Non-targeted cascade mass spectrometry imaging method and device

Abstract

This application relates to the field of mass spectrometry imaging technology, and particularly to a non-targeted cascade mass spectrometry imaging method and apparatus. The method includes: acquiring the secondary mass spectrum of each pixel, performing peak identification and extraction to obtain a first intensity matrix; obtaining multiple parent ion secondary mass spectra and their correspondence with fragment ions through first-order deconvolution calculation; generating a second intensity matrix by combining the molecular composition information of neighboring pixels; performing second-order deconvolution calculation to obtain the relative ion intensities of characteristic fragments generated by each parent ion within each pixel; and generating multi-target mass spectrometry imaging results. This solves the problems in related technologies where mass spectrometry imaging cannot be coupled with chromatographic separation techniques, resulting in limited analytical dimensions and increased data analysis difficulty; and where data acquisition and spectral deconvolution rely on molecular composition modulation techniques, leading to increased instrument complexity and cost, limited application scenarios, and impacts on stability and ease of use.

Description

Technical Field

[0001] This application relates to the field of mass spectrometry imaging technology, and in particular to a non-targeted cascade mass spectrometry imaging method and apparatus. Background Technology

[0002] Among related technologies, data-independent acquisition can improve ion utilization and data acquisition throughput. The key lies in constructing the correspondence between parent and daughter ions to reconstruct standard secondary spectra for structural annotation. Deconvolution techniques, such as ion migration techniques, require modulation of ion composition to achieve this, enabling lipid structure annotation and in-situ tissue imaging. In principle, this technology is also applicable to the spatial imaging analysis of other metabolite ions.

[0003] However, in related technologies, mass spectrometry imaging generally cannot be combined with chromatographic separation technology, which limits the analytical dimensions and increases the difficulty of data interpretation; data acquisition is independent of molecular composition modulation technology and spectral deconvolution depends on molecular composition modulation technology, which increases the complexity and cost of instruments, limits application scenarios, and affects stability and ease of use, and urgently needs to be improved. Summary of the Invention

[0004] This application provides a non-targeted tandem mass spectrometry imaging method and apparatus to solve the problems in related technologies, such as the inability to combine mass spectrometry imaging with chromatographic separation technology, which limits the analytical dimensions and increases the difficulty of data interpretation; and the dependence of data acquisition and spectral deconvolution on molecular composition modulation technology, which increases the complexity and cost of instruments, limits application scenarios, and affects stability and ease of use.

[0005] The first aspect of this application provides a non-targeted cascade mass spectrometry imaging method, comprising the following steps: acquiring a secondary mass spectrum of each pixel; performing peak identification and peak extraction based on the secondary mass spectrum to obtain a first intensity matrix of precursor ions and fragment ions in terms of mass-to-charge ratio and spatial location; performing a first-order deconvolution calculation on the first intensity matrix to obtain secondary mass spectra of multiple precursor ions and obtain the correspondence between precursor ions and fragment ions; based on the obtained correspondence between precursor ions and fragment ions, and combined with the molecular composition information of neighboring pixels, assigning each tissue a second intensity matrix of fragment ions and corresponding precursor ions in terms of mass-to-charge ratio and spatial location; performing a second-order deconvolution calculation on the second intensity matrix to obtain the relative ion intensities of characteristic fragments generated by each precursor ion within each pixel, thereby generating a multi-target mass spectrometry imaging result.

[0006] Through the above technical solution, the embodiments of this application can fully mine mass spectrometry data information by acquiring the secondary mass spectrum of each pixel and subsequent processing. The first intensity matrix obtained by peak identification and extraction lays the foundation for subsequent analysis, and the secondary mass spectrum of the parent ion obtained by first-order deconvolution and its corresponding relationship realize preliminary structural annotation. Combining the information of neighboring pixels to generate a second intensity matrix and calculating the relative ion intensity by second-order deconvolution, the parent ion situation within each pixel is accurately analyzed, and finally multi-target mass spectrometry imaging results are generated, realizing, but not limited to, large-scale lipid and metabolite structure annotation and spatial omics analysis, without the need for functional modules such as ion mobility separation, reducing the hardware requirements of mass spectrometry instruments and improving sample ion utilization and imaging throughput.

[0007] Optionally, in one embodiment of this application, obtaining the secondary mass spectrum of each pixel includes: obtaining the secondary mass spectrum of each pixel in a wide-window fully fragmented data-independent mode, wherein different pixels are determined based on the target coverage of molecular annotations to implement wide-window fragmentation with different mass-to-charge ratio windows.

[0008] Through the above technical solution, the embodiments of this application can acquire secondary mass spectra in a wide-window, fully fragmented, data-independent mode, while simultaneously fragmenting a large number of precursor ions, thereby improving ion utilization and data acquisition throughput. By determining the appropriate mass-to-charge ratio window for wide-window fragmentation based on the coverage of molecular annotation targets, more targeted mass spectrometry information can be acquired, helping to improve the accuracy and comprehensiveness of molecular annotation. This better adapts to the analytical needs of different molecules in complex biological samples, thus providing a higher quality and richer data foundation for subsequent mass spectrometry imaging data processing, structural annotation, and multi-target imaging.

[0009] Optionally, in one embodiment of this application, before obtaining the first intensity matrix of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location, the method further includes: dividing the parent ion and the fragment ions based on the fact that the mass-to-charge ratio of the parent ion is greater than that of the fragment ions, wherein the mass-to-charge ratio of the parent ion is limited to a preset wide window range.

[0010] Through the above technical solution, the embodiments of this application can effectively distinguish between the precursor ion and fragment ion based on a reasonable division of their mass-to-charge ratios. Specifically, the precursor ion's mass-to-charge ratio is greater than that of the fragment ion and is limited to a preset wide window range. This provides a foundation for accurate subsequent analysis and helps to more accurately identify and track ion information during data processing, improving the accuracy of mass spectrometry data interpretation. Limiting the precursor ion within a wide window range ensures sufficient precursor ion information is acquired while avoiding interference from too many irrelevant ions, improving the targeting and effectiveness of data acquisition, thereby optimizing the entire non-targeted cascade mass spectrometry imaging process.

