High-coverage quantitative analysis method and quantitative kit for lipidome

Abstract

The invention relates to a high-coverage quantitative analysis method of lipidome and a quantitative kit, the method adopts a mobile phase A and a mobile phase B with different pH values for elution, the pH value of the mobile phase B is 2.5-5, and the pH value of the mobile phase A is 2.5-3.0. According to the method, good separation of acid-sensitive lipid compounds such as PA, S1P and PS can be realized, ionization inhibition of lipid compounds such as free fatty acid and free cholesterol can be reduced, and the quantitative sensitivity of the lipid compounds can be improved. According to the method, 104 lipid subclasses can be covered, 2375 lipid compounds can be quantified, and the number of the lipid compounds is more than two times that of the lipid compounds in the existing lipidomics quantitative method. The lipidome quantitative kit can be used for quantitatively analyzing lipidomes of seven biological samples such as plasma, urine, cells, liver and excrement of human or animal sources as well as escherichia coli and arabidopsis rosette leaves, and can meet quantitative analysis requirements of lipidomes in different biological samples.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological quantitative analysis, and more particularly to a high-coverage and high-sensitivity quantitative analysis method and quantitative kit for lipidome. BACKGROUND

[0002] The total lipid composition of cells is referred to as the cell lipidome, hereinafter referred to as lipidome. It is currently estimated that the number of lipids in cells can be as high as tens of thousands or even millions, with a content of about amol / mg to nmol / mg of protein. Lipidome quantitative technology has become an important tool for studying the metabolic changes of organisms under different physiological or pathological conditions, and is used to find various lipid biomarkers to diagnose or predict ischemic stroke, coronary sclerosis, lung cancer and many other major diseases. Although nuclear magnetic resonance technology can quantitatively analyze the contents of lipoproteins, total triglycerides and total cholesterol esters in plasma or serum, it cannot achieve quantitative analysis of different subclasses of lipid compounds (J Clin Lipidol.2023, 17(5): 677-687.). Lipidomics quantitative analysis methods based on direct injection-mass spectrometry technology can achieve quantitative analysis of more than 700 lipid compounds in plasma, but there is quantitative interference between phosphatidylcholine (PC) and phosphatidylethanolamine (PE) isomers (J Am Soc Mass Spectrom.2021, 32(11): 2655-2663.). In addition, ultra-high performance liquid chromatography tandem high-resolution mass spectrometry (UPLC-MS) based lipidomics quantitative technology as the main quantitative means can be divided into targeted lipidomics and non-targeted lipidomics quantification. Among them, existing lipidomics quantitative technology generally only covers 36 lipid subclasses in plasma or serum, involving more than 800 lipid compounds (Diabetes Care.2019, 42(11): 2117-2126.; Metabolites.2020, 10(12): 495.; Front Cardiovasc Med.2022, 9: 848840.). Relatively speaking, the quantitative coverage of non-targeted lipidomics reaches more than 2000 lipid compounds, including hydroxy fatty acids, galactosyl diglycerides and coenzyme Q and many other important lipid compounds (Endocr Connect.2023, 12(12): e230212.), but the false positive rate of mass spectrometry matching is high, and it is difficult to quantify low-response lipid compounds.

[0003] Liquid chromatography (LC) separation is an important pretreatment method for mass spectrometry-based lipidomics analysis. Normal phase liquid chromatography (NPLC) or hydrophilic interaction liquid chromatography (HILIC) system can be used to effectively separate amphipathic lipids such as glycerophospholipids (GPs), and reversed phase liquid chromatography (RPLC) can be used to separate other polar lipid compounds. However, the reported chromatographic separation methods have problems such as strong ion suppression, low quantitative sensitivity or low isomer resolution. For example, the mobile phase commonly used in high performance liquid chromatography is neutral or alkaline, which has poor elution effect on acid-sensitive lipid compounds, and reducing the pH value of the mobile phase can easily suppress the response values of other lipid compounds such as free fatty acids, free cholesterol (Cho), etc. Therefore, the current liquid chromatography-mass spectrometry quantitative analysis method urgently needs to improve the chromatographic retention of acid-sensitive lipids such as phosphatidylserine (PS) and sphingosine-1-phosphate (S1P), and improve the quantitative sensitivity of low response lipid compounds.

[0004] Although lipidomics quantitative techniques have been applied to the quantification of single cells, cell lines, plasma and other biological samples, there is still no high-coverage quantitative analysis of fecal lipidomics. The lipid coverage and sensitivity of current lipidomics quantitative analysis methods are still low, and the comparison results of various reported lipid extraction methods are not comprehensive. For example, it is not clear about the extraction efficiency of strong hydrophilic lipid compounds such as phosphatidic acid (PA), S1P and short-chain acyl carnitine (ACar). Therefore, the present application aims to develop a lipidomics quantitative technique with high coverage and high sensitivity, which is mainly used to meet the quantitative analysis needs of lipidomics in plasma, cells, urine, tissues, feces, bacteria and Arabidopsis thaliana and other typical biological matrices. SUMMARY

[0005] In view of the above defects or improvement needs of the prior art, the present application provides a high-coverage and high-sensitivity quantitative analysis method and quantitative kit for a lipidome. The method is based on ultra-high liquid chromatography-mass spectrometry technology, and the purpose is to achieve good separation of various acid-sensitive lipid compounds such as PA, S1P and PS, and to reduce ionization inhibition of free fatty acids and other lipid compounds such as free cholesterol to improve the sensitivity of the chromatographic mobile phase, and to realize quantitative analysis of lipid compounds by combining the retention time of the lipid compounds with the secondary mass spectrum. Especially, the present application adopts two reverse phase chromatography and one hydrophilic interaction chromatography (HILIC) elution, wherein the reverse phase chromatography I (RPLC-I) elution separates acid-sensitive lipid compounds; the reverse phase chromatography II (RPLC-II) elution separates one or more lipid compounds contained in glycerolipids, ceramides and their derivatives, phosphatidylinositol and cholesterol esters; and the HILIC chromatography elution separates one or more lipid compounds contained in phosphatidyl and its derivatives and sphingomyelin. The present application can meet the high-coverage and high-sensitivity quantitative requirements of lipidomics, thereby solving the technical problems that it is difficult to accurately quantify acid-sensitive and low-response lipid compounds in the process of quantifying lipidome by liquid chromatography-mass spectrometry, resulting in low coverage of lipidome quantification.

[0006] To achieve the above-mentioned purpose, according to one aspect of the present application, a high-coverage quantitative analysis method for a lipidome based on liquid chromatography-mass spectrometry technology is provided, which comprises the following steps:

[0007] (1) Extracting the lipidome: extracting the lipidome from the biological matrix to be tested to prepare a sample for injection;

[0008] (2) Chromatographic separation: injecting the first needle with reverse phase chromatography I (RPLC-I), eluting and separating acid-sensitive, low-response and / or strongly hydrophilic lipid compounds, wherein the RPLC-I elution uses different acidic mobile phases A and B with lower pH values, wherein the pH of the mobile phase B is 2.5-5.0, and the pH of the mobile phase A is 2.5-3.0, so that the chromatographic peaks of each target reach baseline separation;

[0009] Preferably, another reverse phase chromatography II (RPLC-II) is used for the second injection to elute and separate one or more lipid compounds from glycerolipids, ceramides and their derivatives, phosphatidylinositol and cholesteryl esters, and / or a hydrophilic interaction chromatography (HILIC) is used for the third injection to elute and separate one or more lipid compounds from phosphoglycerides and their derivatives and sphingomyelin. The first injection, the second injection and the third injection in the present application are not required to be in sequence, and can be eluted in sequence or not in sequence, such as parallel elution.

[0010] (3) Lipidome quantitative analysis: non-targeted lipidomics analysis combined with data-dependent scanning or data-independent scanning mode in high-resolution mass spectrometry, or lipidome targeted quantitative analysis combined with mass spectrometry parameters in dynamic multiple reaction monitoring mode; the mass spectrometry parameters include parent ions, daughter ions, collision energy values, source region temperatures and declustering voltages. In the present application, for lipid compounds with standard products, identification and quantitative analysis can be performed based on their corresponding standard products or existing lipid databases; for lipid compounds without standard products, lipid compounds can be identified using existing or self-built lipid structure-retention time prediction models, and the target is qualitatively analyzed by combining the chromatographic retention time and the corresponding characteristic ions of the mass spectrometry secondary fragments. Finally, the external standard method or the internal standard method is used for quantitative analysis of the separated target, and more preferably the internal standard method is used for quantitative analysis.

[0011] Preferably, in step (2) of the lipidome quantitative analysis method, RPLC-I is used for the first injection, and the pH of the mobile phase A used for elution is 2.5-3.0, and the pH of the mobile phase B is 2.5-5.0; preferably, the mobile phase B is an acidic mobile phase with a pH of 3.0-4.0, and the mobile phase A is a pH of 2.5-3.0; more preferably, the components of the mobile phase A include acetonitrile, methanol and formic acid, and ammonium formate or ammonium acetate, and the components of the mobile phase B include acetonitrile, isopropyl alcohol (IPA) and formic acid, and ammonium formate or ammonium acetate.

[0012] Preferably, in the lipidome quantitative analysis method, the RPLC-I elution and separation includes acid-sensitive lipid compounds such as PA, S1P and PS; the chromatographic column used by the RPLC-I is selected from BEH C 18 , HSS T3, Premier BEH C 18 , Premier HSS T3 or EP C 18 , wherein the chromatographic column of Premier can reduce the adsorption of metal ions to acidic lipid compounds, further reducing the tailing factor. Preferably, the particle size of the EP C 18 is 1.8 μm, the column pressure is lower and the high-throughput stability is better.

