A precise quantitative analysis method for lipidomics

Abstract

This invention discloses a precise quantitative analysis method for lipidomics, belonging to the field of pharmaceutical technology. This invention systematically summarizes the chromatographic-mass spectrometric behavior of existing lipid reference standards, constructs a correlation model between lipid structural parameters and detection parameters, and uses this model to predict the quantitative parameters of lipids lacking reference standards. A quantitative parameter database covering 13 lipid subclasses and 1723 lipids using a "model-prediction-calibration" strategy is established, enabling high-coverage qualitative and quantitative analysis of 1723 lipids in tissues simultaneously. A calibration strategy is also introduced to minimize the consumption of lipid reference standards while ensuring high qualitative accuracy, thus achieving differential lipid analysis. The method of this invention has advantages such as high sensitivity, high accuracy, and wide coverage, and can provide reliable technical support for the early diagnosis and precision treatment of tumors.

Description

Technical Field

[0001] This invention belongs to the field of pharmaceutical technology, specifically relating to a precise quantitative analysis method for lipidomics. Background Technology

[0002] Lipids are widely involved in various physiological and pathological processes in the human body, including cell growth, energy metabolism, and organelle function regulation. Recent studies have shown that lipid metabolic remodeling is one of the most significant metabolic characteristics in tumor development and progression, and certain specific lipid molecules show promise as potential biomarkers for tumor diagnosis. Therefore, developing parallel detection methods for multiple lipid subclasses with both high accuracy and broad coverage is crucial, focusing on the biological and clinical value of lipids.

[0003] Liquid chromatography-mass spectrometry (LC-MS) is one of the most widely used techniques in lipid analysis. LC-MS-based targeted lipidomics analysis typically relies on lipid standards to determine the multiple reaction monitoring (MRM) ions of the analyte. However, lipid structures are highly diverse and their compositions complex; even within the same lipid subclass, there are significant differences in their fatty acid side chains. Furthermore, some lipids are unstable and easily affected by chromatographic conditions and environmental fluctuations.

[0004] Lipids play an important role in cell proliferation and metabolism, and can serve as potential cancer biomarkers. However, due to the complexity and diversity of lipid structures and the difficulty in obtaining reference standards, high-coverage quantitative lipidomics analysis is difficult to achieve. Furthermore, in high-performance liquid chromatography (HPLC) systems, the chromatographic behavior of lipids is easily affected by fluctuations in instrument performance and changes in environmental conditions, leading to shifts in retention times.

[0005] Therefore, there is an urgent need to develop new analytical strategies and calibration methods to overcome the technical bottlenecks faced by targeted lipidomics research under conditions of limited reference standards. Summary of the Invention

[0006] Therefore, the purpose of this invention is to provide a precise quantitative analysis method for lipidomics, which solves the technical bottleneck faced by targeted lipidomics research under the condition of limited reference standards.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a precise quantitative analysis method for lipidomics, comprising the following steps: S1: After sample pretreatment, the liquid to be tested is obtained; S2: Perform HPLC-MS analysis on the test liquid to obtain independent samples. t data; S3: Based on independent samples tData and lipid liquid chromatography-tandem mass spectrometry quantitative parameter database were used for independent sample analysis. t Differential lipids were obtained through testing and OPLS-DA screening. The independent samples t Inspection and retention p For lipid data with a VIP value less than 0.05, the OPLS-DA screening retains lipid data with a VIP value greater than 1 after OPLS-DA differential analysis; S4: Perform statistical analysis on differentially regulated lipids. First, distinguish between upregulated and downregulated lipids, and then classify them into lipid subclasses.

[0009] Based on the above technical solution, the samples in S1 further include liver tumor tissue samples and adjacent liver cancer tissue samples.

