniques have gradually become an indispensable tool in metabolomics research. Compared to NMR, MS offers higher detection sensitivity and broader metabolomics coverage. The analytical throughput of traditional chromatography-mass spectrometry (GC-MS) is limited by chromatographic separation time, and prolonged operation can cause retention time drift and poor repeatability, restricting its application in large-scale metabolomics studies. Direct injection high-resolution mass spectrometry (DI-HRMS) eliminates chromatographic pre-separation, significantly reducing analysis time and organic reagent consumption, making it suitable for high-throughput metabolomics analysis. Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS), as an ultra-high-resolution mass spectrometry method, can achieve ultra-high resolution and higher mass accuracy, providing more reliable results for metabolite molecular annotation. DI-nESI-HRMS uses nanoliter flow rates, has low matrix effects, and high sensitivity, facilitating rapid metabolite detection. Although both methods have been reported for high-throughput metabolite detection, achieving high-throughput acquisition comes at the cost of significantly reduced detection sensitivity, metabolite coverage, and accuracy. Therefore, there is a need to develop a metabolomics mass spectrometry analysis method that takes into account throughput, coverage, and reliability. Summary of the Invention

[0004] This invention provides a novel high-throughput method for detecting metabolites based on direct injection high-resolution mass spectrometry. To achieve the objectives of this invention, the technical solution adopted is as follows:

[0005] High-throughput metabolite detection method based on direct injection high-resolution mass spectrometry:

[0006] 1) A serum metabolite database was constructed by detecting a mixture of more than 10 serum samples to be tested using direct injection Fourier transform ion cyclotron resonance ultra-high resolution mass spectrometry (DI-FTICR MS), including the accurate mass-to-nucleus ratio of the metabolites and their corresponding molecular formulas.

[0007] 2) The MS1 information of metabolites of more than 10 serum samples to be tested was detected by the non-targeting method of direct injection nano-spray ionization high-resolution mass spectrometry (DI-nESI MS); the corresponding molecular formulas were obtained by searching the serum metabolite database, and finally high-throughput analysis of metabolites was achieved.

[0008] According to 1), the number of molecular formulas in the database is divided into segments based on the mass-to-nucleus ratio, and each segment contains between 100 and 500 molecular formulas.

[0009] Specifically:

[0010] 1) Solid-phase microextraction (SPE) was used to pretreat the mixed serum samples, and methanol precipitation was used to pretreat the serum samples to be tested and extract metabolites.

[0011] 2) DI-FTICR MS combined with segmented acquisition method was used to analyze the mixed serum samples after SPE treatment and to construct a serum metabolite database;

[0012] 3) Establish a high-throughput metabolomics analysis method based on DI-nESI Orbitrap MS: Based on the quantity distribution of molecular formulas in different mass ranges in the above database, establish a segmented collection method for the detection of metabolite MS1, which is used for high-throughput analysis of the metabolomics of the serum to be tested after methanol precipitation protein method; and use MS1 to search the above self-built molecular formula database for molecular formula annotation (mass matching window ±5ppm).

[0013] The specific steps for treating the mixed serum sample using SPE in step 1) are as follows: (1) Sample acidification: Take 50-60 uL of mixed serum sample, add 180-220 uL of acetonitrile, vortex for 1-2 min, centrifuge at 1300-14000 rpm / min for 10-15 min, then take 200-220 uL of supernatant and freeze dry it, using 180-220 uL of water and 10-15 uL of 25% formic acid (V 甲酸 / V 水(1 / 3) Redissolve and vortex for 1-2 min to obtain acidified sample; (2) SPE activation (OMIX C18 pipette tip, product number: A57003100): sequentially use 80-120 uL methanol, 80-120 uL formic acid aqueous solution (V 甲酸 / V 水 : 1 / 839) Repeatedly aspirate and drain the SPE C18 tips 10-20 times; (3) times; (3) Sample loading: Use the activated C18 tips to repeatedly aspirate and drain the acidified sample 10-20 times. After the metabolites are adsorbed onto the SPE column, in order to eliminate the interfering substances adsorbed by the SPE, use 80-120uL of formic acid aqueous solution (V 甲酸 / V 水 (1 / 839) Rinse the SPE column 1-3 times with 80-120 μL of 50% methanol / water solution. 甲醇 / V 水 : 1 / 1) Rinse the SPE column 10-20 times with 100 μL of pure methanol to remove metabolites. Combine the two eluents (i.e., 50% methanol and pure methanol wash); use a methanol / water solution with a volume concentration of 0.1% formic acid (V... 甲酸 / V 甲醇 / V 水 Dilute 2 / 99 / 99 to 80 times and wait for DI-FTICRMS analysis.