[0013] Preferably, the lipidome quantitative analysis method, step (2) is injected with three needles, the first needle is eluted with RPLC-I, the second needle is eluted with RPLC-II, and the third needle is eluted with hydrophilic interaction chromatography. The first needle, the second needle and the third needle are sequentially injected and eluted in order or injected and eluted in parallel. In some embodiments, the second needle is injected using RPLC-II, which can elute and separate monoglycerides (MAG), diglycerides (DAG), triglycerides (TAG), ceramides (Cer), dihydroceramides, hexosylceramides, dihexosylceramides, phosphatidylinositol and cholesterol esters. The third needle is injected using HILIC, which can elute and separate phosphatidylglycerol, phosphatidylethanolamine, ether phosphatidylethanolamine, ene ether phosphatidylethanolamine, phosphatidylcholine, ether phosphatidylcholine, ene ether phosphatidylcholine, lysophosphatidylethanolamine, lysophosphatidylcholine and sphingomyelin (SM). The RPLC-II is used to separate cholesterol esters and triglycerides, and the mobile phase B is preferably a neutral mobile phase, and the chromatographic column is preferably a kinetex C 18 , with a particle size of 2.6 μm, which is more suitable for chromatographic separation of non-polar metabolites (cholesterol esters and triglycerides) to reduce ion suppression of the former. The HILIC uses a hydrophilic interaction chromatographic column to elute and separate phospholipid subclasses, and the mobile phase B used is preferably an alkaline mobile phase, and the chromatographic column is preferably BEH HILIC, which is more suitable for chromatographic separation of most phospholipid subclasses. Among the target substances eluted by HILIC, different lipid subclasses are eluted in order from weak to strong according to the polarity of the head group, which can effectively separate different subclasses of lipid compounds and reduce the interference between isomers. Because the structure-retention time rule of lipid compounds eluted by HILIC is obvious, the retention time of a standard in a subclass of lipids can predict the retention time of other lipid compounds in the subclass.

[0014] Preferably, the lipid group quantitative analysis method, the lipid concentration of the first injection needle is higher than the second or third needle, preferably the lipid concentration of the first injection needle is 2-3 times of the lipid concentration of the second or third injection needle. The biological matrix extraction method in the present application is not limited, wherein the biological matrix is plasma, in order to quantitatively determine the acidic, low response and / or strong hydrophilic lipid in human plasma, the first injection sample is extracted by the optimization method proposed by Sarafian ("Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography-mass spectrometry") for quantitative determination. In order to quantitatively determine the high peak degree of lipid compounds, the second and third injection samples can be preferably extracted by the Matyash extraction method ("Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics") for lipid group extraction in plasma. The biological matrix is urine, animal tissue, bacteria, fungi, lower or higher plants, the lipid group can be extracted by the Sarafian lipid extraction method, and 40-60 μL of DCM / MeOH solution (1:1, v / v, 0.1% formic acid FA) is used for reconstitution as the first injection sample; 150-300 μL of dichloromethane (DCM) / methanol (MeOH) mixed solution (1:1, v / v) is used for reconstitution as the second and third injection samples.

[0015] Preferably, in the step (3) of the lipidome quantitative analysis method, the ion source temperature is set to 350-450℃ and the de-clustering voltage is set to 60-80V in the mass spectrometry parameter of the sphingolipids during mass spectrometry data collection, preferably the ion source temperature is set to 350℃ and the de-clustering voltage is set to 70V; the collision energy value of the optimal characteristic fragment of the fatty acyl chain or the ceramide basic skeleton, i.e. the sphingosine related optimal characteristic fragment, is set to 35-60V; the collision energy value of the optimal characteristic fragment of the monoglyceride, sphingosine or free cholesterol is set to 20-40V. The collision energy value required for the breaking of the fatty acyl chain or the ceramide basic skeleton of the lipid compound is set to 35-55V. In contrast, the collision energy value required for the breaking of the ester bond between the glycerol and the phosphate functional group in the lipid compound is relatively low (mostly between 30-40V), such as lysophosphatidic acid (LPA) and lysophosphatidylserine (LPS). The collision energy value required for the dehydroxylation or deamination of MAG, sphingosine (SPH) or Cho is 20-30V.

[0016] The relationship between the total number of carbon (c) or the total number of carbon-carbon double bonds (d) of the fatty acyl chain, ether chain, alkenyl ether chain and sphingosine chain in the lipid compound and the corresponding retention time (t R ) can be fitted by an equation. However, due to the non-linear relationship between the dependent variable and the two independent variables, there may also be polynomial and product terms of these parameters (such as c, c 2 , c 3 , dc 3 , dc 2 , dc 2 ). Therefore, the present application uses the LM function in the R language package to calculate the correlation between the experimental retention time (t R E ) of the known lipid compound and the new variables c, c 2 , c 3 , dc 3 , dc 2 , dc 2 , the variable screening is completed by stepwise regression and full subset regression, and the optimal model is selected by 10-fold cross-validation. In the present application, the lipidome quantitative analysis method based on mass spectrometry data collection in the dynamic multiple reaction monitoring mode, wherein the predicted chromatographic theoretical retention time (retention time prediction accuracy <0.5min) of the lipid compound without standard is predicted by the prediction model constructed according to the following method, and the MRM collection window is set according to the predicted theoretical retention time, as follows:

[0017] Using the identified lipid compounds of the same subclass, a lipid compound structure-retention time quantitative relationship is constructed based on the non-targeted quantitative results, and the experimental retention time t R EFor the dependent variable, the above-mentioned carbon chain number (c) and carbon-carbon double bond number (d) are dependent variables, and a multivariate polynomial nonlinear regression modeling is used to obtain the QSRR model expression t R E = m1c 3 + m2c 2 + m3c + m4d + m5c 3 d + m6c 2 d + m7cd + m8, wherein m1-m7 are coefficients, and m8 is a constant.

[0018] In some embodiments, based on 30 kinds of free fatty acid (FFA) standards and FFA detected in 26 kinds of biological matrix, a QSRR model about FFA experimental retention time t R E and the polynomial regression optimal equation (correlation coefficient R 2 = 0.9984) between c and d, t R E = -0.000200c 3 + 0.000258c 2 + 0.692409c - 5.450528d + 0.000388c 3 d - 0.026387c 2 d + 0.610999cd - 3.945821.

[0019] When Cho is considered as a CE without a fatty acyl chain (i.e., d = 0), Cho and other CE compounds can also construct an optimal polynomial regression equation.

[0020] Since MAG, DAG and TAG only differ in the number of fatty acyl chains on the glycerol skeleton, i.e., the total carbon number (c) or the total carbon-carbon double bond number (d) of 1 to 3 fatty acyl chains, we further speculate that there is a similar optimal equation between the experimental determination t R E and c, d. The experimental results show that the polynomial equation t R E = 0.000056c 3 - 0.010004c 2 + 0.678853c - 1.282941d + 0.000001c 3 d - 0.000453c 2 d + 0.040768cd - 2.401860 can describe the correlation between t R , c and d in glycerolipids such as MAG, DAG and TAG, and the equation fitting degree is high (R 2 = 0.9918).

[0021] The present application also provides a high-coverage and high-sensitivity lipidome quantification kit. The kit is based on ultra-high performance liquid chromatography-mass spectrometry technology for quantification of lipidome, and can be used for quantification of lipidome in 7 biological samples of human or animal origin, including plasma, urine, cells, liver, feces, Escherichia coli and Arabidopsis thaliana rosette leaves. For each biological matrix, a set of internal standard mixtures is prepared, which are stable deuterium-labeled and other lipid compounds that do not exist in vivo (such as odd carbon chain-containing lipid compounds). The internal standard mixture covers a plurality of internal standard compounds in 30 lipid subclasses, and the internal standard mixture is prepared in different concentrations according to the peak area corresponding to the standard of the lipid compound in the same subclass, which is between 0.01-50 times the peak area of the lipid compound in the biological matrix to be detected. The 30 lipid subclasses include cholesteryl ester, ceramide, diglyceride, dihydroceramide, hexosylceramide, dihexosylceramide, lysophosphatidylcholine, lysophosphatidylethanolamine, phosphatidylcholine, phosphatidylethanolamine, sphingomyelin, triglyceride, monoglyceride, phosphatidylglycerol, phosphatidylinositol, phosphatidic acid, phosphatidylserine, sphingosine-1-phosphate, sphingosine, lysophosphatidylinositol, lysophosphatidylserine, lysophosphatidic acid, ganglioside GM3, lysophosphatidylglycerol, acylcarnitine, cholesterol, fatty acid, ganglioside GM1, plant ceramide and cardiolipin.

[0022] Preferably, the biological matrix is plasma, cells, liver or feces, and the internal standard mixture preferably covers 70 internal standard compounds in the 30 subclasses. Preferably, the biological matrix is Escherichia coli or Arabidopsis thaliana rosette leaves, and the internal standard mixture does not contain one or more of the standard compounds of sphingosine-1-phosphate, ganglioside GM3, cholesterol, and ganglioside GM1. Preferably, the biological matrix is urine, and the internal standard mixture does not contain one or more of the standard compounds of sphingosine-1-phosphate, ganglioside GM3, and cholesterol.

