Method and system for detecting cell senescence based on multi-omics metabolites

Abstract

The present application relates to the technical field of biomedical engineering industry, and particularly relates to a cell aging detection method and system based on multi-omics metabolites. The present application obtains multi-omics detection information of a cell sample, and according to the time sequence change similarity of the expression information of molecules between different omics, all stable associated molecule pairs are screened from different omics corresponding molecule pairs. Then the expression activity coefficient of each functional module in each omics is determined, and the expression correlation coefficient between the functional modules of different omics is further obtained. Finally, a multi-omics correlation network is constructed and the aging state of the cell sample is evaluated. The present application deeply integrates multi-omics data, screens the correlation of cross-omics molecules based on time sequence linkage constraints, divides the high-dimensional data in the omics into functional modules with biological significance, reduces the data complexity, constructs a multi-omics correlation network, and thus facilitates the mining of the synergistic regulation mechanism between different omics, and accurately evaluates the cell aging state.

Description

Technical Field

[0001] This invention relates to the field of biomedical engineering technology, specifically to a method and system for detecting cell senescence based on multi-omics metabolites. Background Technology

[0002] Cellular senescence detection is crucial for disease prevention, therapeutic development, regenerative medicine, and basic research. Metabolites provide immediate phenotypic reflections within cells and are highly sensitive to aging-related pathways, serving as a rapid and highly functional layer of biomarkers. Integrating metabolomics with transcriptomics and proteomics to detect cellular senescence using multi-omics technologies can link gene regulatory mechanisms with metabolic functional outputs, helping to deepen our understanding of the multi-layered mechanisms of aging and providing comprehensive insights.

[0003] However, in multi-omics detection of cellular senescence, the data dimensions of different omics vary greatly. For example, single-cell metabolomics data typically contains hundreds of metabolites, while transcriptomics data can reach tens of thousands of gene transcripts. When performing multi-omics integrated analysis, key signals within some low-dimensional omics can easily be diluted or ignored. Traditional dimensionality reduction methods such as LASSO can help screen important features, but in metabolic networks, many metabolites only reflect senescence status when they change synergistically. Single feature selection may miss these combined signals, leading to incomplete identification of senescence biomarkers and consequently poor detection results for cellular senescence. Summary of the Invention

[0004] To address the technical problem of unsatisfactory detection results for cell senescence, the present invention aims to provide a method and system for detecting cell senescence based on multi-omics metabolites, the specific technical solution of which is as follows:

[0005] A method for detecting cell senescence based on multi-omics metabolites, the method comprising:

[0006] Obtain multi-omics detection information from cell samples, including the expression information of each molecule in each omics at each observation time.

[0007] Based on the similarity of temporal changes in the expression information of molecules across different omics, all stable associated molecule pairs are screened from the corresponding molecule pairs of different omics. Within each omics, molecules are grouped based on the similarity of molecular functions to determine all functional modules, and the expression activity coefficient of the corresponding functional module is obtained based on the temporal changes in the expression information of each molecule in each functional module.

[0008] Between different omics functional modules, based on the molecular distribution of all corresponding stable associated molecule pairs and the expression activity coefficient of each functional module, combined with the temporal change characteristics of the expression information of molecules in each stable associated molecule pair, the expression association coefficient is obtained.

[0009] A multi-omics association network is constructed based on the expression correlation coefficients between functional modules of different omics, and the aging status of cell samples is evaluated based on the multi-omics association network.

[0010] Furthermore, the omics includes at least transcriptomics, metabolomics, and proteomics.

[0011] Furthermore, the method for obtaining the stable correlated molecule pairs includes:

[0012] In each omics, the expression information of each molecule is standardized and used as vector elements, and sorted according to the temporal order of the observation time to construct a molecular expression vector; taking any omics as the target omics, the expression vector of any molecule in the target omics is used as the target vector, and the expression vector of each molecule in the non-target omics is used as the reference vector.

[0013] Based on the DTW algorithm, target vectors and reference vectors are matched and vector similarity is obtained. Based on the vector similarity, all candidate molecular pairs are selected from the molecular pairs corresponding to the target vector and each reference vector.

[0014] Based on the temporal differences between the matching vector elements in the two vectors corresponding to each candidate molecule pair, and the temporal change rules of the corresponding molecules of the candidate molecule pair during aging, all associated molecule pairs are screened from all candidate molecule pairs.

[0015] For each associated molecule pair, the molecular cooperative change parameter at each observation time is obtained based on the difference between vector elements in the molecular expression vectors of different molecules at adjacent observation times. When the molecular cooperative change parameter is greater than a preset threshold for a consecutive preset number of observation times, the associated molecule pair is considered a stable associated molecule pair.

[0016] Furthermore, the method for obtaining the associated molecule pairs includes:

[0017] The temporal linkage chains of cross-omics molecules in all aging-related pathways are obtained based on the biological pathway database. For each candidate molecule pair, the main temporal linkage direction of the candidate molecule pair is determined according to the distribution characteristics of the temporal linkage direction of all DTW pairing elements between the expression vectors of the two corresponding molecules. When the main temporal linkage direction is consistent with the temporal linkage direction of the corresponding cross-omics molecule in the temporal linkage chain, the candidate molecule pair is determined to be an associated molecule pair.

