A spatial metabolomics data analysis method, device, equipment and storage medium

Abstract

The present invention relates to the field of life science technology, and provides a spatial metabolomics data analysis method, apparatus, equipment and storage medium. The method determines the theoretical mass-to-charge ratio of each reference metabolite by comparing reference metabolites with different mass-to-charge ratio value ranges; for each reference metabolite, determines the measured mass-to-charge ratio based on data collected by mass spectrometry imaging technology, and calculates the mass-to-charge ratio deviation based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; sets a mass-to-charge ratio tolerance range based on the mass-to-charge ratio deviations of multiple reference metabolites; determines the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite based on the mass-to-charge ratio tolerance range; determines the adapted mass-to-charge ratio tolerance range by introducing reference metabolites and standardized theoretical mass-to-charge ratios, and determines metabolites with theoretical mass-to-charge ratios whose differences from the measured mass-to-charge ratio of the sample are within the mass-to-charge ratio tolerance range as metabolites that match the measured mass-to-charge ratio of the sample, thereby improving the recognition, evaluation and calibration of mass spectrometry data mass drift and achieving accurate matching of metabolites.

Description

Technical Field

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

[0002] The development and application of spatial metabolomics has brought life science research into an emerging spatiotemporal research paradigm. Spatial metabolomics uses mass spectrometry imaging (MSI) technology to perform high-throughput detection of metabolites on sample slices, followed by in situ analysis of metabolite structure, distribution, and content. This method is often combined with liquid chromatography-mass spectrometry (LC-MS) for accurate metabolite identification.

[0003] Most LC-MS and MSI experiments have varying degrees of mass axis deviation. The accuracy of the collected mass-to-charge ratio (m / z) data varies significantly depending on the type of mass spectrometer, the parameter settings, and the instrument status. Obtaining accurate metabolite qualitative results based on deviated data is a formidable challenge. On the one hand, LC-MS mass drift causes a certain degree of deviation in the m / z of metabolite results identified based on LC-MS acquisition data, resulting in inaccurate m / z values for spatial metabolomics. On the other hand, MSI itself has mass drift during data acquisition, and the complexity of mass spectrometry imaging data makes deviations more difficult to assess and calibrate than with LC-MS, resulting in inaccurate accuracy and reliability in spatial metabolomics data analysis. Summary of the Invention

[0004] The present invention provides a spatial metabolomics data analysis method, apparatus, equipment and storage medium to solve the defects of LC-MS mass drift in the prior art, such as a certain degree of deviation in the m / z of metabolite results identified based on LC-MS collected data, and mass drift during MSI data collection itself, thereby achieving accurate and reliable spatial metabolomics data analysis.

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

[0006] For reference metabolites with different mass-to-charge ratio value ranges, determine the theoretical mass-to-charge ratio of each reference metabolite;

[0007] For each reference metabolite, the measured mass-to-charge ratio was determined based on the data collected by mass spectrometry imaging technology, and the mass-to-charge ratio deviation was calculated based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio;

[0008] Setting the mass-to-charge ratio tolerance range based on the mass-to-charge ratio deviation of multiple reference metabolites;

[0009] 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, wherein the measured mass-to-charge ratio of the sample is obtained by collecting the mass spectrometry imaging technology.

[0010] According to a spatial metabolomics data analysis method provided by the present invention, for reference metabolites in different mass-to-charge ratio ranges, determining the theoretical mass-to-charge ratio of each reference metabolite includes:

[0011] determining a plurality of mass-to-charge ratio value ranges in a positive ion mode, and selecting a metabolite in each mass-to-charge ratio value range as a first reference metabolite;

[0012] Determine multiple mass-to-charge ratio value ranges in negative ion mode, and select metabolites within each mass-to-charge ratio value range as second reference metabolites;

[0013] For each first reference metabolite or each second reference metabolite, a theoretical mass-to-charge ratio is determined.

[0014] According to a spatial metabolomics data analysis method provided by the present invention, determining the theoretical mass-to-charge ratio of each reference metabolite includes:

[0015] Obtain the molecular formula of each reference metabolite;

[0016] 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 summing up and calculating.

