Construction method of lipidome background database, lipidome detection method and lipidome detection system

Abstract

The invention discloses a construction method of a lipidome background database, a lipidome detection method and a lipidome detection system.The construction method of the background database comprises the steps that various commercially available blood collection tubes are added into a simulation solution, and sample pretreatment is conducted according to an established sample pretreatment method; then respectively detecting by virtue of an established liquid chromatography-tandem mass spectrometry lipid analysis method; performing peak extraction processing on the collected data to obtain a first-stage peak area list, forming a corresponding chromatographic peak map according to the first-stage peak area list, and summarizing to obtain a background database. Aiming at background interference caused by a blood collection tube, a simulated solution is used for detection through an established lipidome detection method, and a lipidome background database is constructed through chromatographic feature extraction, so that during actual sample lipidome detection, corresponding blood collection tube background features are matched, quantitative deduction is performed, and the detection accuracy is improved. And the accuracy, reliability and stability of sample analysis are improved.

Description

Technical Field

[0001] This invention relates to the field of biological sample detection technology, and more specifically, to a method for constructing a lipidome background database and a lipidome detection method and system. Background Technology

[0002] Serum cohort studies typically refer to studies that involve the continuous collection, processing, and analysis of a series of serum samples. This research design can be used to track the progression of specific diseases, predict disease risk, and assess treatment effectiveness.

[0003] In existing experimental designs, a blank control of the sample within the experimental procedure is typically used to address interference introduced by methods and equipment used in the experimental process. In data analysis, the sample is usually subjected to quantitative or qualitative subtraction based solely on the blank control within the experimental procedure.

[0004] However, during the serum cohort study, researchers found that even serum samples from the same source often showed inconsistent trends in lipid expression, exhibiting a divergent trend.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a method for constructing a lipidome background database, as well as a lipidome detection method and detection system, to improve the above-mentioned technical problems.

[0007] This invention is implemented as follows:

[0008] In a first aspect, the present invention provides a method for constructing a lipidome background database, comprising:

[0009] Obtain various commercially available blood collection tubes;

[0010] Various commercially available blood collection tubes were added to the simulated solution and pretreated according to the established sample pretreatment method.

[0011] The pretreated simulated solutions corresponding to various commercially available blood collection tubes were analyzed by the established liquid chromatography-tandem mass spectrometry lipid analysis method.

[0012] The XCMS software package was used to perform peak extraction processing on the transposed mzXML format acquisition data to obtain a list of primary peak areas for each simulated solution. Based on the list of primary peak areas, the mass-to-charge ratio and retention time of all extracted chromatographic peaks for each simulated solution were marked in the graph to form the corresponding chromatographic peak diagram.

[0013] A list of primary peak areas and chromatograms of simulated solutions corresponding to different commercially available blood collection tubes are compiled.

[0014] In some alternative schemes, the simulated solution is selected from any one of a buffer solution, a 0.7wt% to 1.1wt% sodium salt solution, and a 0.8wt% to 1.2wt% serum protein solution.

[0015] In some alternative solutions, the buffer is PBS; the sodium salt is NaCl; and the serum protein is bovine serum albumin.

[0016] In some alternative schemes, the solvent for the simulated solution is water.

[0017] Among the alternative approaches, established sample pretreatment methods include: first extraction with a mixture of methyl tert-butyl ether and methanol, followed by extraction with a mixture of isopropanol and methanol;

[0018] Among some alternative approaches, established sample pretreatment methods include: adding a simulated solution to a centrifuge tube, then adding a mixture of methyl tert-butyl ether and methanol, vortexing for the first time, centrifuging, and taking the supernatant as the first extract; adding the lower residue to the mixture of methyl tert-butyl ether and methanol again, vortexing for the second time, centrifuging, and taking the supernatant as the second extract; combining the first and second extracts, concentrating and drying the combined extract under vacuum, adding a mixture of isopropanol and methanol to the residue, vortexing for the third time, centrifuging, and taking the supernatant into the sample injection container.

[0019] In some alternative solutions, the volume ratio of methyl tert-butyl ether to methanol in the mixed solution is (3.5–5.5):1.

[0020] In some alternative solutions, the volume ratio of isopropanol to methanol in the mixed solution is (0.9–1.1):1.

[0021] In some alternative schemes, the duration of the first, second, and third vortex oscillations is 50s to 70s.

[0022] In some of the optional schemes, the centrifugation speed after three vortex oscillations is 10,000 rpm to 15,000 rpm, the centrifugation temperature is 0℃ to 5℃, and the centrifugation time is 8 min to 12 min.

[0023] In some alternative schemes, the established liquid chromatography-tandem mass spectrometry (LC-MS / MS) lipid analysis method uses the following chromatographic conditions: C18 column, flow rate of 0.2 mL / min to 0.3 mL / min, column temperature of 38℃ to 42℃, and injection volume of 1 μL to 10 μL; mobile phase A is an acetonitrile aqueous solution with a volume concentration of 35% to 45% and 5 to 15 mM NH4COOH added; mobile phase B is an isopropanol-acetonitrile solution with a volume concentration of 5% to 15% and 5 to 15 mM NH4COOH added; and the elution gradient is as follows:

[0024]

[0025] The mass spectrometry conditions were as follows: the instrument used an electrospray ionization source, positive and negative ion ionization mode, positive ion spray voltage of 3.0-3.50 kV, negative ion spray voltage of 2-3 kV, sheath gas of 30-40 arb, auxiliary gas of 10-20 arb, capillary temperature of 300-350℃, full scan, scan range of 150-2000, and HCD was used for secondary fragmentation, with collision energy between 10% and 90%.

[0026] Some alternative peak enhancement methods include: using the centWave method to find peaks that meet the following conditions: mass-to-charge ratio deviation within 15 ppm, peak width between 5 and 30, minimum response of 5000, and signal-to-noise ratio of 3 or higher.

[0027] Among the available options are various commercially available blood collection tubes, including all types, sizes, manufacturers, and batches of blood collection tubes that are available for purchase.

[0028] Secondly, the present invention also provides a lipidomics detection method, which includes: pre-processing a serum sample according to an established sample pre-processing method;

[0029] Serum samples that have undergone pretreatment were subjected to serum lipidomics analysis using an established liquid chromatography-tandem mass spectrometry method.

[0030] According to the interference feature matching method, the chromatographic peak features of the obtained serum samples are matched with the chromatographic peak features in the lipidomic background database constructed by the above construction method, and then the response is subtracted at the quantitative level.

[0031] The sample pretreatment method and liquid chromatography-tandem mass spectrometry lipid analysis method for serum samples are the same as those used in the above-mentioned method for constructing the lipidome background database.

[0032] In some alternative approaches, the interference feature matching method is as follows: based on the list of primary peak areas of serum samples and lipid background database, primary feature peak matching is performed based on parameter thresholds of mass-to-charge ratio deviation and retention time deviation to find the primary feature of a specific serum blood collection tube in the lipid background database corresponding to each detected lipid primary feature in the sample.