[0014] The specific steps of the DI-FTICR MS analysis in step 2) are as follows: The mass spectrometer is a SolariX XR-15TFTICR MS (Bruker), and the acquisition conditions are: the acquisition range is m / z 70-800, with m / z 30 as the window and m / z 10 as the overlap window for segmented acquisition, and 400 acquisitions are accumulated for each isolation segment. Finally, a metabolite molecular formula database based on mixed serum samples with high coverage and good quality accuracy is obtained by screening relevant thresholds such as elemental composition, mass deviation and isotope distribution.

[0015] In step 2), DI-FTICR MS combined with segmented acquisition method is used to analyze the mixed serum sample after SPE treatment to obtain information such as the accurate mass-to-nucleus ratio, signal intensity and isotope distribution of metabolites. The isotope distribution information here refers to the mass-to-nucleus ratio and response intensity of the isotope corresponding to the single isotope peak of the metabolite.

[0016] A serum metabolite database was constructed, and the reliability of metabolite molecular formula annotation in the database was divided into three levels based on quality accuracy, signal intensity, and isotope distribution scores.

[0017] The reliability levels of self-built databases are mainly classified according to the following rules: Level 1: S / N≥20, quality accuracy within ±0.2ppm and isotope score (Msigma value)≤200; Level 2: S / N≥20, quality accuracy within ±0.2ppm, Msigma>200; Level 3: 10≤S / N<20, quality accuracy within ±0.4ppm.

[0018] The specific steps for serum sample preparation in step 3) are as follows: Serum samples are treated using methanol precipitation. Take 50-60 μL of serum sample, add 180-220 μL of pure methanol, vortex for 1-2 min, centrifuge at 1300-14000 rpm / min for 10-15 min, and then freeze-dry 200-220 μL of the supernatant. Finally, use a methanol / water solution with a volume concentration of 0.1% formic acid (V... 甲酸 / V 甲醇 / V 水 (2 / 99 / 99) Reconstituted and diluted 20 times, to be analyzed by DI-nESI MS.

[0019] The specific steps of the DI-nESI MS analysis in step 3) are as follows, using a Q Exactive mass spectrometer. TM HF MS (Thermo Fisher) The ionization device is based on chip-based multi-channel nanoliter electrospray ionization technology (Advion Inc). It features a single-stage segmented scanning window with an overlap window of m / z 10, a resolution of 240,000, a maximum injection time of 200 ms, a mass spectrometry acquisition time of 0.5-0.7 min, and three repeated acquisitions for each sample.

[0020] This invention is based on direct-injection high-resolution mass spectrometry (DIS). First, it utilizes DIS Fourier transform ion cyclotron resonance (TCR) ultra-high-resolution mass spectrometry to construct a high-coverage, high-quality, and accurate serum metabolite molecular formula database. Second, based on the information from this database, it guides the development of a high-throughput metabolomics detection method based on DIS nanoliter spray ionization (SPEI) high-resolution mass spectrometry. Finally, by searching a self-built database of non-targeted metabolite data obtained from the high-throughput detection method, it achieves high-throughput molecular annotation of metabolites. This invention, through a two-step acquisition strategy, uses a highly reliable and comprehensive metabolomics molecular library as a bridge, balancing detection throughput, coverage, and reliability. The method of this invention has higher reliability and a wider metabolomics coverage, making it suitable for large-scale metabolomics data analysis. Attached Figure Description

[0021] Figure 1 A spectral stitching and acquisition method based on DI-nESI Orbitrap MS was established based on the molecular formula number distribution in a self-built database.