[0023] In addition, the kit containing the above 70 lipid internal standards is also suitable for quantifying the concentrations of other lipid compounds in the lipid subclass similar in structure to the lipid compound standard. For example, the fatty acid internal standard can quantify 2-hydroxy fatty acid, 3-hydroxy fatty acid, dicarboxylic acid and eicosanoid; the acylcarnitine internal standard can quantify hydroxylated and carboxylated acylcarnitine; the diglyceride internal standard can quantify galactosyl diglyceride, digalactosyl diglyceride and ether-type diglyceride; and the lysophosphatidylethanolamine can quantify ether-type and ene ether-type lysophosphatidylethanolamine.

[0024] Preferably, the lipidome quantitative kit further comprises one or more of the following: an extraction reagent for extracting the lipidome from the biological matrix, a reconstitution solvent for reconstituting the lipidome, and one or more eluents in a reverse phase chromatography elution mobile phase; the reverse phase chromatography mobile phase comprises an acidic mobile phase A and an acidic mobile phase B with different pH values, the pH of the mobile phase A is 2.5-3.0, and the pH of the mobile phase B is 2.5-5.0, preferably the pH of the mobile phase B is 3.0-4.0. In some embodiments, the reconstitution solvent comprises a DCM / MeOH mixed (1:1, v / v) reconstitution solvent, and / or an acidic reconstitution solvent mixed with DCM / MeOH and formic acid (1:1, v / v, 0.1% formic acid FA).

[0025] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0026] The lipidome quantitative analysis method provided by the present application extracts the lipidome from the biological matrix, uses RPLC-I for first needle injection, and uses RPLC-I with a mobile phase of acidic mobile phases A and B with different pH values, wherein the pH of the mobile phase B is 2.5-5.0, and the pH of the mobile phase A is 2.5-3.0. It is preferentially used for elution and separation of acid-sensitive, low-response, and strongly hydrophilic lipid compounds in the sample, such as PA, S1P, and PS, and has weak ionization inhibition on free fatty acids and free cholesterol and other lipid compounds, significantly improving the response value. RPLC-I combined with high-resolution mass spectrometry for non-targeted lipidomics analysis, or combined with low-resolution mass spectrometry in a multiple reaction monitoring scanning mode for lipidome targeted quantitative analysis. Compared with the existing traditional reverse phase chromatography or hydrophilic interaction chromatography, the present application can quantitatively analyze more lipid compounds with higher coverage.

[0027] The present application uses two reverse phase chromatography (RPLC-I and RPLC-II) or / and hydrophilic interaction chromatography (HILIC) to separate and elute different lipid subclasses, wherein RPLC-II is used for second needle injection, preferentially used for elution and separation of one or more lipid compounds containing glycerolipids, ceramides and their derivatives, phosphatidylinositol, and cholesterol esters, and HILIC is used for third needle injection, preferentially used for elution and separation of one or more lipid compounds containing phosphatidyl and its derivatives and sphingomyelin. This method can quantitatively cover 104 lipid subclasses and 2375 lipid compounds, and is suitable for high-coverage quantitative analysis of human or animal-derived plasma, urine, cells, liver, feces, and E. coli and Arabidopsis thaliana rosette leaves.

[0028] The lipidome quantitative kit provided by the application can be used for quantitative analysis of the lipidome of 7 biological samples of human or animal sources, plasma, urine, cells, liver, feces, and Escherichia coli and Arabidopsis thaliana rosette leaves. The kit comprises 7 groups of internal standard mixed solutions prepared by stable isotope labeling and lipid compounds (such as lipid compounds containing odd-numbered carbon chains) that do not exist in the biological body to be detected, and a total of 70 internal standard compounds in 30 subgroups. Compared with existing lipidome quantitative kits, the kit of the application can meet the needs of high coverage and high sensitivity quantitative analysis of the lipidome in biological samples of different human, animal, plant and microbial sources. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1Chromatographic elution behavior of representative lipid compounds in 40 lipid subclasses under different chromatographic conditions (chromatographic conditions are shown in Table 2 and Table 3); the standard lipid compounds corresponding to the numbers 1 to 40 are as follows: 1. PS (17:0 / 17:0); 2. PA (17:0 / 17:0); 3. SPH (d17:1); 4. LPC (16:0)-d9; 5. FFA (16:0)-d9; 6. ACar (16:0)-d3; 7. LPE (18:0)-d5; 8. MAG (17:1); 9. PG (15:0 / 15:0); 10. PI (17:0 / 14:1); 11. Hex2Cer (d18:1 / 16:0)-d9; 12. HexCer (d18:1 / 16:0)-d9; 13. Cho-d7; 14. PC (16:0 / 18:1)-d9; 15. Cer-NS (d18:1 / 16:0)-d9; 16. Cer-NDS (d18:0 / 16:0)-d9; 17. PE (18:0 / 18:1)-d5; 18. DAG (16:0 / 18:2)-d9; 19. SM (d18:1 / 24:1)-d7; 20. CE (18:2)-d7; 21. TAG (52:1)-FA18:0-d9; 22. S1P (d17:1); 23. LPS (17:1); 24. LPA (17:0); 25. GM1 (d18:1 / 18:0)-d5; 26. GM3 (d18:1 / 18:0)-d5; 27. MGDG (18:1 / 18:1); 28. CDP-DG (18:1 / 18:1); 29. Cer-NP (t18:1 / 16:0)-d9; 30. CL (14:0 / 14:0 / 14:0 / 14:0); 31. 2HOFA (16:0); 32. 3HOFA (16:0); 33. DCA (16:0); 34. LPI (17:1); 35. LPG (17:1); 36. PC (O-16:0 / 18:1); 37. PE (P-18:0 / 18:1); 38. PC (O-18:1 / O-18:1); 39. LPC (P-18:0); 40. CerPE (d18:1 / 24:0).

[0030] Figure 2 Influence of different chromatographic conditions on the signal intensity of the mass spectrometric acquisition of representative lipid compounds in 40 lipid subclasses (left), and quantitative results under the preferred conditions (right), in which A represents M1-M 12 , and the peak intensity ratio between M 11 , B, C, and D represent M 11 , M 12 , and M 10 , respectively, corresponding to the quantified lipid subclasses;

[0031] Figure 3 MS of sphingolipid subclasses standards in positive or negative mode 2 Mass spectra; A-J are SPH(d17:1), S1P(d17:1), Cer-NDS(d18:0 / 16:0), Cer-NS(d18:1 / 16:0), HexCer(d18:1 / 16:0), Hex2Cer(d18:1 / 16:0), CerPE(t18:0 / 24:0), Cer-NP(t18:0 / 16:0)-d9, GM3 and GM1, respectively.

[0032] Figure 4 Ion source temperature and declustering voltage optimization for 27 lipid subclasses standards, A-C are ion source temperature optimization, D-F are declustering voltage optimization, G-L are collision energy optimization for corresponding lipid standards;

[0033] Figure 5 Extraction efficiency of 40 lipid subclass standards by different extraction methods, A is the extraction recovery of different lipid subclasses by four classical extraction methods, B is the concentration ratio of acylcarnitine series in extraction solution, C is the ratio between matrix effects of different lipid subclasses;

[0034] Figure 6 Quantitative stability of lipid standards, A-C are the batch, intra-day and inter-day stability of different lipid subclass standards, respectively;

[0035] Figure 7 Quantitative stability of large sample size plasma lipidome;

[0036] Figure 8 Concentration range of different subclasses of lipid compounds in plasma samples;

[0037] Figure 9 Quantitative results of lipid compounds in plasma, cells, liver and feces by using the method. DETAILED DESCRIPTION

[0038] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object of the application, the specific embodiments, structures, features and effects thereof according to the present application are described in detail as follows in combination with the accompanying drawings and preferred embodiments.

[0039] The application adopts ultra-high liquid chromatography-mass spectrometry technology to quantitatively determine the lipidome, based on 40 representative lipid subclasses of PA, PS, phosphatidylcholine (PC), phosphatidylethanolamine (PE), TAG, etc. and corresponding 81 kinds of lipid standards, by optimizing chromatographic conditions and mass spectrometry parameters, the problems of poor chromatographic retention and low response of acidic lipids are solved. In the experiment, we found that using different pH values of acidic mobile phase A and mobile phase B for elution (wherein the pH of mobile phase A is 2.5-3.0, and the pH of mobile phase B is 2.5-5.0), not only can realize the good separation of various acid-sensitive lipid compounds such as PA, S1P and PS, but also can significantly improve the quantitative sensitivity of part of lipid compounds such as free fatty acids and free cholesterol.

[0040] The following is an example

[0041] The following experimental biological samples were approved by the ethics committee of Fudan University. Human plasma and urine were obtained from healthy Chinese adult volunteers recruited by the Human Phenome Project and signed informed consent. After the sample was obtained by the clinical standard sampling method, it was immediately frozen with liquid nitrogen and stored at -80℃ for testing. Animal tissue samples were collected in accordance with the national guidelines for experimental animal welfare (Ministry of Science and Technology 2006). The following examples use 81 lipid standards (covering 40 lipid subclasses) in Table 1 and typical biological samples such as human body fluids (plasma, urine), mouse tissues (heart, liver, brain, feces, kidney and lung), bacteria (Escherichia coli), fungi (yeast), lower plants (lichen male and female), higher plants (Arabidopsis thaliana), etc. The SCIEX zeno TOF 7600 and X500R TOF (SCIEX, Chromos, Singapore) and Shimadzu UPLC system (Kyoto, Japan) are used, based on the data-dependent scanning mode of high-resolution mass spectrometry or the dynamic multiple reaction monitoring mode of low-resolution mass spectrometry to collect data, to obtain the retention time (t R ) and mass spectrometry data of lipid compounds in the following standard samples and various biological samples.