[0017] A spatial metabolomics data analysis method provided by the present invention further includes:

[0018] 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 adduct form is calculated based on the molecular weight of each element in the molecular formula of the metabolite through different ion adduct forms;

[0019] A metabolite ion database is established to 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.

[0020] According to a spatial metabolomics data analysis method provided by the present invention, the matching relationship between the measured mass-to-charge ratio of a sample and a metabolite is determined based on a mass-to-charge ratio tolerance range, including:

[0021] The measured mass-to-charge ratios of the samples obtained by mass spectrometry imaging technology were compared with different theoretical mass-to-charge ratios in the metabolite ion database;

[0022] When there is a difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample in the metabolite ion database that meets 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 will be used as the metabolite annotation result of the measured mass-to-charge ratio of the sample.

[0023] The present invention also provides a spatial metabolomics data analysis device, comprising the following modules:

[0024] The theoretical mass-to-charge ratio determination module is used to determine the theoretical mass-to-charge ratio of each reference metabolite for reference metabolites in different mass-to-charge ratio value ranges;

[0025] A mass-to-charge ratio deviation calculation module is used to determine the measured mass-to-charge ratio for each reference metabolite based on data collected using mass spectrometry imaging technology, and to 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;

[0026] A mass-to-charge ratio tolerance range setting module, used to set a mass-to-charge ratio tolerance range based on mass-to-charge ratio deviations of multiple reference metabolites;

[0027] The metabolite matching module is used to 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.

[0028] According to a spatial metabolomics data analysis device provided by the present invention, the theoretical mass-to-charge ratio determination module includes:

[0029] A first reference metabolite determination submodule is configured 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;

[0030] A second reference metabolite determination submodule is used to determine multiple mass-to-charge ratio value ranges in negative ion mode, and select a metabolite in each mass-to-charge ratio value range as a second reference metabolite;

[0031] The theoretical mass-to-charge ratio determination submodule is used to determine the theoretical mass-to-charge ratio for each first reference metabolite or each second reference metabolite.

[0032] According to a spatial metabolomics data analysis device provided by the present invention, the theoretical mass-to-charge ratio determination module includes:

[0033] The molecular formula acquisition submodule is used to obtain the molecular formula of each reference metabolite;

[0034] The sum calculation submodule is used to obtain the theoretical mass-to-charge ratio of the molecular adduct ion by summing up the molecular weight of each element in the molecular formula, the form of the adduct ion produced by molecular ionization, and the charge number.

[0035] A spatial metabolomics data analysis device provided by the present invention further includes:

[0036] The ion database establishment module is used to identify metabolites in samples based on mass spectrometry information using liquid chromatography-mass spectrometry technology. According to the molecular weight of each element in the molecular formula of the metabolite, the theoretical mass-to-charge ratio corresponding to each ion adduct form is calculated through different ion adduct forms; a metabolite ion database is established, and the name of each metabolite, the different ion adduct forms of the metabolite, and the theoretical mass-to-charge ratio corresponding to each ion adduct form are stored in the metabolite ion database.

[0037] According to a spatial metabolomics data analysis device provided by the present invention, the metabolite matching module includes:

[0038] The sample measured mass-to-charge ratio submodule is used to compare the sample measured mass-to-charge ratio obtained by collecting data through mass spectrometry imaging technology with different theoretical mass-to-charge ratios in the metabolite ion database;

[0039] The metabolite annotation submodule is used to use 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 when there is a difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample in the metabolite ion database that meets the mass-to-charge ratio tolerance range.

[0040] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any one of the above-described spatial metabolomics data analysis methods is implemented.

[0041] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described spatial metabolomics data analysis methods.

[0042] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any one of the above-mentioned spatial metabolomics data analysis methods.

