Space metabonomics data analysis method, device and equipment and storage medium

Abstract

The invention relates to the technical field of life science, and provides a space metabonomics data analysis method, device and equipment and a storage medium, and the method comprises the steps: determining the theoretical mass-to-charge ratio of each reference metabolite through reference metabolite in different mass-to-charge ratio numerical ranges; for each reference metabolite, acquiring data according to a mass spectrum imaging technology to determine an actually measured mass-to-charge ratio, and calculating a mass-to-charge ratio deviation according to a difference value between the theoretical mass-to-charge ratio and the actually measured mass-to-charge ratio; setting a mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviations of the plurality of reference metabolites; according to the mass-to-charge ratio tolerance range, determining the matching relationship between the actually measured mass-to-charge ratio of the sample and the metabolite; a reference metabolite and a standardized theoretical mass-to-charge ratio are introduced, the adaptive mass-to-charge ratio tolerance range is determined, and the metabolite with the theoretical mass-to-charge ratio of which the difference value with the actually measured mass-to-charge ratio of the sample is within the mass-to-charge ratio tolerance range is determined as the metabolite matched with the actually measured mass-to-charge ratio of the sample, so that the identification, evaluation and calibration of mass drift of mass spectrum data are improved, and the accuracy of mass drift detection is improved. And accurate matching of metabolites is realized.

Description

Technical Field

[0001] The present invention relates to the field of life science technologies, and in particular, to a method, device, equipment, and storage medium for spatial metabolomics data analysis. Background Art

[0002] The development and application of spatial metabolomics have brought life science research into a new spatio-temporal research mode. Spatial metabolomics is a method that uses mass spectrometry imaging (MSI) technology to detect metabolites on sample sections in a high-throughput manner, and then in-situ analyzes the structure, distribution, and content of metabolites. Usually, it is also necessary to combine liquid chromatography-mass spectrometry (LC-MS) technology to accurately identify metabolites.

[0003] Most LC-MS and MSI experiments have mass axis deviations to varying degrees. For different types of mass spectrometers, different parameter settings, and different instrument states, the accuracy of the mass-to-charge ratio (m / z) data collected is significantly different. How to obtain accurate metabolite qualitative results based on the deviated data is a severe challenge. On the one hand, the mass drift of LC-MS leads to a certain degree of deviation in the m / z of the metabolite results identified based on the data collected by LC-MS. Therefore, the m / z provided for spatial metabolomics is inaccurate. On the other hand, there is mass drift during the data collection of MSI itself, and due to the complexity of the mass spectrometry imaging data, the deviation is more difficult to evaluate and calibrate compared to LC-MS, resulting in insufficient accuracy and reliability in the analysis of spatial metabolomics data. Summary of the Invention

[0004] The present invention provides a method, device, equipment, and storage medium for spatial metabolomics data analysis to solve the defect that the m / z of the metabolite results identified based on the data collected by LC-MS has a certain degree of deviation due to the mass drift of LC-MS in the prior art, and there is mass drift during the data collection of MSI itself, and to achieve accurate and reliable spatial metabolomics data analysis.

[0005] The present invention provides a method for spatial metabolomics data analysis, including the following steps.

[0006] For reference metabolites in different mass-to-charge ratio value ranges, determine the theoretical mass-to-charge ratio of each reference metabolite; For each reference metabolite, determine the measured mass-to-charge ratio according to the data collected by mass spectrometry imaging technology, and calculate the mass-to-charge ratio deviation according to the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; Set the mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviations of multiple reference metabolites; Determine the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite according to the mass-to-charge ratio tolerance range, wherein the measured mass-to-charge ratio of the sample is obtained by mass spectrometry imaging technology.

[0007] According to a spatial metabolomics data analysis method provided by the present invention, for reference metabolites in different mass-to-charge ratio value ranges, determine the theoretical mass-to-charge ratio of each reference metabolite, including: Determine multiple mass-to-charge ratio value ranges in the positive ion mode, and select metabolites as the first reference metabolites within each mass-to-charge ratio value range; Determine multiple mass-to-charge ratio value ranges in the negative ion mode, and select metabolites as the second reference metabolites within each mass-to-charge ratio value range; For each first reference metabolite or each second reference metabolite, determine the theoretical mass-to-charge ratio.