[0042] Example 1 High coverage lipidome quantification method

[0043] Table 1 81 lipid standards (covering 40 lipid subclasses)

[0044]

[0045]

[0046]

[0047]

[0048] The present embodiment is based on 81 lipid standards in Table 1 (covering 40 lipid subgroups), and a high-coverage lipidome quantitative analysis method is established by optimizing chromatographic conditions and mass spectrometry parameters, to solve the technical problems of poor chromatographic retention of acid-sensitive lipid compounds and low mass spectrometry response of free fatty acids and cholesterol, as follows:

[0049] (1) Chromatographic column screening: The above-mentioned standards were subjected to data acquisition by using SCIEX zeno TOF 7600 (SCIEX, Chromos, Singapore) in combination with Shimadzu UPLC system (Kyoto, Japan), wherein the mobile phase of ultra-high performance liquid chromatography comprises mobile phase A and mobile phase B; the mobile phase A is a water acetonitrile methanol solution containing ammonium formate, ammonium acetate, formic acid or / and ammonia water, and the mobile phase B is an acetonitrile isopropanol solution containing ammonium formate, ammonium acetate, formic acid or / and ammonia water; the column temperature of the chromatographic column of ultra-high performance liquid chromatography is 50°C; the injection amount of ultra-high performance liquid chromatography is 1-2 μL, and the specific chromatographic conditions of chromatographic column screening are shown in Table 2.

[0050] Table 2 Chromatographic column settings and elution procedures of 5 kinds of ultra-high performance liquid chromatography

[0051]

[0052]

[0053] The 5 kinds of chromatographic columns of ultra-high performance liquid chromatography in Table 2 are respectively PK C 18 , Phenomenex kinetex C 18 column (2.1*100mm, 2.6μm), BEH HILIC, Waters ACQUITY BEH HILIC column (2.1*100mm, 1.7μm), EPC 18 , Agilent Zorbax Eclipse Plus C 18 column (100*2.1mm, 1.8μm), CSH C 18 , Waters ACQUITY CSH C 18 (100*2.1, 1.7μm), BEH C 18 , Waters ACQUITY BEH C 18(100*2.1,1.7pm) and HSS T3, i.e. Waters ACQUITY HSS T3 (100*2.1,1.7pm) chromatographic column; wherein the total volume of mobile phase A and mobile phase B is 100%, and the gradient elution G4 is as follows: 0-10 min: the volume ratio of mobile phase B is linearly increased from 0% to 20%; 10-11 min: the volume ratio of mobile phase B is linearly increased from 20% to 98%; 11-13 min: the volume ratio of mobile phase B is kept at 98%; 13-13.1 min: the volume ratio of mobile phase B is decreased from 98% to 0.1%; 13.1-14 min: the volume ratio of mobile phase B is kept at 0.1%.

[0054] The SCIEX zeno TOF 7600 high-resolution mass spectrometer was set as follows: DuoSpray ion source temperature was 550°C; curtain gas (CUR) was 35 psi; both atomizing gas (GS1) and drying gas (GS2) were 55 psi; the declustering potential (DP) was ±70 V; the ion spray voltage was set to 5500 V or -4500 V. The mass range of TOF-MS and TOF-MS / MS was set to 100-2000 Da and 50-2000 Da, respectively, and the corresponding collision energy (CE) values were 10 V and 45 V (±15 V), respectively. The data acquisition and processing were performed by OS (v1.7, SCIEX, Chromos, Singapore), and chromatograms and secondary mass spectra of 81 standard samples were obtained, which covered 40 lipid subclasses that could effectively represent the lipid subclasses involved in the existing lipidome quantitative analysis methods, including phosphorylated and hydroxylated lipid compounds. The chromatogram results of one representative lipid compound selected from each of the 40 lipid subclasses are shown in FIG. 1. Figure 1 M1 to M5 in FIG. 2 and Figure 2 M1 to M5 in FIG. 3.

[0055] M1 to M5 in FIG. 4. Figure 1 M1 to M5 in FIG. 5 and Figure 2 M1 to M5 in FIG. 6. It can be known from the results of the chromatographic elution behaviors of the 40 lipid subclasses corresponding to M1 to M5 in FIG. 5 and the effects of different chromatographic columns on the mass spectrometry signal intensity of the 40 lipid subclasses in FIG. 6 that the chromatographic column used in the reversed-phase chromatography is preferably a chromatographic column such as BEH C 18 , HSS T3 or EP C 18 , and more preferably EP C 18 . The column pressure of this chromatographic column is lower, and the lipid compounds of a large number of samples can be quantified without easy column blockage and higher stability.

[0056] (2) Optimization of mobile phase and gradient elution

[0057] SCIEX zeno TOF 7600 (SCIEX, Chromos, Singapore) coupled with Shimadzu UPLC system (Kyoto, Japan) was used for data acquisition, and the retention time (t R ) and mass spectrometry data of different standard lipid compounds were obtained based on high-resolution mass spectrometry information-dependent acquisition (IDA) mode. Among them, the ultra-high performance liquid chromatography method selected the preferred chromatographic column EPC 18 , and further studied the influence of different mobile phase compositions on the quantitative effect of lipid standards, as follows:

[0058] Table 3 Mobile phase and elution program settings of ultra-high performance liquid chromatography

[0059]

[0060] Among them, M8 and M9 are control group 1 and control group 2, respectively, referring to the literature “High-Throughput Plasma Lipidomics: Detailed Mapping of the Associations with Cardiometabolic Risk Factors” and “Untargeted lipidomics reveal association of elevated plasma ceramide levels with reduced survival in metastatic castration-resistant prostate cancer patients”, M 18 , M 11 and M 12 are control group 3 and control group 4, M 11 is the traditional reversed-phase chromatography condition, and M 12 is the hydrophilic interaction chromatography condition; the gradient elution program corresponding to the elution gradient G1 to G4 of the above ultra-high performance liquid chromatography is shown in Table 4.

[0061] Table 4 Elution gradient settings of 4 mobile phases

[0062]

[0063]

[0064] The mass spectrometry parameter settings, data acquisition and processing are the same as above, and the quantitative results of each group are shown in M5-M 12 in Figure 1 and M5-M 12 in Figure 2 .

[0065] From Figure 1 The results corresponding to M5-M9 show that, compared with the mobile phase condition M9 of the traditional reversed-phase chromatography (no acid is added to the mobile phase and the pH values of mobile phases A and B are the same), the addition of acid to the mobile phase to make the pH value be 2.8-3.3 (the pH values of mobile phases A and B are the same in the reversed-phase chromatography mobile phase condition such as M5, M6 and M8) can effectively improve the chromatographic peak shape of acid-sensitive lipid compounds (such as PA, S1P and PS) and reduce the chromatographic tailing factor, but the corresponding low pH value can also inhibit the response values of FFA (such as FFA 16:0-d9) and part of lipid compounds such as Cho. Figure 2 From A: M5-M 10 ).

[0066] Compared with the mobile phase condition such as M5, M6 and M8 of the reversed-phase chromatography, the composition of mobile phases A and B is changed, that is, the pH values of mobile phases A and B, especially when the pH of mobile phase B is less than 3, the chromatographic peak shape of these phosphorylated lipid compounds is effectively improved. However, when the pH is less than 3, the ionization inhibition of a small number of lipid compounds such as FFA is more obvious in the ionization process in the negative ion mode. In order to give priority to the chromatographic separation of most lipid compounds and the ionization of mass spectrometry as much as possible, we use two acid mobile phases for elution. This is the first time we obtain the best results by adjusting the addition amount of formic acid to make the pH value of mobile phase A and the pH value of mobile phase B different. It is found that when the pH value of mobile phase A is 2.5-3.0 and the pH value of mobile phase B is 2.5-5.0, not only the good chromatographic separation of acid-sensitive lipid compounds such as PA, S1P and PS can be achieved, but also the response values of part of lipid compounds such as free fatty acid and free cholesterol can be significantly improved, preferably the pH of mobile phase A is 2.5-3.0 and the pH of mobile phase B is 3.0-4.0, more preferably the pH of mobile phase A is 2.7 and the pH of mobile phase B is 4.0.

[0067] Based on the optimized mobile phase condition (M7), the separation effect of each target lipid compound can be better by adjusting the gradient elution condition thereof. In the present application, the gradient elution condition of the mobile phase can be adjusted according to the specific experimental situation to make the chromatographic peak of the target substance reach baseline separation. In the present embodiment, M 10 corresponding elution condition, the separation effect is better as a whole.

[0068] In addition, under the reversed-phase chromatography M 11 condition containing a neutral mobile phase, the chromatographic column kinetex C 18 with a particle size of 2.6 μM is more suitable for the chromatographic separation of nonpolar metabolites (cholesterol ester and triglyceride) Figure 1 M 11), highly separated to reduce the ion suppression effect of triglycerides on cholesteryl esters. In the presence of a basic mobile phase in reversed-phase chromatography M 12 Under the conditions, the chromatographic column BEH HILIC is more suitable for the chromatographic separation of most phospholipid subgroups, effectively separates different subgroups of lipid compounds according to the polarity of the head group and reduces the interference between isomers Figure 1 In M 12 ).

