Single-cell metabolome analysis method and system with motility rate sensing function

Abstract

The invention belongs to the technical field of single-cell metabolome analysis, and particularly relates to a single-cell metabolome analysis method and system with a motility rate sensing function, and the method comprises the steps: obtaining metabolism spectrum data of a plurality of single cells through a single-cell mass spectrum platform, and forming an initial single-cell metabolism matrix; the initial single cell metabolism matrix comprises a plurality of single cell samples and corresponding metabolite ion strength characteristics; inputting single cell survival rate labels provided by an external survival rate judgment module into the analysis process, wherein the survival rate labels are in one-to-one correspondence with corresponding single cells of the initial metabolism matrix; performing subset division on the initial metabolism matrix according to the motility rate label input in the step 2; reconstructing a living cell metabolism matrix and a dead cell metabolism matrix in groups, performing data processing and analysis operation on the living cell metabolism matrix and the dead cell metabolism matrix, and outputting an analysis result with better biological interpretation based on motility rate correction.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of single-cell metabolome analysis, and particularly relates to a single-cell metabolome analysis method and system with a viability sensing function. BACKGROUND

[0002] Single-cell metabolome analysis technology is a key tool for analyzing cell heterogeneity, revealing disease mechanisms, and evaluating drug responses. It captures the composition and changes of metabolites in individual cells through a single-cell mass spectrometry platform, providing high-resolution data support for cell function annotation, tumor subtype identification, and other research. However, current mainstream single-cell metabolome analysis methods generally do not consider cell viability during data processing. Since dead cells will leak metabolites due to damaged cell membrane integrity, their metabolic profiles will show characteristic attenuation or loss. This type of interference signal mixed into the initial metabolic matrix will severely distort the true metabolic characteristics of living cells, leading to mixed clustering results, misjudgment of differential metabolites, and other problems, significantly reducing the accuracy and reliability of subsequent analysis.

[0003] Although existing technologies have achieved efficient acquisition of single-cell metabolic data, they lack mechanisms for sensing and processing cell viability, which is a core bottleneck restricting the improvement of analysis accuracy. In complex research scenarios, such as multi-cell line metabolic heterogeneity analysis or cell state stratification research after drug treatment, the interference from dead cells will be further amplified, not only masking the inherent metabolic differences between different cell types, but also confusing the real metabolic response induced by drugs with non-specific metabolic changes caused by cell death, making it difficult to accurately distinguish between cell physiological state changes and death-related metabolic disturbances. Ultimately, this limits the depth and breadth of the application of single-cell metabolome technology in precision medicine, tumor research, and other fields. SUMMARY

[0004] The purpose of the present application is to provide a single-cell metabolome analysis method and system with a viability sensing function to address the above technical problems.

[0005] Therefore, the present application provides a single-cell metabolome analysis method with a viability sensing function. Step one: Obtain metabolic profile data of multiple single cells through a single-cell mass spectrometry platform to form an initial single-cell metabolic matrix, which includes several single-cell samples and corresponding metabolite ion intensity characteristics. Step two: Input single-cell viability labels provided by an external viability discrimination module into the analysis process, which are one-to-one corresponding to the single cells of the initial metabolic matrix. Step three: According to the viability labels input in step two, perform grouping reconstruction on the initial metabolic matrix to divide it into live cell and dead cell subsets, and construct live cell metabolic matrix and dead cell metabolic matrix respectively. Step four: performing data processing and analysis operations on the single-cell metabolic matrix subset respectively, and outputting analysis results of the single-cell metabolic profile based on the viability correction.

[0006] Preferably, the sampling mode and ionization mode of the single-cell mass spectrometry platform in step one are not limited, and the metabolic profile data contains ion intensity information of multiple endogenous metabolites.

[0007] Preferably, the viability label in step two is at least one of a binary label, a probability activity score, or a multi-level activity grade label, and the acquisition method of the viability label is not limited.

[0008] Preferably, the results of the single-cell metabolic profile based on the viability correction in step three are statistically analyzed, and the statistical results are used for quality control or subsequent report output. The grouping reconstruction process only filters the sample dimension of the initial metabolic matrix, without changing the structure of the metabolite characteristics.

[0009] Preferably, the data processing in step four includes at least one of normalization, peak intensity filtering, missing value processing, and variance feature selection of the cell metabolic matrix.