[0043] The spatial metabolomics data analysis method, apparatus, equipment and storage medium provided by the present invention determine the theoretical mass-to-charge ratio of each reference metabolite by comparing reference metabolites with 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 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 a 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 LC-MS mass drift in the prior art, in which the m / z of the metabolite result identified based on the LC-MS collected data has a certain degree of deviation, and there is mass drift during the MSI data collection itself, the mass-to-charge ratio deviation is calculated by 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, and the metabolite with a theoretical mass-to-charge ratio whose difference with 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 mass drift, helps reveal the in situ spatial metabolic characteristics of sample slices, and promotes the precise characterization of spatiotemporal metabolism. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 Schematic diagram of the process of the spatial metabolomics data analysis method provided by the present invention;

[0046] Figure 2 is a mass spectrometry image of a representative reference metabolite adduct ion in the positive ion mode provided by the present invention;

[0047] Figure 3 It is a mass spectrometry image of a representative reference metabolite adduct ion in negative ion mode provided by the present invention;

[0048] Figure 4 Schematic diagram of the structure of the spatial metabolomics data analysis device provided by the present invention;

[0049] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0051] The following combination Figure 1-Figure 3 The present invention is described.

[0052] Figure 1 This is one of the flow charts of the spatial metabolomics data analysis method provided by the present invention, such as Figure 1 As shown, the method includes the following:

[0053] Step 101: For reference metabolites in different mass-to-charge ratio ranges, determine the theoretical mass-to-charge ratio of each reference metabolite.

[0054] In step 101, the mass-to-charge ratio refers to the ratio of the mass of a charged ion to its charge. In mass spectrometry, the mass-to-charge ratio is typically expressed as m / z, where m represents the mass of the ion in atomic mass units (AMUs) and z represents the charge of the ion in atomic charge units (ABCs).

[0055] Different mass-to-charge ratio value ranges include m / z value ranges of 50-300 Da, 300-500 Da, and 500-1000 Da.

[0056] Reference metabolites are compounds that are typically present in various sample collection processes. For example, in negative ion mode, the present embodiment selects lactic acid, with an m / z range of 50-300 Da, and arachidonic acid, with an m / z range of 300-500 Da.

[0057] The theoretical mass-to-charge ratio refers to the m / z of a reference metabolite when the mass axis of the mass spectrometer is not deviated. However, most instruments experience mass axis deviation, resulting in an overstated or understated m / z. The theoretical m / z can be calculated by obtaining the exact molecular mass based on the molecular formula of the reference metabolite and then calculating the m / z of the ion based on the adduct ion form. For example, the compound lactic acid, C3H6O3, has an exact molecular mass of 90.0317 and a theoretical m / z of 89.0244 for the [MH]- ion. The [MH]- ion represents a negative ion formed when a molecule loses a positively charged proton. In mass spectrometry analysis, this ion typically appears in negative ion mode. Table 1 shows the theoretical mass-to-charge ratios of more reference metabolites and their adduct ions.

[0058] Optionally, step 101 includes steps A1 to A3:

[0059] Step A1: determining multiple mass-to-charge ratio value ranges in positive ion mode, and selecting a metabolite in each mass-to-charge ratio value range as a first reference metabolite.

[0060] Step A2: determining multiple mass-to-charge ratio value ranges in negative ion mode, and selecting a metabolite in each mass-to-charge ratio value range as a second reference metabolite.

[0061] Step A3: For each first reference metabolite or each second reference metabolite, determine a theoretical mass-to-charge ratio.

[0062] In steps A1 through A3 above, when performing desorption electrospray ionization (DESI)-MSI analysis on frozen sections of colorectal cancer tumors and normal tissue, mass axis deviation may occur during MSI data acquisition. To account for this mass axis deviation during MSI data acquisition, the present invention selects reference metabolites within different mass-to-charge ratio ranges in either positive or negative ion mode.

[0063] Table 1

[0064]

[0065] Optionally, determining the theoretical mass-to-charge ratio of each reference metabolite in step 101 includes steps B1 to B2:

[0066] Step B1: Obtain the molecular formula of each reference metabolite.

[0067] Step B2: Based on the molecular weight of each element in the molecular formula, the form of the adduct ion produced by molecular ionization and the charge number, the theoretical mass-to-charge ratio of the molecular adduct ion is obtained by summing up and calculating.

[0068] In steps B1 and B2 above, the molecular formula represents the molecular composition of a substance using a combination of element symbols and numbers. The molecular formula represents a molecule and its components, including the type and number of atoms of each element in the molecule. The molecular formula is the basis for calculating molecular weight.

[0069] Molecular weight, also known as relative molecular mass, is the sum of the masses of all atoms in a molecule. Molecular weight is an important indicator for measuring molecular size.