[0033] The threshold setting for the mass-to-charge ratio deviation parameter is related to the precision of the mass spectrometer. Higher mass spectrometer precision results in a mass-to-charge ratio closer to the true value, allowing for a smaller threshold setting. The threshold setting for the retention time deviation parameter is related to the stability of the LC-MS system. Generally, a more stable LC-MS system results in smaller retention time fluctuations, allowing for a smaller threshold setting. Preferably, the threshold for the mass-to-charge ratio deviation is between 1 ppm and 30 ppm; the threshold for the retention time deviation is between 10 seconds and 30 seconds. More preferably, the threshold for the mass-to-charge ratio deviation is 15 ppm; and the threshold for the retention time deviation is 20 seconds.

[0034] In some alternative approaches, the correlation coefficient between the primary features of a serum sample and the primary features corresponding to a serum blood collection tube in the lipidomic background database is calculated using the Pearson correlation analysis method. This is done to identify primary features that are interfering features in the serum sample and to subtract these interfering features from the background using a quantitative subtraction method.

[0035] Thirdly, the present invention also provides a lipidomics detection system, which includes a background database constructed by the above-described construction method.

[0036] This invention offers the following advantages: By incorporating the interference caused by blood collection tubes to lipidomics detection into a crucial factor affecting the accuracy, reliability, and stability of serum sample analysis, and specifically using simulated solutions with established lipidomics detection methods, a lipidomics background database corresponding to different types of commercially available blood collection tubes is constructed through chromatographic feature extraction. Therefore, during actual sample lipidomics detection, the background features of corresponding blood collection tubes are matched to this lipidomics background database based on background feature similarity. Through quantitative or qualitative subtraction methods, the influence of blood collection tubes on lipid expression trends is reduced, thereby improving the accuracy, reliability, and stability of sample analysis. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the method for constructing a lipidomic background database according to an embodiment of the present invention.

[0039] Figure 2 This is a schematic flowchart of the lipidomics detection method proposed in the embodiments of the present invention;

[0040] Figure 3 The total ion flow chromatogram of the empty needle in positive ion mode;

[0041] Figure 4 This is the total ion chromatogram of the PBS buffer solution in positive ion mode;

[0042] Figure 5 The total ion chromatogram of the PBS blood collection tube solution in positive ion mode;

[0043] Figure 6 This is the total ion chromatogram of a serum sample in positive ion mode;

[0044] Figure 7 This is the total ion chromatogram of NaCl solution in positive ion mode;

[0045] Figure 8 The total ion chromatogram of the BSA solution in positive ion mode;

[0046] Figure 9 A single chromatographic peak extracted by XCMS;

[0047] Figure 10 The instrument chromatogram corresponding to a single chromatographic peak extracted by xcms;

[0048] Figure 11 This is a schematic diagram of the chromatographic peak characteristic distribution corresponding to the blood collection tubes of the lipidomic background database proposed in the embodiments of the present invention.

[0049] Figure 12 This is a comparison diagram of the chromatographic peak characteristics of different types of blood collection tubes in the method for constructing the lipidomic background database proposed in Embodiment 1 of the present invention;

[0050] Figure 13 This is a comparison diagram of chromatographic peak characteristics of similar blood collection tubes in the method for constructing a lipidomic background database proposed in Embodiment 1 of the present invention.

[0051] Figure 14 This is a comparison diagram of the chromatographic peak characteristics of serum sample 1 and serum sample 2 in Example 2 of the present invention, as well as the blank tubes simulated in the corresponding serum cohort lipidomic background database;

[0052] Figure 15This is a comparison chart used to preliminarily match the chromatographic peak feature maps of each serum sample with those in the lipidomic background database.

[0053] Figure 16 This is a comparison chart of the chromatographic peak characteristic distribution calculated by Pearson correlation analysis in Embodiment 6 of the present invention; the circles and colors in the chart represent the magnitude of the correlation coefficient values ​​between different samples, with red indicating a positive correlation and blue indicating a negative correlation, and the values ​​in the circles representing the correlation coefficient values;

[0054] Figure 17 This is a background subtraction FC comparison image of the sample blood collection tube in Embodiment 6 of the present invention;

[0055] Figure 18 This is a comparison chart of the quantitative correlation of the sample blood collection tube background subtraction in Example 6 of the present invention.

[0056] in, Figures 11-18 In the graph, the horizontal axis represents retention time (Rt), and the vertical axis represents mass-to-charge ratio (m / z). The lines in the graph indicate that a corresponding first-level feature has been detected at the current location. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0058] The following is a detailed description of the method for constructing a lipidomic background database, as well as the lipidomic detection method and system provided by this invention.

[0059] The inventors discovered that existing experimental designs typically use a blank control of the sample within the experimental procedure to account for interference introduced by methods and equipment. In data analysis, samples are generally only quantitatively or qualitatively subtracted based on the blank control. However, this approach overlooks the significant impact of blood collection tubes on lipidomics detection.

[0060] In traditional cohort studies, blood collection tubes are frequently used due to limitations in sample collection and preservation. These tubes may contain various additives or organic compounds that can interfere with the lipid composition of serum, thus affecting the accuracy and reliability of test results. Due to limitations such as usage scenarios, sample collection time and location, different types, manufacturers, and batches of tubes can introduce non-sample-specific differences, creating background noise that interferes with comparisons or analyses between samples. During data analysis, severe background noise from blood collection tubes can cause inconsistencies in comparisons or analyses between samples. Specifically, blood collection tubes can introduce lipid characteristics from sources other than the sample itself, while interfering with the expression trends of existing lipid characteristics in the sample. Samples from different blood collection tubes from the same source show a tendency to separate, while samples from different sources using the same blood collection tube show a tendency to cluster. In other words, background interference can mask the characteristics of the sample itself, leading to unclear or incorrect sample grouping characteristics and unclear lipid characteristic expression trends. Therefore, including blood collection tube characteristics or abnormal characteristics in the candidate biomarker list has a significant impact on biomarker selection in cohort studies.

[0061] However, existing methods often fail to completely eliminate the influence of the blood collection tubes themselves when processing serum samples, and this problem is difficult to solve. On the one hand, it is difficult to isolate the lipid components of serum samples and determine the background interference characteristics caused by the blood collection tubes. On the other hand, the serum samples collected by researchers are based on different types, manufacturers, and batches of blood collection tubes, making it difficult to determine the specific type, model, batch, and manufacturer of the blood collection tubes.

[0062] Based on this, the inventors proposed the following solution after extensive research and practice.

[0063] Some embodiments of the present invention provide a method for constructing a lipidomic background database, comprising:

[0064] Obtain various commercially available blood collection tubes;

[0065] Various commercially available blood collection tubes were added to the simulated solution and pretreated according to the established sample pretreatment method.

[0066] The pretreated simulated solutions corresponding to various commercially available blood collection tubes were analyzed by the established liquid chromatography-tandem mass spectrometry lipid analysis method.

