CN109725046B - Target lipidomics method based on modeling-prediction strategy - Google Patents
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Abstract
The invention belongs to the technical field of medicines, and particularly relates to a target lipidomics method, in particular to a method for carrying out efficient, sensitive, qualitative and quantitative analysis on Lysophosphatidylcholine (LPC) in blood plasma by applying a modeling-predicting strategy. The method takes the carbon chain length (x1) and the number of double bonds (x2) as independent variables, the de-clustering voltage (DP), the Collision Energy (CE), the Retention Time (RT) and the Response Factor (RF) as dependent variables to establish a multiple linear regression model, summarizes the liquid phase mass spectrum parameter rules of an LPC (13:0), an LPC (14:0), an LPC (15:0), an LPC (17:0) LPC (18:1) and an LPC (19:0) LPC (20:0) reference product, and then predicts the corresponding parameters of the LPC of the reference product by using the equation. And quantitatively analyzing LPC in plasma by using the predicted liquid phase mass spectrum parameters, screening biomarkers of different cancers by using independent sample T test, PLS-DA and a single-factor ROC curve, and evaluating the diagnostic capability of the biomarkers by using a multi-factor ROC curve.
Description
Technical Field
The invention belongs to the technical field of medicines, and particularly relates to a target lipidomics method, in particular to a method for carrying out efficient, sensitive, qualitative and quantitative analysis on Lysophosphatidylcholine (LPC) in blood plasma by applying a modeling-predicting strategy. Background
Lipids are skeleton components of biological membranes, and are important substances that participate in life activities of living organisms and provide energy to the living organisms. Lipidomics belongs to an important branch of metabonomics, and is an emerging discipline for systematically analyzing lipid substances in organisms, researching the interaction of the lipid substances and the action of the lipid substances and other biomolecules, and further disclosing the relationship between lipid metabolism and physiological and pathological processes of the organisms. Due to the diversity of lipid backbone, groups and carbon chains, a large number of lipid compounds with different physicochemical properties and dynamic ranges exist in vivo, and one method cannot completely and accurately quantify all lipids qualitatively. Thus, we can classify lipids into several subclasses according to their backbone and then use optimized internal standards, pre-processing methods and LC-MS/MS conditions to more accurately quantify one or several specific lipid subclasses, thus providing more accurate biological interpretation.
Lipid controls are difficult to obtain due to lipid diversity and instability, which makes accurate quantification of lipids a significant challenge. Therefore, it is desirable to provide a strategy and method to solve this problem.
Disclosure of Invention
The first purpose of the present invention is to overcome the drawbacks of the prior art and to establish a method for analyzing lysophosphatidylcholine in plasma using a "modeling-prediction" strategy.
The second object of the present invention is to determine the concentration of lysophosphatidylcholine by established analytical methods.
The third objective of the present invention is to determine the concentration of 60 lysophosphatidylcholine in the plasma of healthy persons and cancer patients by established assays and to screen biomarkers for different cancers by independent sample t-test, PLS-DA, single and multi-factor ROC analysis.
The invention is realized by the following steps:
(1) "modeling-prediction" strategy:
firstly, establishing a multiple linear regression model by taking the carbon chain length (x1) and the number of double bonds (x2) as independent variables, the declustering voltage (DP), the Collision Energy (CE), the Retention Time (RT) and the Response Factor (RF) as dependent variables, summarizing the liquid phase mass spectrum parameter rules of an LPC (13:0), an LPC (14:0), an LPC (15:0), an LPC (17:0), an LPC (18:1), an LPC (19:0) and an LPC (20:0) reference substance to obtain a regression model equation, and then predicting corresponding parameters of the LPC of the reference substance by using the equation.
(2) Pretreatment of a plasma sample:
and (3) taking a plasma sample, adding methanol and an internal standard solution, carrying out vortex, carrying out ultrasonic centrifugation, and taking a supernatant for LC-MS analysis.
Wherein the internal standard solution is LPC (13: 0);
the volume ratio of the blood plasma to the methanol is as follows: 1:3-1:5.