[0069] (3) Mass spectrometry parameter optimization

[0070] In order to improve the quantitative sensitivity of each lipid subgroup as much as possible, this embodiment uses Shimadzu NexeraX2 chromatography coupled with SCIEX QTRAP 6500plus mass spectrometry system to qualitatively and quantitatively analyze various lipid standards, and systematically optimizes multiple mass spectrometry parameters such as parent ions, daughter ions, t R , ion source temperature, declustering voltage, collision energy value, etc. of each lipid subgroup.

[0071] ① Optimization of MRM ion pairs

[0072] In view of the large differences in chemical structures of each lipid subgroup and the differences in response intensities of different ion fragments, this experiment summarizes the ionization modes and MS 2 fragmentation rules of each lipid subgroup in positive and negative ion modes. This experiment first uses three chromatographic conditions (M 10 , M 11 and M 12 ) to obtain MS and MS 2 spectra data of 40 lipid subgroup standards (see Figure 3 ), thereby summarizing the main ionization forms and characteristic fragments for qualitative and quantitative analysis of these lipid subgroups. At the same time, this experiment also combines lipid-related databases (such as LIPID MAPS, LipidBlast and HMDB), MS-DIAL software and published literature to further organize the main ionization forms and corresponding MS 2 qualitative / quantitative fragments of 112 lipid subgroups (see Table 5).

[0073] Table 5 Optimal theoretical characteristic fragment database of lipid compounds

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] Among them, lipid subclasses such as ACar, Cer, HexCer, and Cho, and their esters, have relatively simple ionization forms and MS. 2 Qualitative / quantitative fragmentation. For example, in positive ion mode, ACar primarily consists of [M] fragments. + They are ionized in form, and their characteristic fragments are 85 m / z, i.e., carnitine-related fragments. SPH, S1P, Cer, HexCer, and Hex2Cer are mainly [M+H] ionized. + The ionization process is characterized by 264 Da sphingosine residues containing eighteen carbons (C18). The three GLs subclasses, MAG, DAG, and TAG, all form [M+NH4] in positive ion mode. + The ions exhibit a neutral loss of 35 Da (dehydration and deamination) under different collision energies. Simultaneously, MAG also produces the characteristic fragment [M+NH4-(H2O+NH3)]. + Both DAG and TAG produce [M+NH4-(R i COOH+NH3)] + (i represents the i-th fatty acid chain). Similarly, except for PC, [M+CH3COO] - Apart from formal ionization, most GPs form [MH] in negative ion mode. - Ions generate corresponding characteristic fragments. For example, PE generates a 140 Da phosphate ethanolamine fragment, while PI generates a 241 Da phosphate inositol fragment. To obtain the optimal quantitative sensitivity for lipid compounds, the preferred ion pairs are those shown in the table (i.e., the parent ion with the highest response value in each lipid subclass and the MS). 2 Characteristic fragments) are used as quantitative ion pairs in MRM acquisition mode.

[0080] ② Optimization of mass spectrometry parameters for the optimal ion pairs of lipid standards in 30 lipid subclasses

[0081] Using 30 lipid subclass standards and the optimal MRM qualitative and quantitative ion pairs, the optimal quality spectroscopic parameters for each subclass were determined by optimizing different ion source temperatures (5 values ​​set within the range of 300–550 °C), declustering voltages (20 values ​​set within the range of 10–100 V), and collision energies (20 values ​​set within the range of 10–70 V). The results are as follows: Figure 4 As shown in the figure, A to C represent ion source temperature optimization, and D to F represent cluster removal voltage parameter optimization.

[0082] Depend on Figure 4From the results of A to C, except PE, LPE and FFA, the response intensity of most lipid ion pairs decreased with the increase of temperature in the range of 350-550℃. Therefore, the appropriate ion source temperature was selected as 350℃. From the results of D to F, in the process of increasing the de-clustering voltage from 60V to 100V, the response intensity of most lipid subclasses did not change significantly. Considering that the de-clustering voltage corresponding to the highest response intensity of Cho and CE was 70V, the de-clustering voltage of 70V was preferred as the de-clustering voltage value of each lipid subclass. Figure 4

[0083] From the results of D to F, in the process of increasing the de-clustering voltage from 60V to 100V, the response intensity of most lipid subclasses did not change significantly. Considering that the de-clustering voltage corresponding to the highest response intensity of Cho and CE was 70V, the de-clustering voltage of 70V was preferred as the de-clustering voltage value of each lipid subclass. Figure 4 From the results, the collision energy value of most lipid subclasses containing 2-3 fatty acyl chains (or containing the basic skeleton of Cer) was in the range of 35-55V. It was speculated that because the fatty acids in these lipid subclasses were esterified with the hydroxyl group of glycerol (or the amino group of sphingosine) to form ester bonds, MS needed to provide higher collision energy to fragment the fatty acid fragments in positive or negative ion mode. Conversely, the ester bond formed by the esterification of the phosphate functional group with the hydroxyl group on glycerol required relatively lower collision (mostly between 30-40V), such as LPA, LPS, PA, PS and SM losing the phosphate-containing functional group with the optimal collision energy value of 35-40V, while MAG, SPH and Cho only needed 20-30V collision energy to dehydroxylate or deaminate. Because the hydroxyl group on the Cho skeleton was unstable, Cho and CE only needed 20-25V to produce dehydroxylated cholesterol residue fragments.

[0084] In summary, the ion source temperature, de-clustering voltage and collision energy value of 40 lipid subclasses were investigated and optimized in this experiment. The results showed that the ion source temperature was 350℃, the de-clustering voltage was 70V, and the collision energy value of most lipid subclasses containing 2-3 fatty acyl chains (or containing the basic skeleton of Cer) was in the range of 35-55V. At the same time, the collision energy required for the ester bond of the phosphate functional group with glycerol was 30-40V, such as LPA, LPS, PA, PS and SM losing the phosphate-containing functional group with the optimal collision energy value of 35-40V, while the collision energy of Cho and CE was 20-25V. In addition, the lipid subclasses without standard were subjected to the optimal mass spectrometry parameters of the lipid subclass with similar structure.

[0085] Example 2 Quantitative analysis of lipidome in biological matrix

[0086] Thirteen representative biological matrices were selected for lipidome extraction and quantitative analysis, including human body fluids (plasma, urine), mouse tissues (heart, liver, brain, kidney and lung), bacteria (Escherichia coli), fungi (yeast), and plants (Arabidopsis thaliana rosette leaves, lichen male and female). Among them, plasma was subjected to lipidome extraction by two extraction methods, Matyash method and Sarafian method, and other biological matrices were subjected to lipidome extraction by Sarafian method.​

[0087] To obtain more high-quality secondary mass spectrometry (MS / MS) information for lipid compounds, the lipid extracts from the above 13 biological matrices were mixed and subjected to hydrophilic interaction chromatography (M2C) conditions. 12 The mixture was eluted and collected at elution intervals of 0.5–1 min. After drying under nitrogen at room temperature, it was reconstituted with 50 μL DCM / MeOH (1:1, v / v). Then, it was subjected to two reversed-phase chromatography methods as described above (M... 10 and M 11 ) and hydrophilic chromatographic conditions (M 12 The retention times, characteristic ions, and secondary mass spectra of different lipid compounds in the collected lipid fractions were obtained by combining high-resolution mass spectrometry.

[0088] After preliminary identification using MS-DIAL qualitative analysis software, manual identification and matching were performed based on the established database of characteristic precursor ions and qualitative and quantitative characteristic fragments of different lipid subclasses (Table 5). A total of 2550 lipid compounds (covering 93 lipid subclasses) with retention times and secondary mass spectra were identified, as shown in Table 6.

[0089] Table 6 Comparison of quantitative results under different chromatographic conditions

[0090]

[0091]

[0092]

[0093]

[0094] Table 6 shows that traditional reversed-phase chromatography conditions (M 11 ) and hydrophilic interaction chromatographic conditions (M 12 ) identified 1560 lipid compounds (covering 76 lipid subclasses) and 805 lipid compounds (covering 63 lipid subclasses), respectively. Compared to M 11 and M 12 Two chromatographic conditions, optimized chromatographic conditions (M) 10) can effectively identify 1657 lipid compounds (covering 78 lipid subclasses) containing retention time and secondary mass spectrum, especially 6 LPA, 17 LPS, 21 PA, 34 PS, 1 22,23-dihydrobrassicasterol ester (CASE), 2 sterol glycoside (SHex) and 5 sulfated sterols (SSulfate). At the same time, the optimized chromatographic conditions identified 30 ACar, 52 FFA, 15 2HOFA and 9 DCA, all of which exceed the number of lipid compounds identified under the other two chromatographic conditions, indicating that the overall quantitative coverage of this method is higher.

[0095] In summary, this method not only achieves good chromatographic separation of acid-sensitive lipid compounds such as PA, PS, LPA and LPS, but also significantly improves the response value of some lipid compounds such as free fatty acids and free cholesterol, and is suitable for high-coverage quantitative analysis of lipidomics in various typical biological samples such as human body fluids (plasma, urine), mouse tissues (heart, liver, brain, lung and kidney), bacteria (E. coli), fungi (yeast), lower plants (lichen male and female), higher plants (Arabidopsis thaliana) and the like.

