A DP-tDDA-based untargeted metabolomics analysis method

Abstract

A non-targeted metabolomics analysis method based on DP-tDDA, using full scan data acquisition mode to collect precursor ion MS of sample metabolites 1 Data collected by MS 1 The samples of the data were subjected to one-dimensional statistical analysis to obtain the differential ion list between the two groups; the samples collected on MS 1 The differential metabolites in the data are matched against a database to obtain a pre-identified ion list; the pre-identified ion list and the differential ion list are intersected to obtain a pre-identified differential metabolite ion list (DP-list); a new mass spectrometry method in DDA data acquisition mode is created in the mass spectrometry method editor, and the DP-list is imported into the MS / MS inclusion list; the MS / MS of the DP-list is acquired; and the differential metabolites in the DP-list are identified by MS / MS. This method can significantly improve the MS / MS coverage of differential metabolites.

Description

Technical field:

[0001] The present invention belongs to the field of chemical analysis and relates to a metabolomics analysis method, in particular to a non-targeted metabolomics analysis method based on DP-tDDA. Background technology:

[0002] The goal of non-targeted metabolomics is to comprehensively identify and relatively quantify as many metabolites as possible in biological samples. Given the complexity of metabolites, the overall analysis of metabolites is a serious challenge. In recent years, liquid chromatography-mass spectrometry (LC-MS) has been widely used in non-targeted metabolomics due to its high throughput, high resolution, high sensitivity and high stability. However, relying solely on MS 1 It is often not enough to characterize metabolites with high-quality MS / MS spectra, which can greatly improve the reliability of metabolite identification. Therefore, obtaining higher-quality MS / MS spectra is crucial for the identification of differential metabolites.

[0003] Currently, data dependent acquisition (DDA) and data independent acquisition (DIA) are widely used for MS / MS acquisition in non-targeted metabolomics. However, when dealing with complex biological samples, the limited MS / MS spectrum coverage of DDA will result in a large number of metabolic features not having their MS / MS spectra acquired. Although DIA has a higher MS / MS coverage, the quality of the MS / MS spectra acquired is far inferior to that of DDA. Some studies have suggested that the MS / MS spectra acquired by time-interleaved pre-identification can be 1 Targeted DDA with time-staggered precursor ion lists (tsDDA) can significantly improve the coverage and quality of metabolite MS / MS by acquiring targeted MS / MS spectra (Doi:10.1016 / j.aca.2017.08.044). However, metabolomics studies focus on differentially expressed metabolites, and tsDDA has not significantly improved MS / MS coverage of these differentially expressed metabolites. Therefore, developing new methods with higher MS / MS coverage and quality is crucial for metabolite identification.

[0004] DP-tDDA (target-directed data dependent acquisition with differentialions and pre-identified ions) refers to a targeted data-dependent acquisition mode for differential and pre-identified ions. DP-tDDA is an improved version of tsDDA. While tsDDA requires all pre-identified ions to be divided into three lists and imported into three newly created DDA acquisition methods, three repeated acquisitions are performed on the same sample. The improved DP-tDDA performs a single MS / MS data acquisition on the metabolites in the pre-identified differential metabolite ion list, significantly shortening sample acquisition time and improving MS / MS coverage and data quality. Summary of the invention:

[0005] In response to the above technical problems in the prior art, the present invention provides a non-targeted metabolomics analysis method based on DP-tDDA, which aims to solve the technical problem in the prior art that the MS / MS coverage of differential metabolites has not been significantly improved.

[0006] The present invention provides a non-targeted metabolomics analysis method based on DP-tDDA, comprising the following steps:

[0007] S1. Use full scan data acquisition mode to acquire MS of all metabolites in the sample 1 ;

[0008] S2, based on MS in S1 1 Data results: Single-dimensional statistical analysis of the samples was performed to obtain a list of differential ions between the two groups;

[0009] S3, all metabolites obtained in S1 were MS 1 The data were matched against the database to obtain a list of pre-identified ions;

[0010] S4, taking the intersection of the differential ion list obtained in S2 and the pre-identified ion list obtained in S3 to obtain a pre-identified differential metabolite ion list DP-list;

[0011] S5. Open the mass spectrometry method editor and create a new mass spectrometry method with DDA data acquisition mode.

[0012] The MS / MS inclusion list into which the DP-list is imported;

[0013] S6, collecting MS / MS of the DP-list using the mass spectrometry method of S5;

[0014] S7. Perform MS / MS identification on the differential metabolites in the DP-list in S6.