[0008] According to a spatial metabolomics data analysis method provided by the present invention, determine the theoretical mass-to-charge ratio of each reference metabolite, including: Obtain the molecular formula of each reference metabolite; According to the molecular weight of each element in the molecular formula, the adduct ion form generated by molecular ionization, and the charge number, calculate the theoretical mass-to-charge ratio of the molecular adduct ion by summation.

[0009] According to a spatial metabolomics data analysis method provided by the present invention, it further includes: For identifying metabolites in the sample using the mass spectrometry information of liquid chromatography-mass spectrometry, calculate the theoretical mass-to-charge ratio corresponding to each ion adduct form according to the molecular weight of each element in the molecular formula of the metabolite; Establish a metabolite ion database, and store the name of each metabolite, different ion adduct forms of the metabolite, and the theoretical mass-to-charge ratio corresponding to each ion adduct form in the metabolite ion database.

[0010] According to a spatial metabolomics data analysis method provided by the present invention, determine the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite according to the mass-to-charge ratio tolerance range, including: Compare the measured mass-to-charge ratio of the sample obtained by collecting data using mass spectrometry imaging technology with different theoretical mass-to-charge ratios in the metabolite ion database; When there is a theoretical mass-to-charge ratio in the metabolite ion database whose difference from the measured mass-to-charge ratio of the sample satisfies the mass-to-charge ratio tolerance range, use the name and ion adduct form of the metabolite corresponding to the theoretical mass-to-charge ratio that meets the conditions as the metabolite annotation result of the measured mass-to-charge ratio of the sample.

[0011] The present invention also provides a spatial metabolomics data analysis device, including the following modules: A theoretical mass-to-charge ratio determination module for determining the theoretical mass-to-charge ratio of each reference metabolite for reference metabolites in different mass-to-charge ratio value ranges. A mass-to-charge ratio deviation calculation module for, for each reference metabolite, determining the measured mass-to-charge ratio based on data collected by mass spectrometry imaging technology, and calculating the mass-to-charge ratio deviation based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio. A mass-to-charge ratio tolerance range setting module for setting the mass-to-charge ratio tolerance range based on the mass-to-charge ratio deviations of multiple reference metabolites. A metabolite matching module for determining the matching relationship between the measured mass-to-charge ratio of a sample and metabolites according to the mass-to-charge ratio tolerance range, wherein the measured mass-to-charge ratio of the sample is obtained by mass spectrometry imaging technology.

[0012] A spatial metabolomics data analysis device provided by the present invention, wherein the theoretical mass-to-charge ratio determination module includes: A first reference metabolite determination sub-module for determining multiple mass-to-charge ratio value ranges in positive ion mode, and selecting metabolites within each mass-to-charge ratio value range as the first reference metabolites. A second reference metabolite determination sub-module for determining multiple mass-to-charge ratio value ranges in negative ion mode, and selecting metabolites within each mass-to-charge ratio value range as the second reference metabolites. A theoretical mass-to-charge ratio determination sub-module for determining the theoretical mass-to-charge ratio for each first reference metabolite or each second reference metabolite.

[0013] A spatial metabolomics data analysis device provided by the present invention, wherein the theoretical mass-to-charge ratio determination module includes: A molecular formula acquisition sub-module for acquiring the molecular formula of each reference metabolite. An addition calculation sub-module for calculating the theoretical mass-to-charge ratio of the molecular adduct ion by addition calculation according to the molecular weight of each element in the molecular formula, the adduct ion form generated by molecular ionization, and the charge number.

[0014] A spatial metabolomics data analysis device provided by the present invention further includes: An ion database establishment module for identifying metabolites in a sample according to the mass spectrometry information of liquid chromatography-mass spectrometry technology, calculating the theoretical mass-to-charge ratio corresponding to each ion adduct form according to the molecular weight of each element in the molecular formula of the metabolite, establishing a metabolite ion database, and storing the name of each metabolite, different ion adduct forms of the metabolite, and the theoretical mass-to-charge ratio corresponding to each ion adduct form in the metabolite ion database.

[0015] A spatial metabolomics data analysis device provided by the present invention, wherein the metabolite matching module includes: A sample measured mass-to-charge ratio sub-module for comparing the measured mass-to-charge ratio of a sample obtained by collecting data through mass spectrometry imaging technology with different theoretical mass-to-charge ratios in a metabolite ion database; A metabolite annotation sub-module for, when there is a difference between a theoretical mass-to-charge ratio and the measured mass-to-charge ratio of a sample in the metabolite ion database that satisfies the mass-to-charge ratio tolerance range, taking the name and ion adduct form of the metabolite corresponding to the mass-to-charge ratio that meets the conditions as the metabolite annotation result of the measured mass-to-charge ratio of the sample.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the spatial metabolomics data analysis method as described in any one of the above.