(3) Liquid phase separation:
chromatographic column Kinetex XB-C18 (4.6X 100 mm, 2.6 mu m);
the mobile phase A is 0.1-0.3% of formic acid water, and the mobile phase B is 0.1-0.3% of formic acid methanol;
flow rate of 0.4 ml/min-1；
2 mul of sample size;
column temperature 25oC；
The gradient elution procedure is shown in table 1.
TABLE 1 gradient elution procedure
(4) MS measurement: electrospray ion source, positive ion scan, other parameters and ion channel of the analyte are shown in Table 2.
TABLE 2 LPC Mass Spectrometry parameters
Qualitative ion
(5) Data processing: firstly, independent sample T test, PLS-DA and single-factor ROC curve screen are passed
Biomarkers of different cancers were selected and their diagnostic ability was evaluated by a multifactorial ROC curve.
Drawings
FIG. 1 is a detailed flow diagram of the "modeling-prediction" strategy.
FIG. 2A is a ROC plot of the diagnostic ability of LPC-like biomarkers for lung cancer.
FIG. 2B is a ROC plot of the diagnostic ability of LPC-like biomarkers for breast cancer.
Figure 2C is a ROC plot of diagnostic ability of LPC-like biomarkers for colorectal cancer.
Fig. 2D is a graph of the diagnostic ability ROC of LPC-based biomarkers for gastric cancer.
Detailed Description
Example 1
(1) Firstly, a multivariate linear regression model (table 3) is established by taking the carbon chain length (x1) and the number of double bonds (x2) as independent variables, the declustering voltage (DP), the Collision Energy (CE), the Retention Time (RT) and the Response Factor (RF) as dependent variables, the liquid phase mass spectrum parameter rules of LPC (13:0), LPC (14:0), LPC (15:0), LPC (17:0), LPC (18:1), LPC (19:0) and LPC (20:0) reference products are summarized, and then the corresponding parameters of the LPC of the reference products cannot be obtained by using the equation, and the specific flow is shown in figure 1.
TABLE 3 multiple recurrence regression equation of liquid phase mass spectrometry parameters
(2) Pretreatment of a plasma sample: and (3) taking 100 mu l of a plasma sample, adding 10 mu l of methanol and 50 mu l of internal standard solution into an EP tube, carrying out vortex mixing for 30 s, adding 400 mu l of acetonitrile, carrying out vortex mixing for 3 min, carrying out ice bath ultrasonic treatment for 10 min, centrifuging at 4 ℃ (12000 r/min) for 5 min, and taking the supernatant for LC-MS analysis.
(3) Liquid phase separation: chromatographic column Kinetex XB-C18 (4.6) prepared from radix astragali100 mm, 2.6 μm); the mobile phase A is 0.1 percent of formic acid water B is 0.1 percent of formic acid methanol; flow rate of 0.4 ml/min-1(ii) a 2 mul of sample size; column temperature 25oC; the gradient elution procedure is shown in table 1.
(4) MS measurement: electrospray ion source, positive ion scan, other parameters and ion channel of the analyte are shown in Table 2.
(5) Data processing: the biomarkers of the lung cancer are firstly screened by independent sample T test, PLS-DA and single-factor ROC curve, and the diagnostic capability of the biomarkers is evaluated by a multi-factor ROC curve, so that the biomarkers of the lung cancer are finally determined to be LPC18:1(sn-1), LPC18:2(sn-1), LPC18:2(sn-2) and LPC19:2(sn-2), and the joint prediction capability is shown in figure 2A.
Example 2
(1) Firstly, a multivariate linear regression model (table 3) is established by taking the carbon chain length (x1) and the number of double bonds (x2) as independent variables, the declustering voltage (DP), the Collision Energy (CE), the Retention Time (RT) and the Response Factor (RF) as dependent variables, the liquid phase mass spectrum parameter rules of LPC (13:0), LPC (14:0), LPC (15:0), LPC (17:0), LPC (18:1), LPC (19:0) and LPC (20:0) reference products are summarized, and then the corresponding parameters of the LPC of the reference products cannot be obtained by using the equation, and the specific flow is shown in figure 1.