[0011] Optionally, in one embodiment of this application, the first-order deconvolution calculation of the first intensity matrix includes: using the first intensity matrix to solve a first preset optimization problem to obtain the second-order mass spectrum.

[0012] Through the above technical solutions, the embodiments of this application can effectively integrate the rich information in the first intensity matrix, and transform the relationship between the parent ion and fragment ions in terms of mass-to-charge ratio and spatial position into a clear secondary mass spectrum through optimized calculation, thereby improving the utilization and interpretability of the data. The process of solving the optimization problem can realize intelligent processing of complex mass spectrometry data, avoid the subjectivity and limitations of manual analysis, and enhance the objectivity and accuracy of the results.

[0013] Optionally, in one embodiment of this application, the second-order deconvolution calculation of the second intensity matrix includes: using the second intensity matrix to solve a second preset optimization problem to obtain the relative ion intensity.

[0014] Through the above technical solutions, the embodiments of this application can accurately determine the relative ionic intensities of characteristic fragments generated by each parent ion within each pixel, providing crucial quantitative data support for multi-target mass spectrometry imaging. Simultaneously, it helps to gain a deeper understanding of the distribution and metabolism of molecules in biological tissues, playing an important role in biomedical research, disease diagnosis, and other fields, and enhancing the ability and level of non-targeted cascade mass spectrometry imaging technology for analyzing complex biological samples.

[0015] A second aspect of this application provides a non-targeted cascade mass spectrometry imaging device, comprising: an acquisition module for acquiring a secondary mass spectrum of each pixel; a first data processing module for performing peak identification and peak extraction based on the secondary mass spectrum to obtain a first intensity matrix of precursor ions and fragment ions in terms of mass-to-charge ratio and spatial location; a first calculation module for performing a first-order deconvolution calculation on the first intensity matrix to obtain secondary mass spectra of multiple precursor ions and to obtain the correspondence between precursor ions and fragment ions; a second data processing module for assigning a second intensity matrix of fragment ions and corresponding precursor ions in terms of mass-to-charge ratio and spatial location to each tissue based on the obtained correspondence between precursor ions and fragment ions and combined with molecular composition information of neighboring pixels; and a second calculation module for performing a second-order deconvolution calculation on the second intensity matrix to obtain the relative ion intensities of characteristic fragments generated by each precursor ion within each pixel, thereby generating a multi-target mass spectrometry imaging result.

[0016] Through the above technical solution, the embodiments of this application can fully mine mass spectrometry data information by acquiring the secondary mass spectrum of each pixel and subsequent processing. The first intensity matrix obtained by peak identification and extraction lays the foundation for subsequent analysis, and the secondary mass spectrum of the parent ion obtained by first-order deconvolution and its corresponding relationship realize preliminary structural annotation. Combining the information of neighboring pixels to generate a second intensity matrix and calculating the relative ion intensity by second-order deconvolution, the parent ion situation within each pixel is accurately analyzed, and finally multi-target mass spectrometry imaging results are generated, realizing, but not limited to, large-scale lipid and metabolite structure annotation and spatial omics analysis, without the need for functional modules such as ion mobility separation, reducing the hardware requirements of mass spectrometry instruments and improving sample ion utilization and imaging throughput.

[0017] Optionally, in one embodiment of this application, the acquisition module includes: acquiring the secondary mass spectrum of each pixel in a wide-window fully fragmented data-independent mode, wherein different pixels are determined to implement wide-window fragmentation with different mass-to-charge ratio windows based on the target coverage of molecular annotations.

[0018] Through the above technical solution, the embodiments of this application can acquire secondary mass spectra in a wide-window, fully fragmented, data-independent mode, while simultaneously fragmenting a large number of precursor ions, thereby improving ion utilization and data acquisition throughput. By determining the appropriate mass-to-charge ratio window for wide-window fragmentation based on the coverage of molecular annotation targets, more targeted mass spectrometry information can be acquired, helping to improve the accuracy and comprehensiveness of molecular annotation. This better adapts to the analytical needs of different molecules in complex biological samples, thus providing a higher quality and richer data foundation for subsequent mass spectrometry imaging data processing, structural annotation, and multi-target imaging.

[0019] Optionally, in one embodiment of this application, the first data processing module includes: a segmentation unit, configured to segment the parent ion and the fragment ion based on the fact that the mass-to-charge ratio of the parent ion is greater than that of the fragment ion, wherein the mass-to-charge ratio of the parent ion is limited to a preset wide window range.

[0020] Through the above technical solution, the embodiments of this application can effectively distinguish between the precursor ion and fragment ion based on a reasonable division of their mass-to-charge ratios. Specifically, the precursor ion's mass-to-charge ratio is greater than that of the fragment ion and is limited to a preset wide window range. This provides a foundation for accurate subsequent analysis and helps to more accurately identify and track ion information during data processing, improving the accuracy of mass spectrometry data interpretation. Limiting the precursor ion within a wide window range ensures sufficient precursor ion information is acquired while avoiding interference from too many irrelevant ions, improving the targeting and effectiveness of data acquisition, thereby optimizing the entire non-targeted cascade mass spectrometry imaging process.

[0021] Optionally, in one embodiment of this application, the first calculation module includes: a first solution unit, used to solve a first preset optimization problem using the first intensity matrix to obtain the second-order mass spectrum.

[0022] Through the above technical solutions, the embodiments of this application can effectively integrate the rich information in the first intensity matrix, and transform the relationship between the parent ion and fragment ions in terms of mass-to-charge ratio and spatial position into a clear secondary mass spectrum through optimized calculation, thereby improving the utilization and interpretability of the data. The process of solving the optimization problem can realize intelligent processing of complex mass spectrometry data, avoid the subjectivity and limitations of manual analysis, and enhance the objectivity and accuracy of the results.