[0017] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the spatial metabolomics data analysis method as described in any one of the above.

[0018] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the spatial metabolomics data analysis method as described in any one of the above.

[0019] The spatial metabolomics data analysis method, device, equipment, and storage medium provided by the present invention determine the theoretical mass-to-charge ratio of each reference metabolite by referring to different mass-to-charge ratio value ranges of metabolites; for each reference metabolite, determine the measured mass-to-charge ratio according to the data collected by mass spectrometry imaging technology, and calculate the mass-to-charge ratio deviation based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; set the mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviations of multiple reference metabolites; determine the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite according to the mass-to-charge ratio tolerance range; compared with the prior art where there is a certain degree of deviation in the m / z of metabolite results identified based on LC-MS data collection due to LC-MS mass drift and there is mass drift during the data collection of MSI itself, calculate the mass-to-charge ratio deviation through the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio, set the mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviations of multiple reference metabolites, and determine the metabolite with a theoretical mass-to-charge ratio whose difference from the measured mass-to-charge ratio of the sample is within the mass-to-charge ratio tolerance range as the metabolite matching the measured mass-to-charge ratio of the sample. It improves the recognition, evaluation, and calibration of mass drift in mass spectrometry data. It helps to reveal the in-situ spatial metabolic characteristics of sample sections and promotes the accurate characterization of spatio-temporal metabolism. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0021] Figure 1 It is a schematic flow chart of the spatial metabolomics data analysis method provided by the present invention; Figure 2 It is a mass spectrometry imaging diagram of the adduct ions of representative reference metabolites in the positive ion mode provided by the present invention; Figure 3 It is a mass spectrometry imaging diagram of the adduct ions of representative reference metabolites in the negative ion mode provided by the present invention; Figure 4 It is a schematic structural diagram of the spatial metabolomics data analysis device provided by the present invention; Figure 5 It is a schematic structural diagram of the electronic device provided by the present invention. Detailed implementation manners

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0023] The following will describe the present invention in conjunction with Figures 1-3 Describe the present invention.

[0024] Figure 1 It is one of the schematic flow charts of the spatial metabolomics data analysis method provided by the present invention. As Figure 1 shown, the method includes the following: Step 101: Determine the theoretical mass-to-charge ratio of each reference metabolite for different mass-to-charge ratio value ranges.

[0025] In the above step 101, the mass-to-charge ratio refers to the ratio of the mass of a charged ion to the charge carried. In mass spectrometry analysis, the mass-to-charge ratio is usually represented by m / z, where m represents the mass of the ion in atomic mass units; z represents the number of charges of the ion in elementary charge units.

[0026] The different mass-to-charge ratio value ranges include the m / z value ranges within 50 - 300 Da, 300 - 500 Da, and 500 - 1000 Da.

[0027] Reference metabolites refer to compounds that typically appear in various sample collection processes. By way of example, in the negative ion mode, lactic acid is selected in the embodiments of the present invention, with an m / z range of 50 - 300 Da; arachidonic acid, with an m / z range of 300 - 500 Da.

[0028] The theoretical mass-to-charge ratio refers to the m / z of the reference metabolite when there is no deviation in the mass axis of the mass spectrometry instrument. However, mass axis deviation generally occurs in general instruments, resulting in a larger or smaller m / z. The process of calculating the theoretical m / z can be to obtain the exact molecular mass based on the molecular formula of the reference metabolite, and then calculate the m / z of the ion according to the adduct ion form. By way of example, for the compound lactic acid C3H6O3, the exact molecular mass is 90.0317, and the theoretical m / z of the ion [M - H]- is 89.0244. Among them, the ion [M - H]- represents a negative ion formed after a molecule loses a positively charged proton. In mass spectrometry analysis, such ions usually appear in the negative ion mode. The theoretical mass-to-charge ratios of more reference metabolites and their adduct ions are shown in Table 1.

[0029] Optionally, step 101 includes steps A1 to A3: Step A1: Determine multiple mass-to-charge ratio value ranges in the positive ion mode, and select metabolites within each mass-to-charge ratio value range as the first reference metabolites.