[0067] The XCMS software package was used to perform peak extraction processing on the transposed mzXML format acquisition data to obtain a list of primary peak areas for each simulated solution. Based on the list of primary peak areas, the mass-to-charge ratio and retention time of all extracted chromatographic peaks for each simulated solution were marked in the graph to form the corresponding chromatographic peak diagram.

[0068] A list of primary peak areas and chromatograms of simulated solutions corresponding to different commercially available blood collection tubes are compiled.

[0069] First, by incorporating the interference caused by blood collection tubes to lipidomics detection into the important factors affecting the results of lipidomics detection in serum samples, this provides a direction for further improving the accuracy, reliability, and stability of lipidomics detection. Furthermore, a creative approach is proposed to use simulated solutions instead of serum samples to study interference background characteristics. Specifically, a simulated solution is designed based on the composition of serum samples excluding lipid components. Then, existing, practically used lipidomics detection methods or relevant background chromatographic features are used. Through chromatographic feature extraction, a lipidomics background database for different types of commercially available blood collection tubes corresponding to serum cohorts can be constructed.

[0070] Secondly, given the issue that researchers cannot clearly identify the source of blood collection tubes, a database of background data for all commercially available blood collection tubes can be established. When performing lipidomics analysis on actual samples, the background feature similarity can be used to match the corresponding blood collection tube background features with this lipidomics background database. Then, through quantitative or qualitative subtraction methods, the influence of blood collection tubes on lipid expression trends can be reduced, thereby improving the accuracy, reliability, and stability of sample analysis.

[0071] It should be noted that for samples with known blood collection tube sources, the background characteristics of the corresponding blood collection tubes from the lipidomic background database can be directly subtracted. Generally, the simulated solution does not contain lipid components.

[0072] The purpose of the simulated solution is to dissolve interfering components in the blood collection tube, thereby enabling the detection of background interference from the blood collection tube. This application does not specifically limit the simulated solution. Generally, interfering components in blood collection tubes mostly originate from additives, such as EDTA in purple-capped blood collection tubes, heparin sodium (lithium) in green-capped blood collection tubes, separating gel in yellow-capped blood collection tubes, and coagulants in orange-capped blood collection tubes. When blood enters the blood collection tube, these additives dissolve, resulting in background interference. Therefore, an aqueous phase system similar to blood can be chosen as the simulated solution to dissolve and detect the background. For cases where the simulated solution itself causes interference, the detection results can be subtracted by processing the blood collection tube with the simulated solution, rather than removing the interference from the blood collection tube itself.

[0073] Specifically, participate in Figure 1 The method for constructing a lipidomic background database provided by some embodiments of the present invention includes the following steps:

[0074] S1. Obtain various commercially available blood collection tubes.

[0075] Specifically, commercially available blood collection tubes include all types of blood collection tubes available for purchase, in different specifications, from different manufacturers, and with different batch numbers.

[0076] As a reference, a database of blood collection tubes can be established, and some commercially available serum blood collection tubes are shown in Table 1.

[0077] Table 1 Information on Blood Collection Tubes

[0078]

[0079]

[0080] S2. Add various commercially available blood collection tubes to the simulated solution and perform sample pretreatment according to the established sample pretreatment method.

[0081] For reference, by way of example, the simulated solution is selected from any one of a buffer solution, a 0.7wt% to 1.1wt% sodium salt solution, and a 0.8wt% to 1.2wt% serum protein solution.

[0082] The buffer solution was PBS; the sodium salt was NaCl; the serum protein was bovine serum albumin; and the solvent for the serum simulation solution was water.

[0083] The simulated solution can be selected from PBS, 0.9% NaCl, or 1% bovine serum albumin.

[0084] Furthermore, established sample pretreatment methods refer to existing or actual serum sample pretreatment methods determined during actual detection operations. The sample pretreatment methods here primarily aim to extract lipids. Commonly used methods include those proposed by Folch and Bligh-Dyer, which typically involve modifications to the solvent volume and analytical matrix. Folch's method uses chloroform / methanol (2:1, v / v) as the extraction solvent, while Bligh-Dyer's method uses chloroform / methanol (1:2, v / v), followed by the addition of 1 volume of chloroform and 1 volume of water. Chloroform is highly toxic, so less toxic dichloromethane can be used as a substitute. When using these traditional methods, some problems may arise in the target component collection process because the target component is in the lower layer of the two-phase solvent. Collecting the extract requires inserting the pipette tip through the upper solvent layer into the lower solvent layer, which may contaminate the extract. This problem can be solved using methyl tert-butyl ether (MTBE) as the extraction solvent. MeOH and MTBE (1:5.5, v / v) are added to plasma, followed by the addition of 1.25 times the volume of water to allow for separation. After separation, the lipid compounds are present in the less dense organic solvent and remain on the upper layer, making the collection of the extract solution easy. Furthermore, MTBE has much lower toxicity than chloroform. This method has been shown to extract most lipid compounds (PC, SM, PE, LPC, Cer, ChoIE, TG) with high recoveries.

[0085] For reference, in some embodiments of the present invention, the established sample pretreatment method includes: first extraction with a mixed solution of methyl tert-butyl ether and methanol, and then extraction with a mixed solution of isopropanol and methanol.

[0086] Specifically, the simulated solution was added to a centrifuge tube, followed by a mixture of methyl tert-butyl ether and methanol. After the first vortexing and centrifugation, the upper layer was taken as the first extract. The lower residue was then added to the mixture of methyl tert-butyl ether and methanol again, followed by a second vortexing and centrifugation. The upper layer was taken as the second extract. The first and second extracts were combined, and the combined extract was concentrated and dried under vacuum. A mixture of isopropanol and methanol was added to the residue, followed by a third vortexing and centrifugation. The supernatant was then taken into the sample injection container.

[0087] In some embodiments, the volume ratio of methyl tert-butyl ether to methanol in the mixed solution of methyl tert-butyl ether and methanol is (3.5 to 5.5):1. For example, the volume ratio of methyl tert-butyl ether to methanol can be 3.5:1, 4:1, 4.5:1, 5:1 or 5.5:1, or between any two of the above volume ratios.

[0088] In some embodiments, the volume ratio of isopropanol to methanol in the mixed solution of isopropanol and methanol is (0.9 to 1.1):1. Exemplarily, the volume ratio of isopropanol to methanol may be 0.9:1, 1:1, or 1.1:1, or between any two of the above volume ratios.

[0089] Furthermore, to ensure uniform solution dispersion and better extraction, in some embodiments, the duration of the first, second, and third vortex oscillations is always between 50 and 70 seconds, such as 50, 53, 55, 58, 60, 62, 65, 68, or 70 seconds, or between any two of these times. It should be noted that the first, second, and third vortex oscillations can be the same or identical.