[0023] Optionally, in one embodiment of this application, the second calculation unit includes: a second solution unit, used to solve a second preset optimization problem using the second intensity matrix to obtain the relative ion intensity.

[0024] Through the above technical solutions, the embodiments of this application can accurately determine the relative ionic intensities of characteristic fragments generated by each parent ion within each pixel, providing crucial quantitative data support for multi-target mass spectrometry imaging. Simultaneously, it helps to gain a deeper understanding of the distribution and metabolism of molecules in biological tissues, playing an important role in biomedical research, disease diagnosis, and other fields, and enhancing the ability and level of non-targeted cascade mass spectrometry imaging technology for analyzing complex biological samples.

[0025] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the non-targeted cascade mass spectrometry imaging method as described in the above embodiments.

[0026] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described non-targeted cascade mass spectrometry imaging method.

[0027] A fifth aspect of this application provides a computer program product, including a computer program, which, when executed, is used to implement the above-described non-targeted cascade mass spectrometry imaging method.

[0028] This application's embodiments can fully mine mass spectrometry data by acquiring the secondary mass spectrum of each pixel and performing a series of processing steps. The first intensity matrix obtained from peak identification and extraction lays the foundation for subsequent analysis. First-order deconvolution achieves preliminary structural annotation, and second-order deconvolution, combined with neighboring pixel information, calculates relative ion intensities to accurately analyze the precursor ions within each pixel. Ultimately, multi-target mass spectrometry imaging results are generated, enabling, but not limited to, large-scale lipid and metabolite structure annotation and spatial omics analysis. This requires no specific functional modules, reducing instrument hardware requirements and improving sample ion utilization and imaging throughput. Simultaneously, acquiring secondary mass spectra in a specific data-independent mode improves ion utilization and data acquisition throughput. Determining the mass-to-charge ratio window for different pixels based on molecular annotation targets improves the accuracy and comprehensiveness of molecular annotation, adapting to the needs of complex sample analysis. Furthermore, based on reasonable mass-to-charge ratio division and wide-window limitation of precursor ions, the imaging process is optimized. Solving the optimization problem using the intensity matrix improves data utilization and interpretability, enhances the objectivity and accuracy of results, and provides quantitative support for multi-target imaging, helping to gain a deeper understanding of the molecular structure of biological tissues and improving the technology's analytical capabilities and level for complex samples.

[0029] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. Attached Figure Description

[0030] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0031] Figure 1 This is a flowchart of a non-targeted cascade mass spectrometry imaging method provided according to an embodiment of this application;

[0032] Figure 2 This is a schematic diagram illustrating the data acquisition principle according to an embodiment of this application;

[0033] Figure 3 This is a schematic diagram illustrating the principle of first-order deconvolution according to an embodiment of this application;

[0034] Figure 4 This is a schematic diagram of the result of first-order deconvolution according to a specific embodiment of this application;

[0035] Figure 5 This is a comparison diagram of the first-order deconvolution result and the in-situ targeted cascade mass spectrometry analysis result according to a specific embodiment of this application;

[0036] Figure 6 This is a schematic diagram illustrating the principle of second-order deconvolution according to a specific embodiment of this application;

[0037] Figure 7This is a comparison between a second-order deconvolution mass spectrometry image and an in-situ targeted cascade mass spectrometry image according to a specific embodiment of this application;

[0038] Figure 8 This is a schematic diagram of second-order unconvolution mass spectrometry imaging of multiple groups of different lipid precursor ions according to a specific embodiment of this application;

[0039] Figure 9 This is a schematic diagram of a non-targeted cascade mass spectrometry imaging device according to an embodiment of this application;

[0040] Figure 10 This is a structural example diagram of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0041] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0042] The following describes a non-targeted tandem mass spectrometry imaging method and apparatus according to embodiments of this application, with reference to the accompanying drawings. Addressing the issues mentioned in the background art, where mass spectrometry imaging generally cannot be coupled with chromatographic separation techniques, resulting in limited analytical dimensions and increased data analysis difficulty; and where data acquisition and spectrum deconvolution rely on molecular composition modulation techniques, leading to increased instrument complexity and cost, limited application scenarios, and impacting stability and ease of use, this application provides a non-targeted tandem mass spectrometry imaging method. In this method, mass spectrometry data information can be fully extracted by acquiring the secondary mass spectrum of each pixel and subsequent processing. The first intensity matrix obtained through peak identification and extraction lays the foundation for subsequent analysis, and the secondary mass spectrum of the parent ion obtained by first-order deconvolution and its corresponding relationships achieve preliminary structural annotation. A second intensity matrix is ​​generated by combining information from neighboring pixels, and the relative ion intensity is calculated by second-order deconvolution, accurately analyzing the parent ion situation within each pixel, ultimately generating multi-target mass spectrometry imaging results. This enables, but is not limited to, large-scale lipid and metabolite structure annotation and spatial omics analysis, without requiring ion mobility separation modules, reducing hardware requirements for mass spectrometry instruments and improving sample ion utilization and imaging throughput. This solves the problems in related technologies, such as the inability of mass spectrometry imaging to be combined with chromatographic separation technology, which limits the analytical dimensions and increases the difficulty of data interpretation; and the fact that data acquisition is independent of molecular composition modulation technology and spectral deconvolution depends on molecular composition modulation technology, which increases the complexity and cost of instruments, limits application scenarios, and affects stability and ease of use.

[0043] Specifically, Figure 1This is a schematic flowchart of a non-targeted cascade mass spectrometry imaging method provided in an embodiment of this application.

[0044] like Figure 1 As shown, this non-targeted cascade mass spectrometry imaging method includes the following steps:

[0045] In step S101, the secondary mass spectrum of each pixel is obtained.