[0030] Step A2: Determine multiple mass-to-charge ratio value ranges in the negative ion mode, and select metabolites within each mass-to-charge ratio value range as the second reference metabolites.

[0031] Step A3: For each first reference metabolite or each second reference metabolite, determine the theoretical mass-to-charge ratio.

[0032] In the above steps A1 to A3, when performing desorption electrospray ionization (DESI)-MSI analysis on frozen sections of colorectal cancer tumors and normal tissues, there will be a mass axis deviation during MSI data acquisition. Considering the mass axis deviation during MSI data acquisition, in the embodiments of the present invention, reference metabolites are selected within different mass-to-charge ratio value ranges in the positive ion mode or the negative ion mode respectively.

[0033] Table 1

[0034] Optionally, determining the theoretical mass-to-charge ratio of each reference metabolite in step 101 includes steps B1 to B2: Step B1: Obtain the molecular formula of each reference metabolite.

[0035] Step B2: Calculate the theoretical mass-to-charge ratio of the molecular adduct ion through summation based on the molecular weight of each element in the molecular formula, the form of the adduct ion generated by molecular ionization, and the charge number.

[0036] In the above Steps B1 to B2, the molecular formula represents the composition of a substance molecule using a combination of element symbols and numbers. The molecular formula represents a molecule of a substance and its components, including the types and numbers of atoms of each element in the molecule. The molecular formula is the basis for calculating the molecular weight.

[0037] The molecular weight, also known as the relative molecular mass, is the sum of the masses of all atoms in a molecule. The molecular weight is an important indicator for measuring the size of a molecule.

[0038] Summation calculation refers to the calculation process of adding two or more numerical values to obtain the total sum.

[0039] In the embodiments of the present invention, the precise molecular weight of the entire molecular formula is obtained through summation calculation. The form of the adduct ion and the ion charge number can be obtained through experimental data or by referring to materials. The theoretical mass-to-charge ratio of the ion is obtained based on the precise molecular weight of the entire molecular formula, the form of the adduct ion, and the charge number.

[0040] Step 102: For each reference metabolite, determine the measured mass-to-charge ratio according to the data collected by mass spectrometry imaging technology, and calculate the mass-to-charge ratio deviation based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio.

[0041] In the above Step 102, the measured mass-to-charge ratio refers to the data actually collected by the mass spectrometry instrument. By subjecting the original mass spectrometry signal to a series of data preprocessing methods such as data extraction and peak alignment, the value of the measured m / z can be obtained.

[0042] The calculation formula for the mass-to-charge ratio deviation can be: mass-to-charge ratio deviation = (measured m / z - theoretical m / z) / theoretical m / z × 1E6. For example, the measured m / z is 89.0241, and the theoretical m / z of lactic acid obtained from the database is 89.0244, (89.0241 - 89.0244) / 89.0244 × 1E6 = -3.37 ppm, that is, the mass-to-charge ratio deviation is -3.37 ppm.

[0043] Among them, the embodiments of the present invention provide a database for storing the theoretical mass-to-charge ratios and ion adduct forms of different metabolites.

[0044] Step 103: Set the mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviations of multiple reference metabolites.

[0045] In step 103 above, the degree of mass axis deviation is evaluated based on the mass-to-charge ratio deviations of multiple reference metabolites, and the fluctuation range of the mass-to-charge ratio is determined. The fluctuation range of the mass-to-charge ratio is set as the mass-to-charge ratio tolerance range.

[0046] Exemplarily, the theoretical m / z of the reference metabolite lactic acid in the negative ion mode is 89.0244, the theoretical m / z of arachidonic acid is 303.2330, and the theoretical m / z of PI(38:4) is 885.5499; the measured m / z values are 89.0241, 303.2322, and 885.5475 respectively. The m / z deviations calculated according to the formula are -3.37 ppm, -2.64 ppm, and -2.71 ppm respectively. The degree of mass axis deviation evaluated by these three reference metabolites is between -4 and -2 ppm. According to the requirement standard of expanding 2 ppm on both the left and right, the mass-to-charge ratio tolerance range is set to be within the range of m / z deviation from -6 to 0 ppm.

[0047] Step 104: Determine the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite according to the mass-to-charge ratio tolerance range, where the measured mass-to-charge ratio of the sample is obtained by mass spectrometry imaging technology.

