Method for analyzing toxic effect of environmental pollutants based on single-cell metabonomics

Abstract

The invention relates to a single cell metabonomics-based environmental pollutant toxic effect analysis method, and belongs to the technical field of single cell mass spectrometry. The invention relates to a single-cell metabonomics-based environmental pollutant toxic effect analysis method. The method comprises the following steps: S1, importing mass spectrum flow type original data and extracting a target ion current diagram; s2, identifying a single cell mass spectrum signal and deducting a background signal; s3, matching and identifying metabolites; S4, carrying out data standardization processing and data quality control; S5, identifying differential metabolites; and S6, carrying out pathway enrichment analysis on the screened differential metabolites. According to the analysis method disclosed by the invention, high-quality treatment of single cell metabonomics data is realized, and high-throughput, multi-component and label-free analysis is carried out on pollutants in a single cell.

Description

Technical Field

[0001] The present invention relates to the technical field of single-cell mass spectrometry analysis, and particularly to an analysis method for the toxic effects of environmental pollutants based on single-cell metabolomics. Background Art

[0002] Compared with traditional population-level analysis, single-cell metabolomics can capture the heterogeneous responses of individual cells to pollutants, thus avoiding information loss caused by averaging effects. In recent years, single-cell metabolomics has shown great potential in the assessment of the toxic effects of environmental pollutants. However, single-cell metabolomics also faces challenges in processing highly complex and heterogeneous data. For example, problems such as the distinction between background noise and target signals, and the improvement of signal sensitivity and accuracy. Traditional data processing methods often struggle to effectively handle these complex metabolic data and cannot reveal the specific metabolic effects of pollutants on different cell types.

[0003] The assessment of the toxic effects of environmental pollutants is crucial for environmental and health risk management. Developing advanced technologies suitable for single-cell metabolomic analysis has become one of the key tasks in this field. Traditional toxicity assessment methods mainly rely on population-level biological indicators and are difficult to reveal the heterogeneity at the metabolic level of individual cells. In fact, different types of cells, or even the same type of cells at different developmental stages, may have significant differences in their responses to pollutants. This heterogeneity is particularly important for understanding the toxic mechanisms of pollutants in depth. Per- and polyfluoroalkyl substances (PFAS) are typical environmentally persistent pollutants. Although some PFAS have been gradually phased out, the eco-toxicity and safety issues of their substitutes still remain uncertain. Therefore, studying the metabolic effects of pollutants at the single-cell level is of great significance for accurately revealing the toxic mechanisms and optimizing environmental and health risk assessments.

[0004] The mass cytometry single-cell analysis system combines the high-throughput characteristics of flow cytometry and the high-sensitivity advantages of mass spectrometry analysis, and can detect multiple metabolites in single cells without labeling and with high sensitivity, capturing the heterogeneous responses of different cells to pollutant exposure. This technology provides a new research means for deeply revealing the toxic effects of environmental pollutants at the single-cell level, especially in analyzing the specific responses of different genders, organs, and cell types. However, current research on the effects of environmental pollutants on single-cell metabolic pathways is still insufficient, especially in revealing the specific mechanisms of the effects of pollutants on different types of cells and their specific metabolic pathways, there are many challenges.

[0005] Therefore, developing a method suitable for single-cell metabolomics analysis to deeply reveal the metabolic responses of pollutants at the single-cell level and identify specific metabolic responses of different genders, organs and cell types has become a key issue that needs to be urgently addressed in the current field of environmental analysis science and has important application prospects and technical value. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for analyzing the toxic effects of environmental pollutants based on single-cell metabolomics.

[0007] To achieve the above object, the technical solution adopted by the present invention is:

[0008] In a first aspect, the present invention provides a method for analyzing the toxic effects of environmental pollutants based on single-cell metabolomics, comprising the following steps:

[0009] S1. Importing mass spectrometry raw data and extracting target ion chromatograms;

[0010] S2. Identify single cell mass spectrometry signals and subtract background signals;

[0011] S3. Matching and identification of metabolites;

[0012] S4. Data standardization and data quality control;

[0013] S5. Identify differential metabolites;

[0014] S6. Pathway enrichment analysis was performed on the screened differential metabolites.