[0010] The samples were collected from eight hepatocellular carcinoma patients at Shengjing Hospital of China Medical University. Tumor tissue and its paired adjacent non-tumor tissue were collected from each patient. Based on the above technical solution, the pretreatment in S1 specifically involves: after homogenization, adding 40 μl of methanol and mixed internal standard working solution (SM d18:1 / 16:0 - d 31 PG 16:0 - d 31 / 18:1 5 μg·ml each -1 After adding 20 μl, vortex for 30 s, then add a methyl tert-butyl ether-methanol mixture (5:1.5). v / v Add 1.3 ml of the mixture, vortex for 3 min, sonicate for 5 min, add 290 μl of water, and centrifuge at 4°C (12000 r·min). -1 After 5 min, the organic phase was transferred to another centrifuge tube and dried under a nitrogen stream. The residue was reconstituted with 200 μl of methanol, vortexed for 3 min, sonicated for 5 min, and then centrifuged at 4 °C (12000 r·min). -1 After 5 min, take 5 μl of the supernatant for analysis.

[0011] Based on the above technical solution, the conditions for liquid phase separation in the HPLC-MS analysis in S2 are further as follows: The chromatographic column has an inner diameter of 2.1 mm, a length of 50 mm, and a packing particle size of 1.7 μm. The mobile phase comprises mobile phase A and mobile phase B. Mobile phase A comprises a mixed solution of acetonitrile and water in a volume ratio of 6:4, and mobile phase B comprises a mixed solution of isopropanol and acetonitrile in a volume ratio of 9:1. Both mobile phase A and mobile phase B contain 10 mmol·L⁻¹. -1 ammonium acetate; The flow rate of the mobile phase is 0.3 mL·min.-1 Column temperature 30℃, injection volume 5 μL.

[0012] Based on the above technical solution, the process for establishing the lipid liquid chromatography-tandem mass spectrometry quantitative parameter database is as follows: S301: A multiple linear regression model was established with carbon chain length and number of double bonds as independent variables and retention time as the dependent variable. The prediction equation is: y = aX1 + bX2 + c; Where: X1 is the carbon chain length of the lipid, X2 is the number of double bonds in the lipid, y is the retention time, a is the coefficient corresponding to the carbon chain length, b is the coefficient corresponding to the number of double bonds, and c is a constant term; S302: Based on the liquid chromatography-mass spectrometry parameters of four reference standards in phosphatidic acid, phosphatidylglycerol, phosphatidylethanolamine, diglyceride, phosphatidylserine, phosphatidylinositol, lysophosphatidylcholine, ceramide, lysophosphatidylethanolamine, sphingomyelin, phosphatidylcholine, triglycerides, and fatty acid subclasses, the prediction equation described in S301 was established. The predicted retention time of lipids without reference standards was obtained by substituting the parameters of a self-built library of lipids without reference standards previously established in the laboratory into the above equation. The liquid chromatography-mass spectrometry parameters include carbon chain length and number of double bonds; S303: Establish a database of quantitative parameters for lipid liquid chromatography-tandem mass spectrometry (LC-MS / MS) containing 1723 lipids across 13 subclasses; the database includes the lipid name, parent ion, daughter ion, declustering voltage, collision energy, inlet voltage, collision chamber outlet voltage, and retention time.