[0015] Furthermore, step S1 is specifically as follows:

[0016] 1) Prepare the sample to be tested: Place an appropriate amount of biological sample in a container and add 2-4 times the volume of organic solvent. Vortex for 20-40 seconds. Ultrasonicate in an ice-water bath for 8-15 minutes to extract metabolites. Centrifuge at 12,000-18,000 rpm and collect the supernatant as the sample to be tested.

[0017] 2) Preparation of quality control samples: All samples to be tested for metabolomics analysis were accurately pipetted into the same container with an amount of 10-50 μL. Vortex for 2 minutes to mix thoroughly. This is the quality control sample.

[0018] 3) Use LC-MS to perform full scan data acquisition on the test samples and quality control samples to obtain MS 1 Dataset.

[0019] Furthermore, step S2 is specifically as follows:

[0020] 1) All the samples to be tested and the quality control samples involved in metabolomics analysis were MS 1 The data set was subjected to peak alignment and peak extraction, and a characteristic ion quantification table containing retention time, m / z, and peak area was exported;

[0021] 2) The characteristic ion quantification table was corrected and normalized using StatTarget, an R package that provides omics data correction (Doi: 10.1016 / j.aca.2018.08.002).

[0022] 3) The corrected and normalized characteristic ion quantification table was imported into the MetaboAnalyst platform, where differential ions between groups were screened with an FDR < 0.05 standard to obtain a differential ion list including retention time and m / z. MetaboAnalyst (https: / / www.metaboanalyst.ca / ) is a comprehensive platform for metabolomics data analysis.

[0023] Furthermore, step S3 is specifically as follows:

[0024] 1) All the samples to be tested and the quality control samples involved in metabolomics analysis were MS 1 The datasets were subjected to peak alignment and peak extraction;

[0025] 2) All extracted mass spectrometry peaks were searched and matched with the Human Metabolome Database and LIPID MAPS database to obtain a list of pre-identified metabolite ions including retention time and m / z.

[0026] Furthermore, step S4 is specifically as follows:

[0027] 1) Summarize the differential ion list obtained in step S2 and the identified metabolite ion list obtained in step S3;

[0028] 2) The intersection of the pre-identified metabolite ion list and the differential ion list is obtained by Excel software. The intersection is the pre-identified differential metabolite ion list DP-list containing retention time and m / z.

[0029] Furthermore, step S5 is specifically as follows:

[0030] 1) Open the mass spectrometry method editor, create a new mass spectrometry method, and set the data acquisition mode to DDA. The parameters shared by the full scan data acquisition mode and DDA are consistent with those of the full scan data acquisition method in S1;

[0031] 2) Import the DP-list obtained in step S4 into the MS / MS inclusion list in DDA, and set it to only acquire MS / MS of ions in the MS / MS inclusion list.

[0032] Furthermore, step S6 is specifically as follows:

[0033] 1) Set up the injection sequence for non-targeted metabolomics test samples and quality control samples;

[0034] 2) Use step S5 to acquire MS / MS of the DP-list of the test sample and the quality control sample.

[0035] Furthermore, step S7 is specifically as follows:

[0036] 1) The differential ions obtained in full scan data acquisition mode were analyzed according to the MS 1 The information was first pre-identified and then matched with the differential ion MS / MS information obtained by DP-tDDA for the identification of differential metabolites in metabolomics.

[0037] Furthermore, the present invention is comprehensively compared with the existing methods, as follows:

[0038] 1) Number of characteristic ions: DP-tDDA, conventional DDA and DIA were used to collect data for the test samples and quality control samples, and the MS 1The data sets were peak aligned and extracted, and the number of characteristic ions obtained by the three different analysis methods was compared. The number of characteristic ions obtained by DP-tDDA was significantly higher than that obtained by conventional DDA and DIA.

[0039] 2) Stability: Quality control samples were injected six times using DP-tDDA, conventional DDA, and DIA. The RSD values of the characteristic ion peak areas obtained by the three different analytical methods were compared. The stability of the characteristic ions obtained by DP-tDDA was significantly higher than that of conventional DDA and DIA.