[0018] Furthermore, the method for obtaining the molecular cooperative change parameters includes:

[0019] For each molecule in each associated molecule pair, the difference between the vector elements corresponding to each observation time and the previous adjacent observation time in the corresponding molecule expression vector is used as the change parameter at each observation time.

[0020] At each observation time, the ratio of the change parameters of the two molecules in the associated molecular pair is taken as the molecular cooperative change parameter, wherein the change parameter of the molecule that is earlier in the time sequence in the associated molecular pair is the denominator in the fractional ratio.

[0021] Furthermore, the method for obtaining the expression activity coefficient includes:

[0022] The temporal inflection point in the expression vector of each molecule is taken as the function loss critical point of the corresponding molecule, and the expression information at the function loss critical point is taken as the activity parameter of the corresponding molecule; in each functional module, the activity set parameter is obtained based on the concentrated characteristics of the time corresponding to the function loss critical point of all molecules.

[0023] The activity parameters and the activity set parameters are combined to obtain the activity weight of each molecule; in each functional module, the corresponding expression information is weighted and averaged using the activity weight of each molecule, and the weighted average result is used as the expression activity coefficient of the corresponding functional module.

[0024] Furthermore, the method for obtaining the correlation coefficient includes:

[0025] Based on the distribution of molecules involved in all stable associated molecular pairs between functional modules of different omics, and the temporal linkage direction of the functional loss critical point of the two molecules in each stable associated molecular pair, the functional association coefficient between functional modules of different omics is obtained.

[0026] Based on the distribution characteristics of the expression activity coefficients of the functional modules of different omics, the activity correlation coefficient is determined; the expression correlation coefficient is obtained by fusing the activity correlation coefficient and the functional correlation coefficient.

[0027] Furthermore, the method for obtaining the functional correlation coefficient includes:

[0028] The proportion of molecules involved in all stable associated molecular pairs between functional modules of different omics is used as the functional association weight. The mean value of the activity parameters of the involved molecules is weighted by the functional association weight to obtain the initial functional association coefficient.

[0029] Between functional modules of different omics, the temporal linkage direction of the functional loss critical point of the two molecules in each stable associated molecular pair is determined, and stable associated molecular pairs that are inconsistent with the temporal linkage direction of the temporal linkage chain are screened out. According to the time interval of the functional loss critical point of the two molecules in the screened stable associated molecular pairs, a penalty weight is obtained. The initial functional association coefficient is weighted using the penalty weight to obtain the functional association coefficient.

[0030] Furthermore, methods for assessing the senescence status of cell samples include:

[0031] Using any functional module as the target module, and each functional module of the other omics as a reference module, strongly correlated modules are selected from all reference modules based on the expression correlation coefficient; a multi-omics association network is constructed based on all strongly correlated modules corresponding to each functional module; and the aging status of cell samples is assessed based on the multi-omics association network and preset aging biomarkers.

[0032] A cell senescence detection system based on multi-omics metabolites includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the cell senescence detection method based on multi-omics metabolites.

[0033] The present invention has the following beneficial effects:

[0034] This invention acquires multi-omics detection information from cell samples. Based on the similarity of temporal changes in molecular expression information across different omics, it performs initial screening of molecular pairs across different omics using temporal linkage constraints to identify all stable associated molecular pairs. Within each omics, molecules are grouped based on functional similarity characteristics to determine all functional modules, reducing analytical complexity. The expression activity coefficient of each functional module is obtained based on the temporal changes in the expression information of each molecule within that module. Then, between functional modules of different omics, expression association coefficients are obtained based on the molecular distribution of all stable associated molecular pairs, the expression activity coefficient of each functional module, and the temporal changes in the expression information of molecules within each stable associated molecular pair. Finally, a multi-omics association network is constructed based on the expression association coefficients between functional modules of different omics, and the aging state of cell samples is assessed based on this multi-omics association network. This invention deeply integrates multi-omics data, performs temporal linkage-based association screening of cross-omics molecules, and divides high-dimensional data in omics into biologically meaningful functional modules to reduce data complexity and construct multi-omics association networks. This facilitates the discovery of synergistic regulatory mechanisms between different omics and accurately assesses cellular senescence status. Attached Figure Description

[0035] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating a method for detecting cell senescence based on multi-omics metabolites, provided in one embodiment of the present invention;

[0037] Figure 2 This is a flowchart illustrating a method for obtaining stable correlated molecule pairs according to an embodiment of the present invention.

[0038] Figure 3 This is a flowchart illustrating a method for obtaining correlation coefficients according to an embodiment of the present invention. Detailed Implementation

[0039] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a cell senescence detection method and system based on multi-omics metabolites proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0041] The following description, in conjunction with the accompanying drawings, details a specific scheme for a cell senescence detection method and system based on multi-omics metabolites provided by the present invention.

[0042] Please see Figure 1 The diagram illustrates a flowchart of a cell senescence detection method based on multi-omics metabolites provided by an embodiment of the present invention, specifically including:

[0043] Step S1: Obtain multi-omics detection information of cell samples. The multi-omics detection information includes the expression information of each molecule in each omics at each observation time.