[0046] It is understood that sample ionization uses a soft ionization source, from which a variety of soft ionization sources can be selected. For example, in the embodiments of this application, collision-induced dissociation technology can be used to excite ions using a fixed excitation energy. In actual operation, technicians can also select other ion fragmentation technologies, including but not limited to ultraviolet light dissociation, surface-induced dissociation, electron capture-induced dissociation, and electron transfer-induced dissociation, to achieve ion excitation according to specific needs. They are not limited to these technologies and can flexibly choose according to the actual situation.

[0047] Optionally, in one embodiment of this application, obtaining the secondary mass spectrum of each pixel includes: obtaining the secondary mass spectrum of each pixel in a wide-window fully fragmented data-independent mode, wherein different pixels are determined to implement wide-window fragmentation with different mass-to-charge ratio windows based on the target coverage of molecular annotations.

[0048] Specifically, secondary mass spectra of each pixel are acquired in a wide-window, fully fragmented, data-independent mode. Based on the target coverage of molecular annotation, different pixels are selected for wide-window fragmentation with varying mass-to-charge ratio windows. The range of the wide mass-to-charge ratio window is adjustable. All ions within the wide mass-to-charge ratio window of each pixel are simultaneously fragmented, and tandem mass spectrometry data are then acquired. When ionizing compounds on the sample surface, there are no limitations on spatial resolution; the ionization method should be adapted to the spatial resolution range achievable by the selected method. This ensures effective data acquisition under different spatial resolution conditions, meeting the needs of various application scenarios.

[0049] Furthermore, after acquiring the cascade mass spectra of each pixel, the raw data file is converted into a .mzML format data file using the open-source software MSConvert (Mass Spectrometry Convert, a tool in the ProteoWizard software package, primarily used for format conversion of mass spectrometry data files). It should be noted that although mass spectrometry data acquired by instruments from different manufacturers may differ in format, the analytical method in this embodiment is not limited to this. Then, the .mzML format data file is processed using the open-source Python library pymzml (Python mzML module, a Python library for processing .mzML format mass spectrometry data files), converting it into a .npy format data file. This .npy format data file contains cascade mass analysis data of each pixel with a wide window of fully fragmented data for subsequent processing and analysis.

[0050] In actual execution, pixels are clustered based on initial data. Various clustering algorithms can be used, such as the KNN (K-Nearest Neighbors) algorithm used in this embodiment. In specific operations, clustering algorithms including but not limited to K-means (K-means clustering algorithm), DBSCAN (Density-Based Spatial Clustering of Applications with Noise), GMM (Gaussian Mixture Model), and SOM (Self-Organizing Map) can be selected to directly cluster pixels. Alternatively, pixel clustering can be performed indirectly through dimensionality reduction algorithms such as UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding). Based on the clustering results, each category corresponds to an average mass spectrum.

[0051] This application's embodiments can employ soft ionization sources for ionization with flexible technology selection. Multiple ion fragmentation techniques can be chosen based on actual needs, without being limited to a specific technology. This adapts to different experimental conditions and research requirements, effectively stimulating ions to obtain mass spectra. Mass spectra are obtained using a wide-window, fully fragmented data-independent mode. Different mass-to-charge ratio windows are selected based on the coverage of molecular annotation targets, improving the comprehensiveness of molecular annotation. The adjustable window range increases the method's flexibility. There are no strict limitations on spatial resolution, adapting to the resolution range of different ionization methods, enabling the acquisition of effective data under various spatial resolution conditions and broadening application scenarios. During data format conversion, open-source software can process data from different vendors and convert it to a specific format, facilitating subsequent analysis. Furthermore, diverse clustering algorithms are available, including direct use of various classic clustering algorithms and indirect clustering through dimensionality reduction algorithms. The most suitable method can be selected based on the actual situation, thereby obtaining an average mass spectrum based on the clustering results, laying the foundation for subsequent accurate analysis.

[0052] In step S102, peak identification and extraction are performed based on the secondary mass spectrum to obtain the first intensity matrix of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial position.

[0053] Understandably, the intensity matrices of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location are obtained from the secondary mass spectrometry data, eliminating the need for primary mass spectrometry acquisition. This data acquisition method, based on specific technical principles, effectively utilizes the information contained in the secondary mass spectrometry data, reduces unnecessary data acquisition procedures, improves data acquisition efficiency, and provides a data foundation for subsequent analysis and processing.

[0054] Optionally, in one embodiment of this application, before obtaining the first intensity matrix of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location, the method further includes: dividing the parent ion and fragment ions based on the fact that the mass-to-charge ratio of the parent ion is greater than that of the fragment ion, wherein the mass-to-charge ratio of the parent ion is limited to a preset wide window range.

[0055] Specifically, the intensity matrices of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location are dynamically obtained. The division is based on the parent ion having a higher mass-to-charge ratio than the fragment ions, and the parent ion's mass-to-charge ratio is limited to a set wide window range. For example, in practice, if the mass-to-charge ratio window is set to m / z 750-m / z 900 (this is just an example and can be adjusted according to specific needs), then during data processing, the parent ion and fragment ions will be accurately distinguished based on this window range and the magnitude of their mass-to-charge ratios. This ensures the accuracy and validity of the data in the intensity matrix, providing a reasonable data classification basis for subsequent peak identification and extraction.

[0056] Further, based on the clustering results of step S101, peak extraction is performed in each category, extracting a peak list from each category and summarizing it. Then, the original data is binned according to the list and a width of ±0.02 Daltons to reasonably divide the data, so as to more accurately obtain the intensity information of the parent ion and fragment ions in terms of spatial location and mass-to-charge ratio, finally obtaining the intensity matrix, as shown below. Figure 2 As shown, by using a fixed set of isotope distribution ratios and a judgment tolerance of ±0.005 Daltons, the influence of isotope distribution is initially eliminated by direct subtraction. This reduces the interference of isotope distribution on the intensity measurement of parent ions and fragment ions, making the obtained intensity matrix more accurately reflect the actual ion situation.