[0015] The method of the present invention achieves high-quality processing of single-cell metabolomics data and performs high-throughput, multi-component, label-free analysis of pollutants in single cells. The present invention can reveal the specific response mechanism of different types of cells to pollutants at the single-cell level, and combine metabolomics data processing to deeply analyze the toxic effects of pollutants.

[0016] As a preferred embodiment of the present invention, in step S1, the raw data includes: accurate mass, retention time, ion response intensity and tandem mass spectrometry information of metabolites.

[0017] As a preferred embodiment of the present invention, in step S1, data are collected in positive and negative ion modes, wherein the parameters of the positive ion mode are as follows: the operating voltage is +1.5 kV, the capillary temperature is 320 °C, the maximum injection time is 20 ms, and the AGC target value is 1×10 6, the S-lens is 100; drying gas temperature: 0 °C; drying gas flow rate: 0; nebulizing gas pressure: 0; sheath gas temperature: 0 °C; sheath gas flow rate: 0 L / min; the parameters in negative ion mode are as follows: working voltage is -1.5 kV, capillary temperature is 320 °C, maximum injection time is 20 ms, AGC target value is 1×10 6 , the S-lens is 100; drying gas temperature: 0 °C; drying gas flow rate: 0; nebulizing gas pressure: 0; sheath gas temperature: 0 °C; sheath gas flow rate: 0 L / min; the mass-to-charge ratio of the characteristic target ion in positive ion mode is: 806.5685; the mass-to-charge ratio of the characteristic target ion in negative ion mode is: 885.5499. Preferably, the characteristic target is lipid.

[0018] In order to better detect the lipid substances with a mass-to-charge ratio of 806.5685 in positive ion mode and 885.5499 in negative ion mode, the present invention optimizes the data acquisition parameters in positive and negative ion modes and finds that the lipid metabolites of liver single cells can be better detected under the above parameters, and finally 64 and 74 differential metabolites are detected in male and female groups respectively.

[0019] As a preferred embodiment of the present invention, step S1 includes the following steps:

[0020] a) Use Python programming to read and process the original data;

[0021] b) Extract the target ion chromatogram within the mass accuracy threshold range of 5 ppm;

[0022] c) Correct the baseline of the extracted ion chromatogram.

[0023] As a preferred embodiment of the present invention, step S2 includes the following steps:

[0024] a) Apply the Peak Finding algorithm to identify the effective peaks with a response intensity greater than 20%;

[0025] b) Identify the background signal through a machine learning algorithm;

[0026] c) Perform denoising and background subtraction processing on each peak.

[0027] Preferably, the machine learning algorithm in step b) specifically adopts the following method:

[0028] a. Establish a data point pair for the signal time point and response intensity: (t, I);

[0029] b. Assume that the signal distribution is a combination of multiple normal distributions, and use the following GMM model for signal distribution to identify the background signal:

[0030]

[0031] Where: I is the signal strength, π k is the weight of the kth Gaussian component, μ k , is the mean and variance of the kth Gaussian component. The parameter π is estimated using the expectation maximization (EM) algorithm k , μ k , σ k Then, according to the probability density of the GMM model, the probability that each signal belongs to the background signal is calculated.

[0032] The present invention compares other methods for removing background signals, such as baseline correction, smoothing filter model, and Bayesian model, and finds that compared with other models or methods, when the GMM model is used, the background signal can be more accurately identified, and the background signal can be deducted and denoised, and the mass spectrometry signal of a single cell can be obtained more accurately.

[0033] As a preferred embodiment of the present invention, step S3 includes the following steps:

[0034] a) Combine and summarize the mass-to-charge ratio data of all pulse peaks within an error range of 5ppm;

[0035] b) Matching using the HMDB metabolite database;

[0036] c) Metabolite identification by API query or local database search.