[0010] Preferably, the metabolic analysis in step four includes at least one of dimensionality reduction analysis, clustering analysis, and differential metabolism and pathway analysis. The dimensionality reduction analysis uses at least one of linear or nonlinear dimensionality reduction methods such as PCA, UMAP, and t-SNE. The clustering analysis uses at least one of clustering methods such as K-means, hierarchical clustering, and density clustering. The differential metabolism and pathway analysis includes at least one of differential metabolite screening, volcano plot analysis, and metabolic pathway enrichment analysis.

[0011] Preferably, the method is suitable for at least one of the following metabolomics tasks: cell subtype analysis, drug response evaluation, and cell state identification.

[0012] Preferably, the method is suitable for single-cell metabolomics analysis of human cell lines, including at least one of HeLa cells, K562 cells, HepG2 cells, and colorectal cancer cell lines.

[0013] Preferably, the method can be used to analyze the single-cell multi-level metabolic response after drug treatment, and the drug includes 5-fluorouracil, and the multi-level metabolic response includes overall drug effect, metabolic loss caused by dead cells, and metabolic heterogeneity within the living cell population.

[0014] A single-cell metabolomics analysis system with viability awareness function, the system comprising: The data acquisition module is configured to acquire metabolic profile data of a plurality of single cells through a single cell mass spectrometry platform to form an initial single cell metabolic matrix; The label input module is configured to receive a single cell viability label provided by an external viability discrimination module and to correspond the viability label to single cell samples in the initial metabolic matrix one by one. The grouping reconstruction module is configured to group and reconstruct the metabolic matrix according to the viability label, divide the live cells and the dead cells into subsets, and construct a live cell metabolic matrix and a dead cell metabolic matrix, respectively. The metabolic analysis module is configured to perform data processing and analysis operations on the subsets of the single cell metabolic matrix, and output analysis results.

[0015] The present application has the following advantages: By introducing a cell viability perception and grouping reconstruction mechanism, the core problem of interference of dead cells in traditional single cell metabolomics analysis is solved from the root. The introduction of the viability label and the targeted grouping operation can effectively distinguish the distorted metabolic profile caused by metabolite leakage, ensure that the subsequent analysis is based on the viability correction to maintain the true metabolic characteristics of the cells, and avoid problems such as spectrum deviation and clustering confusion caused by dead cells, thereby significantly improving the accuracy and reliability of the metabolomics analysis results. Meanwhile, the mechanism does not depend on a specific sampling or ionization method, and can be adapted to various single cell mass spectrometry platforms, has good universality, and can be stably applied to metabolomics analysis tasks in different cell types and different research scenarios.

[0016] In actual research and application, the present application can significantly enhance the resolving power and application value of single cell metabolomics technology. In the cell subtype identification scene, by excluding the interference of dead cells, the inherent metabolic differences of different cell types or subgroups can be more clearly presented, thereby assisting in accurately identifying cell identity characteristics. In complex scenes such as drug response evaluation, the real metabolic response induced by drugs and the non-specific metabolic changes caused by cell death can be effectively distinguished, the metabolic adaptation rules of cells under external intervention can be accurately disassembled, more accurate metabolic basis for revealing the physiological state changes of cells and analyzing disease mechanisms can be provided, and the application depth and breadth of single cell metabolomics technology in the field of precision medicine and tumor research can be promoted. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A single cell metabolomics analysis flowchart with a viability perception function is provided. The core process of the method includes the following steps: step one, single cell metabolomics data acquisition; step two, cell dead and live label input; step three, single cell metabolite data grouping reconstruction, dividing the dead and live cells into two subsets; and step four, grouping analysis, including feature extraction, dimension reduction, clustering, and pathway enrichment.

[0018] Figure 2Schematic diagram of mass spectrometry difference between live and dead cells; This figure demonstrates the metabolite mass spectrum difference between live and dead cells in single-cell mass spectrometry. A variety of characteristic metabolites reflecting cell activity and metabolic integrity can be detected in the metabolic profile of live cells, while the metabolic profile of dead cells shows significantly attenuated or missing metabolic characteristics. This figure is used to illustrate the necessity of live rate label input and grouping reconstruction technology.