[0070] Addition is the process of adding two or more values to get the total.

[0071] The present invention uses additive calculation to obtain the precise molecular weight of the entire molecular formula. The adducted ion form and ion charge number can be obtained through experimental data or reference, and the theoretical mass-to-charge ratio of the ion is obtained based on the precise molecular weight of the entire molecular formula and the adducted ion form and charge number.

[0072] Step 102: For each reference metabolite, determine the measured mass-to-charge ratio based on data collected using 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.

[0073] In step 102, the measured mass-to-charge ratio refers to the data actually collected by the mass spectrometer. The raw mass spectrometer signal is subjected to a series of data preprocessing methods such as data extraction and peak alignment to obtain the measured m / z value.

[0074] The calculation formula of 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=-3.37ppm, that is, the mass-to-charge ratio deviation is -3.37ppm.

[0075] Among them, an embodiment of the present invention provides a database for storing theoretical mass-to-charge ratios and ion addition forms of different metabolites.

[0076] Step 103: Setting a mass-to-charge ratio tolerance range according to mass-to-charge ratio deviations of a plurality of reference metabolites.

[0077] In step 103, the degree of mass axis deviation is evaluated based on the mass-to-charge ratio deviations of the multiple reference metabolites, and the mass-to-charge ratio fluctuation range is determined. The mass-to-charge ratio fluctuation range is set as the mass-to-charge ratio tolerance range.

[0078] For example, in negative ion mode, the theoretical m / z for the reference metabolites lactate is 89.0244, arachidonic acid is 303.2330, and PI(38:4) is 885.5499. The measured m / z values are 89.0241, 303.2322, and 885.5475, respectively. Calculated according to the formula, the m / z deviations are -3.37 ppm, -2.64 ppm, and -2.71 ppm, respectively. The estimated mass axis deviations for these three reference metabolites range from -4 to -2 ppm. Based on the requirement of a 2 ppm increase on both sides, the mass-to-charge ratio tolerance range is set to -6 to 0 ppm.

[0079] Step 104: determining a 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 acquired by mass spectrometry imaging technology.

[0080] In step 104, when the measured mass-to-charge ratio of the sample is obtained through MSI data acquisition, the measured mass-to-charge ratio of the sample is matched with the theoretical mass-to-charge ratios of the multiple metabolites stored in the database. If there is a difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample that is within the mass-to-charge ratio tolerance range, a 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.

[0081] In this embodiment of the present invention, the mass drift in MSI data acquisition is evaluated by screening the fixed universal metabolite theoretical m / z in positive and negative ion modes to assess the degree of mass axis deviation and fluctuation range. Based on the calculated theoretical m / z deviation, the mass-to-charge ratio tolerance is precisely set to ensure the optimal adaptability of the result matching and obtain accurate and reliable metabolite annotation results.

[0082] Optionally, the spatial metabolomics data analysis method according to the embodiment of the present invention further includes step 100:

[0083] Step 100: For metabolites in a sample identified by mass spectrometry information using liquid chromatography-mass spectrometry, a theoretical mass-to-charge ratio corresponding to each ion adduct form is calculated based on the molecular weight of each element in the molecular formula of the metabolite through different ion adduct forms; a metabolite ion database is established, and the name of each metabolite, the different ion adduct forms of the metabolite, and the theoretical mass-to-charge ratio corresponding to each ion adduct form are stored in the metabolite ion database.

[0084] In step 100, high-resolution primary and secondary mass spectrometry information from liquid chromatography-high-resolution mass spectrometry / mass spectrometry (LC-HRMS / MS) is used to identify metabolites in the sample. Specifically, LC-HRMS / MS is used for non-targeted metabolomics analysis. Metabolite identification is performed based on characteristic information such as retention time, high-resolution primary and secondary mass spectrometry information, and isotopic information, to determine highly accurate primary and secondary metabolites. For ease of description, the highly accurate primary and secondary metabolites are referred to as identified metabolites, and the molecular formula of the identified metabolites is obtained. Based on the molecular formula of the identified metabolites, the theoretical molecular weight and theoretical m / z of the identified metabolites are reconstructed. In this embodiment of the present invention, each element in the molecular formula is accurate to at least 6 decimal places, and the molecular weight and theoretical m / z of the molecular formula as a whole are calculated by summing them up to 5 decimal places. In this case, the calculated molecular weight and theoretical m / z are relatively accurate.