[0037] As a preferred embodiment of the present invention, step S4 includes the following steps:

[0038] a) Integrate compound information with mass spectrometry-response intensity information of individual cell metabolite composition;

[0039] b) Z-score standardization or minimum-maximum standardization is used to process the data to make the data between different batches and samples comparable;

[0040] c) Use interpolation methods to fill missing values ​​and remove features with more than 70% missing values;

[0041] d) Perform batch effect correction on multiple batches of data.

[0042] As a preferred embodiment of the present invention, step S5 comprises the following steps:

[0043] a) Independent sample t-test and partial least squares discriminant analysis (OPLS-DA) were used to screen differential metabolites;

[0044] b) Identify differential metabolites by the VIP value > 1 method and the significance test (p-value < 0.05) method.

[0045] In a second aspect, the present invention provides a method for preparing a liver single-cell suspension, comprising the following steps:

[0046] (1) Add trypsin and a liver digestion solution to the obtained liver tissue for digestion;

[0047] (2) After the digestion is terminated, perform centrifugation and filtration to collect cells;

[0048] (3) Resuspend the collected cells with a mixed solution of methanol and an aqueous ammonium formate solution to obtain a liver single-cell suspension.

[0049] The present invention discovers that when methanol and an aqueous ammonium formate solution are combined as the suspension of liver cells, the dissolution efficiency and detection sensitivity of metabolites can be improved, enabling better detection of single-cell metabolites. When methanol is combined with other solutions, such as ethanol, acetonitrile, formic acid, ammonium formate acetate solution, etc., their dissolution efficiency and detection sensitivity are inferior to the combination of methanol and an aqueous ammonium formate solution. Therefore, the present invention combines methanol and an aqueous ammonium formate solution as the single-cell suspension of liver cells.

[0050] As a preferred embodiment of the present invention, in the mixed aqueous solution of methanol and ammonium formate, the ratio of methanol to ammonium formate is 1:(3 - 5); preferably, it is 1:3.

[0051] The present invention's research finds that when the volume ratio of methanol to the aqueous ammonium formate solution in the single-cell suspension is 1:(3 - 5), the dissolution efficiency and detection sensitivity of metabolites can be improved, enabling better detection of single-cell metabolites, and it can also improve the stability of the system. Especially when the ratio of methanol to ammonium formate is 1:3, the effect is optimal.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] The present invention provides an analysis method for the toxic effects of environmental pollutants based on single-cell metabolomics to achieve high-quality processing of single-cell metabolomics data and perform high-throughput, multi-component, and label-free analysis of pollutants in single cells. The present invention can reveal the specific response mechanism of liver cells to pollutants at the single-cell level and, in combination with metabolomics data processing, deeply analyze the toxic effects of pollutants, providing scientific support for the risk assessment and management decision-making of environmental pollutants. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic structural diagram of the mass cytometry single-cell analysis system used in the present invention;

[0055] Figure 2Schematic diagram of the effect of the ratio of methanol to ammonium formate on the number of detected cells;

[0056] Figure 3 Content distribution diagram of chlorinated polyfluoroalkyl ether sulfonates in single hepatocytes of zebrafish of different genders after exposure;

[0057] Figure 4 Flow chart of single-cell mass spectrometry information extraction and metabolite feature analysis;

[0058] Figure 5 Mass spectrometry diagrams of metabolic characteristics in positive and negative ion modes in the single-cell mass spectrometry flow method after exposure to chlorinated polyfluoroalkyl ether sulfonates;

[0059] Figure 6 Heat maps of metabolites in positive ion mode in hepatocytes of female and male zebrafish before and after exposure to chlorinated polyfluoroalkyl ether sulfonates;

[0060] Figure 7 Heat maps of metabolites in negative ion mode in hepatocytes of female and male zebrafish before and after exposure to chlorinated polyfluoroalkyl ether sulfonates;

[0061] Figure 8 Differential metabolite maps of hepatocytes of male zebrafish in positive and negative ion modes before and after exposure to chlorinated polyfluoroalkyl ether sulfonates ((1) OPLS-DA map, (2) loading plot, (3) S-plot);