[0019] Figure 3 Schematic diagram of metabolic profiling difference between live and dead cells; This figure is used to demonstrate the systematic difference in metabolic characteristics and metabolic pathways between live and dead cells. This figure is used to illustrate that live rate-based grouping reconstruction can significantly improve the accuracy and reliability of metabolic profiling.

[0020] Figure 4 Schematic diagram of metabolic clustering results of various human cell lines after live rate grouping reconstruction; This figure demonstrates the metabolic space distribution of representative cell lines (HeLa, K562, HepG2) after grouping reconstruction, and the dimensionality reduction or clustering results show that different cell lines have distinguishable population structures in metabolic characteristics. This figure is used to illustrate that the method of the present application can enhance the metabolic difference analysis capability in multiple cell types.

[0021] Figure 5 Schematic diagram of dimensionality reduction analysis results of six colorectal cancer cell lines before and after grouping reconstruction; This figure demonstrates the changes in cell population structure, clustering results or subtype differentiation of six colorectal cancer cell lines with different sources in metabolic profiling before and after grouping reconstruction. After grouping reconstruction, the metabolic differences between different cell lines are significantly enhanced, and the population structure is more clear, indicating that the method of the present application can effectively identify dead cell interference and improve the performance of subtype identification.

[0022] Figure 6 Schematic diagram of single-cell multi-level metabolic analysis after drug treatment; This figure takes a representative drug treatment model (colorectal cancer HCT116 cells treated with 5-fluorouracil) as an example to demonstrate the flow of hierarchical analysis of single-cell metabolic profiling after grouping reconstruction, including clustering structure of cell population, identification of differential metabolites, and classification trend of subpopulation. This figure is used to illustrate the application ability of the method of the present application in drug response research and the analysis advantage of cell state stratification. DETAILED DESCRIPTION

[0023] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly described. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art are within the scope of protection of the present application.

[0024] The method introduces an external viability label into a single-cell metabolic data processing flow, constructs a metabolic matrix of living cells and a metabolic matrix of dead cells respectively by performing grouped reconstruction on an initial metabolic matrix, and thus significantly improves the reliability and resolution of single-cell metabolomics analysis. The method can avoid spectrum deviation caused by metabolite leakage of dead cells, is suitable for various single-cell mass spectrometry platforms, and can be used for cell subtype analysis, drug response evaluation, and cell state recognition and other metabolomics tasks.

[0025] To achieve the above object, the present application provides the following technical scheme (such as Figure 1 ): Step one: single-cell metabolomics data acquisition. Obtain metabolic profile data of multiple single cells through a single-cell mass spectrometry platform to form an initial single-cell metabolic matrix. The single-cell metabolic data can be obtained by various cell mass spectrometry analysis methods, and the sampling method and ionization method are not specified. The initial metabolic matrix includes a plurality of single cell samples (Cell 1, Cell 2, …) and corresponding metabolite ion intensity characteristics.

[0026] Step two: cell viability label input. Input the single-cell viability label provided by the external viability discrimination module into the analysis flow. The viability label can be a binary label (such as live / dead), a probabilistic viability score or a multi-level viability grade label, and the specific acquisition method is not limited. Each label corresponds to the corresponding single cell of the initial metabolic matrix.

[0027] Step three: grouped reconstruction. According to the viability label input in step two, perform grouped operation on the initial metabolic matrix: divide the metabolic profile labeled as “living cell” into a subset to construct a living cell metabolic matrix; divide the metabolic profile labeled as “dead cell” into a subset to construct a dead cell metabolic matrix. The results after reconstruction can be statistically analyzed for quality control or subsequent report output. The viability grouped reconstruction process does not change the structure of the metabolite characteristics, but only filters the sample dimension. Typical mass spectra of dead and living cells are shown in FIG. 2. Figure 2 .

[0028] Step four: feature extraction, dimension reduction, clustering, and other metabolic analysis. The reconstructed single-cell metabolic matrix is subjected to data processing, including but not limited to: normalization of the metabolic matrix, peak intensity screening, missing value processing, variance feature selection, and other processing. At least one analysis step is performed on the processed data matrix, including but not limited to: dimension reduction analysis, using PCA, UMAP, t-SNE, or other nonlinear or linear dimension reduction methods to reduce the dimension of the metabolic data of living cells to reveal the metabolic difference structure between cells. Clustering analysis is performed on the dimension-reduced data, including K-means, hierarchical clustering, density-based clustering (DBSCAN), and other methods to identify potential cell subpopulation distribution patterns. Differential metabolism and pathway analysis is performed on cells under different subpopulations or different treatment conditions to screen for differential metabolites, volcano plot analysis, metabolic pathway enrichment analysis, and other methods to construct the functional metabolic characteristics of cells. The final output is the clustering results, subtype structure, differential metabolite list, and metabolic pathway analysis results based on the metabolic profile of living cells. Typical metabolic differences between dead and living cells are shown in FIG. 6. Figure 3 .