(2) Pretreatment of a plasma sample: and (3) taking 100 mu l of a plasma sample, adding 10 mu l of methanol and 50 mu l of internal standard solution into an EP tube, carrying out vortex mixing for 30 s, adding 400 mu l of acetonitrile, carrying out vortex mixing for 3 min, carrying out ice bath ultrasonic treatment for 10 min, centrifuging at 4 ℃ (12000 r/min) for 5 min, and taking the supernatant for LC-MS analysis.
(3) Liquid phase separation: chromatographic column Kinetex XB-C18 (4.6X 100 mm, 2.6 mu m); the mobile phase A is 0.1 percent of formic acid water B is 0.1 percent of formic acid methanol; flow rate of 0.4 ml/min-1(ii) a 2 mul of sample size; column temperature 25oC; the gradient elution procedure is shown in table 1.
(4) MS measurement: electrospray ion source, positive ion scan, other parameters and ion channel of the analyte are shown in Table 2.
(5) Data processing: the biomarkers of the breast cancer are firstly screened by independent sample T test, PLS-DA and single-factor ROC curve, and the diagnostic capability of the biomarkers is evaluated by a multi-factor ROC curve, so that the biomarkers of the breast cancer are finally determined to be LPC18:2(sn-1), LPC18:2(sn-2) and LPC 22:4 (sn-1), and the joint predictive capability is shown in figure 2B.
Example 3
(1) Firstly, a multivariate linear regression model (table 3) is established by taking the carbon chain length (x1) and the number of double bonds (x2) as independent variables, the declustering voltage (DP), the Collision Energy (CE), the Retention Time (RT) and the Response Factor (RF) as dependent variables, the liquid phase mass spectrum parameter rules of LPC (13:0), LPC (14:0), LPC (15:0), LPC (17:0), LPC (18:1), LPC (19:0) and LPC (20:0) reference products are summarized, and then the corresponding parameters of the LPC of the reference products cannot be obtained by using the equation, and the specific flow is shown in figure 1.
(2) Pretreatment of a plasma sample: and (3) taking 100 mu l of a plasma sample, adding 10 mu l of methanol and 50 mu l of internal standard solution into an EP tube, carrying out vortex mixing for 30 s, adding 400 mu l of acetonitrile, carrying out vortex mixing for 3 min, carrying out ice bath ultrasonic treatment for 10 min, centrifuging at 4 ℃ (12000 r/min) for 5 min, and taking the supernatant for LC-MS analysis.
(3) Liquid phase separation: chromatographic column Kinetex XB-C18 (4.6X 100 mm, 2.6 mu m); the mobile phase A is 0.1 percent of formic acid water B is 0.1 percent of formic acid methanol; flow rate of 0.4 ml/min-1(ii) a 2 mul of sample size; column temperature 25oC; the gradient elution procedure is shown in table 1.
(4) MS measurement: electrospray ion source, positive ion scan, other parameters and ion channel of the analyte are shown in Table 2.
(5) Data processing: firstly, independent samples are tested by T, PLS-DA and a single-factor ROC curve to screen the biomarkers of the colorectal cancer, and the diagnosis capability of the biomarkers is evaluated by a multi-factor ROC curve, so that the biomarkers of the colorectal cancer are finally determined to be LPC 17:0 (sn-1), LPC19: 0 (sn-2), LPC19: 1 (sn-2) and LPC19:2(sn-2), and the joint prediction capability is shown in figure 2C.
Example 4
(1) Firstly, a multivariate linear regression model (table 3) is established by taking the carbon chain length (x1) and the number of double bonds (x2) as independent variables, the declustering voltage (DP), the Collision Energy (CE), the Retention Time (RT) and the Response Factor (RF) as dependent variables, the liquid phase mass spectrum parameter rules of LPC (13:0), LPC (14:0), LPC (15:0), LPC (17:0), LPC (18:1), LPC (19:0) and LPC (20:0) reference products are summarized, and then the corresponding parameters of the LPC of the reference products cannot be obtained by using the equation, and the specific flow is shown in figure 1.