[0040] 3) Pre-identification of metabolite ion quantity: DP-tDDA, conventional DDA and DIA were used to collect data for the test samples and quality control samples, and the MS 1 The data set was subjected to peak alignment, peak extraction, and peak identification. The number of pre-identified metabolite ions obtained by the three different analytical methods was compared. The number of pre-identified metabolite ions obtained by DP-tDDA was significantly higher than that obtained by conventional DDA and DIA.

[0041] 4) Differential ion number: DP-tDDA, conventional DDA and DIA were used to collect data for the test samples and quality control samples, and the MS 1 The dataset was peak aligned and extracted, and a characteristic ion quantification table was derived. After data correction and normalization using StatTarget, the data were imported into the MetaboAnalyst platform. Differential ions between groups were screened using an FDR < 0.05 standard. The number of differential ions obtained by different analytical methods was compared, and the number of differential ions obtained by DP-tDDA was significantly higher than that by conventional DDA and DIA.

[0042] 5) Number of pre-identified differential metabolites: The pre-identified differential metabolites were obtained by taking the intersection of the pre-identified metabolite ions and differential ions of the three analytical methods. The number of differential metabolites obtained by different analytical methods was compared. The number of differential metabolites obtained by DP-tDDA was significantly higher than that obtained by conventional DDA and DIA.

[0043] 6) MS / MS coverage of differential metabolites: DP-tDDA, conventional DDA, and DIA were used to acquire data for test and quality control samples, and peak alignment and extraction were performed on the MS / MS data sets. The number of differential metabolites obtained by the different analysis methods was compared. The MS / MS coverage of differential metabolites obtained by DP-tDDA was significantly higher than that of conventional DDA and DIA.

[0044] The present invention has the following advantages over the prior art:

[0045] 1. The full scan mode adopted by the present invention acquires MS 1The dataset can effectively increase the MS of characteristic ions 1 It reduces scanning time, improves the analysis and detection of co-eluting substances, broadens the coverage of metabolites and increases the stability of the instrument.

[0046] 2. The targeted DDA strategy of differential ions and pre-identification ions adopted in the present invention can increase the MS / MS coverage of differential metabolites by targeting the MS / MS acquisition of differential metabolite ions, thereby improving the reliability of differential metabolite identification.

[0047] Compared with the existing technology, the technical effect of the present invention is positive and obvious. The analysis method of the present invention ensures that all metabolites have sufficient MS 1 Scan time improves the detection of co-eluting ions, can effectively expand the detection coverage of metabolite ions, and through targeted MS / MS acquisition of pre-identified differential metabolites, ensures that all pre-identified metabolite ions have sufficient MS / MS scanning time, thereby improving the MS / MS coverage of differential metabolites. Compared with conventional data acquisition methods, this method has better stability, higher characteristic ion coverage, higher differential metabolite MS / MS coverage and high-quality MS / MS spectra.

[0048] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. Description of the drawings:

[0049] Figure 1 Figure 2 is a workflow diagram of the DP-tDDA non-targeted metabolomics analysis method.

[0050] Figure 2 This is a comparison of the number of characteristic ions obtained by the DP-tDDA non-targeted metabolomics analysis method in this example, as well as DDA and DIA.

[0051] Figure 3 This is a comparison of the stability of characteristic ions obtained by the DP-tDDA non-targeted metabolomics analysis method in this example with those obtained by DDA and DIA.

[0052] Figure 4 This is a comparison of the number of pre-identified metabolite ions obtained by the DP-tDDA non-targeted metabolomics analysis method in this example, as well as DDA and DIA.

[0053] Figure 5 This is a comparison of the number of characteristic ions obtained by the DP-tDDA non-targeted metabolomics analysis method in this example, as well as DDA and DIA.

[0054] Figure 6 This example compares the number of differential metabolites obtained based on the DP-tDDA non-targeted metabolomics analysis method and DDA and DIA.

[0055] Figure 7 This example compares the number of metabolites with MS / MS differences obtained based on the DP-tDDA non-targeted metabolomics analysis method and DDA and DIA. Specific implementation method:

[0056] Embodiment 1:

[0057] A non-targeted metabolomics analysis method based on DP-tDDA.