[0044] First, cell samples at a specific aging stage are prepared, and multi-omics detection information of the cell samples is obtained, including at least transcriptomics, metabolomics, and proteomics; this is already an existing technology, and the general acquisition process is described here:

[0045] Cell samples at specific aging stages are subjected to a rapid quenching step to fix their metabolic and transcriptional states. The rapid quenching is achieved by directly adding cold methanol solution to the cell suspension. After quenching, the samples are immediately subjected to mechanical homogenization or ultrasonic homogenization to uniformly disperse the cell components in the buffer medium to obtain a homogenate. In the case of single-cell detection, single-cell separation should be performed before quenching, and the processing steps of different single-cell populations should be performed synchronously as much as possible to reduce changes in metabolic state caused by time delay.

[0046] Samples were collected at multiple key time points after induction, namely observation times (0h, 12h, 24h, 36h, 48h, 60h, and 72h). Three biological replicates were set up at each observation time. The resulting homogenates were aliquoted into three types of functional assay portions: metabolite extract portion for metabolomics detection; nucleic acid extract portion for transcriptomics detection; and protein and its post-translational modifications extract portion for proteomics detection. For samples at each observation time, metabolomics, transcriptomics, and proteomics data were collected simultaneously.

[0047] Metabolomics data acquisition: An optimized single-cell extraction method was used, and cells were rapidly lysed with a low-temperature extraction solution to ensure that metabolites were not degraded during the extraction process. The extracted metabolite samples were immediately analyzed by LC-MS. By checking the stability of the total ion chromatogram, the reproducibility of the retention time, and the response intensity of the internal standard, unqualified samples were removed, and the abundance value of each metabolite was obtained.

[0048] Transcriptome data acquisition: Using single-cell RNA sequencing technology, such as the 10xGenomics platform, tens of thousands of RNA expression levels are acquired within the same cell population; quality filtering is also required to remove low-quality reads and samples with an alignment rate below 80%.

[0049] Proteomics data acquisition: Using mass spectrometry-based proteomics technologies, such as SILAC labeling, proteins were extracted and separated from the same cell samples; the quality of the mass spectrometry data was evaluated using a high-resolution mass spectrometer, including the number of peptides identified, protein identification coverage, and quantification accuracy, and samples with fewer than 500 peptides identified and less than 30% protein identification coverage were removed; the detection data were matched with a protein database to confirm the protein identity of the peptides and to calculate the corresponding abundance values;

[0050] The multi-omics detection information includes the expression information of each molecule in each omics at each observation time; among them, the molecules in metabolomics are metabolites, and the expression information is the abundance value of the metabolites; the molecules in transcriptomics are RNA, and the expression information is the expression level; the molecules in proteomics are proteins, and the expression information is the abundance value of the proteins.

[0051] In another embodiment of the present invention, preliminary dimensionality reduction of the transcriptome data can also be performed:

[0052] Combining prior biological knowledge, β-galactosidase (SA-β-gal) activity was used as a quantitative indicator of cellular senescence phenotypes. Before multi-omics testing of each sample, live cells were stratified for SA-β-gal phenotype using a cleavable fluorescent substrate. Specific inhibitors were added and immediately quenched after sorting, allowing stratified samples to undergo simultaneous preparation in the metabolic / transcriptional / protein pathways. The source of SA-β-gal activity was distinguished from total β-gal activity using a pH-specific control. The fluorescence intensity, population positive rate, and enzyme activity rate of the lysate were normalized to form an activity phenotype vector, and the vector modulus was used as the SA-β-gal activity of the current sample.

[0053] The correlation between each gene and SA-β-gal activity was calculated using Pearson correlation coefficient. An empirical correlation threshold of 0.35 was set, and genes with an absolute correlation coefficient greater than or equal to the empirical correlation threshold were retained. Based on prior biological knowledge, key genes involved in aging pathways were screened, and functionally important genes were manually retained. Genes that were not retained were removed to reduce the matrix dimension.

[0054] It should be noted that the above operations are all existing technologies, and the detailed process will not be described in detail.

[0055] Step S2: Based on the similarity of temporal changes in the expression information of molecules among different omics, screen all stable associated molecule pairs from the corresponding molecule pairs of different omics; in each omics, group molecules based on the similarity of molecular functions, determine all functional modules, and obtain the expression activity coefficient of the corresponding functional module based on the temporal changes in the expression information of each molecule in each functional module.

[0056] Given the differences in the order of magnitude and distribution characteristics of data from different omics, direct association analysis may lead to bias towards high-dimensional transcriptional data due to inconsistent data scales, thus masking the synergistic effects of metabolites and proteins. In addition, cellular senescence involves a temporal chain of metabolic perturbation → gene expression changes → protein abundance adjustment. This feature can be used to add time constraints to the biological priors to screen for metabolite-gene-protein associations with temporal consistency, eliminate spurious associations caused by temporal mismatches, and improve the specificity of the initial association screening.