[0096] Example 3 Fitting of the relationship between the structure of lipid compounds and retention time (QSRR)

[0097] Based on the above identified biological matrix lipidomics data, the chemical characteristics of different lipid compounds in the same subclass were quantitatively processed according to the total number of carbon (c) and the number of carbon-carbon double bonds (d) contained in the fatty acyl chain, ether chain, alkenyl ether chain or / and sphingosine in the compound structure. Among them, 30 of the 56 identified FFA were verified by standard retention time. Due to the nonlinear relationship between the dependent variable and the independent variable, polynomial and product terms (such as c, c 2 , c 3 , dc 3 , dc 2 , dc 2 ) of these parameters also need to be obtained. The LM function of R language software (V4.1.0) was used to construct a QSRR new retention time prediction model based on a multivariate polynomial regression equation; variable selection was completed by stepwise regression and full subset regression, and the optimal model was selected by 10-fold cross-validation. Finally, the t R values of the validation set were predicted using the optimal model, and the correlation analysis and deviation value (Δt R ) calculation were performed with the experimental t R values to evaluate the accuracy of the new retention time prediction model.

[0098] The results show that when d = 0, the relationship between c and t R can be described by the equation t R= -0.0016c 3 + 0.0692c 2 - 0.3835c + 1.1522 (correlation coefficient R 2 = 0.9981). When c is a constant, the relationship between t R and d can be described by the equation y = ki d + k2 (ki and k2 are constants). For example, in FFA containing 22 carbon numbers (i.e., c = 22), the corresponding t R E and d is t R E = -0.6321d + 9.2227, R 2 = 0.9942; in FFA containing 20 carbon numbers (i.e., c = 20), the corresponding t R E and d is t R E = -0.6760d + 8.3933, R 2 = 0.9955; in FFA containing 18 carbon numbers (i.e., c = 18), the corresponding t R E and d is t R E = -0.7630d + 7.4840, R 2 = 0.9955; in FFA containing 16 carbon numbers (i.e., c = 16), the corresponding t R E and d is t R E = -0.8540d + 6.4060, R 2 = 0.9978.

[0099] We analyzed and tried to use a polynomial regression equation to describe the relationship between the retention time of lipid compounds t R , c and d at the same time. Using the LM function of R language software (V4.1.0), variable screening was completed by stepwise regression and full subset regression, and the optimal model was selected by 10-fold cross-validation. It was found that the equation t R E = m1c 3 + m2c 2 + m3c + m4d + m5 + m6c 3 d + m7c 2 d + m8cd can be used to predict the retention time of lipid compounds. For example, the QSRR prediction model of FFA is shown in Table 7, and the QSRR prediction model of glycerolipid is shown in Table 8.

[0100] Table 7 The corresponding tR E Polynomial equation fitting results between c and d

[0101] New variable Coefficient Significance (p) Polynomial equation constant term -3.945821 4.09E-15 c 3 ]]> -0.000200 0.003793 c 2 ]]> 0.000258 0.945175 c 0.692409 1.26E-14 d -5.450528 2.5E-06 dc 3 ]]> 0.000388 0.000466 dc 2 ]]> -0.026387 0.00027 dc 0.610999 0.0001

[0102] Note: the fitting degree (R 2 ) of this equation fitting model is 0.9984, and the new variables are all generated from variables c and d.

[0103] Table 8 t R E Polynomial equation fitting results between c and d

[0104] New variable Coefficient Significance (p) Polynomial equation constant term -2.401860 4.32E-12 c 3 ]]> 0.000056 4.54E-32 c 2 ]]> -0.010004 5.58E-52 c 0.678853 2E-102 d -1.282941 5.87E-31 dc 3 ]]> 0.000001 0.163436 dc 2 ]]> -0.000453 0.002486 dc 0.040768 5.67E-09

[0105] Note: the fitting degree (R 2 ) of this equation fitting model is 0.9918, and the new variables are all generated from variables c and d.

[0106] Evaluation of the prediction model: the polynomial equation fitting modeling was performed for each lipid subclass of the 1871 lipid compounds identified in the above different biological samples. Among them, the theoretical calculation and experimental determination t R deviation of 1760 lipid compounds (accounting for 94.10% of the total) were less than ±0.15 min, and the correlation equation coefficient and R 2 were both 0.9993, i.e. the correlation was very high. It can be seen that the polynomial regression equation (t R E = m1c 3 + m2c 2 + m3c + m4d + m5 + m6c 3 d + m7c 2 d + m8cd) fitting modeling can accurately predict the chromatographic retention time of different lipid compounds in the same subclass, and provides a more optimal MRM retention time collection window for the qualitative and quantitative analysis of lipid compounds without commercial standards.

[0107] Example 4 High-coverage lipidome quantitative analysis kit for multiple biological matrices

[0108] Reagent 1 of the kit: 70 lipid compound standard powders or stock solutions containing stable isotope labels and odd-numbered carbon fatty acyl chains (covering 30 lipid subclass standards) were appropriately diluted or dissolved in dichloromethane:methanol (1:1, v / v) or chloroform:methanol (1:1, v / v) to prepare individual stock solutions (0.1–10 mM). These standard stock solutions were diluted, mixed, and dried under nitrogen, then added to chloroform:methanol (1:1, v / v) to obtain lipid internal standard mixtures (Table 9). The lipid internal standard mixtures were serially diluted 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, and 1024 times to prepare lipid standard working curves L1–L13.

[0109] Table 9 shows the concentration ranges (in μM) of the internal standard mixtures corresponding to the seven biological matrix groups in Reagent 1. B-1 to B-7 are suitable for lipidomic quantitative analysis of the seven biological matrix samples: plasma (B-1), cells (B-2), Arabidopsis thaliana rosette leaves (B-3), Escherichia coli (B-4), feces (B-5), liver (B-6), and urine (B-7), respectively.

[0110]

[0111]

[0112]

[0113]

[0114] The “-” in the table indicates that the standard is not contained.

[0115] The preparation of other reagents in the kit is shown in Table 10:

[0116] Reagent number Reagent solvent preparation composition Reagent 2 Chromatographic grade isopropanol (Merck, Germany, purity all >99%) Reagent 3 Ultrapure water (Merck Millipore Ltd, Milli-Q ultrapure water system purification, Germany) Reagent 4 Chromatographic grade methanol (Merck, Germany, purity all >99%) Reagent 5 Chromatographic grade methyl tert-butyl ether (Merck, Germany, purity all >99%) Reagent 6 Dichloromethane and methanol (1:1, v / v) Reagent 7 Dichloromethane and methanol (1:1, v / v, containing 0.1% FA)

[0117] The extraction efficiency of different extraction methods for 40 lipid subclass standards was compared, and the results are as follows: Figure 6 As shown. The Sarafiah extraction method, i.e., the single-phase extraction method using IPA-H2O solution, achieves recoveries of 80-120% for most lipid subclasses. Figure 6 (A) However, the Matyash extraction method, i.e., the two-phase extraction method using MTBE-MeOH-H2O solution, has an extraction efficiency of less than 60% for hydrophilic lipid compounds such as LPA, LPG, LPI, GM1, and GM3. Figure 5 (A). However, the Sarafiah extraction method exhibits stronger inhibition of ionization of LPE and DAG compounds in plasma, i.e., matrix effect. Therefore, in this invention, plasma samples were subjected to characteristic lipidome extraction using both of the above extraction methods, as detailed below:

[0118] For plasma samples: To quantitatively determine acidic, low response and / or strongly hydrophilic lipids in human plasma, the optimized method proposed by Sarafian was adopted for extraction. Briefly, 12 μΐ^of plasma sample was accurately pipetted into a 1.5 mL EP tube, and 500 μΐ^of pre-cooled reagent 2 and 12 μΐ^of reagent 1 were added on ice. After mixing for 1 min, 88 μΐ^of pre-cooled reagent 3 was added and mixed for 5 s. After protein precipitation and lipidome extraction for 1 h, the supernatant was centrifuged at 14,000 rpm for 10 min at 4 °C, and then blown dry with nitrogen at room temperature, followed by re-dissolution with 36 μΐ^of reagent 7, which was recorded as plasma extract A. 8 μΐ^of plasma sample was accurately pipetted into a 1.5 mL EP tube, and 225 μΐ^of ice-cold reagent 4 and 8 μΐ^of reagent 1 were added on ice. After vortex mixing for 1 min, 750 μΐ^of low-temperature reagent 5 was added and stirred for 5 s. After extraction on ice for 1 h, 180 μΐ^of reagent 6 was added, inverted 10 times, and centrifuged at 14,000 rpm for 10 min at 4 °C, and the upper organic phase was collected and transferred to a new tube. After adding 750 μΐ^of reagent 5 to the lower aqueous phase, the tube was inverted 10 times and centrifuged under the above-mentioned conditions for 10 min. The two upper organic phases were mixed and blown dry with nitrogen at room temperature, and then re-dissolved with 80 μΐ^of reagent 6, which was recorded as plasma extract B. For non-plasma samples, the Sarafian extraction method can be used for lipidome extraction of each biological matrix, and the specific extraction method is as follows:

[0119] For urine samples, 50 μΐ^of urine sample was accurately pipetted into a 1.5 mL EP tube, and 50 μΐ^of ice-precooled reagent 3, 500 μΐ^of reagent 2, and 20 μΐ^of reagent 1 were added. After mixing for 1 min, and after protein precipitation and lipidome extraction for 1 h, the supernatant was centrifuged at 14,000 rpm for 10 min at 4 °C, and then blown dry with nitrogen at room temperature. Re-dissolution was performed with 150 μΐ^of reagent 6, which was recorded as urine extract B. 75 μΐ^of extract B was taken and blown dry with nitrogen, and re-dissolution was performed with 40 μΐ^of reagent 7, which was recorded as urine extract A.