[0057] This application's embodiments can obtain intensity matrices from secondary mass spectrometry data, avoiding primary mass spectrum acquisition, simplifying the process, improving efficiency, and laying a data foundation for subsequent analysis. Based on the mass-to-charge ratio relationship between parent ions and fragment ions and a preset wide window range, the two are divided to ensure the accuracy and effectiveness of the intensity matrix data, providing a reasonable classification basis for peak identification, etc. Peak extraction and binning operations are performed based on clustering results. By summarizing the peak list and binning according to a specific width, the intensity information of ions in spatial and mass-to-charge ratio dimensions can be accurately obtained, which helps to construct a high-quality intensity matrix. The influence of isotopes is eliminated by directly subtracting the fixed isotope distribution ratio and judgment tolerance, reducing interference and making the intensity matrix more accurately reflect the actual ion situation, improving overall data quality and analytical reliability.

[0058] In step S103, a first-order deconvolution calculation is performed on the first intensity matrix to obtain the secondary mass spectra of multiple precursor ions and to obtain the correspondence between precursor ions and fragment ions.

[0059] Those skilled in the art should understand that in first-order deconvolution, it is assumed that the quantitative relationship between the parent ion and the corresponding fragment ion remains unchanged within the same cascade mass spectrometry analysis, and that this quantitative relationship is independent of the parent ion intensity. This assumption provides a theoretical basis for subsequent calculations based on mathematical models, enabling the simplification of complex ion interactions when processing data, focusing on using the intensity matrix to solve for the relationship between the parent ion and fragment ions.

[0060] Optionally, in one embodiment of this application, performing a first-order deconvolution calculation on the first intensity matrix includes: using the first intensity matrix to solve a first preset optimization problem to obtain a second-level mass spectrum.

[0061] In actual implementation, the elastic regression network algorithm is used to perform first-order deconvolution calculations on the intensity matrices of the parent ion and fragment ion corresponding to the selected pixel data, such as... Figure 3As shown in the diagram. This algorithm has unique advantages in processing this type of data, effectively assigning fragment ions to the cascade spectra of their parent ions. In the specific calculation process, the data from the intensity matrix is ​​input into the elastic regression network algorithm model. Through model calculation and iteration, the accurate assignment of fragment ions is gradually achieved. Secondary mass spectra of each parent ion are obtained through first-order deconvolution calculation. These secondary mass spectra are crucial for further analysis of the parent ion structure. Then, according to pre-defined annotation rules, the structure of the parent ion is annotated using the obtained secondary mass spectra. Annotation rules may include, but are not limited to, information based on known mass spectrometry features, ion fragmentation patterns, etc., through comparison and analysis to determine the structural characteristics of the parent ion.

[0062] Furthermore, based on the annotation results, the correspondence between the parent ion and fragment ions was obtained. It is important to note that this correspondence primarily covers qualitative information about the molecular structure, excluding quantitative information. This provides fundamental structural data for subsequent quantitative analysis and helps to understand the relationship between the parent ion and fragment ions at the molecular structure level.

[0063] The following is a detailed description of step S103 using a specific embodiment.

[0064] In some embodiments, it is assumed that under consistent tandem mass spectrometry conditions, the theoretical tandem mass spectrum generated by a parent ion with a signal intensity of 1 unit remains unchanged, and the portion of the tandem mass spectrometry data containing only fragment ions is denoted as a vector. When the parent ion signal p i When the intensity is p, there exists a corresponding phasor. The following equations exist:

[0065]

[0066] in, This represents the fragment ion portion of the cascade mass spectrum collected during the actual experiment, where L is the number of channels for the fragment ions.

[0067] In particular, since the embodiments of this application adopt a wide-window, fully fragmented, data-independent mode, several different precursor ions within the window will fragment simultaneously, and the collected tandem mass spectrometry data are tandem mass spectra of multiple precursor ions. The weighted sum of (M being the number of parent ion channels) is given by the following equation:

[0068]

[0069] Among them, matrix Let it be a matrix In this embodiment, matrix C is the variable to be solved. After solving, the precursor ions can be annotated. Given the distribution of certain precursor ions in specific regions, the pixel selection strategy in this embodiment aims to ensure representativeness of each region. Specifically, an equal number of pixels are randomly selected from each category. This solvability depends on whether the rank of matrix P is greater than M. Therefore, determining matrix C requires data from at least M pixels. Given that some randomly selected pixels may exhibit low heterogeneity in lipid composition, this embodiment selects N pixels (where N>M) to maintain the basic solvability condition. in Represented as a matrix Therefore, the following relationship holds true:

[0070] P×C=F,

[0071] Among them, matrix This represents the intensity information of fragment ions in terms of their spatial location (N pixels) and mass-to-charge ratio.

[0072] It is important to note that in this embodiment, only tandem mass spectrometry data after ion excitation is collected, and first-order mass spectrometry data is not collected, so the intensity of the precursor ion before fragmentation cannot be directly obtained. In this example, the intensity of the precursor ion after fragmentation is used instead of the intensity before fragmentation, which does not affect the final solution. The first-order deconvolution ultimately transforms into solving this problem:

[0073]

[0074] After obtaining an approximate solution to matrix C, the coefficient matrix for the generation of fragment ions from the parent ion can be obtained. It should be noted that, in the specific implementation process, those skilled in the art can also solve the optimization problem using methods including but not limited to non-negative least squares methods and Lasso regression, and no specific restrictions are imposed on this.

[0075] In this embodiment, matrix C is solved column by column, and matrix P changes dynamically accordingly. When solving the Kth column of matrix C, the mass-to-charge ratio is m / z. k The fragment ions. The mass-to-charge ratio of the parent ion must be greater than that of the fragment ions, and the mass-to-charge ratio of the parent ion must fall within a set wide mass-to-charge ratio window. In this embodiment, the mass-to-charge ratio window is m / z 750 to m / z 900; data that do not meet the conditions in the mass-to-charge ratio dimension are temporarily set to zero. It should be noted that this embodiment does not limit the range of the mass-to-charge ratio window. The data deconvolution method in this embodiment does not rely on a database and is characterized by speed, simplicity, automation, and accuracy.