[0057] Based on this, the embodiments of the present invention first screen all stable associated molecule pairs from the corresponding molecule pairs of different omics according to the similarity of temporal changes in the expression information of molecules among different omics; the molecules in the stable associated molecule pairs have a biologically significant synergistic change relationship in the time dimension, which prepares for the subsequent construction of a multi-omics association network to assess the aging state.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining stable correlated molecule pairs includes:

[0059] Please see Figure 2 The flowchart illustrates a method for obtaining stable correlated molecule pairs according to an embodiment of the present invention, specifically including:

[0060] Step S201: In each omics, the expression information of each molecule is standardized and used as vector elements, and sorted according to the temporal order of the observation time to construct a molecular expression vector; taking any omics as the target omics, the expression vector of any molecule in the target omics is used as the target vector, and the expression vector of each molecule in the non-target omics is used as the reference vector.

[0061] Specifically, the expression information of molecules is Z-score standardized to bring the data from the three omics systems to the same numerical scale. For each molecule in each omics system, the standardized molecular expression information is used as vector elements to construct a molecular expression vector of the form (Y1, Y2, ..., Yn), where n is the sequence number of the observation time and Yn is the standardized molecular expression information at the nth observation time. Target vectors and reference vectors are then determined to prepare for the subsequent identification of stable associated molecule pairs.

[0062] Step S202: Match the target vector and the reference vector based on the DTW algorithm and obtain the vector similarity. Based on the vector similarity, select all candidate molecule pairs from the molecule pairs corresponding to the target vector and each reference vector.

[0063] Dynamic Time Warping (DTW) is an algorithm commonly used for time series data alignment and similarity calculation. By matching and pairing target vectors with cross-omics molecules in reference vectors, molecular pairs can be identified. This facilitates subsequent evaluation of whether the molecular pairs exhibit aging-related temporal changes by combining the temporal chain of metabolic perturbations → gene expression changes → protein abundance adjustments associated with cellular senescence.

[0064] Specifically, all matching pairs between the target vector and the reference vector can be determined based on the DTW algorithm, and the DTW similarity between the target vector and the reference vector, i.e., the vector similarity, can be calculated. Then, the vector similarity is sorted in descending order to construct a sorted sequence. The sorted sequence is subjected to first-order difference, and all reference vectors before the reference vector corresponding to the larger sequence element in the largest difference value are taken as candidate pairs to be associated with the target vector.

[0065] Step S203: Based on the temporal differences between the matching vector elements in the two vectors corresponding to each candidate molecule pair, and the temporal change rules of the corresponding molecules of the candidate molecule pair during aging, all associated molecule pairs are screened from all candidate molecule pairs.

[0066] Since the temporal association rules of cross-omics molecules in aging-related pathways are ordered, the temporal association rules of candidate association pairs are verified by combining the DTW matching results of the candidate association pairs, thereby screening out all associated molecule pairs.

[0067] In a preferred embodiment of the present invention, the method for obtaining associated molecule pairs includes:

[0068] The temporal linkage chains of cross-omics molecules in all aging-related pathways are obtained based on the biological pathway database. For each candidate molecule pair, the main temporal linkage direction of the candidate molecule pair is determined according to the distribution characteristics of the temporal linkage direction of all DTW pairing elements between the expression vectors of the two corresponding molecules. When the main temporal linkage direction is consistent with the temporal linkage direction of the corresponding cross-omics molecule in the temporal linkage chain, the candidate molecule pair is determined to be an associated molecule pair.

[0069] It should be noted that the specific extraction of temporal association rules (such as the temporal linkage of metabolite A → transcript B → protein C) from the KEGG and Reactome databases to establish a temporal association rule library containing the temporal linkages of cross-omics molecules in all aging-related pathways is an existing technology, and the specific construction process will not be described in detail here.

[0070] Since the molecular expression information of different omics in each matching pair determined by the DTW algorithm may correspond to different observation times and there is a temporal sequence (the essence of a matching pair is a molecular pair, but it is manifested as the pairing of expression information of molecules in the molecular pair at different observation times), the temporal linkage direction of the matching pair can be determined based on the observation time.

[0071] For example, if a candidate molecule pair is metabolite A-transcription product B, where the molecular expression vectors corresponding to metabolite A are (A1, A2, ..., An) and the molecular expression vectors corresponding to transcription product B are (B1, B2, ..., Bn), and a matching pair A1-B2 conforms to the temporal linkage relationship of metabolite A → transcription product B, then the temporal linkage direction of the matching pair A1-B2 is set as positive; conversely, if the temporal linkage direction of the matching pair A5-B3 is negative.

[0072] The temporal linkage direction of all matching pairs in the candidate molecular pairs is determined, and the temporal linkage direction with the highest frequency is taken as the main temporal linkage direction. When the sequential main temporal linkage direction conforms to the directional characteristics of the temporal linkage chain, the candidate molecular pair is determined to be an associated molecular pair. It should be noted that when the main temporal linkage direction cannot be determined, the candidate molecular pair is determined not to be an associated molecular pair.

[0073] Step S204: For each associated molecule pair, obtain the molecular cooperative change parameter at each observation time based on the difference between vector elements in the molecular expression vectors of different molecules at adjacent observation times. When the molecular cooperative change parameter is greater than a preset threshold for a preset number of consecutive observation times, the associated molecule pair is regarded as a stable associated molecule pair.