[0022] Figure 2 In the mixed standard detection based on the DI-nESI Orbitrap MS method, the number of unique molecular formulas obtained by searching the self-built database and the online database is compared. Detailed Implementation

[0023] The following detailed description of the implementation of the present invention is provided in conjunction with the accompanying drawings: This embodiment is implemented based on the technical solution of the present invention, and provides detailed implementation methods and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0024] Example

[0025] A high-throughput metabolite detection method based on direct injection high-resolution mass spectrometry: A high-coverage, high-quality, and high-accuracy serum metabolite database was constructed using DI-FTICR MS; based on the information in the constructed database, a high-throughput metabolomics detection method based on DI-nESI MS was established; and high-throughput analysis of metabolites was achieved by searching the self-built database of non-targeted metabolite data from serum extracts.

[0026] 1. Sample processing

[0027] 1.1 Preparation and Detection of Mixed Standards

[0028] The standards were obtained from Sigma's Mass Spectrometry Metabolite Library (MSMLS). Each standard was placed in a 96-well plate and included 466 metabolites, primarily consisting of amino acids such as phenylalanine, carboxylic acids such as maleic acid, and lipids such as LA-decanoylphosphatidylcholine. For polar metabolites, standards were prepared using a 95% (v / v) methanol / water solution. 甲醇 / V 水 Dissolve 19 / 1) for lipid metabolite standards using a 50% methanol / chloroform (V / v) solution. 甲醇 / V 氯仿 Dissolve 1 / 1) of the standard. Then mix all the single standards in equal volumes, resulting in a final mixed standard with a concentration of approximately 0.2 μg / mL for each standard. Use a methanol / water solution with a volume concentration of 0.1% formic acid (V / mL). 甲酸 / V 甲醇 / V 水 Dilute 2 / 99 / 99) 10 times and wait for DI-nESI MS detection.

[0029] 1.2 Serum Sample Processing

[0030] Blood samples from 69 adults (including 38 healthy individuals and 31 patients with type 2 diabetes) were collected, allowed to stand for 30 minutes, centrifuged at 4500 rpm / min and 4°C for 15 minutes, and then the serum was collected and stored at -80°C for later use. Mixed serum samples were prepared by mixing equal volumes of the above serum samples. All samples were thawed in ice water during pretreatment.

[0031] The specific steps for processing mixed serum samples using SPE are as follows: (1) Sample acidification: Take 50uL of mixed serum sample, add 200uL of acetonitrile, vortex for 1min, centrifuge at 14000rpm / min for 10min, then take 220uL of supernatant and freeze dry, using 100uL of water and 10uL of 25% (V) solution. 甲酸 / V 水 (2) SPE activation: SPE C18 tips (OMIX C18 pipette tips, catalog number: A57003100) were repeatedly aspirated and drained 100uL of methanol and 100uL of formic acid aqueous solution (V formic acid / V water: 1 / 839) 10 times; (3) Sample loading: The acidified sample was repeatedly aspirated and drained 10 times using the activated C18 tips to allow the metabolites to be analyzed to be adsorbed onto the SPE column. In order to eliminate the interfering substances adsorbed by SPE, 100uL of formic acid aqueous solution (V formic acid / V water: 1 / 839) was used to re-dissolve and vortex for 1 min; 甲酸 / V 水 (1 / 839) Rinse the SPE column once with 100 μL of 50% methanol / water (V) solution. 甲醇 / V 水 : 1 / 1) Rinse the SPE column 10 times with 100 μL of pure methanol to remove the eluent metabolites. Combine the two eluents (i.e., 50% methanol wash and pure methanol wash); use a methanol / water solution with a volume concentration of 0.1% formic acid (V... 甲酸 / V 甲醇 / V 水 Dilute 2 / 99 / 99 to 80-fold and wait for DI-FTICRMS analysis.

[0032] Sixty-nine serum samples were treated using methanol precipitation. 50 μL of serum sample was taken, 200 μL of methanol was added, vortexed for 1 min, centrifuged at 14000 rpm for 10 min, and 220 μL of the supernatant was collected and freeze-dried. Finally, the protein was separated into 0.1% formic acid in a methanol / water solution (V...). 甲酸 / V 甲醇 / V 水 Reconstitute with 2 / 99 / 99 solution and dilute 20 times, then analyze with DI-nESI MS.