[0076] This application simplifies complex ion interactions based on reasonable assumptions, focusing on solving the relationship between parent ions and fragment ions using intensity matrices. This provides a solid theoretical foundation for the calculations, making the process more targeted and feasible. By using the first intensity matrix to solve a pre-defined optimization problem, secondary mass spectra of multiple parent ions can be effectively obtained, providing crucial evidence for in-depth analysis of the parent ion structure. The use of an elastic regression network algorithm ensures that fragment ions are accurately assigned to the cascade spectra of their respective parent ions. In specific calculations, precise allocation is achieved through model calculation and iteration. Based on known mass spectrometry features, ion fragmentation patterns, and other annotation rules, the parent ion can be structurally annotated to determine its structural characteristics. Although the final obtained correspondence between parent ions and fragment ions does not involve quantitative information, it provides important structural-level foundational data for subsequent quantitative analysis, contributing to a comprehensive understanding of their molecular structural relationship.

[0077] In step S104, based on the obtained correspondence between the parent ion and fragment ions, and combined with the molecular composition information of neighboring pixels, each tissue is assigned a second intensity matrix of fragment ions and corresponding parent ions in terms of mass-to-charge ratio and spatial location.

[0078] Understandably, the introduction of molecular composition information from neighboring pixels is crucial. Because tissues exhibit a degree of spatial continuity and correlation, the molecular composition of neighboring pixels can supplement and reference the analysis of the target tissue. By collecting and integrating the molecular composition information of neighboring pixels, a more comprehensive molecular distribution can be obtained, thereby providing a better understanding of the target tissue's position and characteristics within the overall environment.

[0079] In practice, by integrating the correspondence between the parent ion and fragment ions, as well as the neighboring pixel molecules, a second intensity matrix is ​​assigned to each tissue, representing the fragment ions and their corresponding parent ions in terms of mass-to-charge ratio and spatial location. For each tissue, the intensity information of its fragment ions and corresponding parent ions is reorganized and reconstructed along these two important dimensions: mass-to-charge ratio and spatial location. In the mass-to-charge ratio dimension, the distribution of ion intensity with different mass-to-charge ratios is accurately reflected, providing a basis for identifying and distinguishing different types of ions. In the spatial location dimension, the distribution location and pattern of ions within the tissue are clearly displayed, helping to reveal the spatial heterogeneity of molecules within the tissue and providing a solid data foundation for subsequent second-order deconvolution calculations and multi-target mass spectrometry imaging.

[0080] In step S105, a second-order deconvolution calculation is performed on the second intensity matrix to obtain the relative ion intensity of the characteristic fragments generated by each parent ion within each pixel, thereby generating a multi-target mass spectrometry imaging result.

[0081] Those skilled in the art need to understand that second-order deconvolution is based on two important assumptions. First, in the same cascade mass spectrometry analysis, the quantitative relationship between the parent ion and the corresponding fragment ion remains constant, which provides a basic premise for subsequent calculation of the quantitative relationship based on mathematical models. Second, it is assumed that the proportion of isomers / materials of the same weight is consistent among neighboring pixels. This assumption allows the information of neighboring pixels to be used to infer the situation of the target pixel, thus making it possible to obtain a more accurate quantitative relationship.

[0082] In the actual execution process, based on the correspondence between the parent ion and the fragment ion obtained in the first-order deconvolution in step S104, and based on the intensity information of the fragment ion and the corresponding parent ion of the target pixel and nearby pixels (e.g., using a 2x2 pixel square to cover the target pixel, the size of the square can be adjusted according to the number of parent ions corresponding to the fragment ions), this intensity information comes from the second intensity matrix constructed earlier. Combining these data provides a sufficient data foundation for the second-order deconvolution calculation.

[0083] Optionally, in one embodiment of this application, performing a second-order deconvolution calculation on the second intensity matrix includes: using the second intensity matrix to solve a second preset optimization problem to obtain the relative ion intensity.

[0084] Specifically, based on the above data, it is substituted into a preset optimization problem to perform second-order deconvolution calculation. In particular, through specific mathematical models and algorithms (such as optimization methods including but not limited to minimizing a specific function, the specific optimization method is not limited), the quantitative relationship between the parent ion and fragment ions of the target pixel is solved, thereby obtaining the relative ion intensity of the characteristic fragments generated by each parent ion in each pixel.

[0085] Furthermore, based on the second-order deconvolution results, the proportional relationship between isomers / isomers of equal weight is obtained. These results are used to reconstruct the spatial distribution of isomers / isomers of equal weight. By integrating and visualizing the quantitative information and isomer / isomer ratio information in each pixel in space, multi-target mass spectrometry imaging is finally realized, generating multi-target mass spectrometry imaging results with rich information, which can clearly show the spatial distribution and relative content of different parent ions and their characteristic fragments in biological tissues.

[0086] The following specific embodiment will provide a detailed description of step S105.

[0087] Specifically, in this embodiment, a cascade mass spectrum of 175 precursor ions was obtained on a single frozen section of the mouse cerebellum through first-order deconvolution. For example... Figure 4As shown, the second-order mass spectrum obtained by deconvolution using the non-targeted cascade mass spectrometry method is highly similar to that obtained by targeted cascade mass spectrometry. It should be noted that the isolation window for targeted cascade mass spectrometry is often set to ±0.5 Daltons. Figure 5 As shown, the interfering precursor ion and the target precursor ion break down simultaneously, but the embodiments of this application can avoid this interference.

[0088] This embodiment does not involve additional analytical methods, thus helping to reduce ion loss. The data-independent acquisition mode is relatively favorable for low-abundance ions, such as... Figure 5 As shown, even with a relative signal strength as low as 0.12%, this embodiment can still solve for its corresponding cascade spectrum.