[0013] Where: carbon chain length is abbreviated as X1, number of double bonds as X2, retention time as RT, parent ion as Q1, daughter ion as Q3, declustering voltage as DP, collision energy as CE, inlet voltage as EP, collision chamber outlet voltage as CXP, and internal standard as IS; Phosphatidic acid is available in PA 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, and 16:0 / 18:1. Phosphatidylglycerol is available in PG ratios of 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, and 16:0 / 18:1. Phosphatidylethanolamine is available in PE ratios of 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, and 16:0 / 18:1. Diglycerides are DG 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1; Phosphatidylserine is available in PS ratios of 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, and 16:0 / 18:1. Phosphatidylinositol has a PI ratio of 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, and 16:0 / 18:1. Lysophosphatidylcholine was LPC 18:0, 16:0, 18:1, 20:0; Ceramides were available in Cer d18:1 / 18:0, d18:1 / 16:0, d18:1 / 18:1, and d18:1 / 20:0. Lysophosphatidylethanolamine is available in LPE ratios of 18:0, 16:0, 18:1, and 14:0. The sphingomyelin is SM d18:1 / 18:0, d18:1 / 16:0, d18:1 / 16:1, d18:1 / 17:0; Phosphatidylcholine is available in PC ratios of 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, and 13:0 / 13:0. Triglycerides are TG 18:1 / 18:1 / 18:1, 16:0 / 16:0 / 18:1, 18:2 / 18:2 / 18:2, 16:0 / 18:1 / 18:1; The fatty acids are FA 16:0, 18:0, 18:1, 18:2.

[0014] Based on the above technical solution, the multiple linear regression model further introduces a constant term A, that is, the original y=aX1+bX2+c is changed to y=aX1+bX2+c+A, and the constant term A is the retention time difference between two repeated measurements of any lipid in the same lipid subclass.

[0015] Secondly, the present invention provides the application of the above-mentioned precise quantitative analysis method for lipidomics in determining the concentrations of phosphatidic acid, phosphatidylglycerol, phosphatidylethanolamine, diglycerides, phosphatidylserine, phosphatidylinositol, lysophosphatidylcholine, ceramide, lysophosphatidylethanolamine, sphingomyelin, phosphatidylcholine, triglycerides and fatty acids.

[0016] Thirdly, the present invention provides the application of the above-mentioned precise quantitative analysis method of lipidomics in screening lipid biomarkers in tumor tissues and adjacent normal tissues of liver cancer patients.

[0017] Liver cancer is abbreviated as HCC.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention systematically summarizes the chromatographic-mass spectrometric behavior of existing lipid reference standards, constructs a correlation model between lipid structural parameters and detection parameters, and uses it to predict the quantitative parameters of lipids lacking reference standards. It establishes a quantitative parameter database covering 13 lipid subclasses and 1723 lipids using a "model-prediction-calibration" strategy, enabling high-coverage qualitative and quantitative analysis of 1723 lipids in tissues. At the same time, a calibration strategy is introduced to minimize the consumption of lipid reference standards while ensuring high qualitative accuracy, thus achieving differential analysis of lipids.

[0019] 2. The method of the present invention has the advantages of high sensitivity, high accuracy and wide coverage, and can provide reliable technical support for the early diagnosis and precision treatment of tumors. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention, the accompanying drawings involved in the embodiments will be briefly described below.

[0021] Figure 1 This is a schematic diagram of the "model-prediction-calibration" strategy process in Embodiment 6 of the present invention; Figure 2 This is a schematic diagram showing the average relative error of the relative retention time of each lipid in different lipid subclasses in Example 6 of the present invention; Figure 3 Example 6 of this invention presents the OPLS-DA score graphs of tumor tissue and adjacent normal tissue in HCC patients, and the results of 200 permutation tests used to verify the reliability of the OPLS-DA model: A is the OPLS-DA score graph of tumor tissue and adjacent normal tissue in HCC patients, and B is the results of 200 permutation tests used to verify the reliability of the OPLS-DA model (R... 2 X=0.304, R 2 Y=0.924,Q 2 =0.717); Figure 4 This is a bar chart showing the statistical analysis of the upregulated and downregulated lipids between tumor tissue and adjacent normal tissue in Example 6 of the present invention. Figure 5 This is a heatmap showing significant differences in lipids between tumor tissue and adjacent normal tissue in Example 6 of the present invention. Figure 6 Volcano plot (|log2FC| ≥ 1) of significantly different lipids (highlighting lipids with a change of more than 2-fold) in Example 6 of the present invention. Figure 7 The following is a diagram showing the distribution of lipid types between tumor tissue and adjacent normal tissue in Embodiment 6 of the present invention: A is a diagram showing the distribution of upregulated lipid types between tumor tissue and adjacent normal tissue, and B is a diagram showing the distribution of downregulated lipid types between tumor tissue and adjacent normal tissue. Detailed Implementation