[0090] For reference, the centrifugation speed after three vortex oscillations is 10,000 rpm to 15,000 rpm, such as 10,000 rpm, 11,000 rpm, 12,000 rpm, 13,000 rpm, 14,000 rpm or 15,000 rpm, or between any two of the above speeds. The centrifugation temperature is 0℃ to 5℃, such as 0℃, 1℃, 2℃, 3℃, 4℃ or 5℃, and the centrifugation time is 8 min to 12 min, such as 8 min, 9 min, 10 min, 11 min or 12 min.

[0091] For example, the established sample pretreatment method can be as follows: Add 4 mL of PBS, 0.9% NaCl solution, and 1% BSA to a blood collection tube and vortex to mix. Take 1 mL of the sample simulation solution from the blood collection tube into a 2 mL centrifuge tube. Add 750 μL of MTBE-MeOH (4:1, v / v) solution and vortex for 60 s. Place on ice for 10 min, centrifuge at 12000 rpm and 4℃ for 10 min, and collect the supernatant into another 2 mL centrifuge tube; add 500 μL of MTBE-MeOH (4:1, v / v) solution to the lower residue and vortex for 60 s; centrifuge at 12000 rpm and 4℃ for 10 min, collect the supernatant and combine the extracts; concentrate and dry under vacuum, add 200 μL of ISO-MeOH (1:1, v / v) solution to the residue, vortex for 60 s; centrifuge at 12000 rpm and 4℃ for 10 min, and collect the supernatant into a sample vial.

[0092] S3. The pretreated simulated solutions corresponding to various commercially available blood collection tubes were analyzed using the established liquid chromatography-tandem mass spectrometry lipid analysis method.

[0093] Specifically, in some embodiments, the established liquid chromatography-tandem mass spectrometry (LC-MS / MS) lipid analysis method uses the following chromatographic conditions: a C18 column, a flow rate of 0.2 mL / min to 0.3 mL / min, a column temperature of 38°C to 42°C, and an injection volume of 1 μL to 10 μL; mobile phase A is an acetonitrile aqueous solution with a volume concentration of 35% to 45% and 5 to 15 mM NH4COOH added; mobile phase B is an isopropanol-acetonitrile solution with a volume concentration of 5% to 15% and 5 to 15 mM NH4COOH added. For example, in another established LC-MS / MS lipid analysis method, the chromatographic conditions are: a C18 column, an autosampler temperature of 4°C to 10°C, a flow rate of 0.25 mL / min, a column temperature of 38°C to 42°C, and an injection volume of 2 μL; mobile phase A is CAN:H2O = 6:4 and 10 mM NH4COOH added. The elution gradient is as follows: NH4COOH is added to mobile phase B, which is isopropanol:acetonitrile = 9:1, and mobile phase B contains 10 mM NH4COOH.

[0094]

[0095] The mass spectrometry conditions were as follows: the instrument used an electrospray ionization source in positive and negative ion ionization mode; the positive ion spray voltage was 3.0-3.50 kV, the negative ion spray voltage was 2-3 kV, the sheath gas was 30-40 alb, the auxiliary gas was 10-20 alb, the capillary temperature was 300-350 °C, and a full scan was performed with a scan range of 150-2000. Secondary fragmentation was performed using an HCD with collision energies between 10% and 90%. For example, the mass spectrometry conditions were as follows: the instrument used an electrospray ionization source in positive and negative ion ionization mode; the positive ion spray voltage was 3.50 kV, the negative ion spray voltage was 2.40 kV, the sheath gas was 30 alb, the auxiliary gas was 10 alb, the capillary temperature was 325 °C, the scan range was 150-2000, and secondary fragmentation was performed using an HCD with collision energies of 15%, 30%, and 40%.

[0096] It should be noted that the liquid chromatography-tandem mass spectrometry lipid analysis method in the above embodiments is only an example. The above detection method can have better accuracy, reliability and stability of lipidomics detection, but it is not intended to limit the embodiments of the present invention. That is, in other embodiments, some existing liquid chromatography-tandem mass spectrometry lipid analysis methods can also be used to perform the above operations. However, it is necessary to maintain consistency with the above operations when detecting actual samples.

[0097] S4. Using the XCMS software package, peak extraction processing is performed on the transposed mzXML format acquisition data to obtain a list of primary peak areas for each simulated solution. Based on the list of primary peak areas, the mass-to-charge ratio and retention time of all extracted chromatographic peaks for each simulated solution are marked in the graph to form the corresponding chromatographic peak diagram.

[0098] This application does not limit the specific methods and parameters of peak enhancement processing; the peak enhancement processing only needs to obtain normal chromatographic peaks. As an example, peak enhancement processing includes: using the centWave method to find peaks, where the found chromatographic peaks meet the following conditions: mass-to-charge ratio deviation (m / z deviation) within 15 ppm, peak width between 5 and 30, minimum response of 5000, and signal-to-noise ratio (SNR) greater than 3. For example, depending on the characteristics of the sample itself, the parameter settings can be: m / z = 5–15 ppm, peak width 5–30, sn thresh (SNR threshold) > 3, prefilter > 6 & > 5000 (the chromatographic peak must contain at least 6 sampling points with an intensity greater than 5000 cps), and noise (minimum response / background noise) > 5000. Through these parameter settings, obviously abnormal detection results (abnormal m / z, extremely low response m / z) can be removed, and the primary chromatographic peaks corresponding to each m / z can be obtained. The selection of the ppm range for the m / z deviation of a substance is generally based on the resolution of the mass spectrometer used. Under current mass spectrometry conditions, the m / z deviation of lipid substances is generally within 15 ppm, essentially covering the entire m / z range of a single substance. Peak width parameters are set based on the chromatographic separation of substances, in seconds (s), meaning peaks from 5 to 30 ppm can be identified. Peak acquisition points and background noise are also set based on the current instrument and system, generally referencing the average background response during non-substance detection periods. Setting these parameters according to specific mass spectrometry setup conditions can maximize the quality and accuracy of chromatographic peaks.

[0099] S5. Compile a list of primary peak areas and chromatograms for simulated solutions corresponding to different commercially available blood collection tubes.

[0100] Specifically, the list of primary peak areas and chromatographic peak diagrams can be stored on a computer and categorized to clearly correspond to the background characteristic peak diagrams of different blood collection tubes.

[0101] Furthermore, participate Figure 2 Some embodiments of the present invention also provide a lipidomics detection method, which includes:

[0102] S1. Perform sample pretreatment on serum samples according to the established sample pretreatment method.

[0103] S2. The pretreated serum samples were subjected to serum lipidomics analysis using the established liquid chromatography-tandem mass spectrometry lipid analysis method.

[0104] S3. According to the interference feature matching method, the chromatographic peak features of the obtained serum sample are matched with the chromatographic peak features in the lipidomic background database constructed by the above construction method, and then the response is subtracted at the quantitative level.

[0105] The sample pretreatment method and liquid chromatography-tandem mass spectrometry lipid analysis method for serum samples are the same as those used in the above-mentioned method for constructing the lipidome background database.