[0074] Furthermore, considering the synergistic changes in the molecular expression levels of different molecules in the associated molecular pair, it can help verify the functional coupling relationship of different omics molecules, further uncover causal clues from time-series data, and avoid spurious associations. Based on this, we can obtain the molecular synergistic change parameters of each associated molecular pair at each observation time. When the molecular expression levels show certain synergistic changes at consecutive observation times, it indicates that the more stable the temporal association changes, the more likely it is to be a stable associated molecular pair.

[0075] In a preferred embodiment of the present invention, the method for obtaining molecular cooperative change parameters includes:

[0076] For each molecule in each associated molecule pair, the difference between the vector elements corresponding to each observation time and the previous adjacent observation time in the corresponding molecule expression vector is used as the change parameter at each observation time.

[0077] At each observation time, the ratio of the change parameters of the two molecules in the correlated molecular pair is taken as the molecular cooperative change parameter, where the change parameter of the molecule that comes first in the time sequence in the correlated molecular pair is the denominator in the fractional ratio.

[0078] As an example, taking the associated molecular pair (metabolite A - transcript B) as an example, the molecular co-variation parameters at the i-th observation time. Among them B i For the expression information of transcription product B at observation time i, B i-1 For the expression information of transcription product B at observation time i-1, A i For the expression information of metabolite A at observation time i, A i-1 This provides information on the expression of metabolite A at observation time i; B i -B i-1 Let A be the parameter representing the change of transcription product B at observation time i. i -A i-1 Let be the parameter representing the change of metabolite A at observation time i;

[0079] The molecular co-change parameter reflects the co-change slope of molecules in the associated molecular pair. When the value is positive and greater than the preset threshold, it indicates that the two molecules are changing in the same direction. When the value is positive and the absolute value is greater than the preset threshold, it indicates that they are changing in opposite directions. The preset threshold is set to the vector similarity of the associated molecular pair, and the preset number is set to 2. When the molecular co-change parameter of the associated molecular pair is greater than the preset threshold for at least two consecutive observation times, the associated molecular pair is considered a stable associated molecular pair.

[0080] At this point, the associated molecular pairs have been identified.

[0081] Considering that genes typically function through functional modules such as pathways and complexes, and that genes within the same module jointly regulate metabolites or proteins, grouping genes by functional modules and associating them with metabolites and proteins at the module level can balance dimensional differences and highlight synergistic effects. Metabolites and proteins can be similarly grouped to determine their corresponding functional modules. Based on this, this embodiment of the invention first determines all functional modules in each omics based on molecular functional similarity characteristics. Reducing the dimensionality of high-dimensional data in omics to biologically meaningful modules can reduce data complexity and facilitate subsequent exploration of synergistic regulatory mechanisms between different omics.

[0082] Identifying all functional modules based on molecular functional similarity is an existing technique; the general process is briefly described here:

[0083] Based on the Gene Ontology database, metabolites are functionally annotated, and transcripts are divided into several functional modules according to functional similarity. Each module contains genes with the same function.

[0084] Using metabolic pathway databases such as KEGG and MetaCyc, metabolites are divided into several functional modules according to the pathways they participate in.

[0085] Using databases such as UniProt and STRING, proteins are divided into several functional modules according to the signaling pathways they participate in.

[0086] Once all functional modules in each omics are identified, the expression activity coefficient of the corresponding functional module can be obtained based on the temporal changes in the expression information of each molecule in each functional module. The expression activity coefficient reflects the overall activity level of the corresponding functional module in the cellular senescence process, preparing for subsequent assessment of cellular senescence.

[0087] Preferably, in one embodiment of the present invention, considering that aging is not a uniform process, but rather accompanied by the phased collapse of functional modules, the temporal inflection point of expression information can reflect the functional collapse characteristics of molecules; and when the time corresponding to the functional loss critical points of all molecules with similar functions in a functional module is more synchronously concentrated, it indicates that these molecules are co-responding to a certain aging stimulus or regulatory event; thus, the expression activity of the functional module can be evaluated by combining the expression information of all molecules in the functional module; therefore, the method for obtaining the expression activity coefficient includes:

[0088] The temporal inflection point in the expression vector of each molecule is taken as the function loss critical point of the corresponding molecule, and the expression information at the function loss critical point is taken as the activity parameter of the corresponding molecule; in each functional module, the activity set parameter is obtained based on the concentrated characteristics of the time corresponding to the function loss critical points of all molecules.

[0089] The activity weight of each molecule is obtained by fusing activity parameters and activity concentration parameters. In each functional module, the corresponding expression information is weighted and averaged using the activity weight of each molecule, and the weighted average result is used as the expression activity coefficient of the corresponding functional module.

[0090] As an example, the expression information in the molecular expression vector of each molecule can be sorted and fitted into a time-series curve to obtain the time-series inflection point (a well-known technique, the detailed process will not be elaborated here). The time-series inflection point is used as the critical point of loss of function of the molecule, and the corresponding expression information is used as the activity parameter of the corresponding molecule. Then, the variance of the time corresponding to the loss of function critical point can be mapped to a negative exponential function with the natural constant e as the base to measure the ensemble features and obtain the activity ensemble parameters.

[0091] Furthermore, in each functional module, the activity parameter of each molecule is multiplied and fused with the activity set parameter of the functional module, and the fusion result is linearly normalized to obtain the activity weight of each molecule; then, the activity weight of each molecule is used to perform weighted averaging on the corresponding expression information to obtain the expression activity coefficient of the corresponding functional module.