(2) Pretreatment of a plasma sample: and (3) taking 100 mu l of a plasma sample, adding 10 mu l of methanol and 50 mu l of internal standard solution into an EP tube, carrying out vortex mixing for 30 s, adding 400 mu l of acetonitrile, carrying out vortex mixing for 3 min, carrying out ice bath ultrasonic treatment for 10 min, centrifuging at 4 ℃ (12000 r/min) for 5 min, and taking the supernatant for LC-MS analysis.
(3) Liquid phase separation: chromatographic column Kinetex XB-C18 (4.6X 100 mm, 2.6 mu m); the mobile phase A is 0.1 percent of formic acid water B is 0.1 percent of formic acid methanol; flow rate of 0.4 ml/min-1(ii) a 2 mul of sample size; column temperature 25oC; the gradient elution procedure is shown in table 1.
(4) MS measurement: electrospray ion source, positive ion scan, other parameters and ion channel of the analyte are shown in Table 2.
(5) Data processing: the biomarkers of the gastric cancer are firstly screened by independent sample T test, PLS-DA and a single-factor ROC curve, and the diagnostic capability of the biomarkers is evaluated by a multi-factor ROC curve, so that the biomarkers of the gastric cancer are finally determined to be LPC18: 0 (sn-1), LPC19: 0 (sn-2), LPC 20:0 (sn-1) and LPC 20:0 (sn-2), and the joint prediction capability is shown in figure 2D.
Claims (10)
1. A target lipidomics method based on a modeling-prediction strategy is characterized by comprising the following steps:
(1) "modeling-prediction" strategy:
firstly, establishing a multiple linear regression model by taking the carbon chain length (x1) and the number of double bonds (x2) as independent variables, the de-clustering voltage (DP), the Collision Energy (CE), the Retention Time (RT) and the Response Factor (RF) as dependent variables, summarizing the liquid phase mass spectrum parameter rules of an LPC (13:0), an LPC (14:0), an LPC (15:0), an LPC (17:0), an LPC (18:1), an LPC (19:0) and an LPC (20:0) reference substance to obtain a regression model equation, and then predicting corresponding parameters of the LPC of the reference substance by using the equation;
(2) pre-treating a plasma sample;
(3) liquid phase separation;
(4) MS measurement;
(5) data processing: biomarkers for different cancers were first screened by independent sample T-test, PLS-DA, single and multifactorial ROC curves.
2. The method of claim 1,
taking a plasma sample, adding methanol and an internal standard solution, carrying out vortex and ultrasonic centrifugation, and taking a supernatant for LC-MS analysis; wherein, the internal standard solution is LPC (13:0), and the volume ratio of the plasma to the methanol is: 1:3-1:5.
3. The method of claim 1,
the conditions for liquid phase separation in step (3) are:
a chromatographic column of Kinetex XB-C18, 4.6 multiplied by 100 mm, 2.6 mu m;
the mobile phase A is 0.1-0.3% of formic acid water, and the mobile phase B is 0.1-0.3% of formic acid methanol;
flow rate of 0.4 ml/min-1；
2 mul of sample size;
column temperature 25oC；
The gradient elution procedure is shown in table 1:
TABLE 1 gradient elution procedure
。
4. The method of claim 1, wherein the cancer is lung cancer, breast cancer, colorectal cancer, or gastric cancer.
5. The method of claim 4, wherein the biomarkers of lung cancer are LPC18:1(sn-1), LPC18:2(sn-1), LPC18:2(sn-2), LPC19:2 (sn-2).
6. The method of claim 4, wherein the biomarker for breast cancer is LPC18:2(sn-1), LPC18:2(sn-2), LPC 22:4 (sn-1).
7. The method of claim 4, wherein the biomarker for colorectal cancer is LPC 17:0 (sn-1), LPC19: 0 (sn-2), LPC19: 1 (sn-2), LPC19:2 (sn-2).
8. The method of claim 4, wherein the biomarkers of gastric cancer are LPC18: 0 (sn-1), LPC19: 0 (sn-2), LPC 20:0 (sn-1), LPC 20:0 (sn-2).
9. Use of the method of claim 1 for determining the concentration of lysophosphatidylcholine.
10. Use of the method of claim 1 for screening for cancer biomarkers.
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