[0022] Based on the aforementioned issues, our previous research revealed that the same lipid subclass exhibits regular and predictable HPLC-MS / MS behavior under certain conditions: its mass spectrometry parameters (such as DP, CE), retention time (RT), and response factor (RF) all show systematic changes with the increase of the number of carbon atoms in the fatty acid side chain or the change of the number of double bonds. Based on this inherent pattern, we have successfully achieved the quantitative determination of 1201 lipids in plasma by constructing a multiple linear regression model using the number of carbon atoms in the side chain and the number of double bonds, focusing on 13 lipid subclasses. Building upon this, this invention further establishes a high-coverage, high-accuracy lipid analysis parameter database that incorporates a systematic calibration strategy, which is of great significance for overcoming the limitations of reference standards and achieving comprehensive and reliable quantitative analysis in lipidomics.

[0023] The present invention will be described in detail below with reference to the embodiments. However, the implementation of the present invention is not limited thereto. Obviously, the embodiments described below are only some embodiments of the present invention. For those skilled in the art, other similar embodiments can be obtained without creative effort and all fall within the protection scope of the present invention.

[0024] Example 1 Establishment of a database of quantitative parameters for lipid liquid chromatography-tandem mass spectrometry: First, a multiple linear regression model was established with carbon chain length (X1) and the number of double bonds (X2) as independent variables and retention time (RT) as the dependent variable. The results were summarized for phosphatidic acid (PA 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), phosphatidylglycerol (PG 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), phosphatidylethanolamine (PE 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), diglycerides (DG 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), and phosphatidylserine (PS 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), phosphatidylinositol (PI 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), lysophosphatidylcholine (LPC18:0, 16:0, 18:1, 20:0), ceramide (Cer d18:1 / 18:0, d18:1 / 16:0, d18:1 / 18:1, d18:1 / 20:0), lysophosphatidylethanolamine (LPE 18:0, 16:0, 18:1, 14:0), sphingomyelin (SM d18:1 / 18:0, d18:1 / 16:0, d18:1 / 16:1, d18:1 / 17:0), phosphatidylcholine (PC 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 13:0 / 13:0), triglycerides (TG 18:1 / 18:1 / 18:1, 16:0 / 16:0 / 18:1, 18:2 / 18:2 / 18:2, 16:0 / 18:1 / 18:1), fatty acids (FA 16:0, 18:0, 18:1, 18:2) To predict the corresponding parameters of lipids for which reference standards cannot be obtained, a liquid chromatography-tandem mass spectrometry quantitative parameter database containing 1723 lipids in 13 subclasses was established. The database includes the name of each lipid, the parent ion (Q1), the daughter ion (Q3), the declustering voltage (DP), the collision energy (CE), the inlet voltage (EP), the collision chamber outlet voltage (CXP), and the retention time (RT).

[0025] Next, the relative retention time (RRT) of each lipid was calculated as a function of the retention time of the added internal standard (IS). Although the RRT of the same lipid varies greatly under different influencing factors, its RRT relative to the internal standard is relatively stable.

[0026] To address this, this invention proposes a calibration strategy based on the variation law of RRT. Specifically, a constant term can be introduced into the original prediction equation to establish an updated RT model. When the relative error (RE) of RRT for a certain lipid is within 10%, a constant A can be introduced, defined as the RT difference between two repeated measurements for any lipid in the same lipid subclass. This method optimizes the RT prediction for the entire lipid subclass by measuring the RT of only one representative lipid, thereby avoiding the need to remeasure all lipids and significantly reducing the consumption of reference standards. Although the RT predicted by the updated equation cannot be completely consistent with the original equation's prediction, its deviation can be controlled within the practically acceptable range of 60 s. This calibration method significantly improves the accuracy of RT prediction and helps to achieve robust and reliable lipid identification within the detection window.