[0106] In some implementations, the interference feature matching method is as follows: based on the list of primary peak areas of serum samples and lipid background database, primary feature peak matching is performed based on parameter thresholds of mass-to-charge ratio deviation of 15 pmm and retention time deviation of 20 seconds to find that each detected lipid primary feature in the sample corresponds to a primary feature of a serum blood collection tube in the lipid background database.

[0107] Furthermore, in some embodiments, the correlation coefficient of the quantitative level between the primary features of the serum sample and the primary features corresponding to a certain serum blood collection tube in the lipidomic background database is calculated by Pearson correlation analysis, so as to identify the primary features that are interfering features in the serum sample, and the background subtraction of the interfering features in the serum sample is performed by quantitative subtraction.

[0108] It should be noted that other implementation methods are not limited to the above methods; other subjective or objective matching methods with a certain degree of accuracy can also be used for matching.

[0109] Some embodiments of the present invention also provide a lipidomics detection system, which includes a background database constructed by the above-described construction method.

[0110] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0111] The experimental materials and equipment used in the following examples are shown in Tables 2, 3 and 4.

[0112] Table 2 Experimental Materials and Equipment

[0113]

[0114]

[0115] Table 3 Main Experimental Reagents

[0116]

[0117] Table 4 Material Information

[0118]

[0119] The discovery process of background interference introduced by blood collection tubes

[0120] During blood lipidomics research, the inventors unexpectedly discovered the same interference in the test results of different samples. To find the source of this interference, a detailed study was conducted, as follows:

[0121] (1) Solution preparation

[0122] PBS buffer: Commercially available 1X PBS buffer.

[0123] PBS blood collection tube solution: Add 4 mL of PBS solution to the yellow-headed separating gel blood collection tube (Jiangsu Yuli Medical Equipment Co., Ltd.) and vortex to mix.

[0124] Serum sample: Blood samples from healthy individuals were collected using yellow-headed separation gel blood collection tubes (Jiangsu Yuli Medical Equipment Co., Ltd.), centrifuged, and serum samples were obtained.

[0125] (2) Preprocessing

[0126] Take 1 mL of the above-mentioned PBS buffer, PBS blood collection tube solution, and serum sample, respectively, add 750 μL of MTBE-MeOH (4:1, v / v) solution, and vortex for 60 s. Then place on ice for 10 min, centrifuge at 12000 rpm at 4℃ for 10 min, and collect the supernatant.

[0127] (3) LC-MS detection conditions

[0128] The chromatographic conditions were as follows: XBridge Premier Oligonucleotide BEH C18 2.5 μm (2.1 × 100 mm) column, flow rate of 0.25 mL / min, column temperature of 40 °C, and injection volume of 2 μL. Mobile phase A was ACN:H2O = 6:4 (10 mM NH4COOH), and mobile phase B was ISO:ACN = 9:1 (10 mM NH4COOH).

[0129] The elution gradient is as follows:

[0130]

[0131] Mass spectrometry conditions are:

[0132] The instrument uses an electrospray ionization (ESI) source in both positive and negative ionization modes. The positive ion spray voltage is 3.50 kV, and the negative ion spray voltage is 2.40 kV. The sheath gas is 30 arb, and the auxiliary gas is 10 arb. The capillary temperature is 325 °C. A full scan is performed, with a scan range of 150–2000 Å. Secondary pyrolysis is performed using HCD, with collision energies of 15%, 30%, and 40%.

[0133] Administer one syringe under the LC-MS conditions, along with pre-treated PBS buffer, PBS blood collection tube solution, and serum sample, as follows: Figure 3-6 The figures represent the total ion flow maps of empty syringes, PBS buffer, PBS blood collection tube solutions, and serum samples, respectively, under positive ion mode.

[0134] Depend on Figures 3-6 It was found that the settling time (RT) values ​​of the total ion chromatograms of PBS blood collection tube solution and serum samples showed the same "bulge" (broad peak) between 10 min and 14 min, while this broad peak was not present in the total ion chromatograms of empty syringes and PBS buffer. This indicates that the blood collection tube introduced background interference. Furthermore, the total ion chromatogram of the serum sample showed that the RRT value of this "bulge" was very close to the RRT value of the target peak in the serum sample, making this background interference more likely to lead to inaccurate test results.

[0135] Background interference under different simulated solutions

[0136] Furthermore, different simulated solutions were placed in blood collection tubes and processed with serum samples, as follows:

[0137] 0.9% Sodium Chloride (0.9% NaCl) Solution: Dissolve 0.9g of NaCl in 100mL of water. Add 4mL of the solution to a yellow-tipped separating gel blood collection tube (Jiangsu Yuli Medical Equipment Co., Ltd.) and vortex to mix.

[0138] 1% Bovine Serum Albumin (1% BSA): Dissolve 1g of BSA in 100mL of water. Transfer 4mL to a yellow-tipped separating gel blood collection tube (Jiangsu Yuli Medical Equipment Co., Ltd.) and vortex to mix.

[0139] Following the same pretreatment and LC-MS conditions used to detect background interference introduced by the blood collection tube, 0.9% NaCl and 1% BSA were detected, and their representative total ion chromatograms are shown below. Figures 7-8 As shown.

[0140] Comparison of detection results for empty syringes, PBS blood collection tube solutions, 0.9% NaCl, and 1% BSA revealed background interference from the blood collection tubes within the first 10-14 minutes. While the detection results for different simulated solutions were generally similar, some differences existed, indicating that in addition to background interference from the blood collection tubes, the simulated solutions themselves also introduced minor background interference. Among these, PBS blood collection tube solutions showed the least interference, followed by 0.9% NaCl, and then 1% BSA.

[0141] Example 1

[0142] This embodiment provides a method for constructing a lipidomics background database, which specifically includes the following steps:

[0143] (1) Reagent preparation

[0144] PBS buffer: Commercially available 1X PBS buffer.

[0145] 5mM NH4COOH: Accurately weigh 3.15g of NH4COOH, add 10mL of H2O, and mix until dissolved.

[0146] ISO:ACN = 9:1 (10mM NH4COOH): Measure 900mL of ISO and 100mL of...

[0147] Add ACN to the mobile phase bottle, add 2 mL of 5 M NH4COOH, mix well, cover the bottle and sonicate for 5 min.

[0148] ACN:H₂O = 6:4 (10mM NH₄COOH): Measure 600mL of ACN and 400mL of...

[0149] Add H2O to the mobile phase bottle, add 2 mL of 5 M NH4COOH, mix well, cover the bottle and sonicate for 5 min.