[0029] Example 1 To verify the applicability of the method of the present application in different cell sources and different culture systems, three typical human cell lines, including HeLa (cervical cancer cells), K562 (leukemia cells), and HepG2 (liver cancer cells), were selected for single-cell metabolome data analysis with viability awareness to evaluate the robustness of the method in the context of different cell types and the ability to analyze metabolic heterogeneity. The three selected cell types have significant differences in cell structure, culture method, and metabolic pathways, thus reflecting the universality of the method of the present application in diverse cell systems.

[0030] In the specific experimental process, first, the three cell lines were cultured according to the conventional method and single-cell metabolic data were collected to obtain the initial metabolic matrix containing multiple endogenous metabolites. Then, the external viability discrimination module was used to assign a viability label to each single cell, and the data of living cells and dead cells were distinguished.

[0031] Low-dimensional embedding and clustering analysis were performed on the single-cell metabolome data of the three different cell lines to evaluate the ability of the method of the present application to detect intercellular and intracellular metabolic heterogeneity. Figure 4 After dimension reduction processing of the single-cell metabolic profiles of the three types of cells, it can be seen that different cell lines form distinct cluster structures that can be distinguished from each other in the metabolic space, showing stable and distinguishable cell identity characteristics. In addition, within each cell line, living cells and dead cells also show a clear separation of clustering distribution, and the two types of cells maintain stable distinguishability in metabolic characteristics.

[0032] This "double-layer separation" phenomenon indicates that the method of this invention can not only accurately capture intercellular metabolic differences in a cross-cell type context, but also simultaneously reveal the subpopulation structure of different metabolic states (such as living cells and dead cells) within the same cell line. This characteristic enables the method of this invention to simultaneously resolve cell type characteristics and physiological state characteristics at single-cell resolution, thus having significant implications for phenotypic classification, assessment of metabolic heterogeneity in tumor cell populations, and cell function annotation based on metabolic characteristics.

[0033] Example 2: To verify the applicability and classification ability of the method of the present invention in the context of complex tumor heterogeneity, this example selected six colorectal cancer (CRC) cell lines from different sources and with different molecular characteristics, and conducted a subtype differentiation study based on single-cell metabolomics data. Colorectal cancer exhibits significant molecular and phenotypic heterogeneity. Traditional classification methods usually rely on genomic or proteomic information, while metabolomics-based classification studies are relatively limited. This example aims to utilize the viability-sensing single-cell metabolomics analysis method proposed in this invention to identify metabolic subtypes of six CRC cell lines and analyze the role of group reconstruction in improving classification performance.

[0034] During the experiment, a large number of single-cell metabolic profiles of six CRC cell lines were first collected. Dimensionality reduction and cluster analysis were then performed on all cells without distinguishing cell viability. The results showed that the distribution of different cell lines in the metabolic space was highly mixed, making it difficult to form a clear population structure, resulting in poor classification performance. (See appendix) Figure 5 (Left). This indicates that in the absence of viability information, the loss and significant attenuation of metabolites from dead cells significantly interfere with the true metabolic differences between cells, severely limiting the ability to distinguish cell subtypes based on the original metabolic profile. Subsequently, viability tags were introduced according to the method of this invention, and viable cells were identified using endogenous metabolite characteristics. Dead cells were then separated from the metabolic matrix, and subtype analysis was performed based on the viable cell metabolic profile. The results showed that the six CRC cell lines formed six independent and clearly defined distribution regions in the metabolic space, significantly improving the subtype distinguishing ability ( Figure 5 (In the middle). The reconstructed metabolic data accurately reflect the inherent metabolic characteristics of different cell lines, demonstrating that the method of this invention has a significant resolution advantage in complex multi-cell type systems.