[0058] See Figure 1 , which is a workflow diagram of the DP-tDDA-based non-targeted metabolomics analysis method. First, the full scan data acquisition mode (Doi: 10.1021 / acs.analchem.9b05135) was used to acquire the MS of all metabolites in the non-targeted metabolomics sample. 1 Then, the non-targeted metabolomics data were subjected to unidimensional statistical analysis to obtain the differential ion list between the two groups, and the MS of all metabolites was analyzed. 1 With LIPID MAPS( https: / / lipidmaps.org / ) and the Human Metabolome Database (https: / / hmdb.ca / ) to obtain a pre-identified ion list. The pre-identified ion list and the differential ion list are then intersected to obtain a pre-identified differential metabolite ion list (DP-list). The mass spectrometry method editor is then opened, a new mass spectrometry method in DDA data acquisition mode is created, and the DP-list is imported into the MS / MS inclusion list. The MS / MS of the DP-list is acquired using the mass spectrometry method described above. The differential metabolites in the DP-list are then identified by MS / MS. The present invention also comprehensively compares the DP-tDDA acquisition data with existing methods.

[0059] This example analyzes rat serum, specifically comprising the following steps:

[0060] (1) Sample collection: Taking SD rat serum as an example, 16 serum samples were collected from two groups of rats, the normal group and the myocardial ischemia model group. The serum samples were centrifuged at 12,000 rpm for 10 min at 4°C, and the supernatant was frozen at -80°C for later use.

[0061] (2) Sample preparation: 100 μL of serum sample was added to 400 μL of methanol, vortexed for 30 seconds, sonicated in an ice-water bath for 10 minutes, and then centrifuged at 13,000 rpm for 10 minutes at 4°C. The supernatant was evaporated to dryness using a nitrogen blower at 30°C, dissolved in 100 μL of methanol, and centrifuged at 13,000 rpm for 10 minutes before being collected for testing.

[0062] (3) Preparation of quality control samples: 10 μL of each rat serum pre-treated sample was transferred to a 1.5 mL centrifuge tube and vortexed to mix to form a new sample, i.e., the quality control sample.

[0063] (5) Chromatographic conditions: The chromatographic separation of the samples was performed using a Waters Acquity I-class UPLC system equipped with a binary pump, autosampler, degasser, and column oven. The chromatographic column was a Waters Acquity UPLC HSS T3 (2.1 mm × 100 mm, 1.8 μm) column. The column temperature was 40°C. The mobile phase consisted of water (A) containing 0.1% formic acid and acetonitrile (B). The flow rate was 0.3 mL / min. The gradient elution program was as follows: 0-1 min, 5%-5% B; 1-13 min, 5%-100% B, 13-15 min, 100%-100% B, 15-15.1 min, 100%-5% B, 15.1-20 min, 5%-5% B. The injection volume was 2 μL.

[0064] (6) Mass spectrometry conditions: Mass spectrometry detection was performed on a SYNAPT G2-Si HDMS system equipped with an electrospray ion source. MS and MS / MS data acquisition were performed in positive and negative ion modes using MS and fast DDA modes, respectively. The mass spectrometry conditions were set as follows: drying gas flow rate, 800 L / h; drying gas temperature, 400°C; ion source temperature, 120°C; capillary voltage, 2.2 kV (positive ion mode), 2.0 kV (negative ion mode); cone voltage, 40 V, source offset, 80 V; cone gas flow rate, 50 L / h; mass range, 50–1200 Da; MS scan rate, 0.2 s. The parameters of the fast DDA mode were set as follows: the maximum number of MS / MS ions per single MS scan was 5; the low-mass collision energy was 10–40 V, and the high-mass collision energy was 40–120 V. Real-time data calibration was performed using a 200 pg / μL leucine enkephalin solution as an external reference. The leucine enkephalin lock masses were m / z 556.2771 in positive ionization mode and m / z 554.2615 in negative ionization mode, respectively. The flow rate was 5 μL / min. Data were acquired using MassLynx V4.1.

[0065] (7) Differential ion analysis: The MS of all test samples and quality control samples involved in metabolomics analysis is analyzed. 1The dataset was peak aligned and extracted, and a characteristic ion quantification table containing grouping information, retention time, m / z, and peak area for all samples was derived. After data correction and normalization using StatTarget software, the characteristic ion quantification table was imported into the MetaboAnalyst platform. Ions that differed between groups were screened using an FDR < 0.05 standard to obtain a differential ion list containing retention time and m / z.

[0066] (8) Data pre-identification: All samples to be tested and quality control samples involved in metabolomics analysis are pre-identified by MS. 1 The data sets were peak aligned and extracted, and all the extracted MS 1 The mass spectrum peaks were searched and matched with the Human Metabolome Database and LIPID MAPS database to obtain a list of pre-identified metabolite ions including retention time and m / z.