[0062] Figure 9 Differential metabolite maps of hepatocytes of female zebrafish in positive and negative ion modes before and after exposure to chlorinated polyfluoroalkyl ether sulfonates ((1) OPLS-DA map, (2) loading plot, (3) S-plot);

[0063] Figure 10 Gender difference pathway enrichment bubble map of chlorinated polyfluoroalkyl ether sulfonates in metabolic pathways. Detailed implementation manners

[0064] To better illustrate the purpose, technical solution and advantages of the present invention, the present invention will be further described below in conjunction with specific embodiments.

[0065] Example 1

[0066] 1. Preparation of single-cell suspension

[0067] 6:2 Chlorinated polyfluoroalkyl ether sulfonate (6:2Cl-PFESA) is a fluorinated organic pollutant with amphiphilic properties. Its structure contains 6 perfluorinated carbon chains and 2 hydrogen-substituted carbon chains. This property makes it exhibit both high chemical stability, hydrophobicity, and certain water solubility, and it is widely used in industry. Zebrafish (Danio rerio) were exposed to 6:2 chlorinated polyfluoroalkyl ether sulfonate. After the exposure ended, the zebrafish livers were collected, and a single-cell suspension of the liver was obtained by digestion with trypsin and liver enzyme solution. The specific experimental procedures are as follows:

[0068] The specific formula of the liver enzyme solution is as follows: Each milliliter of the enzyme solution contains 1 mg of type I collagenase, 0.5 mg of type II collagenase, 0.5 mg of neutral protease, 20 U of papain, and 0.5 mg of type I deoxyribonuclease. After the enzyme solution is prepared, it needs to be stored at 4 °C and equilibrated to room temperature before use to ensure enzyme activity.

[0069] Adult AB wild-type zebrafish (Danio rerio) were raised in fish tanks filled with tap water filtered through a carbon membrane filter. The environmental temperature in the fish-raising room was 28 ± 1 °C, with a 14 h light / 10 h dark cycle, and an air stone was placed to maintain the dissolved oxygen concentration in the water at 7.2 ± 0.3 mg L -1 between. Adult fish were fed regularly 2 times a day, and the excreta and uneaten food in the fish tank were removed every day. After being cultured in the water tank for 1 week, the next relevant experiment could be carried out without obvious diseases and deaths.

[0070] Two hundred healthy adult fish were randomly divided into 4 groups according to different genders and raised in 4 fish tanks, with 50 fish in each group, namely the blank female control group and the blank male control group; the other two groups were single-exposure groups with an exposure concentration of 200 ng / L. The glass fish tanks used in the laboratory had a capacity of 10 L, the exposure liquid volume was 4 L, and the exposure time was 28 days.

[0071] After the exposure ended, the livers of zebrafish in each experimental group were taken. Add 1 mL of trypsin to every 30 mg of liver tissue, let it stand at room temperature for 10 min, and add an appropriate amount of DPBS to terminate the digestion. Centrifuge for 5 min, remove the supernatant, add an appropriate amount of DPBS to resuspend the cell tissue, then filter the evenly pipetted cells through a 40 μm cell filter sieve, centrifuge for 5 min (300 xg / min, 4 °C), remove the supernatant, and dilute and resuspend with the suspension to obtain a single-cell suspension, and then perform cell viability counting statistics. The total number of cells was not less than 10 5 cells, and then the single-cell suspension of the zebrafish liver was analyzed using a mass cytometry single-cell analysis system.

[0072] In the single-cell suspension, the suspension is a mixed solution of methanol and ammonium formate aqueous solution, where the volume ratio of methanol to ammonium formate aqueous solution is 1:(3 - 5); preferably, it is 1:3.