[0085] An ion adduct form refers to the interaction of ions with other atoms, molecules, or ions during a chemical reaction or mass spectrometry analysis, forming a compound or ion cluster with a specific mass and charge state. This embodiment of the present invention selects multiple possible ion adduct forms and calculates the theoretical mass-to-charge ratio corresponding to each ion adduct form.

[0086] In this embodiment of the present invention, a Python-based code tool was developed that can automatically and quickly calculate the theoretical molecular weight of metabolites and the theoretical m / z of various ion adduct forms within seconds, enabling efficient and automated creation of a metabolite ion database.

[0087] Optionally, the above step 104 includes steps C1 to C2:

[0088] Step C1: 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.

[0089] Step C2: If there is a difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample in the metabolite ion database that meets 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 will be used as the metabolite annotation result of the measured mass-to-charge ratio of the sample.

[0090] In the above steps C1 to C2, for example, the measured m / z is 124.0071, the theoretical m / z of the [MH]-ion of taurine in the metabolite ion database is 124.0074, and 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 the mass-to-charge ratio tolerance is set from -6 to 0 ppm, -2.42 ppm is within the set mass-to-charge ratio tolerance. Therefore, the measured m / z matches the [MH]-form of taurine. The metabolite name corresponding to taurine, Taurine, and the ion adduct form, [MH]-form, are assigned as the metabolite annotation result for the sample's measured mass-to-charge ratio of 124.0071.

[0091] To further explain the present invention, the following specific example of spatial metabolomics analysis of clinical colorectal cancer tissues is provided.

[0092] Tumor and normal tissue samples from colorectal cancer (CRC) patients were collected and pre-treated. Non-targeted metabolomics analysis was performed using LC-HRMS / MS. Metabolite identification was based on retention time, high-resolution primary and secondary mass spectrometry information, and isotopic signatures. Level 1 and level 2 metabolites were accurately identified and their molecular formulas were obtained.

[0093] Next, a module was established to accurately reconstruct the molecular weight and high-resolution m / z based on the molecular formula. On the one hand, it corrected the m / z inaccuracy caused by the mass axis deviation during LC-MS acquisition, and on the other hand, it efficiently established the ion database of metabolites.

[0094] DESI-MSI analysis was then performed on frozen sections of CRC tumors and normal tissues. For the mass axis deviation in MSI data acquisition, reference metabolites were selected in the range of m / z 50-300, 300-500, and 500-1000 in positive or negative ion mode, 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 precise matching of the MSI collected m / z data 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, thereby improving the accuracy and efficiency of metabolite matching, and then obtaining accurate and reliable metabolite annotation results, where the metabolite annotation results are the metabolite name and the corresponding ion addition form. Finally, a spatial metabolomics analysis of CRC was performed to obtain the distribution characteristics of tumor- and normal tissue-specific metabolites, revealing the spatial metabolic changes in tumor occurrence and development. Figure 2As shown, Figure 2 is a mass spectrometric image of a representative reference metabolite adduct ion in positive ion mode, such as Figure 3 As shown, Figure 3 It is the mass spectrometric image of the representative reference metabolite adduct ion in negative ion mode.

[0095] The present invention provides a spatial metabolomics data analysis method, which determines the theoretical mass-to-charge ratio of each reference metabolite by comparing reference metabolites with different mass-to-charge ratio value ranges; for each reference metabolite, determines the measured mass-to-charge ratio based on data collected by mass spectrometry imaging technology, and calculates the mass-to-charge ratio deviation based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; sets a mass-to-charge ratio tolerance range based on the mass-to-charge ratio deviations of multiple reference metabolites; determines the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite based on the mass-to-charge ratio tolerance range; compared with the prior art in which the m / z of metabolite results identified based on LC-MS collected data has a certain degree of deviation due to LC-MS mass drift, and there is mass drift during MSI data collection itself, the mass-to-charge ratio deviation is calculated by 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 based on the mass-to-charge ratio deviations of multiple reference metabolites, and the metabolites with theoretical mass-to-charge ratios whose differences with the measured mass-to-charge ratio of the sample are within the mass-to-charge ratio tolerance range are determined as metabolites that match the measured mass-to-charge ratio of the sample. This improves the identification, evaluation, and calibration of mass spectrometry data mass drift, helps reveal the in situ spatial metabolic characteristics of sample slices, and promotes the precise characterization of spatiotemporal metabolism.