[0089] Furthermore, second-order spectral deconvolution is performed. The principle of second-order deconvolution is as follows: Figure 6 As shown. After the first stage of deconvolution, the annotation column labels are obtained, establishing the relationship between the parent ion and its corresponding fragment ion. It is important to note that to achieve a quantitative relationship between the parent ion and fragment ions of the target pixel, the molecular composition and intensity information of the neighboring pixels are needed. In this embodiment, since most fragment ions originate from at most four different parent ions, a 2x2 pixel grid (covering the target pixel) is used. The intensity information of the fragment ion and its corresponding parent ion of a single pixel within this grid can be represented as:

[0090]

[0091] in, The total ionic strength of the i-th parent ion of the j-th fragment ion and all isomers / isomeric compounds of that parent ion is given; in the examples, this strength can be directly obtained; α i Let p be the ionic strength of the i-th parent ion. i and The ratio of the i-th parent ion to the ratio of the j-th fragment ion it produces, c i The product of:

[0092]

[0093] Among them, c i It is the ratio of the intensity of the parent ion to the intensity of the fragment ions it produces. In this embodiment, it is assumed that within a 2x2 pixel square... The ratio remains constant, that is, the ratio between isomers and isotopes remains unchanged, thus α i Can replace c i It reflects quantitative relationships.

[0094] Furthermore, in this embodiment, a 2x2 pixel grid (covering the target pixel) is used. The intensity information of fragment ions and their corresponding parent ions for all pixels within this grid can be represented as follows: (where k is the number of parent ions capable of producing the fragment), then the second-order deconvolution is solved by the following equation:

[0095]

[0096] Thus, a vector is obtained. An approximate solution is provided. It should be noted that this embodiment does not limit the optimization method for solving this problem.

[0097] In this embodiment, the pixel grid is slid with a step size of 1 to progressively solve for the vector of each target pixel. like Figure 6 As shown.

[0098] It should be noted that the size of the pixel square in this embodiment is determined by the number of parent ions corresponding to the fragment ions. If there are more than 4 parent ions, a 3x3 pixel square is used, and so on.

[0099] Furthermore, by reconstructing the spatial distribution of each parent ion, this embodiment can achieve spatial visualization of isomeric substances and isomers. For example... Figure 7 As shown, this embodiment visualizes the spatial distribution of a pair of isomers with the same weight, revealing significant differences, and the results are in high agreement with those obtained from in-situ targeted tandem mass spectrometry imaging. Figure 8 As shown, this embodiment visualizes the spatial distribution of six pairs of isomeric objects / isomers.

[0100] This application's embodiments can be based on two reasonable assumptions to lay the foundation for calculating quantitative relationships, effectively utilizing information from neighboring pixels to infer the target pixel's condition and improve the accuracy of quantitative relationships. During execution, the correspondence obtained from first-order deconvolution and the intensity information of the target and nearby pixels are fully integrated, providing solid data support for second-order deconvolution. By solving the second preset optimization problem, the relative ionic intensity of the characteristic fragments generated by the parent ion within each pixel can be accurately obtained, thereby obtaining the proportional relationship between isomers / objects of equal weight.

[0101] The non-targeted cascade mass spectrometry imaging method proposed in this application can acquire cascade mass spectrometry data in a non-data-dependent mode, obtaining the intensity matrices of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location. First-order deconvolution is performed on the intensity information of the parent ion and fragment ions of multiple pixels to obtain the correspondence between the parent ion and fragment ions, enabling automatic annotation of molecular structures in the mass spectrometry imaging data. Second-order deconvolution is performed on the intensity information of fragment ions and corresponding parent ions of the target pixel and its neighboring pixels to obtain the quantitative relationship between the parent ion and fragment ions, realizing non-targeted cascade mass spectrometry imaging. This simplifies the mass spectrometry imaging process, eliminates the need for other gas phase separation techniques, automatically annotates mass spectrometry imaging data, and reduces the hardware requirements for mass spectrometry imaging instruments. Simultaneously, it enables mass spectrometry imaging of multiple target ions, improving the utilization rate of analytical samples and the throughput of mass spectrometry imaging.

[0102] Next, a non-targeted cascade mass spectrometry imaging device according to an embodiment of this application is described with reference to the accompanying drawings.

[0103] Figure 9 This is a block diagram of a non-targeted cascade mass spectrometry imaging device according to an embodiment of this application.

[0104] like Figure 9 As shown, the non-targeted cascade mass spectrometry imaging device 10 includes: an acquisition module 100, a first data processing module 200, a first calculation module 300, a second data processing module 400, and a second calculation module 500.

[0105] The acquisition module 100 is used to acquire the secondary mass spectrum of each pixel.

[0106] The first data processing module 200 is used to identify and extract peaks based on the secondary mass spectrum to obtain the first intensity matrix of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location.

[0107] The first calculation module 300 is used to perform first-order deconvolution calculation on the first intensity matrix to obtain the secondary mass spectra of multiple parent ions and to obtain the correspondence between parent ions and fragment ions.

[0108] The second data processing module 400 is used to assign a second intensity matrix of fragment ions and corresponding parent ions in terms of mass-to-charge ratio and spatial location to each tissue based on the obtained correspondence between parent ions and fragment ions and the molecular composition information of neighboring pixels.

[0109] The second calculation module 500 is used to perform second-order deconvolution calculation on the second intensity matrix to obtain the relative ion intensity of each parent ion producing characteristic fragments within each pixel, and generate multi-target mass spectrometry imaging results.

[0110] Optionally, in one embodiment of this application, the acquisition module 100 includes: acquiring the secondary mass spectrum of each pixel in a wide-window fully fragmented data-independent mode, wherein different pixels are determined to implement wide-window fragmentation with different mass-to-charge ratio windows based on the target coverage of molecular annotations.