[0092] Step S3: Among the functional modules of different omics, based on the molecular distribution of all corresponding stable associated molecular pairs and the expression activity coefficient of each functional module, and combined with the temporal change characteristics of the expression information of molecules in each stable associated molecular pair, the expression association coefficient is obtained.

[0093] Since cellular senescence is an orderly breakdown of multiple functional modules rather than a change in a single molecule, it is necessary to analyze the strength of the associations between functional modules of different omics. According to the temporal chain analysis of metabolic perturbation → gene expression change → protein abundance adjustment in cellular senescence, the more stable molecular pairs exist between functional modules of different omics, and the more obvious the activity expression level of the molecules in the stable molecular pairs in the corresponding functional modules, the stronger the association between the two functional modules. At the same time, the more the functional loss critical point of the molecules in the stable molecular pairs conforms to the prior temporal relationship, the stronger the mutual association between the two functional modules.

[0094] Based on this, in this embodiment of the invention, the expression correlation coefficient is obtained between functional modules of different omics, according to the molecular distribution of all corresponding stable associated molecular pairs and the expression activity coefficient of each functional module, combined with the temporal change characteristics of the expression information of molecules in each stable associated molecular pair. The expression correlation coefficient reflects the correlation strength between functional modules of different omics, and prepares for the subsequent construction of multi-omics association network.

[0095] Preferably, in one embodiment of the present invention, the method for obtaining the correlation coefficient includes:

[0096] Please see Figure 3 The flowchart illustrates a method for obtaining an expression correlation coefficient according to an embodiment of the present invention, specifically including:

[0097] Step S301: Based on the distribution of molecules involved in all stable associated molecular pairs between functional modules of different omics, and the temporal linkage direction of the functional loss critical point of the two molecules in each stable associated molecular pair, obtain the functional association coefficient between functional modules of different omics.

[0098] In a preferred embodiment of the present invention, the method for obtaining the functional correlation coefficient includes:

[0099] The proportion of molecules involved in all stable associated molecular pairs between functional modules of different omics is used as the functional association weight. The initial functional association coefficient is obtained by weighting the mean of the activity parameters of the involved molecules using the functional association weight.

[0100] Between functional modules of different omics, the temporal linkage direction of the functional loss critical point of the two molecules in each stable associated molecular pair is determined, and stable associated molecular pairs that are inconsistent with the temporal linkage direction of the temporal linkage chain are screened out. Based on the time interval of the functional loss critical point of the two molecules in the screened stable associated molecular pairs, a penalty weight is obtained. The initial functional association coefficient is weighted using the penalty weight to obtain the functional association coefficient.

[0101] As an example, firstly, all stable associated molecular pairs are screened out from all molecular pairs between functional modules of different omics (determined in step S2), and the proportion of molecules in the stable associated molecular pairs in the molecules of the two functional modules is used as the functional association weight; the mean of the activity parameters of all molecules involved in all stable associated molecular pairs is multiplied by the functional association weight to obtain the initial functional association coefficient.

[0102] Then, the temporal linkage direction of the functional loss critical point of the two molecules in each stable associated molecule pair is determined, and stable associated molecule pairs that are inconsistent with the temporal linkage direction of the temporal linkage chain are screened out. This is the same as the method in step S203 for determining the temporal linkage direction of the matching pair and determining whether it is consistent with the temporal linkage direction of the temporal linkage chain, and will not be repeated here.

[0103] The negative correlation mapping result of the time interval between the critical points of functional loss of the two molecules in the screened stable associated molecular pairs is used as the penalty weight. Specifically, the time interval can be mapped to a negative exponential function with the natural constant e as the base, or other negative correlation mapping methods can be used. The value range of the penalty weight is 0-1. The larger the time interval, the less the change in molecular expression in the stable associated molecular pair conforms to the temporal linkage characteristics of the temporal linkage chain, the lower the reference value, and the smaller the penalty weight. After weighting the initial functional association coefficient with the penalty weight, the greater the reduction of the initial functional association coefficient, the smaller the functional association coefficient is obtained.

[0104] Step S302: Among the functional modules of different omics, determine the activity association coefficient based on the distribution characteristics of the expression activity coefficient of the functional modules; fuse the activity association weight and the functional association coefficient to obtain the expression association coefficient.

[0105] Considering that between functional modules of different omics, the larger the expression activity coefficient of the functional module and the smaller the difference, the stronger and more similar the expression of the two functional modules in the aging process, and the higher the association strength should be;

[0106] Based on this, as an example, between functional modules of different omics, the expression activity coefficient of the functional module is used as the numerator of the fractional ratio, the absolute value of the difference in the expression activity coefficients of the functional modules is used as the denominator of the fractional ratio, and the fractional ratio is used as the activity association weight; then the activity association weight is multiplied and fused with the functional association coefficient to obtain the expression association coefficient between functional modules of different omics.

[0107] Step S4: Construct a multi-omics association network based on the expression correlation coefficients between functional modules of different omics, and evaluate the aging status of cell samples based on the multi-omics association network.

[0108] After obtaining the expression correlation coefficients between functional modules of different omics, a multi-omics association network can be constructed based on the expression correlation coefficients between functional modules of different omics, thereby assessing the aging status of cell samples.