[0048] In step 104 above, when obtaining the measured mass-to-charge ratio of the sample by collecting data through MSI, the measured mass-to-charge ratio of the sample is matched with the theoretical mass-to-charge ratios of various metabolites stored in the database. When there is a difference between a theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample within the mass-to-charge ratio tolerance range, the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite corresponding to the theoretical mass-to-charge ratio is determined.

[0049] For the mass drift existing in the MSI data collection in the embodiments of the present invention, the degree of mass axis deviation and the fluctuation range are evaluated based on the theoretical m / z of the universal metabolites screened in the positive and negative ion modes, and the mass-to-charge ratio tolerance is accurately set according to the calculated theoretical m / z deviation to ensure the best adaptability of the result matching and obtain accurate and reliable metabolite annotation results.

[0050] Optionally, the spatial metabolomics data analysis method of the embodiments of the present invention further includes step 100: Step 100: For identifying metabolites in a sample by using the mass spectrometry information of liquid chromatography-mass spectrometry technology, calculate the theoretical mass-to-charge ratio corresponding to each ion adduct form according to the molecular weight of each element in the molecular formula of the metabolite; establish a metabolite ion database, and store the name of each metabolite, different ion adduct forms of the metabolite, and the theoretical mass-to-charge ratio corresponding to each ion adduct form in the metabolite ion database.

[0051] In the above step 100, high-resolution first-order and second-order mass spectrometry information of liquid chromatography-high resolution mass spectrometry / mass spectrometry (LC-HRMS / MS) is used to identify metabolites in the sample. Specifically, LC-HRMS / MS technology is used for untargeted metabolomics analysis, and metabolites are identified based on characteristic information such as retention time, high-resolution first-order mass spectrometry information, second-order mass spectrometry information, and isotopes to determine first-order and second-order ranked metabolites with relatively high accuracy. For the convenience of description, the first-order and second-order ranked metabolites with relatively high accuracy are called identified result metabolites, and the molecular formulas of the identified result metabolites are obtained. Based on the molecular formulas of the identified result metabolites, the theoretical molecular weights and theoretical m / z of the identified result metabolites are reconstructed. In the embodiments of the present invention, each element in the molecular formula is accurate to more than 6 decimal places, and the molecular weight and theoretical m / z of the overall molecular formula accurate to 5 decimal places are obtained through summation calculation. At this time, the calculated molecular weight and theoretical m / z are relatively accurate.

[0052] Ion adduct form refers to the compound or ion group with specific mass and charge state formed by the interaction of ions with other atoms, molecules or ions during chemical reactions or mass spectrometry analysis. In the embodiments of the present invention, multiple possible ion adduct forms are selected, and the theoretical mass-to-charge ratios corresponding to each ion adduct form are calculated.

[0053] In the embodiments of the present invention, a code tool is developed based on python, which can automatically and quickly calculate the theoretical molecular weight of metabolites and the theoretical m / z of various ion adduct forms within a few seconds. It realizes the efficient establishment of an ion database of metabolites by itself.

[0054] Optionally, the above step 104 includes steps C1 to C2: Step C1: Compare the measured mass-to-charge ratio of the sample obtained by collecting data through mass spectrometry imaging technology with different theoretical mass-to-charge ratios in the metabolite ion database.

[0055] Step C2: When there is a theoretical mass-to-charge ratio in the metabolite ion database whose difference from the measured mass-to-charge ratio of the sample satisfies the mass-to-charge ratio tolerance range, the name and ion adduct form of the metabolite corresponding to the theoretical mass-to-charge ratio that meets the conditions are used as the metabolite annotation result of the measured mass-to-charge ratio of the sample.

[0056] In the above steps C1 to C2, for example, the measured m / z is 124.0071, and the theoretical m / z of the [M-H]- ion of taurine in the metabolite ion database is 124.0074. The deviation between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample is (124.0071 - 124.0074) / 124.0074 1E6 = -2.42 ppm. Assuming that the set mass-to-charge ratio tolerance range is -6 to 0 ppm, and -2.42 ppm is within the set mass-to-charge ratio tolerance range, the measured m / z matches the [M-H]- form of taurine. The name of the metabolite corresponding to taurine, taurine, and the ion adduct form [M-H]- form are used as the metabolite annotation result for the measured mass-to-charge ratio of the sample, 124.0071

[0057] To further explain the present invention, the following specific examples of spatial metabolomics analysis of clinical colorectal cancer tissues are provided