[0150] (2) Sample pretreatment

[0151] To simulate the dissolution of interfering substances in blood collection tubes, various blood collection tubes listed in Table 5 were used. For each type of blood collection tube, 4 mL of PBS was added and vortexed to mix. Then, 1 mL of the solution from each blood collection tube was transferred to a 2 mL centrifuge tube, and 750 μL of MTBE-MeOH (4:1, v / v) solution was added and vortexed for 60 s. Then place on ice for 10 min, centrifuge at 12000 rpm at 4℃ for 10 min, and collect the supernatant into another 2 mL centrifuge tube; add 500 μL of MTBE-MeOH (4:1, v / v) solution to the lower residue, vortex for 60 s; centrifuge at 12000 rpm at 4℃ for 10 min, and collect the supernatant again and combine it with the supernatant from the first extraction; then vacuum concentrate and dry the combined extract (SCIENTZ-1LS centrifuge concentrator, speed 2000 r / min, temperature 25℃, time 2.5 h), add 200 μL of ISO-MeOH (1:1, v / v) solution to the residue, vortex for 60 s; centrifuge at 12000 rpm at 4℃ for 10 min, and collect the supernatant into a sample vial.

[0152] (3) Instrument data acquisition method

[0153] The processed serum simulant solution was analyzed using liquid chromatography-tandem mass spectrometry (LC-MS / MS).

[0154] The chromatographic conditions were as follows: XBridge Premier Oligonucleotide BEH C18 2.5 μm (2.1 × 100 mm) column, autosampler temperature 8℃, flow rate 0.25 mL / min, column temperature 40℃, and injection volume 2 μL. Mobile phase A was ACN:H₂O = 6:4 (10 mM NH₄COOH), and mobile phase B was ISO:ACN = 9:1 (10 mM NH₄COOH). The elution gradient was as follows:

[0155]

[0156] Mass spectrometry conditions are:

[0157] The instrument uses an electrospray ionization (ESI) source in both positive and negative ionization modes. The positive ion spray voltage is 3.50 kV, and the negative ion spray voltage is 2.40 kV. The sheath gas is 30 arb, and the auxiliary gas is 10 arb. The capillary temperature is 325 °C. A full scan is performed, with a scan range of 150–2000 Å. Secondary pyrolysis is performed using HCD, with collision energies of 15%, 30%, and 40%.

[0158] (4) Chromatographic feature extraction

[0159] Based on the RxCMS software package, peak extraction was performed on the transposed acquired data (mzXML format). Peak finding was conducted using the centWave method, with the following parameters set: mass-to-charge ratio error (ppm) of 5–15 ppm, peak width of 5–30, signal-to-noise ratio threshold of 3, prefilter parameter >6 and >5000 (indicating that the chromatographic peak contains at least 6 acquisition points with intensities above 5000 cps), and minimum noise >5000 (i.e., the chromatographic peak intensity is greater than 5000). Primary chromatographic peaks meeting these parameters were found, resulting in a primary data list containing peak area, rt (retention time), and peak area. For example, [example data would be inserted here]. Figure 9 and Figure 10 The images are, in order, the instrument chromatogram and the XCMS extraction chromatogram corresponding to the primary chromatographic peak with m / z = 574.34.

[0160] The mz and rt dimensions of all chromatographic peaks for each sample are identified in the graph, forming a chromatographic peak characteristic map (e.g., Figure 11 ).

[0161] (5) Compile a list of primary peak areas and chromatographic peak characteristics for different commercially available blood collection tubes.

[0162] The collected blood collection tubes included four types: yellow-tipped procoagulant tubes, red-tipped additive-free tubes, and purple-tipped anticoagulant tubes. Under the same experimental conditions, lipidomics analysis was performed on the collected blood collection tubes using the methods described above. Based on the chromatographic peak characteristic maps obtained by XCMS software processing, the collected blood collection tubes included seven characteristic types (see Table 5).

[0163] Table 5. Classification of Blood Collection Tube Characteristics

[0164]

[0165]

[0166] The chromatographic peak characteristics of various blood collection tubes reveal that different types, such as 3_3 and 3_6, differ in the presence or absence of additives and their functions. Even from the same manufacturer, they exhibit different background characteristics. Figure 12 As shown in a and b in the figure.

[0167] Blood collection tubes of the same type, such as 3_2 and 3_6, showed variations in process and contents due to different batches and manufacturers. Upon testing, these tubes also exhibited different background characteristics (e.g., Figure 13 (as shown in a and b in the diagram).

[0168] The lipidome background database was obtained by storing the list of primary peak areas and chromatographic peak characteristic maps for different commercially available blood collection tubes.

[0169] It should be noted that, in addition to the blood collection tube itself, PBS buffer or other simulated solutions will also bring background interference. The background interference brought by PBS buffer is very small (negligible). When constructing the (blood collection tube) background database, the PBS buffer can be left unremoved; or the background interference brought by PBS buffer can be removed. In this case, before injecting the PBS blood collection tube solution, inject one dose of PBS buffer (without blood collection tube treatment, otherwise the same) and use the detection result of PBS buffer as a blank. Perform blank removal processing on the detection result of PBS blood collection tube solution, and then perform the above steps (4) and (5).

[0170] Example 2

[0171] This embodiment provides a method for constructing a lipidomics background database, which specifically includes the following steps:

[0172] The background features of different blood collection tubes were obtained in the same way as in Example 1. Then, data corresponding to these background feature regions were searched in the primary peak area list and filtered out to obtain the background features and mass spectrometry data of different blood collection tubes, and a lipidome background database was constructed.

[0173] Example 3

[0174] This embodiment provides a method for constructing a lipidomics background database, which specifically includes the following steps:

[0175] Use 0.9% NaCl instead of PBS buffer, otherwise the same as in Example 1.

[0176] Example 4

[0177] This embodiment provides a method for constructing a lipidomics background database, which specifically includes the following steps:

[0178] Use 1% BSA instead of PBS buffer, otherwise the same as in Example 1.

[0179] Example 5

[0180] This embodiment provides a method for lipidomics detection of serum samples, which includes:

[0181] Serum was collected using blood collection tubes to obtain serum samples, which were then tested according to the sample pretreatment method (2) and instrument collection method (3) of Example 1.

[0182] The detection results were processed using XCMS software to obtain chromatographic peak characteristic maps. These maps were then compared with those of blood collection tubes in the lipidomic background database. The blood collection tube with the closest background characteristics and its mass spectrometry data (first-level peak area list) were selected and subjected to blank subtraction to obtain the detection results for the serum samples.

[0183] As an example, serum samples from the same healthy individual were collected using blood collection tubes from two different manufacturers. Blood collection tube 1 (yellow-headed coagulation-promoting tube, batch number S2301009) and blood collection tube 2 (yellow-tube separation gel coagulation-promoting tube, batch number 20231021) were designated as serum sample 1 and serum sample 2, respectively. The obtained mass spectrometry data were processed using XCMS software to obtain the following results: Figure 14 The chromatogram peak characteristics shown in a and c are visible. Figure 14 There is a significant difference between 'a' and 'c' in the equation.