[0035] Further analysis showed that the metabolic characteristics of live cells not only distinguished the six CRC cell lines, but also closely related to their clinically important microsatellite instability (MSI) status. The experimental results showed that the cell lines of MSI-High (MSI-H) type (such as RKO, LoVo, HCT116) and the cell lines of microsatellite stable (MSS) type (such as Colo205, SW620, HT29) were obviously separated in the live cell metabolic space, showing differences in metabolic pathways related to genomic stability. This shows that the metabolic information of live cells can reflect the deep differences in genetic background and signal pathways of cells, and has potential application value for the identification of CRC subtypes, disease stratification, and prediction of immune therapy response.

[0036] To further verify the interference effect of dead cells on metabolic characteristics, the metabolic data of dead cells of the six CRC cell lines were also analyzed in this embodiment. The results showed that in the data set composed of dead cells, different CRC cell lines could hardly be distinguished, showing a highly mixed metabolic distribution structure (Fig. 4B). Figure 5 Right). This phenomenon is consistent with the characteristics of metabolic outflow and metabolic pathway inactivation of dead cells after cell membrane damage, which masks the inherent metabolic differences between different cell types. Therefore, the live rate grouping reconstruction is crucial for maintaining the inherent metabolic characteristics of cells and improving the subtype identification ability.

[0037] This embodiment proves that the live rate-aware metabolic profiling method proposed in the present application can effectively improve the subtype discrimination ability of complex cancer cell systems. By reconstructing the single-cell metabolic matrix through grouping and analyzing only based on the metabolic profile of live cells, the detection ability of metabolic differences between CRC cells can be significantly enhanced, and the six CRC cell lines and their MSI status can be accurately distinguished, providing important technical support for cancer classification and precision treatment strategies based on metabolic characteristics.

[0038] Example 3To further verify the application potential of the method of the present application in drug action analysis and cell state stratification, this embodiment selects the colorectal cancer HCT116 cell widely used in chemotherapy research as the research object, and analyzes the multi-level metabolic response of the cell under the treatment of 5-fluorouracil (5-FU) (Fig. 5). Figure 6 a) FU is a commonly used anti-tumor drug in clinical practice, which mainly interferes with nucleotide synthesis and induces oxidative stress to cause cell death, and thus can be used as a typical model for evaluating the live rate-aware analysis ability of the present application.

[0039] In this embodiment, single-cell metabolomics data of HCT116 cells in untreated control group and 5-FU treated group were collected respectively to obtain metabolic profiles containing a large number of endogenous metabolites. First, the metabolic profiles of the two groups of cells were compared as a whole, and the results showed that compared with the control group, the cells after drug treatment showed significant differences in many types of metabolites, involving lipids, amino acids and many other metabolic categories, suggesting that drug treatment triggered extensive metabolic remodeling. Figure 6 b). Further metabolic annotation showed that many metabolites such as linoleic acid, arachidonic acid, glutamate, glutamate, etc. were significantly changed after drug treatment, indicating that 5-FU could intervene in multiple metabolic pathways.

[0040] Subsequently, the viability-aware analysis method of the present application was used to distinguish between live cells and dead cells and extract metabolic characteristics respectively. The analysis results showed that dead cells showed significant attenuation in many key metabolites, including glutathione, AMP, glutamate, isoleucine, etc. closely related to redox balance and energy metabolism, which indicated that drug-induced cell death would be accompanied by extensive metabolite reduction. Figure 6 c). If the metabolic data of dead cells is mixed with live cells for analysis, it will cause the true metabolic response to be significantly disturbed. Therefore, the method of the present application can effectively identify interference signals by grouping and reconstructing metabolic matrix subsets, ensuring the biological authenticity of the analysis results.

[0041] Further pattern recognition of the metabolic characteristics of live cells only showed that the live cell population could still be divided into two subpopulations with significantly different metabolic characteristics, reflecting the different adaptive strategies of cells under drug action. Figure 6 d). Analysis of the differential metabolic characteristics of the two subpopulations showed that they showed significant differences in redox metabolism, energy metabolism and other pathways, suggesting that live cells may adopt different metabolic coping strategies under the same drug pressure. Figure 6 e). The revelation of this internal heterogeneity not only reflects the complex and diverse adaptive mechanisms of cancer cells to chemotherapy drugs, but also demonstrates the advantages of the method of the present application in high-resolution detection of cell state changes.