[0120] For E. coli (DH5a) samples, 25 mL was taken and centrifuged at 4 °C and 3000 rpm for 15 min. After discarding the supernatant, 4 mg of wet sample was added with 200 μΐ^of pre-cooled reagent 3, 1 mL of reagent 2, and 20 μΐ^of reagent 1. After vortex mixing for 30 s and repeated freezing and thawing with liquid nitrogen-ordinary water for 3 times, ice water bath ultrasonic was performed for 10 cycles (45 Hz and 80 Hz, each for 30 s). After centrifugation at 4 °C and 12,000 rpm for 10 min, the supernatant was taken out. The above extraction operation was repeated twice, and the supernatant was combined and blown dry with nitrogen at room temperature. Re-dissolution was performed with 200 μΐ^of reagent 6, which was recorded as E. coli extract B. 100 μΐ^of extract B was taken and blown dry with nitrogen, and re-dissolution was performed with 40 μΐ^of reagent 7, which was recorded as E. coli extract A.

[0121] For cell sample, accurately take 20 mg, add 200 μL reagent 3, 1000 μL reagent 2, 20 μL reagent 1 pre-cooled by ice. After vortexing for 30 s, freeze-thaw for 3 times with liquid nitrogen-ordinary water, and then ultrasonic for 10 cycles (45 Hz and 80 Hz, 30 s each) in ice water bath. After vortexing for 1 min, centrifuge at 12,000 rpm for 10 min at 4 ℃, and take the supernatant. Repeat the above extraction operation for 2 times, combine the supernatants, and dry under nitrogen at room temperature. Redissolve with 200 μL reagent 6, and mark as cell extract B. Take 100 μL extract B, dry under nitrogen, and redissolve with 40 μL reagent 7, and mark as cell extract A.

[0122] For liver sample, accurately take 20 mg, add 200 μL reagent 3, 1000 μL reagent 2, 40 μL reagent 1 pre-cooled by ice. After vortexing for 30 s, crush twice by tissue crusher (30 Hz, 60 s). After standing for extraction for 30 min, centrifuge at 14,000 rpm for 10 min at 4 ℃, and take the supernatant. Repeat the above extraction operation for 3 times, combine all the supernatants, and dry under nitrogen at room temperature. Redissolve with 300 μL reagent 6, and mark as liver extract B. Take 150 μL extract B, dry under nitrogen, and redissolve with 60 μL reagent 7, and mark as liver extract A.

[0123] For fecal sample, accurately take 20 mg, add 200 μL reagent 3, 1000 μL reagent 2, 20 μL reagent 1 pre-cooled by ice. After vortexing for 30 s, crush twice by tissue crusher (30 Hz, 60 s). After standing for extraction for 30 min, centrifuge at 14,000 rpm for 10 min at 4 ℃, and take the supernatant. Repeat the above extraction operation for 3 times, combine all the supernatants, and dry under nitrogen at room temperature. Redissolve with 300 μL reagent 6, and mark as fecal extract B. Take 150 μL extract B, dry under nitrogen, and redissolve with 60 μL reagent 7, and mark as fecal extract A.

[0124] For Arabidopsis rosette leaf sample, accurately take 10 mg, add 200 μL reagent 3, 1000 μL reagent 2, 20 μL reagent 1 pre-cooled by ice. After vortexing for 30 s, crush twice by tissue crusher (30 Hz, 60 s). After standing for extraction for 30 min, centrifuge at 14,000 rpm for 10 min at 4 ℃, and take the supernatant. Repeat the above extraction operation for 3 times, combine all the supernatants, and dry under nitrogen at room temperature. Redissolve with 300 μL reagent 6, and mark as Arabidopsis extract B. Take 150 μL extract B, dry under nitrogen, and redissolve with 60 μL reagent 7, and mark as Arabidopsis extract A.

[0125] In the ultra-high performance liquid system, the extract A of different biological matrixes is eluted and separated in turn by using the mobile phase conditions (M 10 ) of reversed-phase chromatography, which can elute and separate sphingosine, fatty acid, lysophosphatidic acid and other lipid subclasses with many acid-sensitive, low response or / and strong hydrophilic characteristics in turn. The extract B of biological matrixes can be eluted and separated in turn by using one or more mobile phases in M 10 , M 11 and / or M 12 to elute and separate conventional lipid compounds.

[0126] In the present embodiment, the extract B of different biological matrixes is eluted and separated in turn by using the conditions (M 11 ) of reversed-phase chromatography to elute and separate monoglyceride, diglyceride, ceramide, dihydroceramide, hexosylceramide, dihexosylceramide, phosphatidylinositol, triglyceride and cholesterol in turn; and by using the conditions (M 12 ) of hydrophilic interaction chromatography to elute and separate phosphatidylglycerol, phosphatidylethanolamine, ether phosphatidylethanolamine, ene ether phosphatidylethanolamine, phosphatidylcholine, ether phosphatidylcholine, ene ether phosphatidylcholine, lysophosphatidylethanolamine, lysophosphatidylcholine and sphingomyelin in turn.

[0127] The optimized MS parameters of all the above lipids based on MRM are as follows: collision gas (CAD), medium; CUR, 40 psi; GS1 and GS2 are both 55 psi; the ion spray voltage value is set to 5500 V or -4500 V; the ion source temperature and DP are 350 °C and ± 70 V respectively. At the same time, based on the characteristic ion pair information of different lipid subclasses summarized in the above embodiment 1, combined with the identified and predicted lipid compound species (including retention time), the MRM scanning parameters are constructed, including characteristic parent ions, characteristic qualitative and quantitative fragments, optimized collision energy values (Table 5), retention times and their collection windows. The data acquisition and processing use Analyst and OS (v1.7, SCIEX, Chromos, Singapore).

[0128] To determine whether the quantitative sensitivity, the quantitative linear range and the stability of the plasma lipidome quantitative analysis method meet the actual quantitative needs, the present study was performed to investigate the linear range, the limit of detection (LoD) and the limit of quantitation (LLoQ), the extraction recovery and accuracy, the intra-day and inter-day precision, the storage stability at 4℃ and -80℃, etc. The calculation method of lipid concentration was to divide the peak area of each analyte by the peak area of the corresponding internal standard (IS), and then multiply by the concentration value of IS. The lipid compounds were quantitatively analyzed using the structurally similar internal standard, while the lipid compounds without suitable internal standard could only be relatively quantitatively analyzed by peak area and batch correction method was used to eliminate batch effects. We used the calibration curve of internal standard to evaluate the linearity and sensitivity of the method, and the sensitivity, linearity, extraction efficiency, precision and stability of the method were systematically verified. According to the guidelines of the U.S. Food and Drug Administration (FDA), the limits of detection (LOD) and the limits of quantitation (LLOQ) of different subclasses were defined as the amount of analyte on the chromatographic column at signal-to-noise ratios (S / N) of 3 and 10, respectively, which were calculated from the lowest amount on the calibration curve and the corresponding signal-to-noise ratio. The lipid internal standard was added before and after the extraction of the blank matrix, and the extraction recovery rate (n=6) was calculated by the ratio of the corresponding peak areas. The precision and stability of the plasma lipidome were evaluated by using representative low (concentration ratio <0.3), medium (concentration ratio 0.3-0.7) and high (concentration ratio 0.7-1.0) concentration internal standard lipid compounds. The concentration ratio was obtained by the ratio of the concentration of each compound to the highest concentration in the same subclass. The precision was evaluated by the coefficient of variation (CV) of intra-batch, intra-day and inter-day measurements. The extracted plasma lipidome was stored at -4℃ and -80℃ for 1 day and 7 days, respectively, to evaluate its stability, and the results were as follows:

[0129] Table 11 Linear range, LoDs and LLoQs of 27 representative lipid subclasses and their 67 standard products in the plasma lipidome quantitative analysis method

[0130]

[0131]

[0132]

[0133] The results showed that the linear regression coefficient R of 67 lipid compounds in 27 representative lipid subclasses 2All values ​​were greater than 0.99. The upper limits of quantification (ULOQ) for TAG, LPC, PC, FFA, MAG, and CE were 0.008, 0.020, 0.031, 0.031, 0.074, 0.109, and 0.610 mM, respectively. Considering that the extracts were diluted 3-fold (or 10-fold) before quantification, the ULOQs for these subclasses in plasma were 0.080, 0.198, 0.307, 0.094, 0.745, 1.091, and 1.828 mM, respectively. Meanwhile, the limits of detection (LOD) for most lipids (such as S1P, LPA, LPS, LPI, LPG, and PA) ranged from 0.012 amol to 0.869 fmol, while the lower limits of quantification (LLOQ) ranged from 0.004 to 1.962 pmol. The lipid quantification method developed in this invention can simultaneously meet the quantitative analysis needs of both low-content and high-content lipid compounds (such as TAG lipid compounds with a total carbon number of C44 to C60 in the fatty acyl chain), covering a wide range of lipid compounds. The reported concentration range in the plasma of 1950 people.