[0184] By comparing the chromatographic peak characteristics of each blood collection tube in the lipidome background database, the chromatographic peak characteristics of the blood collection tube corresponding to serum sample 1 were matched as follows: Figure 14 As shown in b, serum sample 2 is as follows Figure 14 As shown in c in the figure, the collection areas of serum sample 1 and the corresponding blood collection tube in the 100-1000s and 400-1100m / z ranges are as follows: Figure 14 The peaks in the red boxes (a and b) have the same chromatographic peak distribution, indicating interference from sources other than the sample. Serum sample 2 and its corresponding blood collection tube are located in the collection regions of 50-300s and 400-1100m / z. Figure 14The peaks in the red boxes (c and d in the image) have the same chromatographic peak distribution, indicating interference from sources other than the sample. Then... Figure 14 The mass spectrometry data corresponding to 'b' in the data is used as a blank. Blank subtraction is performed on the detection data of serum sample 1. Figure 14 The mass spectrometry data corresponding to d in the sample is used as a blank. The detection data of serum sample 1 is processed by subtracting the blank to obtain the detection results of serum sample 1 and serum sample 2. The interference of blood collection tube is eliminated, and the detection results of the two are consistent.

[0185] Example 6

[0186] This embodiment provides a method for lipidomics detection of serum samples, which includes:

[0187] (1) Serum samples 125, 126, 127 and 147, 240 and 296 from a hospital in the south were pretreated according to the sample pretreatment method in Example 1.

[0188] Serum samples 125, 126, and 127 are in yellow-tipped tube background (EP1), while samples 147, 240, and 296 are in purple-tipped tube background (EP4). Assuming that the blood collection tube model used for each serum sample is one of EP1, EP3, and EP4 from the lipidome background database, but the specific model is unknown, the blood collection tube corresponding to each serum sample is found by following these steps.

[0189] (2) The pretreated serum sample was detected by liquid chromatography-tandem mass spectrometry under the conditions of Example 1.

[0190] (3) Blood collection tubes used for serum samples 125, 126, 127 and 147, 240, and 296 were identified in the lipidome background database constructed in Example 1. Specifically, this included:

[0191] Comparison Method 1:

[0192] Similar to Example 1, the chromatographic peak feature maps of serum sample detection results were obtained using XCMS software. The chromatographic peak feature maps of each serum sample were matched with the chromatographic peak feature maps in the lipidomic background database to find chromatographic peak feature maps with the same background features and corresponding blood collection tubes and mass spectrometry data.

[0193] Depend on Figure 15 It can be seen that the chromatographic peak characteristics of serum samples 125, 126, and 127 are similar, and they have the same background characteristics as blood collection tube EP1 in the database. Therefore, it is preliminarily determined that the blood collection tubes used for serum samples 125, 126, and 127 are EP1.

[0194] The chromatographic peak characteristics of serum samples 147, 240, and 296 are similar, and they have similar background characteristics to blood collection tube EP4 in the database. Therefore, it is preliminarily determined that the blood collection tubes used for serum samples 125, 126, and 127 are EP4.

[0195] Comparison Method Two:

[0196] Based on the primary peak area lists of serum samples and blank blood collection tubes, primary characteristic peak matching was performed using parameter thresholds of m / z 15ppm and rt adjust 20s (i.e., mass-to-charge ratio deviation of 15ppm and retention time deviation of 20 seconds). The similarity between the characteristics detected in the blank blood collection tubes and those in the samples was calculated using Pearson correlation analysis, as follows:

[0197] (1) Based on the first-level characteristic peak of the combined multiple samples, the characteristic peak that can match the interference of the blank tube is selected.

[0198] (2) Mark the first-level features that have a response in different samples as 1, and mark the ones with a response of 0 as 0, so as to represent the detection of different samples in different mz rt. The same operation is performed to mark the blank tubes.

[0199] (3) Generate pairwise matched samples and variables detected by blank features, and use the Pearson correlation coefficient, formula: X represents the detected features of the sample, and Y represents the detected features of the blank tube. The indexes for the strength and direction of the linear relationship between the two sets of variables are generated. The strength of the linear relationship is the magnitude of the correlation coefficient r, which is the absolute value (0 to 1), and the direction is positive or negative. Positive indicates positive correlation, and negative indicates negative correlation.

[0200] The results are as follows Figure 16 As shown, blood collection tube samples with different backgrounds and their corresponding blood collection tubes exhibit relatively high similarity. Serum samples 125, 126, and 127 show the highest Pearson correlation coefficients with EP1, at 0.28, 0.3, and 0.28 respectively, further indicating that the blood collection tubes used in serum samples 125, 126, and 127 are EP1. Serum samples 147, 240, and 296 show the highest Pearson correlation coefficients with EP4, at 0.23, 0.26, and 0.22 respectively, also further indicating that the blood collection tubes used in serum samples 125, 126, and 127 are EP4. In summary, the blood collection tubes used in the serum samples matched by the method in this embodiment are the same as those actually used, demonstrating that the method is accurate and reliable.

[0201] Furthermore, the data from lipidome background database blood collection tube EP1 was subtracted from the test results of serum samples 125, 126, and 127 to obtain the actual data for serum samples 125, 126, and 127. Similarly, the data from lipidome background database blood collection tube EP4 was subtracted from the test results of serum samples 147, 240, and 296 to obtain the actual data for serum samples 125, 126, and 127.

[0202] Using QC (quality control) samples as the same benchmark, the FC (fat density) of detected lipid characteristics is calculated, revealing that samples with different blood collection tube backgrounds (such as...) Figure 17 As shown in Figures a and c, Figure a uses the average peak area of ​​each chromatographic peak detected in samples 125, 126, and 127, while Figure c uses the average peak area of ​​each chromatographic peak detected in samples 147, 240, and 296. The background characteristics of the blood collection tubes can cause abnormal increases or decreases in the lipid characteristic FC. By using quantitative subtraction to remove background interference from the samples, the overall FC value distribution in the interference region (approximately 0-800 seconds) becomes more consistent with the region without interference after 800 seconds, and the abnormal changes in FC are essentially eliminated (e.g., Figure 17 (as shown in b and d).

[0203] To verify the effectiveness of background subtraction, Pearson correlation analysis of the area of ​​primary characteristic peaks in samples of the same serum type before background subtraction showed that the samples exhibited high similarity to the background of the same blood collection tube, and low similarity between different backgrounds (e.g., ...). Figure 18 (As shown in a). The sample characteristic peaks that match the interference characteristic peaks in the blank tube are quantitatively subtracted, i.e., the area of ​​the current characteristic peak is directly subtracted from the area of ​​the corresponding interference characteristic peak in the blank tube. After background subtraction, samples of the same serum type show consistently high similarity (e.g., ...). Figure 18 (as shown in b).

[0204] The results above demonstrate the positive impact of accurate matching of blood collection tube interference features and background subtraction on subsequent analysis.