[0073] The preparation method when the volume ratio of methanol to ammonium formate aqueous solution is 1:3 is as follows: Weigh 0.03153 g of ammonium formate and dissolve it in 6 mL of ultrapure water to obtain an ammonium formate aqueous solution (the concentration of ammonium formate in the ammonium formate aqueous solution is 50 millimoles per liter); then add 2 mL of methanol to the ammonium formate aqueous solution and mix evenly to obtain 10 mL of suspension.

[0074] The research of the present invention finds that when methanol and ammonium formate aqueous solution are combined as the suspension of hepatocytes, it can improve the dissolution efficiency and detection sensitivity of metabolites, enabling better detection of single-cell metabolites. When methanol is combined with other solutions, such as ethanol, acetonitrile, formic acid, ammonium formate acetate solution, etc., their dissolution efficiency and detection sensitivity are not as good as the combination of methanol and ammonium formate aqueous solution. Therefore, the present invention combines methanol and ammonium formate aqueous solution as the suspension of hepatocytes.

[0075] In addition, the research of the present invention finds that when the ratio of methanol to ammonium formate aqueous solution in the single-cell suspension is 1:(3 - 5), it can improve the dissolution efficiency and detection sensitivity of metabolites, enabling better detection of single-cell metabolites, and can also improve the stability of the system. Especially when the ratio of methanol to ammonium formate aqueous solution is 1:3, the effect is the best ( Figure 2 ).

[0076] 2. Mass cytometry single-cell detection

[0077] The above liver single-cell suspension is detected using the mass cytometry single-cell analysis system and method in the patent application document with the publication number CN 117092017 A.

[0078] 3. Data processing and analysis

[0079] The data obtained from the above mass cytometry single-cell detection is analyzed, and the specific steps are as follows:

[0080] The original data obtained from mass cytometry single-cell detection includes: accurate mass of lipid metabolites, retention time, ion response intensity, and tandem mass spectrometry information.

[0081] (1) Import the mass cytometry original data and extract the target ion current map:

[0082] Import the raw data of mass spectrometry cytometry, and process the data obtained according to the characteristic metabolites (lipids) in the positive and negative ion modes separately. Use the automated processing program developed in Python to read the data, set the mass accuracy threshold within 5 ppm to extract the target ion chromatogram, and perform baseline correction on the obtained ion chromatogram;

[0083] Among them, the raw data includes: the exact mass of lipid metabolites, retention time, ion response intensity, and tandem mass spectrometry information;

[0084] The parameters in the positive ion mode are: the working voltage is +1.5 kV, the capillary temperature is 320 °C, the maximum injection time is 20 ms, and the AGC target value is 1×10 6 , and the S-lens is 100;

[0085] The parameters in the negative ion mode are: the working voltage is -1.5 kV, the capillary temperature is 320 °C, the maximum injection time is 20 ms, and the AGC target value is 1×10 6 , and the S-lens is 100;

[0086] The present invention discovers that when the maximum injection time is set to 20 ms, the signal data of liver single cells can be obtained better.

[0087] The mass-to-charge ratio of the characteristic target (lipid) ions in the positive ion mode is: 806.5685

[0088] The mass-to-charge ratio of the characteristic target (lipid) ions in the negative ion mode is: 885.5499.

[0089] (2) Identify the mass spectrometry signal of single cells and subtract the background signal:

[0090] Taking the characteristic lipid signal on the cell membrane in single liver cells of zebrafish as the recognition basis, apply the PeakFinding algorithm to identify the peaks in the target ion chromatogram, set the response intensity threshold to 20% to confirm as valid peaks, and use the machine learning algorithm to automatically identify the background signal based on the data distribution characteristics to achieve accurate identification of the background signal. Perform denoising and background subtraction processing on each peak point to obtain the mass spectrometry-response intensity information representing the metabolite composition of single cells ( Figure 5 ).

[0091] The mass spectrometry signal consists of the background signal and the real peak signal, and the background signal usually has a lower response intensity. Signal modeling and classification are performed through probability distribution.