[0027] Glycerophospholipids (PEs, PAs, PGs, PSs, PIs) typically consist of two hydrophobic fatty acyl chains and a polar head group. This structure facilitates stable intermolecular interactions on the chromatographic column. The bifatty chain structure makes their retention behavior less susceptible to changes in chromatographic conditions, thus exhibiting high stability. In contrast, lysophospholipids (LPEs, LPCs) contain only one fatty acyl chain, with the other chain replaced by a highly polar hydroxyl group, significantly increasing molecular polarity and reducing hydrophobicity. This structural feature enhances their interactions with polar groups in the solvent system, making lysophospholipids more sensitive to changes in environmental and instrumental conditions, resulting in greater fluctuations in their relative response time (RRT).

[0028] The highly polar phosphate groups in lysophospholipids (LPEs, LPCs) further enhance their overall polarity, making them more polar than fatty acids (FAs) which also have a single-chain structure. This increased polarity makes lysophospholipids more susceptible to interactions beyond hydrophobic interactions, such as interactions with polar solvents or intermolecular hydrogen bonding, thus significantly improving their sensitivity to changes in experimental conditions such as solvent composition, temperature, and column efficiency.

[0029] Example 2 Tumor tissue sample pretreatment: Tumor tissue and adjacent normal tissue samples were homogenized, and an appropriate amount of the homogenate was taken. An internal standard solution was added, and the mixture was extracted with solvent, followed by vortexing, sonication, centrifugation, nitrogen blowing, reconstitution, vortexing, sonication, centrifugation, and the supernatant was collected for HPLC-MS analysis. The internal standard solution was SM (d18:1 / 16:0-d...). 31 ) and PG (16:0-d 31 / 18:1), wherein the extraction solvent is methyl tert-butyl ether-methanol-water (5:1.5:1.45, v / v / v ).

[0030] Example 3 Liquid phase separation: Column: ACQUITY UPLC BEH C18 (2.1×50 mm, 1.7 μm); Mobile phases: A: acetonitrile-water (6:4, v / v), B: isopropanol-acetonitrile (9:1, v / v), both containing 10 mmol·L⁻¹ ammonium acetate; Flow rate: 0.3 mL·min⁻¹; Column temperature: 30℃; Injection volume: 5 μL; The gradient elution procedures are shown in Tables 1 and 2.

[0031] Table 1: Positive ion scanning gradient elution procedure.

[0032]

[0033] Table 2: Negative ion scanning gradient elution procedure.

[0034]

[0035] Example 4 MS measurement: Electrospray ionization source, positive and negative ion scanning, multiple reaction monitoring mode, ion channels and other parameters of the reference standard are shown in Table 3.

[0036]

[0037] Example 5 Differential lipids in tumor tissue and adjacent normal tissue of HCC patients were screened using independent samples t-test and OPLS-DA, and then classified and statistically analyzed.

[0038] Example 6 First, a multiple linear regression model was established with carbon chain length (X1) and the number of double bonds (X2) as independent variables and retention time (RT) as the dependent variable. The results were summarized for phosphatidic acid (PA 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), phosphatidylglycerol (PG 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), phosphatidylethanolamine (PE 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), diglycerides (DG 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), and phosphatidylserine (PS 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), phosphatidylinositol (PI 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 16:0 / 18:1), lysophosphatidylcholine (LPC18:0, 16:0, 18:1, 20:0), ceramide (Cer d18:1 / 18:0, d18:1 / 16:0, d18:1 / 18:1, d18:1 / 20:0), lysophosphatidylethanolamine (LPE 18:0, 16:0, 18:1, 14:0), sphingomyelin (SM d18:1 / 18:0, d18:1 / 16:0, d18:1 / 16:1, d18:1 / 17:0), phosphatidylcholine (PC 18:0 / 18:0, 16:0 / 16:0, 18:1 / 18:1, 13:0 / 13:0), triglycerides (TG 18:1 / 18:1 / 18:1, 16:0 / 16:0 / 18:1, 18:2 / 18:2 / 18:2, 16:0 / 18:1 / 18:1), fatty acids (FA 16:0, 18:0, 18:1, 18:2) To predict the corresponding parameters of lipids for which reference standards cannot be obtained, a liquid chromatography-tandem mass spectrometry quantitative parameter database containing 1723 lipids in 13 subclasses was established. The database includes the name of each lipid, the parent ion (Q1), the daughter ion (Q3), the declustering voltage (DP), the collision energy (CE), the inlet voltage (EP), the collision chamber outlet voltage (CXP), and the retention time (RT).