[0058] Tumor and normal tissue samples of clinical colorectal cancer (CRC) patients were collected and subjected to tissue sample pretreatment respectively. Non-targeted metabolomics analysis was performed using LC-HRMS / MS technology. Based on retention time, high-resolution first-order mass spectrometry, second-order mass spectrometry information, isotope and other characteristic information, metabolites were identified, and level1 and level2 metabolites with relatively high accuracy were determined and their molecular formulas were obtained

[0059] Then, a module for accurately reconstructing the theoretical exact molecular weight and high-resolution m / z based on the molecular formula was established. On the one hand, it corrected the inaccuracy of m / z caused by the mass axis deviation during LC-MS acquisition, and on the other hand, it efficiently established a metabolite ion database by itself

[0060] Then, DESI-MSI analysis was performed on frozen sections of CRC tumors and normal tissues. For the mass axis deviation existing in MSI data acquisition, in the positive or negative ion mode, within the ranges of m / z 50 - 300, 300 - 500, and 500 - 1000, reference metabolites were selected and the theoretical m / z was calculated. The degree of mass axis deviation was evaluated by the difference between the theoretical m / z and the measured m / z, and the fluctuation range was determined. According to the calculated m / z deviation, the mass-to-charge ratio tolerance range was accurately set to ensure the accurate matching of the m / z data collected by MSI and the self-built database. An automated annotation module was further developed to match the self-built metabolite ion database with the measured MSI data after debugging the mass-to-charge ratio tolerance range, improving the accuracy and efficiency of metabolite matching, and then obtaining accurate and reliable metabolite annotation results, where the metabolite annotation result is the metabolite name and the corresponding ion adduct form. Finally, spatial metabolomics analysis of CRC was performed, and the specific metabolite distribution characteristics of tumors and normal tissues were obtained, revealing the spatial metabolic changes during tumorigenesis and development. As Figure 2As shown Figure 2 is the mass spectrometry imaging map of the adduct ions of representative reference metabolites in the positive ion mode, as Figure 3 shown Figure 3 is the mass spectrometry imaging map of the adduct ions of representative reference metabolites in the negative ion mode.

[0061] A method for analyzing spatial metabolomics data provided by the present invention determines the theoretical mass-to-charge ratio of each reference metabolite by referring to metabolites within different mass-to-charge ratio value ranges; for each reference metabolite, the measured mass-to-charge ratio is determined according to the data collected by mass spectrometry imaging technology, and the mass-to-charge ratio deviation is calculated based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; a mass-to-charge ratio tolerance range is set according to the mass-to-charge ratio deviations of multiple reference metabolites; the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite is determined according to the mass-to-charge ratio tolerance range; compared with the prior art in which there is a certain degree of deviation in the m / z of metabolite results identified based on LC-MS data collection due to LC-MS mass drift and there is mass drift during the data collection of MSI itself, the mass-to-charge ratio deviation is calculated based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio, a mass-to-charge ratio tolerance range is set according to the mass-to-charge ratio deviations of multiple reference metabolites, and the metabolite with a theoretical mass-to-charge ratio whose difference from the measured mass-to-charge ratio of the sample is within the mass-to-charge ratio tolerance range is determined as the metabolite matching the measured mass-to-charge ratio of the sample. This improves the identification, evaluation, and calibration of mass spectrometry data quality drift. It helps to reveal the in-situ spatial metabolic characteristics of sample sections and promotes the accurate characterization of spatio-temporal metabolism.

[0062] The spatial metabolomics data analysis device provided by the present invention is described below. The spatial metabolomics data analysis device described below can be correspondingly referred to the spatial metabolomics data analysis method described above.

[0063] Figure 4 is one of the schematic flowcharts of the spatial metabolomics data analysis device provided by the present invention, as Figure 4 shown. The device includes the following: A theoretical mass-to-charge ratio determination module 201 for determining the theoretical mass-to-charge ratio of each reference metabolite by referring to metabolites within different mass-to-charge ratio value ranges.

[0064] Optionally, the theoretical mass-to-charge ratio determination module 201 includes: A first reference metabolite determination sub-module for determining multiple mass-to-charge ratio value ranges in the positive ion mode and selecting metabolites within each mass-to-charge ratio value range as the first reference metabolites; A second reference metabolite determination sub-module for determining multiple mass-to-charge ratio value ranges in the negative ion mode and selecting metabolites within each mass-to-charge ratio value range as the second reference metabolites; A theoretical mass-to-charge ratio determination sub-module for determining the theoretical mass-to-charge ratio for each first reference metabolite or each second reference metabolite.