[0033] 2. Analytical Methods

[0034] 2.1 DI-FTICR MS Analysis

[0035] The mass spectrometer used was a SolariX XR-15T DI-FTICR MS (Bruker Daltonics). Before sample acquisition, the instrument underwent external calibration using a 0.1 mg / ml sodium formate acetonitrile solution. After calibration, actual samples were acquired: the acquisition range was m / z 70-800, with segmented acquisition using m / z 30 as the isolation window and m / z 10 as the overlap window. Optimized time-of-flight and resolution parameters were used, with 400 acquisitions accumulated per isolation segment. The obtained mass spectrometry data were used to generate peak tables using the instrument's built-in Data Analysis software, and internal standard calibration was performed using known compounds in the sample and solvent, based on prior knowledge. Then, the SmartFormula function was used to generate the corresponding molecular formula based on the mass-to-nucleus ratio (the addition ion type mainly considered [M+H)). + [M+Na] + [M+K] + Results with only one candidate molecular formula were retained. The reliability of the obtained molecular formulas was classified into levels based on intensity, isotope distribution, and mass deviation, mainly according to the following rules: Level 1: S / N ≥ 20, mass accuracy within ±0.2 ppm and isotope score (Msigma value) ≤ 200; Level 2: S / N ≥ 20, mass accuracy within ±0.2 ppm, Msigma > 200; Level 3: 10 ≤ S / N < 20, mass accuracy within ±0.4 ppm.

[0036] Ultimately, through screening based on thresholds such as elemental composition, mass deviation, and isotopic distribution, a total of 3734 unique molecular formulas were obtained. Their distribution across different mass-to-nucleus ratio ranges is shown below. Figure 1 As shown in the black bar chart, after ion fusion (retaining only the additive ion form with the highest confidence level), 3333 molecular formulas were obtained. The specific confidence level distribution and the corresponding number of molecular formulas are as follows: Level 1: 767, Level 2: 1549, Level 3: 1017.

[0037] 2.2 DI-nESI MS Analysis

[0038] The specific steps for analysis using DI-nESI MS are as follows, with the mass spectrometer being a Q Exactive. TMHF MS (thermoelectric) ionization devices utilize chip-based multichannel nanoliter electrospray ionization technology. The mass spectrometry data below was acquired from serum samples using a spectral stitching method, segmented primarily based on the distribution of molecular formula numbers within different mass-to-nucleus ratio ranges in a self-built database. Specifically... Figure 1 As shown, the black bars represent the distribution of molecular formulas in the database across different mass-to-nucleus ratios. It can be seen that the number of molecular formulas is relatively small in the low-mass and high-mass ranges, resulting in a wider segmentation range in DI-nESI MS acquisition. Figure 1 As shown by the red line segment. In the range of mass-to-nucleus ratio of 370-470, there are a large number of molecular formulas in the database, so the segmentation range is narrow. Based on this strategy, the final spectrum splicing acquisition method based on DI-nESI Orbitrap MS is used, which includes 12 segments, each containing 100-500 molecular formulas. The specific ranges of the mass-to-nucleus ratio and the corresponding number of molecular formulas in the database are as follows: m / z 70-230: 390, m / z 230-310: 413, m / z 310-350: 304, m / z 350-390: 307, m / z 390-430: 349, m / z 430-470: 335, m / z 470-510: 184, m / z 510-567: 296, m / z 567-604: 299, m / z 604-650: 341, m / z 650-710: 328, m / z 710-800: 188. The overlapping window at both ends was m / z 5 (i.e., each low-mass segment extends m / z 5 from the intersection in its respective direction), with a resolution of 240,000, a maximum injection time of 200 ms, and a mass spectrometry acquisition time of 0.6 min. Each sample was tested three times, and subsequent experimental processing only retained mass-to-nucleus ratio features appearing two or more times. The mass spectrometry data was primarily processed using a self-developed Python program for peak table export, 3x signal-to-noise ratio filtering, blank removal, and peak alignment (mass range 5 ppm). The final molecular formula annotation relied on a self-developed program to retrieve data from a self-built database, with a mass deviation range of ±5 ppm.