[0096] The spatial metabolomics data analysis device provided by the present invention is described below. The spatial metabolomics data analysis device described below and the spatial metabolomics data analysis method described above can be referenced to each other.

[0097] Figure 4 This is one of the flow diagrams of the spatial metabolomics data analysis device provided by the present invention, such as Figure 4 As shown, the device includes the following:

[0098] The theoretical mass-to-charge ratio determination module 201 is used to determine the theoretical mass-to-charge ratio of each reference metabolite for reference metabolites in different mass-to-charge ratio value ranges.

[0099] Optionally, the theoretical mass-to-charge ratio determination module 201 includes:

[0100] A first reference metabolite determination submodule is configured 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;

[0101] A second reference metabolite determination submodule is used to determine multiple mass-to-charge ratio value ranges in negative ion mode, and select a metabolite in each mass-to-charge ratio value range as a second reference metabolite;

[0102] The theoretical mass-to-charge ratio determination submodule is used to determine the theoretical mass-to-charge ratio for each first reference metabolite or each second reference metabolite.

[0103] Optionally, the theoretical mass-to-charge ratio determination module 201 includes:

[0104] The molecular formula acquisition submodule is used to obtain the molecular formula of each reference metabolite;

[0105] The sum calculation submodule is used to obtain the theoretical mass-to-charge ratio of the molecular adduct ion by summing up the molecular weight of each element in the molecular formula, the form of the adduct ion produced by molecular ionization, and the charge number.

[0106] A mass-to-charge ratio deviation calculation module 202 is configured to determine the measured mass-to-charge ratio for each reference metabolite based on data collected using 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;

[0107] A mass-to-charge ratio tolerance range setting module 203 is used to set a mass-to-charge ratio tolerance range according to mass-to-charge ratio deviations of multiple reference metabolites;

[0108] The metabolite matching module 204 is configured to determine a matching relationship between the actual mass-to-charge ratio of the sample and a metabolite according to a mass-to-charge ratio tolerance range, wherein the actual mass-to-charge ratio of the sample is acquired through mass spectrometry imaging technology.

[0109] Optionally, the spatial metabolomics data analysis device provided by the present invention further includes:

[0110] The ion database establishment module is used to identify metabolites in samples based on mass spectrometry information using liquid chromatography-mass spectrometry technology. According to the molecular weight of each element in the molecular formula of the metabolite, the theoretical mass-to-charge ratio corresponding to each ion adduct form is calculated through different ion adduct forms; a metabolite ion database is established, and the name of each metabolite, the different ion adduct forms of the metabolite, and the theoretical mass-to-charge ratio corresponding to each ion adduct form are stored in the metabolite ion database.

[0111] Optionally, the present invention provides a spatial metabolomics data analysis device, the metabolite matching module 204, comprising:

[0112] The sample measured mass-to-charge ratio submodule is used to compare the sample measured mass-to-charge ratio with different theoretical mass-to-charge ratios in the metabolite ion database;

[0113] The metabolite annotation submodule is used to 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 sample's measured mass-to-charge ratio when there is a difference between the theoretical mass-to-charge ratio and the sample's measured mass-to-charge ratio in the metabolite ion database that meets the mass-to-charge ratio tolerance range.