[0065] Optionally, the theoretical mass-to-charge ratio determination module 201 includes: A molecular formula acquisition sub-module for acquiring the molecular formula of each reference metabolite; An addition calculation sub-module for obtaining the theoretical mass-to-charge ratio of the molecular adduct ion through addition calculation based on the molecular weight of each element in the molecular formula, the adduct ion form generated by molecular ionization, and the charge number.

[0066] A mass-to-charge ratio deviation calculation module 202 for determining the measured mass-to-charge ratio according to the data collected by mass spectrometry imaging technology for each reference metabolite, and calculating the mass-to-charge ratio deviation based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; A mass-to-charge ratio tolerance range setting module 203 for setting the mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviations of multiple reference metabolites; A metabolite matching module 204 for determining the matching relationship between the measured mass-to-charge ratio of the sample and a metabolite according to the mass-to-charge ratio tolerance range, where the measured mass-to-charge ratio of the sample is obtained by mass spectrometry imaging technology.

[0067] Optionally, a spatial metabolomics data analysis device provided by the present invention further includes: An ion database establishment module for identifying metabolites in a sample according to the mass spectrometry information of liquid chromatography-mass spectrometry, calculating the theoretical mass-to-charge ratio corresponding to each ion adduct form according to the molecular weight of each element in the molecular formula of the metabolite, establishing a metabolite ion database, and storing the name of each metabolite, different ion adduct forms of the metabolite, and the theoretical mass-to-charge ratio corresponding to each ion adduct form in the metabolite ion database.

[0068] Optionally, for the metabolite matching module 204 of the spatial metabolomics data analysis device provided by the present invention, it includes: A sample measured mass-to-charge ratio sub-module for comparing the measured mass-to-charge ratio of the sample with different theoretical mass-to-charge ratios in the metabolite ion database; A metabolite annotation sub-module for, when there is a theoretical mass-to-charge ratio in the metabolite ion database whose difference from the measured mass-to-charge ratio of the sample satisfies the mass-to-charge ratio tolerance range, taking the name and ion adduct form of the metabolite corresponding to the theoretical mass-to-charge ratio that meets the conditions as the metabolite annotation result of the measured mass-to-charge ratio of the sample.

[0069] A spatial metabolomics data analysis device provided by the present invention determines the theoretical mass-to-charge ratio of each reference metabolite by referring to metabolites within different mass-to-charge ratio value ranges. For each reference metabolite, the measured mass-to-charge ratio is determined based on data collected by mass spectrometry imaging technology, and the mass-to-charge ratio deviation is calculated according to the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio. A mass-to-charge ratio tolerance range is set according to the mass-to-charge ratio deviations of multiple reference metabolites. The matching relationship between the measured mass-to-charge ratio of the sample and the metabolite is determined based on the mass-to-charge ratio tolerance range. Compared with the prior art where there are certain deviations in the m / z of metabolite results identified based on LC-MS data collection due to LC-MS mass drift and there is mass drift during MSI data collection itself, the mass-to-charge ratio deviation is calculated according to the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio, and the mass-to-charge ratio tolerance range is set according to the mass-to-charge ratio deviations of multiple reference metabolites. Metabolites with a theoretical mass-to-charge ratio whose difference from the measured mass-to-charge ratio of the sample is within the mass-to-charge ratio tolerance range are determined as metabolites matching the measured mass-to-charge ratio of the sample. This improves the identification, evaluation, and calibration of mass spectrometry data quality drift. It helps to reveal the in-situ spatial metabolic characteristics of sample sections and promotes the accurate characterization of spatio-temporal metabolism.

[0070] Figure 5 An example of a schematic physical structure diagram of an electronic device is shown as Figure 5 shown. The electronic device may include: a processor (Processor) 810, a communication interface (Communications interface) 820, a memory (Memory) 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logic instructions in the memory 830 to execute the spatial metabolomics data analysis method.

[0071] In addition, when the logic instructions in the above-mentioned memory 830 are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-only memory), random access memories (RAM, Random access memory), magnetic disks, or optical discs, etc., which can store program codes.

[0072] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the spatial metabolomics data analysis method provided by each of the above methods.

[0073] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the spatial metabolomics data analysis method provided by each of the above methods.