[0039] Next, the relative retention time (RRT) of each lipid is calculated as a function of the retention time of the added internal standard (IS). Although the RT of the same lipid varies greatly under different influencing factors, its RRT relative to the internal standard is relatively stable. Therefore, this invention proposes a calibration strategy based on the RRT variation law. Specifically, a constant term can be introduced into the original prediction equation to establish an updated RT model. When the relative error (RE) of the RRT of a certain lipid is within 10%, a constant A can be introduced, which is defined as the difference in RT between two repeated measurements of any lipid in the same lipid subclass. This method can optimize the prediction of RT for the entire lipid subclass by measuring the RT of only one representative lipid, thereby avoiding the need to remeasure all lipids and significantly reducing the consumption of reference standards. Although the RT predicted by the updated equation cannot be completely consistent with the original equation prediction, its deviation can be controlled within the practically acceptable range of 60 s. The calibration process diagram is shown below. Figure 1 As shown in the figure. This calibration method significantly improves the accuracy of RT prediction and facilitates robust and reliable lipid identification within the detection window. The calibrated retention time and relative retention time error, as well as the retention time prediction equation, are shown in Table 4.

[0040] Based on the information in the table, we calculated the mean relative error (RE) of the relative retention time (RRT) of each lipid between the two assays, and the results are as follows. Figure 2 As shown, glycerophospholipids exhibited the lowest RE values, all below 2.40%, including phosphatidylethanolamine (PEs, RE=1.38%), phosphatidylcholine (PCs, RE=2.30%), phosphatidic acid (PAs, RE=1.46%), phosphatidylglycerol (PGs, RE=2.15%), phosphatidylserine (PSs, RE=1.37%), and phosphatidylinositol (PIs, RE=2.40%). This was followed by fatty acyls, such as fatty acids (FAs, RE=4.38%); glycerides, including diacylglycerols (DGs, RE=0.84%) and triacylglycerols (TGs, RE=5.77%); and sphingolipids, such as ceramides (Cers, RE=2.22%) and sphingomyelins (SMs, RE=6.95%). The relative efficiency (RE) values ​​of the aforementioned lipids all did not exceed 6.95%, indicating that their relative response time (RRT) remained relatively stable despite changes in instrument performance and experimental conditions over time. In contrast, lysophosphatidylcholine (LPC) exhibited significantly larger RRT fluctuations, with lysophosphatidylcholine (LPC) showing an RE of -44.53% and lysophosphatidylethanolamine (LPE) showing an RE of -29.56%.

[0041] Table 4: Retention Time (RT) and Relative Retention Time (RRT) Correction Parameter Information.

[0042]

[0043] Note: PEs, DGs, LPCs, LPEs, PCs, Cers, TGs, and SMs are all classified under SM (d18:1 / 16:0-d). 31 ) as the internal standard for positive ions; FAs, PAs, PGs, PSs and PIs are used as PG (16:0-d) 31 ( / 18:1) is used as an internal standard for negative ions.