[0092] Therefore, the machine learning algorithm adopts a background signal recognition machine learning algorithm based on the Gaussian Mixture Model (GMM), specifically as follows:

[0093] a. Establish data point pairs for signal time points and response intensities: (t, I);

[0094] b. Assume that the signal distribution is a combination of multiple normal distributions, and use the following GMM model to identify the background signal for the signal distribution:

[0095]

[0096] where: I is the signal intensity, π k is the weight of the k-th Gaussian component, μ k , is the mean and variance of the k-th Gaussian component. Use the Expectation-Maximization (EM) algorithm to estimate the parameters π k , μ k , σ k . Then, according to the probability density of the GMM model, calculate the probability that each signal belongs to the background signal.

[0097] The present invention compares with other models, such as: baseline correction method, smoothing filter model, Bayesian model, and finds that compared with other models or methods, when using the GMM model, it can more accurately identify the background signal, subtract the background signal and denoise, and can more accurately obtain the mass spectrometry signal of a single cell.

[0098] (3) Match and identify metabolites:

[0099] Merge and summarize the mass-to-charge ratio data in all pulse peaks within an error range of 5 ppm, construct a total mass-to-charge ratio table, and retrieve and identify through the API interface of the HMDB metabolite database or the local database, completing the rapid identification of metabolites. The identification results are shown in Tables 1 and 2. 110 metabolites were identified in single zebrafish hepatocytes in the positive ion mode (Table 1), and 69 metabolites were identified in single zebrafish hepatocytes in the negative ion mode (Table 2). It can be seen that there are obvious differences in the metabolite expressions among different experimental groups ( Figures 6-7 ).

[0100] (4) Data normalization processing and data quality control:

[0101] Data normalization processing: Integrate the compound information with the mass spectrometry-response intensity information of the metabolites of a single cell, and use Z-score normalization or minimum-maximum normalization to process the data to make the data between different batches and samples comparable;

[0102] Data quality control: Use the interpolation method to fill in the missing values, remove the features with missing values exceeding 70%, and perform batch effect correction on multi-batch data to eliminate the influence of the experimental batch on data analysis.

[0103] (5) Identify differential metabolites:

[0104] The partial least squares discriminant analysis (OPLS-DA) and independent samples t-test were used to compare the groups before and after exposure of male and female fish, respectively. Differential metabolites were screened by the criteria of VIP value > 1 method and significance test (p-value < 0.05) method. Figure 8 、 9 )

[0105] (6) Perform pathway enrichment analysis on the screened differential metabolites:

[0106] KEGG pathway enrichment analysis was performed on the screened differential metabolites. Among them, 64 and 74 differential metabolites were identified in the male and female groups, respectively (Tables 3 and 4), which were involved in 15 and 11 metabolic pathways, respectively (Table 5). As Figure 10 shown, the changes in the metabolic pathways of liver cells of zebrafish of different genders caused by chlorinated polyfluoroalkyl ether sulfonates exposure. Specifically, there were significant differences in the metabolic pathways such as lipid metabolism, amino acid metabolism, and oxidative stress between the liver single cells of zebrafish in the exposure group and the control group. Especially in terms of lipid homeostasis and energy metabolism, the response of male zebrafish was significantly enhanced, while females showed more obvious amino acid metabolism and antioxidant stress responses.

[0107] Through the above methods, 64 and 74 differential metabolites were identified from male and female zebrafish, as well as 15 and 11 metabolic pathways. It shows that gender will have different responses to environmental pollutants. The method of the present invention is more accurate than the traditional method and can identify more differential metabolites. The method of the present invention realizes metabolomics analysis of environmental pollutants at the single cell level, revealing the heterogeneous characteristics of the cell response to pollutants. This method first studies the gender difference effect of environmental pollutants at the single cell level. By analyzing the metabolic response characteristics of hepatocytes of male and female individuals, potential gender-specific metabolic pathway changes were found.