[0074] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0075] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A spatial metabolomics data analysis method, characterized in that: include: For reference metabolites with different mass-to-charge ratio value ranges, determining a theoretical mass-to-charge ratio of each of the reference metabolites; For each of the reference metabolites, determining the measured mass-to-charge ratio according to data collected using mass spectrometry imaging technology, and calculating the mass-to-charge ratio deviation according to the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; setting a mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviation of a plurality of reference metabolites; The matching relationship between the actual mass-to-charge ratio of the sample and the metabolite is determined according to the mass-to-charge ratio tolerance range, wherein the actual mass-to-charge ratio of the sample is acquired by mass spectrometry imaging technology.

2. The spatial metabolomics data analysis method according to claim 1, characterized in that: The step of determining the theoretical mass-to-charge ratio of each reference metabolite of different mass-to-charge ratio ranges comprises: Determine multiple mass-to-charge ratio value ranges in positive ion mode, and select a metabolite in each mass-to-charge ratio value range as a first reference metabolite; Determine multiple mass-to-charge ratio value ranges in negative ion mode, and select metabolites in each mass-to-charge ratio value range as second reference metabolites; For each of the first reference metabolites or each of the second reference metabolites, a theoretical mass-to-charge ratio is determined.

3. The spatial metabolomics data analysis method according to claim 1, characterized in that: Determining the theoretical mass-to-charge ratio of each of the reference metabolites comprises: obtaining a molecular formula of each of the reference metabolites; According to the molecular weight of each element in the molecular formula, the form and charge number of the adduct ion produced by molecular ionization, the theoretical mass-to-charge ratio of the molecular adduct ion is obtained by addition calculation.

4. The spatial metabolomics data analysis method according to claim 1, characterized in that: Also includes: For the identification of metabolites in samples using mass spectrometry information from liquid chromatography-mass spectrometry, the theoretical mass-to-charge ratio corresponding to each ion addition form is calculated through different ion addition forms according to the molecular weight of each element in the molecular formula of the metabolite; A metabolite ion database is established, in which the name of each metabolite, different ion addition forms of the metabolite, and the theoretical mass-to-charge ratio corresponding to each ion addition form are stored.

5. The spatial metabolomics data analysis method according to claim 4, characterized in that: Determining the matching relationship between the sample's measured mass-to-charge ratio and the metabolite according to the mass-to-charge ratio tolerance range includes: Comparing the measured mass-to-charge ratio of the sample obtained by collecting data through mass spectrometry imaging technology with different theoretical mass-to-charge ratios in the metabolite ion database; When there is a difference between a theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample in the metabolite ion database that satisfies the mass-to-charge ratio tolerance range, the name and ion addition form of the metabolite corresponding to the theoretical mass-to-charge ratio that meets the conditions are used as the metabolite annotation result of the measured mass-to-charge ratio of the sample.

6. A spatial metabolomics data analysis device, characterized in that: include: A theoretical mass-to-charge ratio determination module, for determining the theoretical mass-to-charge ratio of each reference metabolite in different mass-to-charge ratio value ranges; A mass-to-charge ratio deviation calculation module, for each of the reference metabolites, collecting data using mass spectrometry imaging technology to determine the measured mass-to-charge ratio, and calculating the mass-to-charge ratio deviation based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; A mass-to-charge ratio tolerance range setting module, used to set a mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviations of a plurality of reference metabolites; The metabolite matching module is used to determine the matching relationship between the actual mass-to-charge ratio of the sample and the metabolite according to the mass-to-charge ratio tolerance range, wherein the actual mass-to-charge ratio of the sample is collected by mass spectrometry imaging technology.

7. The spatial metabolomics data analysis device according to claim 6, characterized in that: The theoretical mass-to-charge ratio determination module comprises: A first reference metabolite determination submodule is used to determine a plurality of mass-to-charge ratio value ranges in a positive ion mode, and select a metabolite in each mass-to-charge ratio value range as a first reference metabolite; A second reference metabolite determination submodule is used to determine a plurality of mass-to-charge ratio value ranges in a negative ion mode, and select a metabolite in each mass-to-charge ratio value range as a second reference metabolite; The theoretical mass-to-charge ratio determination submodule is used to determine a theoretical mass-to-charge ratio for each of the first reference metabolites or each of the second reference metabolites.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the spatial metabolomics data analysis method according to any one of claims 1 to 5 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the spatial metabolomics data analysis method according to any one of claims 1 to 5 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the spatial metabolomics data analysis method according to any one of claims 1 to 5 is implemented.

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