[0039] 3. Results

[0040] 1) High throughput

[0041] To demonstrate that this method can improve the throughput of metabolite detection, it is compared with a literature method (reference: Thompson CJ, Witt M, Forcisi S, et al. Journal of the American Society for Mass Spectrometry, 2020, 31(10): 2025-2034.). In the literature, to improve the coverage of metabolite detection, a segmented acquisition method is used for a single biological sample, with each segment taking about 4.5 min, for a total of 14 segments, and a total time of about 63 min. When detecting 69 serum samples, even ignoring the time for cleaning the ion source between injections, the detection time is still about 4347 min. In contrast, in the method of this invention, for the mixed sample to be tested, a self-built database can be established in about 600 min by detecting the mixed sample at one time. Subsequent single biological samples are introduced using an automated nESI injection method, which does not require cleaning the ion source and only takes about 0.6 min. When detecting 69 samples, including three repeated injections, only 724.2 min is required, indicating that this method has high throughput characteristics.

[0042] 2) High coverage

[0043] To demonstrate the necessity of the self-built database, the DI-nESI MS segmented acquisition method established by analysis method 2 (i.e., based on the distribution of the number of molecular formulas in the self-built database within different mass-to-nucleus ratio ranges) was first used to detect the mixed standard solution prepared in step 1.1, as described in section 2.2. Then, molecular formula annotation (mass deviation ±5 ppm) was performed by searching the self-built database (as shown in section 2.1) and the online database HMDB (Human Metabolomics Database) using the mass-to-nucleus ratio (both of which contain metabolites from the mixed standard). Finally, the number of unique molecular formulas obtained from the two databases is as follows: Figure 2 The bar chart shows that the filled diagonal lines represent the number of molecular formulas in the mixed table, the colorless filler represents the number of isomers, and the sum of the two represents the number of metabolites in the mixed standard. The chart shows that the number of unique molecular formulas obtained from the self-built database is 18% higher than that from the HMDB database. Using the same 0.6-minute detection method, the number of unique molecular formulas matched significantly increased when this strategy was adopted, demonstrating the superiority of this method in terms of detection coverage.

[0044] In summary, the established direct-injection high-resolution mass spectrometry technique, through a two-step acquisition strategy and using a highly reliable and comprehensive metabolomics library as a bridge, balances detection throughput, coverage, and reliability, making it suitable for high-throughput metabolomics research.

Claims

1. A high-throughput method for detecting metabolites based on direct injection high-resolution mass spectrometry, characterized by: 1) Constructing a serum metabolite database by detecting a mixed sample of more than 10 serum samples to be detected using direct injection Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS), including the accurate mass-to-charge ratio of metabolites and the corresponding molecular formula; 2) Detecting the MS1 information of metabolites in each of the more than 10 serum samples to be detected using a non-target method of direct injection nanoelectrospray ionization high-resolution mass spectrometry (DI-nESI MS); searching the serum metabolite database to obtain the corresponding molecular formula, and finally realizing high-throughput analysis of metabolites.

2. The method of claim 1, characterized by: According to step 1), the number of molecular formulas in the database is segmented according to the mass-to-charge ratio, and each segment contains between 100 and 500 molecular formulas. In step 2), the MS1 information of metabolites is collected in segments.

3. The method of claim 1 or 2, characterized by: 1) Using solid-phase microextraction (SPE) to pretreat the mixed serum sample, using methanol to precipitate proteins for pretreatment of the serum sample to be detected, and extracting metabolites; 2) Using DI-FTICR MS combined with segmented collection to analyze the mixed serum sample after SPE treatment to construct a serum metabolite database; 3) Establishing a high-throughput metabolomics analysis method based on DI-nESI Orbitrap MS: according to the number distribution of molecular formulas in the database at different mass-to-charge ratios, a method for collecting metabolite MS1 is established for high-throughput analysis of the serum metabolome after treatment with the methanol protein precipitation method; and using MS1 to search the serum metabolite database for molecular formula annotation, with a mass matching window of ±5 ppm during molecular formula annotation.