[0044] (2) Pretreatment of tumor tissue samples: Collect tumor tissue and adjacent normal tissue samples (20-30 mg) from HCC patients. After rinsing with physiological saline and weighing, add physiological saline at 10 times the weight. Then cut the tissue into small pieces and homogenize using an ultrasonic cell disruptor for 1 min under ice bath conditions (150 W, ultrasonic on for 2 s, off for 2 s). After centrifuging the homogenate at 4℃ and 12000 rpm for 10 min, collect the supernatant as the tissue homogenate sample.

[0045] Take 200 μL of the above homogenized sample into an EP tube, add 40 μL of methanol and 20 μL of internal standard, vortex for 30 s, add 1.3 ml of methyl tert-butyl ether-methanol mixed solution (5:1.5, v / v), vortex for 3 min, sonicate for 5 min, add 290 μL of water, centrifuge at 4°C (12000 r / min) for 5 min, take the organic phase and place it in another EP tube to air dry, add 200 μL of methanol to redissolve the residue, vortex for 3 min, sonicate for 5 min, centrifuge at (12000 r·min-1) for 5 min, take the supernatant for analysis.

[0046] (3) Liquid phase separation: Chromatographic column: ACQUITY UPLC BEH C 18 (2.1 × 50 mm, 1.7 μm); Mobile phase: A: Acetonitrile-water (6:4, v / v B: Isopropanol-acetonitrile (9:1) v / v Each contains 10 mmol·L -1 Ammonium acetate; gradient elution programs are shown in Tables 1 and 2.

[0047] (4) MS determination: electrospray ion source, positive and negative ion scanning, multiple reaction monitoring mode, ion channels and other parameters of the reference standard are shown in Table 3.

[0048] (5) Data processing: Differential lipids in tumor tissue and adjacent normal tissue of HCC patients were screened using independent samples t-test and OPLS-DA, and then classified and statistically analyzed. For example... Figure 3The lipids measured using the "model-prediction-calibration" strategy in tumor and adjacent normal tissues showed good intergroup separation, and the OPLS-DA 200-permutation test also showed no overfitting. Further statistical analysis showed that, compared to adjacent normal tissues, 621 lipids were downregulated in tumor tissues, of which 146 were significantly downregulated; 408 lipids were upregulated, of which 43 were significantly upregulated. Figure 4 Significant changes in lipids are shown in the form of a heatmap. Figure 5 Next, we further screened for up- and down-regulated lipids based on FoldChange values ​​greater than 2 or less than 1 / 2, and the results are as follows. Figure 6 As shown, a total of 145 lipids were downregulated and 35 lipids were upregulated. These lipids belong to the following lipid subclasses: Figure 7 As shown.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A precise quantitative analysis method for lipidomics, characterized in that, Includes the following steps: S1: After sample pretreatment, the liquid to be tested is obtained; S2: Perform HPLC-MS analysis on the test liquid to obtain independent samples. t data; S3: Based on independent samples t Data and lipid liquid chromatography-tandem mass spectrometry quantitative parameter database were used for independent sample analysis. t Differential lipids were obtained through testing and OPLS-DA screening. The independent samples t Inspection and retention p For lipid data with a VIP value less than 0.05, the OPLS-DA screening retains lipid data with a VIP value greater than 1 after OPLS-DA differential analysis; S4: Perform statistical analysis on differentially regulated lipids. First, distinguish between upregulated and downregulated lipids, and then classify them into lipid subclasses.

2. The precise quantitative analysis method for lipidomics according to claim 1, characterized in that, The samples in S1 include liver tumor tissue samples and adjacent liver cancer tissue samples.