[0134] To evaluate the stability of high-coverage lipidomics quantification methods, the intra-batch and inter-batch precision of most detected lipid compound standards was less than 30%. Figure 6 The total concentrations of Cho, CE, TAG, DAG, PC, PE, PI, LPC, LPE, SM, Cer-NS, HexCer, Hex2Cer, S1P, and FFA in plasma QC were compared with... The total concentrations reported in the plasma of 1950 individuals were similar, spanning eight orders of magnitude (from pM to mM). Compared to other literature reports, this invention also describes the concentration ranges and total concentrations of other key functional lipid subclasses (such as LPA, PA, LPG, GM3, PC-O, LPE-P, LPC-O, LPC-P, SPH, dhS1P, and ACar). Figure 7 ).

[0135] Furthermore, through six consecutive quantitative analyses of the lipidome of human plasma, cells, urine, mouse liver and feces, Escherichia coli, and Arabidopsis thaliana rosette leaves, it was found that ( Figure 8), a total of 2375 lipid compounds (covering 104 sub-classes) can be quantitatively analyzed. Among them, the current reported literature method can only quantitatively analyze 901, 730 and 316 in cells, plasma and liver (J Am Soc Mass Spectrom. 2021, 32(11): 2655-2663.; Front Cardiovasc Med. 2022, 9:848840.; J Lipid Res. 2021, 62:100104.), while the present application can quantitatively analyze 1810, 1722, 1476 and 1592 lipid compounds (covering 85, 78, 94 and 91 sub-classes, respectively) in cells, plasma, feces and liver, which is more than 2 times the number of lipid compounds quantified in the prior art Figure 9 ). It is worth noting that high-coverage quantitative analysis of lipidomics in fecal samples has not been reported in the prior art. In addition, the present application also realizes the quantitative analysis of many special lipid compounds such as S1P, DGTS, SHexCer-HS, HexCer_HS, HexCer_AP, HexCer_HDS, Cer_HDS, Cer_HS, Cer_NP, Cer_AP, CerPE, ASM, AHexCer, SISE, BASulfate, BRSE, CASE, AHexBRS, AHexCAS, AHexSIS, AHexSTS, DCAE, SHex, etc. modified by phosphorylation, hydroxylation, glycosylation and sulfation, indicating that the present application can be suitable for high-coverage quantitative analysis of lipidomics in different typical biological matrices, and provides an important innovative quantitative analysis technology for lipid structure and function, mechanism related research.

[0136] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the scope of the technical solution of the present application. Any brief introduction, modification, equivalent change and modification of the above embodiments made according to the technical essence of the present application, as long as it does not deviate from the technical solution content of the present application, still belongs to the scope of the technical solution of the present application.

Claims

1. A method for high-coverage quantitative analysis of lipidome based on liquid chromatography-mass spectrometry technology, characterized in that, Specifically comprising the following steps: (1) extracting lipids: extracting lipids from the biological matrix to be tested to prepare a sample for injection; (2) chromatographic separation: the first needle adopts reverse phase chromatography I (RPLC-I) to elute and separate acid-sensitive lipid compounds, and the elution of the RPLC-I uses different acidic mobile phases A and B with lower pH values, wherein the mobile phase B has a pH of 2.5-5.0, and the mobile phase A has a pH of 2.0-3.0; Preferably, reverse phase chromatography II (RPLC-II) and / or hydrophilic interaction (HILIC) chromatography are also used to elute and separate different lipid compounds, wherein RPLC-II is used to elute and separate one or more of glycerolipids, ceramides and derivatives thereof, phosphatidylinositol, and cholesterol esters; and hydrophilic interaction chromatography is used to elute and separate one or more of phosphatidyl compounds and derivatives thereof, and sphingomyelin; (3) quantitative analysis of lipid groups: non-targeted lipidomics analysis is performed in combination with data-dependent scanning or data-independent scanning mode in high-resolution mass spectrometry, or lipid group quantitative analysis is performed in combination with mass spectrometry parameters of dynamic multiple reaction monitoring mode; the mass spectrometry parameters include parent ions, daughter ions, collision energy values, source region temperatures, and declustering voltages.

2. The method for quantitative analysis of a lipid panel according to claim 1, wherein, The RPLC-I elution uses an acidic mobile phase B with a pH of 2.5-5.0, and an acidic mobile phase A with a pH of 2.5-3.0; preferably, the mobile phase B is an acidic mobile phase with a pH of 3.0-4.

0.

3. The method for quantitative analysis of a lipid panel according to claim 1 or 2, wherein The RPLC-I elution and separation include acid-sensitive lipid compounds such as phosphatidic acid, sphingosine-1-phosphate, and phosphatidylserine.

4. The method of quantitative analysis of a lipid panel according to claim 3, wherein, The RPLC-I uses a chromatographic column selected from BEH C 18 , HSS T3, Premier BEH C 18 , Premier HSS T3 or EP C 18 , preferably EP C 18 with a particle size of 1.8 pm.

5. The method of quantitative analysis of a lipid panel according to claim 4, wherein, Step (2) injection of three needles, the first needle adopts RPLC-I elution, the second needle adopts RPLC-II elution, and the third needle adopts HILIC elution, and the first needle, the second needle, and the third needle are sequentially injected and eluted or injected and eluted in parallel; The RPLC-II elution separates cholesteryl esters from triglycerides, the mobile phase B is preferably a neutral mobile phase, the chromatographic column is preferably a kinetex C 18 with a particle size of 2.6 pm; The hydrophilic interaction chromatography separates different subclasses of lipid compounds according to the polarity of the head group and reduces the interference between isomers, and the mobile phase B thereof is preferably an alkaline mobile phase, and the chromatographic column is preferably a BEH HILIC.

6. The method of quantitative analysis of a lipid panel according to claim 5, wherein, The lipid concentrations of the samples of the second needle and the third needle are the same, and the lipid concentration of the sample of the first needle is higher than that of the second needle or the third needle, and preferably the lipid concentration of the sample of the first needle is 2-3 times that of the sample of the second needle or the third needle.

7. The method for quantitative analysis of a lipidome according to any one of claims 1 to 6, wherein In mass spectrometry data acquisition, the ion source temperature in the mass spectrometry parameters of sphingolipids is set to 350-450℃, and the declustering voltage is set to 60-80V; The optimal characteristic fragments related to fatty acyl chains or ceramide basic skeletons, i.e., sphingosine, have a collision energy value set to 35-60V; the optimal characteristic fragments related to monoglycerides, sphingosine, or free cholesterol have a collision energy value set to 20-40V.

8. The method of quantitative analysis of a lipid panel according to claim 7, wherein, Mass spectrometry data acquisition based on dynamic multiple reaction monitoring mode, wherein for lipid compounds without standard products, a prediction model constructed according to the following method is used to predict the chromatographic theoretical retention time (retention time prediction accuracy <0.5min), and the optimal MRM acquisition window is set according to the predicted theoretical retention time: Using the identified same sub-class of lipid compounds, a quantitative structure-retention time relationship of the lipid compounds was constructed based on the non-targeted quantitative results, with the experimental retention time t R E As the dependent variable, the total number of carbon c and the number of carbon-carbon double bonds d of the fatty acyl chain, ether chain, alkenyl ether chain or / and sphingosine contained in the structure of the compound were used as the independent variable, and a QSRR model expression was obtained by using multivariate polynomial nonlinear regression modeling as t R E = m1c 3 + m2c 2 + m3c + m4d + m5c 3 d + m6c 2 d + m7cd + m8, wherein m1-m7 are coefficients and m8 is a constant.

9. A high-coverage and high-sensitivity lipidome quantification kit, characterized in that, The kit is used for quantitative analysis of lipidome of plasma, urine, cells, liver, feces, E. coli or Arabidopsis thaliana rosette leaves of human or animal origin, which comprises lipid compounds with odd carbon chain and labeled by stable deuterium isotope as internal standard; The internal standard mixture comprises 70 kinds of lipid compounds of the following multiple subgroups: cholesteryl ester, ceramide, diglyceride, dihydroceramide, hexosylceramide, dihexosylceramide, lysophosphatidylcholine, lysophosphatidylethanolamine, phosphatidylcholine, phosphatidylethanolamine, sphingomyelin, triglyceride, monoglyceride, phosphatidylglycerol, phosphatidylinositol, phosphatidic acid, phosphatidylserine, sphingosine-1-phosphate, sphingosine, lysophosphatidylinositol, lysophosphatidylserine, lysophosphatidic acid, ganglioside GM3, lysophosphatidylglycerol, acylcarnitine, cholesterol, free fatty acid, ganglioside GM1, plant ceramide and cardiolipin; the internal standard mixture is prepared in different concentrations according to the peak area of the standard corresponding to the peak area of the lipid compound in the biological matrix to be detected.

10. The lipidome quantification kit of claim 9, wherein, The biological matrix is plasma, cells, liver or feces, and the internal standard mixture comprises 70 kinds of lipid compound standards covering 30 lipid subgroups. The biological matrix is E. coli or Arabidopsis thaliana rosette leaves, and the internal standard mixture does not contain one or more of the following lipid compound standards: sphingosine-1-phosphate, ganglioside GM3, cholesterol and ganglioside GM1. The biological matrix is urine, and the internal standard mixture does not contain one or more of the following lipid compound standards: sphingosine-1-phosphate, ganglioside GM3 and cholesterol. Preferably, the kit further comprises one or more of the following: an extraction reagent for extracting lipids from the biological matrix, a reconstitution agent for reconstituting lipids and a reverse phase chromatography elution mobile phase; the reverse phase chromatography elution mobile phase comprises acidic mobile phase A and acidic mobile phase B with different pH values, wherein the pH of mobile phase A is 2.5-3.0, and the pH of mobile phase B is 2.5-5.0.

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