4. The method of claim 3, characterized by: The specific steps for processing the mixed serum sample in step 1) are as follows, (1) acidification of the sample: take 50-60 uL of the mixed serum sample, add 180-220 uL of acetonitrile, vortex for 1-2 min, then centrifuge at 1300-14000 rpm / min for 10-15 min, then take 200-220 uL of supernatant, freeze-dry, reconstitute with 80-120 uL of water and 10-15 uL of formic acid aqueous solution with a volume concentration of 25%, and vortex for 1-2 min to obtain the acidified sample; (2) SPE C18 pipette tip activation: sequentially use 80-120 uL of methanol, 80-120 uL of V 甲酸 / V 水 =1 / 839 formic acid aqueous solution to repeatedly draw and discharge to rinse the SPE C18 tips 10-20 times; (3) sample loading: use the activated C18 tips to repeatedly draw and discharge to rinse the acidified sample 10-20 times, and after the analyte metabolites are adsorbed on the SPE column, in order to remove the interfering substances adsorbed by the SPE, use 80-120 uL of V 甲酸 / V 水 =1 / 839 formic acid aqueous solution to draw and discharge to rinse the SPE column 1-3 times, and finally use 80-120 uL of methanol aqueous solution with a volume concentration of 50% to draw and discharge to rinse the SPE column 10-20 times, 100 uL of pure methanol to repeatedly draw and discharge to rinse the SPE column 10-20 times to elute the metabolites, and finally combine the two eluents, i.e., the 50% methanol eluent and the pure methanol eluent; dilute with formic acid-containing methanol aqueous solution to 80 times, the formic acid-containing methanol aqueous solution has V 甲酸 / V 甲醇 / V 水 : 2 / 99 / 99, and is ready for DI-FTICR MS analysis.

5. The method of claim 3, wherein, The specific steps for analyzing DI-FTICR MS in step 2) are as follows: the mass spectrometer is SolariX XR-15T FTICR MS, and the acquisition conditions are: the acquisition range is m / z 70-800, the window is m / z 30, and the overlap window is m / z 10 for segmented collection, with 400 accumulations per isolation segment. Finally, a metabolite molecular formula database based on mixed serum samples with high coverage and good mass accuracy is obtained through element composition, mass deviation, and isotope distribution screening.

6. The method of claim 3, characterized by: In step 2), DI-FTICR MS combined with segmented collection is used to analyze the mixed serum sample after SPE treatment to obtain the accurate mass-to-charge ratio, signal intensity, and isotope distribution information of metabolites. The isotope distribution information here refers to the isotope mass-to-charge ratio and response intensity corresponding to the single isotope peak of the metabolite; Constructing a serum metabolite database, according to the mass accuracy, signal intensity, and isotope distribution score, the reliability of metabolite molecular formula annotation in this database is divided into three levels; The self-built database confidence level classification is based on the following rules, Level 1: S / N ≥ 20, mass accuracy is within ±0.2 ppm range and isotope score, i.e. Msigma value ≤ 200; Level 2: S / N ≥ 20, mass accuracy is within ±0.2 ppm range, Msigma > 200; Level 3: 10 ≤ S / N < 20, mass accuracy is within ±0.4 ppm range.

7. The method of claim 3, wherein, The specific steps of the serum sample in step 3) are as follows: the serum sample is treated by the method of precipitating protein with methanol, 50-60 uL of the serum sample is taken, 180-220 uL of pure methanol is added, vortexed for 1-2 min, centrifuged at 1300-14000 rpm / min for 10-15 min, and then 200-220 uL of supernatant is taken and freeze-dried, finally diluted 20 times with a methanol aqueous solution containing formic acid, and the methanol aqueous solution containing formic acid is V 甲酸 / V 甲醇 / V 水 =2 / 99 / 99, and is ready for DI-nESI MS analysis.

8. The method of claim 3, wherein, The specific steps of using DI-nESI MS analysis in step 3) are as follows, the mass spectrometer is Q Exactive™ HF MS, and the ionization device is a multi-channel nanoelectrospray ionization technology based on a chip; one-stage splicing type segment scanning window, the overlapping window is m / z 10, the resolution is 240,000, the maximum injection time is 200 ms, the mass spectrum acquisition time is 0.5-0.7 min, and each sample is repeatedly collected for 3 times.

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