[0042] This embodiment shows that the viability-aware single-cell metabolomics analysis method proposed by the present application can decompose the drug response process into multiple levels, including overall drug action, metabolic loss caused by dead cells, and metabolic heterogeneity within the live cell population. Using this hierarchical analysis strategy, the metabolic adaptation of tumor cells under chemotherapy pressure can be more accurately described, which is of great significance for understanding drug mechanisms, identifying drug resistance characteristics, and conducting individualized treatment research.

[0043] The embodiments of the present application are described above with reference to the drawings, and the embodiments and features in the embodiments of the present application can be combined with each other without conflict, and the present application is not limited to the above-described specific embodiments, and the above-described specific embodiments are only illustrative but not restrictive, and a person of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, and all belong to the protection of the present application.

Claims

1. A single-cell metabolomics analysis method with viability-awareness, comprising: step one: obtaining metabolic profile data of a plurality of single cells through a single-cell mass spectrometry platform to form an initial single-cell metabolic matrix, wherein the initial single-cell metabolic matrix comprises a plurality of single-cell samples and corresponding metabolite ion intensity features; step two: inputting single-cell viability labels provided by an external viability discrimination module into an analysis process, wherein the viability labels correspond to the single cells in the initial metabolic matrix one by one; step three: performing grouping reconstruction on the initial metabolic matrix according to the viability labels input in step two to divide the initial metabolic matrix into a live cell subset and a dead cell subset, and constructing a live cell metabolic matrix and a dead cell metabolic matrix, respectively; and step four: performing data processing and analysis operations on the single-cell metabolic matrix subsets, respectively, to output single-cell metabolic profile analysis results based on viability correction. The metabolic profile data comprises ion intensity information of a plurality of endogenous metabolites. The viability labels in step two are at least one of binary labels, probabilistic activity scores, or multi-level activity grade labels. In step three, statistics of the single-cell metabolic profile analysis results based on viability correction are obtained, and the statistics are used for quality control or subsequent report output. The grouping reconstruction process only filters the sample dimension of the initial metabolic matrix without changing the structure of the metabolite features.

2. The single-cell metabolome analysis method with viability sensing function according to claim 1, characterized in that: In step four, the data processing includes at least one of normalization, peak intensity filtering, missing value processing, and variance feature selection of the cell metabolic matrix.

3. The single-cell metabolome analysis method with viability sensing function according to claim 1, wherein: In step four, the metabolic analysis includes at least one of dimensionality reduction analysis, clustering analysis, and differential metabolism and pathway analysis.

4. The single-cell metabolome analysis method with viability sensing function according to claim 1, wherein: The method comprises at least one of cell subtype analysis, drug response evaluation, and cell state identification. The method comprises single-cell metabolomics analysis of human cell lines, wherein the human cell lines comprise at least one of HeLa cells, K562 cells, HepG2 cells, and colorectal cancer cell lines.

5. The single-cell metabolome analysis method with viability sensing function according to claim 1, wherein: The method is used for analyzing single-cell multi-level metabolic responses after drug treatment.

6. The single-cell metabolome analysis method with viability sensing function according to claim 5, wherein: The drug comprises 5-fluorouracil, and the multi-level metabolic responses comprise overall drug effects, metabolic loss caused by dead cells, and metabolic heterogeneity within a live cell population.

7. The single-cell metabolome analysis method with viability sensing function according to claim 1, wherein: The system comprises:

8. The single-cell metabolome analysis method with viability sensing function according to claim 1, wherein: a data acquisition module configured to obtain metabolic profile data of a plurality of single cells through a single-cell mass spectrometry platform to form an initial single-cell metabolic matrix; 9. The single-cell metabolome analysis method with viability sensing function according to claim 1, wherein: a label input module configured to receive single-cell viability labels provided by an external viability discrimination module and to correspond the viability labels to single-cell samples in the initial metabolic matrix one by one; a grouping reconstruction module configured to reconstruct the initial metabolic matrix according to the viability labels to construct a live cell metabolic matrix and a dead cell metabolic matrix, respectively; 10. A single-cell metabolome analysis system with viability sensing function based on the single-cell metabolome analysis method with viability sensing function according to any one of claims 1-9, characterized in that: a metabolic analysis module configured to perform data processing and analysis operations on single-cell metabolic matrix subsets, respectively, to output analysis results. ​ ​ ​ ​

Images