[0067] (9) DP-tDDA data acquisition: The pre-identified metabolite ion list and the differential ion list are intersected to obtain the pre-identified differential metabolite ion list (DP-list). Then, the mass spectrometry method editor is opened to create a new mass spectrometry method in DDA data acquisition mode. The DP-list is imported into the MS / MS inclusion list, and the MS / MS of the DP-list is acquired using the above mass spectrometry method.

[0068] (10) DP-tDDA was comprehensively compared with existing methods: DDA and DIA were used as comparison objects, and DP-tDDA, DDA, and DIA were used to analyze the test samples and quality control samples, respectively. The number of characteristic ions, the number of identified metabolites, the number of differential characteristic ions, the number of differential metabolites, and the number of differential metabolites with MS / MS obtained by the three analytical methods were compared. Six quality control samples were repeatedly analyzed, and the relative standard deviation (RSD) of the peak area of the characteristic ions was used as an indicator of stability.

[0069] Figure 2 This example compares the number of characteristic ions obtained using the DP-tDDA non-targeted metabolomics analysis method with those obtained using DDA and DIA. Data were collected using DP-tDDA, conventional DDA, and DIA for both test and quality control samples. Peak alignment and peak extraction were performed on the MS1 datasets. A characteristic ion quantification table containing grouping information, retention time, m / z, and peak area for all test samples was derived. The number of characteristic ions obtained using the three analysis methods was compared. The results showed that DP-tDDA significantly increased the number of characteristic ions.

[0070] Figure 3In this example, the stability of characteristic ions obtained by DP-tDDA was compared with that of DDA and DIA. Quality control samples were injected six times using DP-tDDA, conventional DDA, and DIA. Peak alignment and extraction were performed, and a quantitative table of characteristic ions containing the retention time, m / z, and peak area of all quality control samples was generated. The RSD values of the characteristic ion peak areas obtained by the three analytical methods were compared. The results showed that the stability of characteristic ions obtained by DP-tDDA was higher than that of DDA and DIA.

[0071] Figure 4 This example compares the number of pre-identified metabolite ions obtained by DP-tDDA, DDA, and DIA: Data were collected for test and quality control samples using DP-tDDA, conventional DDA, and DIA, respectively. Peak alignment, peak extraction, and peak identification were performed on the MS1 datasets to generate a list of pre-identified metabolite ions, including retention time and m / z. The number of pre-identified metabolite ions obtained by the three analytical methods was compared. The results showed that DP-tDDA significantly increased the number of pre-identified metabolite ions.

[0072] Figure 5 In this example, the number of characteristic ions obtained by DP-tDDA, DDA and DIA was compared: DP-tDDA, conventional DDA and DIA were used to collect data on the test samples and quality control samples, and the MS 1 The dataset was peak aligned and extracted, and a characteristic ion quantification table containing grouping information, retention time, m / z, and peak area for all samples was derived. After data correction and normalization using StatTarget software, the table was imported into the MetaboAnalyst platform. Ions that differed between groups were screened using an FDR < 0.05 standard. This yielded a differential ion list containing retention time and m / z. The number of differential ions obtained by the three analytical methods was then compared. The results showed that DP-tDDA significantly increased the number of differential ions.

[0073] Figure 6 In this example, the number of differential metabolites obtained by DP-tDDA was compared with that obtained by DDA and DIA. The pre-identified metabolite ion lists obtained by DP-tDDA, DDA, and DIA were intersected with the differential ion lists to obtain a pre-identified differential metabolite ion list. The pre-identified differential metabolite ion counts obtained by the three analytical methods were then compared. The results showed that DP-tDDA significantly increased the number of pre-identified differential metabolite ions.

[0074] Figure 7This example compares the number of differentially expressed metabolites by MS / MS using DP-tDDA, DDA, and DIA. Data were acquired for test and quality control samples using DP-tDDA, conventional DDA, and DIA, respectively. Peak alignment and peak extraction were performed on the MS / MS datasets, and the number of differentially expressed metabolites by MS / MS was compared using the three analytical methods. The results showed that DP-tDDA significantly increased the number of differentially expressed metabolites by MS / MS.