[0111] Optionally, in one embodiment of this application, the first data processing module 200 includes: a segmentation unit, used to segment parent ions and fragment ions based on the fact that the mass-to-charge ratio of the parent ion is greater than that of the fragment ions, wherein the mass-to-charge ratio of the parent ion is limited to a preset wide window range.

[0112] Optionally, in one embodiment of this application, the first calculation module 300 includes: a first solution unit, used to solve a first preset optimization problem using a first intensity matrix to obtain a second-order mass spectrum.

[0113] Optionally, in one embodiment of this application, the second calculation unit 500 includes: a second solution unit, used to solve a second preset optimization problem using a second intensity matrix to obtain the relative ion intensity.

[0114] It should be noted that the foregoing explanation of the non-targeted cascade mass spectrometry imaging method embodiment also applies to the non-targeted cascade mass spectrometry imaging device of this embodiment, and will not be repeated here.

[0115] The non-targeted cascade mass spectrometry imaging device proposed in this application can acquire cascade mass spectrometry data in a non-data-dependent mode, obtaining the intensity matrices of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location. First-order deconvolution is performed on the intensity information of the parent ion and fragment ions of multiple pixels to obtain the correspondence between the parent ion and fragment ions, enabling automatic annotation of molecular structures in the mass spectrometry imaging data. Second-order deconvolution is performed on the intensity information of fragment ions and corresponding parent ions of the target pixel and its neighboring pixels to obtain the quantitative relationship between the parent ion and fragment ions, realizing non-targeted cascade mass spectrometry imaging. This simplifies the mass spectrometry imaging process, eliminates the need for other gas phase separation techniques, automatically annotates mass spectrometry imaging data, and reduces the hardware requirements for mass spectrometry imaging instruments. Simultaneously, it enables mass spectrometry imaging of multiple target ions, improving the utilization rate of analytical samples and the throughput of mass spectrometry imaging.

[0116] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0117] The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0118] When the processor 1002 executes the program, it implements the non-targeted cascade mass spectrometry imaging method provided in the above embodiments.

[0119] Furthermore, electronic devices also include:

[0120] Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0121] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0122] The memory 1001 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0123] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0124] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0125] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0126] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described non-targeted cascade mass spectrometry imaging method.

[0127] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described non-targeted cascade mass spectrometry imaging method.

[0128] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0129] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0130] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0131] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0132] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0133] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0134] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0135] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A non-targeted cascade mass spectrometry imaging method, characterized in that, This method is used to understand the distribution and metabolism of molecules in biological tissues, and includes the following steps: Obtain the secondary mass spectrum of each pixel; Peak identification and extraction are performed based on the secondary mass spectrum to obtain the first intensity matrix of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location. The first intensity matrix is ​​subjected to first-order deconvolution calculation to obtain the second-order mass spectra of multiple precursor ions and to obtain the correspondence between precursor ions and fragment ions. Based on the obtained correspondence between the parent ion and fragment ions, and combined with the molecular composition information of neighboring pixels, each biological tissue is assigned a second intensity matrix of fragment ions and corresponding parent ions in terms of mass-to-charge ratio and spatial location. The second intensity matrix is ​​subjected to second-order deconvolution calculation to obtain the relative ion intensity of each parent ion producing characteristic fragments within each pixel, thereby generating multi-target mass spectrometry imaging results.

2. The method according to claim 1, characterized in that, The process of obtaining the secondary mass spectrum of each pixel includes: The secondary mass spectrum of each pixel is obtained in a wide-window, fully fragmented, data-independent mode, wherein different pixels are determined to be fragmented in a wide-window manner with different mass-to-charge ratio windows based on the target coverage of molecular annotations.

3. The method according to claim 1, characterized in that, Before obtaining the first intensity matrix of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location, the process also includes: The parent ion and the fragment ion are distinguished based on the fact that the mass-to-charge ratio of the parent ion is greater than that of the fragment ion, wherein the mass-to-charge ratio of the parent ion is limited to a preset wide window range.

4. The method according to claim 1, characterized in that, The first-order deconvolution calculation of the first intensity matrix includes: The first intensity matrix is ​​used to solve the first preset optimization problem to obtain the second-order mass spectrum.

5. The method according to claim 1, characterized in that, The second-order deconvolution calculation of the second intensity matrix includes: The relative ion intensity is obtained by solving the second preset optimization problem using the second intensity matrix.

6. A non-targeted cascade mass spectrometry imaging device, characterized in that, This device is used to understand the distribution and metabolism of molecules in biological tissues, including: The acquisition module is used to acquire the secondary mass spectrum of each pixel; The first data processing module is used to identify and extract peaks based on the secondary mass spectrum to obtain the first intensity matrix of the parent ion and fragment ions in terms of mass-to-charge ratio and spatial location. The first calculation module is used to perform first-order deconvolution calculation on the first intensity matrix to obtain the second-order mass spectra of multiple parent ions and to obtain the correspondence between parent ions and fragment ions. The second data processing module is used to assign each biological tissue a second intensity matrix in terms of mass-to-charge ratio and spatial location dimensions of fragment ions and corresponding parent ions based on the obtained correspondence between the parent ions and fragment ions and combined with the molecular composition information of neighboring pixels. The second calculation module is used to perform second-order deconvolution calculation on the second intensity matrix to obtain the relative ion intensity of each parent ion producing feature fragments within each pixel, and generate multi-target mass spectrometry imaging results.

7. The apparatus according to claim 6, characterized in that, The acquisition module includes: The secondary mass spectrum of each pixel is obtained in a wide-window, fully fragmented, data-independent mode, wherein different pixels are determined to be fragmented in a wide-window manner with different mass-to-charge ratio windows based on the target coverage of molecular annotations.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the non-targeted cascade mass spectrometry imaging method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the non-targeted cascade mass spectrometry imaging method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the non-targeted cascade mass spectrometry imaging method as described in any one of claims 1-5.

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