[0109] Preferably, in one embodiment of the present invention, the method for assessing the aging state of a cell sample includes:

[0110] Using any functional module as the target module, and each functional module of the other omics as a reference module, strongly correlated modules are selected from all reference modules based on the expression correlation coefficient; a multi-omics association network is constructed based on all strongly correlated modules corresponding to each functional module; and the aging status of cell samples is assessed based on the multi-omics association network and preset aging biomarkers.

[0111] As an example, the expression correlation coefficient between the target module and each reference module is determined. These coefficients are then sorted in descending order to construct a ranking sequence. This ranking sequence is then subjected to first-order differencing. All reference modules preceding the reference module with the largest difference value are considered strongly correlated modules of the target module. Alternatively, the implementer can filter reference modules with high expression correlation coefficients to the target module by setting a threshold. By changing the target module, all strongly correlated modules of each functional module can be determined. Furthermore, a correlation chain is formed based on each functional module and its corresponding strongly correlated modules. Functional modules are considered network nodes, and correlation chains are considered topological connections between nodes, thus constructing a multi-omics correlation network. This multi-omics correlation network is the dimensionality reduction and integration result of multi-omics detection. It should be noted that constructing correlation networks based on known correlation chains is an existing technique and will not be elaborated upon further.

[0112] After constructing a multi-omics association network, it is possible to further analyze cellular senescence status by combining it with preset senescence biomarkers, thereby quantifying senescence levels and identifying key molecules. This is already an existing technology, and the general steps are briefly described below:

[0113] 1. Mapping aging features to association networks: Mapping known aging biomarkers, such as SA-β-gal activity, telomere length, or aging-related genes, to module nodes of multi-omics association networks;

[0114] 2. Module activity assessment: The activity of a module is measured by the expression activity coefficient of each functional module. The larger the expression activity coefficient, the more significantly the module is perturbed during the aging process.

[0115] 3. Time series trend integration: By combining stable molecular pairs and functional loss thresholds within modules, we analyze whether changes in module activity are consistent with biological prior sequences in the aging process (e.g., metabolic perturbation → transcriptional changes → protein abundance adjustment); by superimposing module activity analysis in the time dimension, we can identify early perturbed modules and late functionally collapsed modules, thereby constructing a module-level dynamic map of the aging process.

[0116] 4. Quantification of aging degree: Based on the activity of each module, the strength of the correlation between modules, and the number of time-stable correlation pairs within the module, the comprehensive aging index is calculated by multiplication and normalization. The aging index is used to quantify the aging degree of different samples or different time points. The higher the value, the higher the level of aging of the cells.

[0117] 5. Identification of key modules and core molecules: Through betweenness centrality network topology analysis, modules or molecular nodes that play a core role in the aging process are identified; core nodes may correspond to early perturbation metabolites, key regulatory genes or functional proteins, and serve as potential aging biomarkers or intervention targets.

[0118] Based on the same inventive concept, the present invention also proposes a cell senescence detection system based on multi-omics metabolites, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the cell senescence detection method based on multi-omics metabolites described in steps S1-S4.

[0119] In summary, this invention acquires multi-omics detection information from cell samples and, based on the similarity of temporal changes in molecular expression information across different omics, screens all stable associated molecule pairs from corresponding molecule pairs across different omics. Then, it identifies all functional modules within each omics and obtains the expression activity coefficient of each functional module. Furthermore, it obtains the expression correlation coefficients between functional modules of different omics. Finally, it constructs a multi-omics association network based on the expression correlation coefficients between functional modules of different omics and assesses the aging state of cell samples based on this multi-omics association network. This invention deeply integrates multi-omics data, performs temporal linkage-based association screening of cross-omics molecules, and divides high-dimensional data in omics into biologically meaningful functional modules, reducing data complexity and constructing a multi-omics association network. This facilitates the discovery of synergistic regulatory mechanisms between different omics and accurately assesses the aging state of cells.