[0075] In summary, this embodiment adopts full scan data acquisition mode to increase the MS of all ions to be detected. 1 The targeted DDA acquisition strategy for differential metabolite ions can effectively increase the MS / MS scan time and improve the MS / MS coverage of differential metabolite ions. The DP-tDDA analysis method used in this example can set different MS / MS inclusion lists based on different samples, making this analysis method widely applicable.

[0076] The above-described embodiments merely illustrate several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A non-targeted metabolomics analysis method based on DP-tDDA, characterized in that The following steps are involved: S1. Use full scan data acquisition mode to acquire MS of all metabolites in the sample 1 data; S2, based on MS in S1 1 Data results: Single-dimensional statistical analysis of the samples was performed to obtain a differential ion list between two groups, the two groups being the normal group and the myocardial ischemia model group; S3, all metabolites obtained in S1 are MS 1 The data were matched against the database to obtain a list of pre-identified ions; S4, taking the intersection of the differential ion list obtained in S2 and the pre-identified ion list obtained in S3 to obtain a pre-identified differential metabolite ion list DP-list; S5. Open the mass spectrometry method editor, create a new mass spectrometry method in DDA data acquisition mode, and import the DP-list into the MS / MS inclusion list; S6. Acquire MS / MS of the DP-list using the mass spectrometry method of S5; the details are as follows: 1) Set up the injection sequence for non-targeted metabolomics samples and quality control samples; 2) Acquire MS / MS of the DP-list of the test sample and quality control sample through step S5; S7. Perform MS / MS identification on the differential metabolites in the DP-list in S6; the details are as follows: 1) The differential ions obtained in full scan data acquisition mode were analyzed according to the MS 1 The information was first pre-identified and then matched with the MS / MS information of the differential ions obtained by DP-tDDA for the identification of differential metabolites in metabolomics.

2. The non-targeted metabolomics analysis method based on DP-tDDA according to claim 1, characterized in that: Step S1 is specifically as follows: 1) Prepare the sample to be tested: Place an appropriate amount of biological sample in a container and add 2-4 times the volume of organic solvent. Vortex for 20-40 seconds. Ultrasonicate in an ice-water bath for 8-15 minutes to extract metabolites. Centrifuge at 12,000-18,000 rpm and take the supernatant as the sample to be tested. 2) Prepare quality control samples: Accurately pipette 10-50 μL of equal amounts of all samples to be analyzed into the same container and vortex for 2 minutes to mix thoroughly. This is the quality control sample. 3) Use LC-MS to perform full scan data acquisition on the test samples and quality control samples to obtain MS 1 Dataset.

3. The non-targeted metabolomics analysis method based on DP-tDDA according to claim 1, characterized in that: Step S2 is specifically as follows: 1) MS of all test samples and quality control samples involved in metabolomics analysis 1 The data set was subjected to peak alignment and peak extraction, and a characteristic ion quantification table containing retention time, m / z, and peak area was exported; 2) The characteristic ion quantification table was corrected and normalized using StatTarget software; 3) The corrected and normalized characteristic ion quantification table was imported into the MetaboAnalyst platform, and the differential ions between groups were screened with an FDR < 0.05 standard to obtain a differential ion list including retention time and m / z.

4. The non-targeted metabolomics analysis method based on DP-tDDA according to claim 1, characterized in that: Step S3 is as follows: 1) MS of all test samples and quality control samples involved in metabolomics analysis 1 The datasets were subjected to peak alignment and peak extraction; 2) All extracted mass spectrometry peaks were searched and matched with the Human Metabolome Database and LIPID MAPS database to obtain a list of pre-identified metabolite ions including retention time and m / z.

5. The non-targeted metabolomics analysis method based on DP-tDDA according to claim 1, characterized in that: Step S4 is specifically as follows: 1) Summarize the differential ion list obtained in step S2 and the pre-identified ion list obtained in step S3; 2) Use Excel software to take the intersection of the pre-identified ion list and the differential ion list. The intersection is the pre-identified differential metabolite ion list DP-list containing retention time and m / z.

6. The non-targeted metabolomics analysis method based on DP-tDDA according to claim 1, characterized in that: Step S5 is specifically as follows: 1) Open the mass spectrometry method editor, create a new mass spectrometry method, and set the data acquisition mode to DDA. The parameters shared by the full scan data acquisition mode and DDA should be consistent with those of the full scan data acquisition method in S1. 2) Import the DP-list obtained in step S4 into the MS / MS inclusion list in DDA and set it to acquire MS / MS only for ions in the MS / MS inclusion list.

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