3. The precise quantitative analysis method for lipidomics according to claim 1, characterized in that, The pretreatment in S1 is as follows: After homogenization, 40 μl of methanol and 20 μl of mixed internal standard working solution are added, followed by vortexing for 30 s. Then, 1.3 ml of a methyl tert-butyl ether-methanol mixed solution with a volume ratio of 5:1.5 is added, and the mixture is vortexed for 3 min, sonicated for 5 min, and then 290 μl of water is added. The mixture is then incubated at 4°C and 12000 r·min. -1 Centrifuge for 5 min, transfer the organic phase to another centrifuge tube and dry it with nitrogen gas. Redissolve the residue in 200 μl of methanol, vortex mix for 3 min, sonicate for 5 min, and then incubate at 4 °C and 12000 r·min. -1 Centrifuge for 5 min, and inject 5 μl of the supernatant for analysis; the mixed internal standard working solution includes SMd18:1 / 16:0-d 31 And PG16:0-d 31 / 18:1 5μg·ml each -1 .

4. The precise quantitative analysis method for lipidomics according to claim 1, characterized in that, The conditions for liquid phase separation in the HPLC-MS analysis of S2 are: The chromatographic column has an inner diameter of 2.1 mm, a length of 50 mm, and a packing particle size of 1.7 μm. The mobile phase comprises mobile phase A and mobile phase B. Mobile phase A comprises a mixed solution of acetonitrile and water in a volume ratio of 6:4, and mobile phase B comprises a mixed solution of isopropanol and acetonitrile in a volume ratio of 9:

1. Both mobile phase A and mobile phase B contain 10 mmol·L⁻¹. -1 ammonium acetate; The flow rate of the mobile phase is 0.3 mL·min. -1 Column temperature 30℃, injection volume 5 μL.

5. The precise quantitative analysis method for lipidomics according to claim 1, characterized in that, The process for establishing the lipid liquid chromatography-tandem mass spectrometry quantitative parameter database is as follows: S301: A multiple linear regression model was established with carbon chain length and number of double bonds as independent variables and retention time as the dependent variable. The prediction equation is: y = aX1 + bX2 + c; Where: X1 is the carbon chain length of the lipid, X2 is the number of double bonds in the lipid, y is the retention time, a is the coefficient corresponding to the carbon chain length, b is the coefficient corresponding to the number of double bonds, and c is a constant term; S302: Based on the liquid chromatography-mass spectrometry parameters of four reference standards in phosphatidic acid, phosphatidylglycerol, phosphatidylethanolamine, diglyceride, phosphatidylserine, phosphatidylinositol, lysophosphatidylcholine, ceramide, lysophosphatidylethanolamine, sphingomyelin, phosphatidylcholine, triglycerides, and fatty acid subclasses, the prediction equation described in S301 was established. The predicted retention time of lipids without reference standards was obtained by substituting the parameters of a self-built library of lipids without reference standards previously established in the laboratory into the above equation. The liquid chromatography-mass spectrometry parameters include carbon chain length and number of double bonds; S303: Establish a database of quantitative parameters for lipid liquid chromatography-tandem mass spectrometry (LC-MS / MS) containing 1723 lipids across 13 subclasses; the database includes the lipid name, parent ion, daughter ion, declustering voltage, collision energy, inlet voltage, collision chamber outlet voltage, and retention time.

6. The precise quantitative analysis method for lipidomics according to claim 5, characterized in that, The multiple linear regression model introduces a constant term A, which is changed from the original y=aX1+bX2+c to y=aX1+bX2+c+A. The constant term A is the difference in retention time between two repeated measurements of any lipid in the same lipid subclass.

7. The application of the precise quantitative lipidomics analysis method according to any one of claims 1 to 6 in determining the concentrations of phosphatidic acid, phosphatidylglycerol, phosphatidylethanolamine, diglycerides, phosphatidylserine, phosphatidylinositol, lysophosphatidylcholine, ceramide, lysophosphatidylethanolamine, sphingomyelin, phosphatidylcholine, triglycerides, and fatty acids.

8. The application of the precise quantitative lipidomics analysis method as described in any one of claims 1 to 6 in screening lipid biomarkers in tumor tissues and adjacent normal tissues of liver cancer patients.

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