[0108] Table 1 List of metabolites identified in single liver cells of zebrafish in positive ion mode

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Table 2 List of metabolites identified in single liver cells of zebrafish in negative ion mode

[0115]

[0116]

[0117]

[0118] Table 3 Differentially expressed metabolites in male zebrafish after exposure

[0119]

[0120]

[0121]

[0122] Table 4 Differentially expressed metabolites in female zebrafish after exposure

[0123]

[0124]

[0125] Table 5 Metabolic pathways related to the effects of 6:2Cl-PFESA on male and female zebrafish hepatocytes

[0126]

[0127]

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for analyzing the toxic effects of environmental pollutants based on single-cell metabolomics, characterized in that: The method comprises the following steps: S1. Importing mass spectrometry raw data and extracting target ion chromatograms; S2. Identify single cell mass spectrometry signals and subtract background signals; S3. Matching and identification of metabolites: S4. Data standardization and data quality control: S5. Identification of differential metabolites: S6. Pathway enrichment analysis was performed on the screened differential metabolites.

2. The analysis method according to claim 1, characterized in that In step S1, the raw data includes: metabolite accurate mass, retention time, ion response intensity and tandem mass spectrometry information.

3. The analysis method according to claim 1, characterized in that In step S1, data is collected in positive and negative ion modes, wherein: The parameters of the positive ion mode were as follows: operating voltage was +1.5 kV, capillary temperature was 320 °C, maximum injection time was 20 ms, and AGC target value was 1 × 10 6 , S-lens is 100; drying gas temperature: 0℃; drying gas flow rate: 0; spray gas pressure: 0; sheath gas temperature: 0℃; sheath gas flow rate: 0L / min; The parameters of the negative ion mode were as follows: operating voltage was -1.5 kV, capillary temperature was 320 °C, maximum injection time was 20 ms, and AGC target value was 1 × 10 6 , S-lens is 100; drying gas temperature: 0℃; drying gas flow rate: 0; spray gas pressure: 0; sheath gas temperature: 0℃; sheath gas flow rate: 0L / min; Among them, the mass-to-charge ratio of the characteristic target ion in the positive ion mode is: 806.5685; the mass-to-charge ratio of the characteristic target ion in the negative ion mode is: 885.5499.

4. The analysis method according to claim 1, characterized in that The step S1 comprises the following steps: a) Use Python programming to read and process raw data; b) extracting the target ion chromatogram within a mass accuracy threshold of 5 ppm; c) Perform baseline correction on the extracted ion chromatogram.

5. The analysis method according to claim 1, characterized in that The step S2 comprises the following steps: a) Apply the Peak Finding algorithm to identify valid peaks with response intensity greater than 20%; b) Identify background signals through machine learning algorithms; c) Perform denoising and background subtraction on each peak.

6. The analysis method according to claim 1, characterized in that The step S3 comprises the following steps: a) Combine and summarize the mass-to-charge ratio data of all pulse peaks within an error range of 5ppm; b) Matching using the HMDB metabolite database; c) Metabolite identification by API query or local database search.

7. The analysis method according to claim 1, characterized in that The step S4 comprises the following steps: a) Integrate compound information with mass spectrometry-response intensity information of individual cell metabolite composition; b) Z-score standardization or minimum-maximum standardization is used to process the data to make the data between different batches and samples comparable; c) Use interpolation methods to fill missing values ​​and remove features with more than 70% missing values; d) Perform batch effect correction on multiple batches of data.

8. The analysis method according to claim 1, characterized in that The step S5 comprises the following steps: a) Independent sample t-test and partial least squares discriminant analysis were used to screen differential metabolites; b) Differential metabolites were identified by VIP value > 1 method and significance test method.

9. A method for preparing a liver single cell suspension, characterized in that: The steps include: (1) adding trypsin and liver enzymatic solution to the obtained liver tissue for digestion; (2) After digestion is completed, centrifuge and filter to collect cells; (3) The collected cells are resuspended in a mixed solution of methanol and ammonium formate aqueous solution to obtain a liver single cell suspension.

10. The preparation method according to claim 9, characterized in that: In the mixed aqueous solution of methanol and ammonium formate, the volume ratio of methanol to the aqueous solution of ammonium formate is 1:(3-5).

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