[0205] In summary, this study established a serum sampling tube background database and a background subtraction method by performing lipidomics analysis on serum blood collection tubes in a cohort study. Different types and sources of blood collection tubes were systematically considered, and their characteristics were comprehensively compared and analyzed. Different feature classifications were summarized to ensure the comprehensiveness and accuracy of the database. A blood collection tube feature comparison method was developed. For samples with known blood collection tube backgrounds, the corresponding blood collection tubes were used for background subtraction. For samples with unknown blood collection tubes, the established serum blood collection tube background database was used to ensure that the blood collection tubes most closely matching the sample background were selected for background subtraction based on the similarity of the sample and blood collection tube background features. A blood collection tube background subtraction method was developed. By accurately extracting and identifying various interferences from blood collection tubes and comparing them with sample lipids, background features or interfered sample lipid features were distinguished. Quantitative or qualitative subtraction methods were used to reduce the influence of blood collection tubes on lipid expression trends, improving the accuracy, reliability, and stability of sample analysis.

[0206] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing a lipidomic context database, characterized by, It comprises: acquiring various commercially available blood collection tubes; adding various commercially available blood collection tubes to simulation solutions respectively, and performing sample pretreatment according to an established sample pretreatment method; detecting the simulation solutions corresponding to various commercially available blood collection tubes after pretreatment by an established liquid chromatography tandem mass spectrometry lipid analysis method; using an XCMS software package to perform peak extraction processing on the transposed mzXML format acquisition data to obtain a first peak area list of each simulation solution, and according to the first peak area list, identifying the mass-to-charge ratio and retention time dimensions of all chromatographic peaks of each extracted simulation solution in a graph to form a corresponding chromatographic peak graph; summarizing the first peak area list and the chromatographic peak graph of the simulation solutions corresponding to different commercially available blood collection tubes.

2. The method of claim 1, wherein the lipidomic context database is constructed by, The simulation solution is selected from any one of a buffer, a 0.7wt%-1.1wt% sodium salt solution, and a 0.8wt%-1.2wt% serum protein solution.

3. The method of claim 2, wherein the lipidomic context database is constructed by, The buffer is PBS; and / or, the sodium salt solution is a NaCl solution; and / or, the serum protein solution is a bovine serum albumin solution; and / or, the solvent of the simulation solution is water.

4. The method for constructing a lipidomic background database according to any one of claims 1 to 3, characterized in that, The established sample pretreatment method comprises: first using a mixed solution of methyl tert-butyl ether and methanol for extraction, and then using a mixed solution of isopropanol and methanol for extraction; Preferably, the established sample pretreatment method comprises: adding the simulation solution into a centrifugal tube, then adding a mixed solution of methyl tert-butyl ether and methanol, first vortexing, taking the upper liquid as the first extraction liquid after centrifugation, adding a mixed solution of methyl tert-butyl ether and methanol to the lower residue again, second vortexing, taking the upper liquid as the second extraction liquid after centrifugation, combining the first extraction liquid and the second extraction liquid, vacuum concentrating and drying the combined extraction liquid, adding a mixed solution of isopropanol and methanol to the residue, third vortexing, and taking the upper clear liquid in a sample container after centrifugation; Preferably, in the mixed solution of methyl tert-butyl ether and methanol, the volume ratio of methyl tert-butyl ether to methanol is (3.5-5.5):1; Preferably, in the mixed solution of isopropanol and methanol, the volume ratio of isopropanol to methanol is (0.9-1.1):1; Preferably, the time of the first vortexing, the second vortexing, and the third vortexing is 50s-70s; Preferably, the rotation speed of centrifugation after the three vortexings is 10000rpm-15000rpm, the centrifugation temperature is 0℃-5℃, and the centrifugation time is 8min-12min.

5. The method for constructing a lipidomic background database according to any one of claims 1 to 3, characterized in that, In the established liquid chromatography tandem mass spectrometry lipid analysis method, the chromatographic conditions are as follows: a C18 chromatographic column, a flow rate of 0.2mL / min-0.3mL / min, a column temperature of 38℃-42℃, and a sample injection amount of 1μL-10μL; the mobile phase A is a 35%-45% acetonitrile aqueous solution and 5-15mM NH4COOH is added to the mobile phase A, the mobile phase B is a 5%-15% isopropanol acetonitrile solution and 5-15mM NH4COOH is added to the mobile phase B, and the elution gradient is as follows: The mass spectrometry conditions are as follows: the instrument uses an electrospray ion source, positive and negative ionization modes, a positive ion spray voltage of 3.0-3.50 kV, a negative ion spray voltage of 2-3 kV, a sheath gas of 30-40 arb, an auxiliary gas of 10-20 arb, a capillary temperature of 300-350 DEG C, full scan, a scan range of 150-2000, and HCD for secondary cracking with a collision energy of 10%-90%.

6. The method for constructing a lipidomic background database according to any one of claims 1 to 3, characterized in that, The peak searching includes: using the centWave method to search for chromatographic peaks that meet the conditions of a mass-to-charge ratio deviation within 15 ppm, a peak width of 5-30, a minimum response of 5000, and a signal-to-noise ratio of 3 or more.

7. The method of constructing a lipidomic background database according to any one of claims 1 to 3, wherein, Various commercially available blood collection tubes include all kinds of blood collection tubes of different types, different specifications, different manufacturers, and different batches that can be purchased.

8. A method of detecting a lipid panel, comprising: It comprises: The serum sample is subjected to sample pretreatment according to the established sample pretreatment method; The pretreated serum sample is subjected to serum lipidomics detection by the established liquid chromatography tandem mass spectrometry lipid analysis method; According to the interference feature matching method, the obtained chromatographic peak features of the serum sample are matched with the chromatographic peak features in the lipidomic background database constructed by the construction method of any one of claims 1-7, and the response is deducted at the quantitative level again; The sample pretreatment method and the liquid chromatography tandem mass spectrometry lipid analysis method used for the serum sample are the same as the sample pretreatment method and the liquid chromatography tandem mass spectrometry lipid analysis method in the construction method of the lipidomic background database of any one of claims 1-7.

9. The method of claim 8, wherein the lipidome is detected by mass spectrometry. The interference feature matching method is: according to the first peak area list of the serum sample and the lipidomic background database, based on the mass-to-charge ratio deviation and the retention time deviation parameter threshold, the first feature peak matching is performed, and each lipid first feature in the sample is found to correspond to the first feature of a serum blood collection tube in the lipidomic background database. Preferably, the mass-to-charge ratio deviation parameter threshold is between 1 ppm and 30 ppm; and the retention time deviation parameter threshold is between 10 seconds and 30 seconds.

10. The method of claim 9, wherein the lipid panel is selected from the group consisting of: total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, and combinations thereof. By the Pearson correlation analysis method, the correlation coefficient between the quantitative level of the first feature peak of the serum sample and the first feature peak corresponding to the serum blood collection tube in the lipidomic background database is calculated to realize the first feature peak belonging to the interference feature in the serum sample, and the interference feature in the serum sample is background deducted by the quantitative deduction method.

11. A lipid panel testing system, characterized by, The system comprises the background database constructed by the construction method of any one of claims 1-7.

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