[0114] The present invention provides a spatial metabolomics data analysis device, which determines the theoretical mass-to-charge ratio of each reference metabolite by comparing reference metabolites with different mass-to-charge ratio value ranges; for each reference metabolite, determines the measured mass-to-charge ratio based on data collected by mass spectrometry imaging technology, and calculates the mass-to-charge ratio deviation based on the difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio; sets a mass-to-charge ratio tolerance range based on the mass-to-charge ratio deviations of multiple reference metabolites; determines the matching relationship between the measured mass-to-charge ratio of the sample and the metabolite based on the mass-to-charge ratio tolerance range; compared with the LC-MS mass drift in the prior art, in which the m / z of the metabolite result identified based on the LC-MS collected data has a certain degree of deviation, and there is mass drift during the MSI data collection itself, the mass-to-charge ratio deviation is calculated by 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 based on the mass-to-charge ratio deviations of multiple reference metabolites, and the metabolite with a theoretical mass-to-charge ratio whose difference with 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 mass drift, helps reveal the in situ spatial metabolic characteristics of sample slices, and promotes the precise characterization of spatiotemporal metabolism.

[0115] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call logic instructions in the memory 830 to execute the spatial metabolomics data analysis method.

[0116] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0117] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program 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 the above methods.

[0118] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is configured to execute the spatial metabolomics data analysis method provided by the above methods when the computer program is executed by a processor.

[0119] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0120] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0121] 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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments 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 the theoretical mass-to-charge ratio of each reference metabolite; For each of the reference metabolites, determining a measured mass-to-charge ratio based on data collected using mass spectrometry imaging technology, and calculating a mass-to-charge ratio deviation based on a 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 the plurality of reference metabolites; Determining a 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; Wherein, for reference metabolites with different mass-to-charge ratio value ranges, determining the theoretical mass-to-charge ratio of each reference metabolite comprises: determining a plurality of mass-to-charge ratio value ranges in a positive ion mode, and selecting 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 within 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, determining a theoretical mass-to-charge ratio; The spatial metabolomics data analysis method further 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 adduct form is calculated based on the molecular weight of each element in the molecular formula of the metabolite through different ion adduct forms; Establishing a metabolite ion database, storing the name of each metabolite, different ion adduct forms of the metabolite, and a theoretical mass-to-charge ratio corresponding to each ion adduct form in the metabolite ion database; Wherein, determining 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 includes: Comparing 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 difference between a theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample in the metabolite ion database that meets 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.

2. 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 for 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 summing up and calculating.

3. 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 for reference metabolites in different mass-to-charge ratio value ranges; a mass-to-charge ratio deviation calculation module, configured to determine the measured mass-to-charge ratio for each of the reference metabolites based on data collected using 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; A mass-to-charge ratio tolerance range setting module, configured to set a mass-to-charge ratio tolerance range according to the mass-to-charge ratio deviations of a plurality of reference metabolites; a metabolite matching module, configured to determine a matching relationship between a sample's measured mass-to-charge ratio and a metabolite according to the mass-to-charge ratio tolerance range, wherein the sample's measured mass-to-charge ratio is acquired through mass spectrometry imaging technology; Wherein, the theoretical mass-to-charge ratio determination module includes: A first reference metabolite determination submodule is configured 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 multiple mass-to-charge ratio value ranges in negative ion mode, and select a metabolite in each mass-to-charge ratio value range as a second reference metabolite; a theoretical mass-to-charge ratio determination submodule, configured to determine a theoretical mass-to-charge ratio for each of the first reference metabolites or each of the second reference metabolites; The spatial metabolomics data analysis device further includes: An ion database establishment module is used to identify metabolites in samples using mass spectrometry information using liquid chromatography-mass spectrometry technology. Based on the molecular weight of each element in the molecular formula of the metabolite, the theoretical mass-to-charge ratio corresponding to each ion adduct form is calculated through different ion adduct forms; Establishing a metabolite ion database, storing the name of each metabolite, different ion adduct forms of the metabolite, and a theoretical mass-to-charge ratio corresponding to each ion adduct form in the metabolite ion database; Among them, the metabolite matching module includes: A sample measured mass-to-charge ratio submodule, for comparing the sample measured mass-to-charge ratio with different theoretical mass-to-charge ratios in the metabolite ion database; The metabolite annotation submodule is used to use the name and ion addition 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 when there is a difference between the theoretical mass-to-charge ratio and the measured mass-to-charge ratio of the sample in the metabolite ion database that meets the mass-to-charge ratio tolerance range.

4. 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 2 is implemented.

5. 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 2 is implemented.

6. 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 2 is implemented.

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