[0120] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting cell senescence based on multi-omics metabolites, characterized by, The method comprises: Obtaining multi-omics detection information of a cell sample, the multi-omics detection information comprising expression information of each molecule in each omics at each observation time; wherein the molecules in the metabolomics are metabolites, and the expression information is the abundance value of the metabolites; the molecules in the transcriptomics are RNAs, and the expression information is the expression amount; the molecules in the proteomics are proteins, and the expression information is the abundance value of the proteins; According to the time sequence change similarity of the expression information of the molecules between different omics, all stable associated molecule pairs are screened from the corresponding molecule pairs of different omics; in each omics, the molecules are grouped based on the similar characteristics of the molecular functions, all functional modules are determined, and the expression activity coefficient of the corresponding functional module is obtained according to the time sequence change of the expression information of each molecule in each functional module; According to the molecular distribution of all the stable associated molecule pairs and the expression activity coefficient of each functional module between different omics, and combining the time sequence change characteristics of the expression information of the molecules in each stable associated molecule pair, an expression correlation coefficient is obtained. Based on the expression correlation coefficient between the functional modules of different omics, a multi-omics correlation network is constructed, and the aging state of the cell sample is evaluated based on the multi-omics correlation network. The method for obtaining the expression correlation coefficient comprises: In each omics, the expression information of each molecule is standardized as a vector element, and is sorted according to the time sequence order of the observation time to construct a molecule expression vector; the time sequence inflection point in each molecule expression vector is taken as the functional loss critical point of the corresponding molecule, and the expression information at the functional loss critical point is taken as the activity parameter of the corresponding molecule; According to the distribution of the molecules involved in all the stable associated molecule pairs between the functional modules of different omics, and the time sequence linkage direction of the functional loss critical points of the two molecules in each stable associated molecule pair, a functional correlation coefficient between the functional modules of different omics is obtained. According to the distribution characteristics of the expression activity coefficient of the functional modules, an activity correlation coefficient is determined between the functional modules of different omics; the activity correlation coefficient and the functional correlation coefficient are fused to obtain an expression correlation coefficient. The method for obtaining the functional correlation coefficient comprises: The number proportion of the molecules involved in all the stable associated molecule pairs between the functional modules of different omics in the functional modules is taken as a functional correlation weight, the mean value of the activity parameters of the involved molecules is weighted by using the functional correlation weight to obtain an initial functional correlation coefficient; The time sequence linkage direction of the functional loss critical points of the two molecules in each stable associated molecule pair is determined between the functional modules of different omics, and the stable associated molecule pairs whose time sequence linkage direction is inconsistent with the time sequence linkage direction of the time sequence linkage chain are screened out; a penalty weight is obtained according to the time interval of the functional loss critical points of the two molecules in the screened stable associated molecule pairs; the initial functional correlation coefficient is weighted by using the penalty weight to obtain a functional correlation coefficient.

2. The method of claim 1, wherein the method is a multi-omics based metabolite- based cell senescence detection method. The omics at least comprises transcriptomics, metabolomics and proteomics. 3.The multi-omics based metabolic marker for cell aging detection method according to claim 1, wherein, The method for obtaining the stable associated molecule pairs comprises: The target omics is any omics, any molecular expression vector in the target omics is a target vector, and each molecular expression vector in the non-target omics is a reference vector; The target vector and the reference vector are matched based on the DTW algorithm to obtain vector similarity, and all candidate molecular pairs are screened from the corresponding molecular pairs of the target vector and each reference vector according to the vector similarity; The time sequence difference between the matching vector elements in the two vectors corresponding to each candidate molecular pair is determined, and the time sequence change rule of the candidate molecular pair corresponding molecules in the aging process is determined, so as to screen all associated molecular pairs from all candidate molecular pairs. For each associated molecular pair, the molecular synergistic change parameter at each observation time is obtained according to the vector element difference between adjacent observation times in the molecular expression vectors corresponding to different molecules, and when the molecular synergistic change parameter at a continuous preset number of observation times is greater than a preset threshold, the associated molecular pair is taken as a stable associated molecular pair.

4. The method of claim 3, wherein the method is a multi-omic metabolite-based cell senescence detection method. The method for obtaining the associated molecular pair comprises: Based on the biological pathway database, the time sequence linkage chain of the cross-omics molecules in all aging-related pathways is obtained, and for each candidate molecular pair, the time sequence linkage main direction of the candidate molecular pair is determined according to the distribution characteristics of the time sequence linkage direction of all DTW pairing elements between the two molecular expression vectors corresponding to the candidate molecular pair, and when the time sequence linkage main direction is consistent with the time sequence linkage direction of the corresponding cross-omics molecules in the time sequence linkage chain, the candidate molecular pair is determined as an associated molecular pair.

5. The multi-omic metabolite-based cell senescence detection method of claim 3, wherein, The method for obtaining the molecular synergistic change parameter comprises: For each molecule in each associated molecular pair, the difference between the vector elements corresponding to each observation time and the adjacent previous observation time in the corresponding molecular expression vector is taken as the change parameter at each observation time. At each observation time, the ratio of the change parameters of the two molecules in the associated molecular pair is taken as the molecular synergistic change parameter, wherein the change parameter of the molecule with a time sequence in the associated molecular pair is taken as the denominator in the fractional ratio.

6. The multi-omic metabolite-based cell senescence detection method of claim 4, wherein, The method for obtaining the expression activity coefficient comprises: In each functional module, the activity concentration parameter of each molecule is obtained according to the concentration characteristics of the functional loss critical point corresponding time of all molecules; The activity weight of each molecule is obtained by fusing the activity parameter and the activity concentration parameter, and the expression activity coefficient of the corresponding functional module is obtained by using the activity weight of each molecule to weight and average the expression information in each functional module.

7. The multi-omic metabolite-based cell senescence detection method of claim 1, wherein, The method for evaluating the aging state of the cell sample comprises: Each functional module is taken as a target module, and each functional module of the remaining omics is taken as a reference module, and the strongly associated modules are screened from all reference modules based on the expression association coefficient; the multi-omics association network is constructed based on all strongly associated modules corresponding to each functional module; and the aging state of the cell sample is evaluated based on the multi-omics association network and the preset aging marker.

8. A cell senescence detection system based on multi-omic metabolites, characterized by, The computer program stored in the memory and executable on the processor realizes the steps of the cell aging detection method based on multi-omics metabolites according to any one of claims 1-7 when the processor executes the computer program.

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