622 results about "Single cell sequencing" patented technology

The invention discloses a space transcriptomics cell type deconvolution method based on a generative adversarial network and a comparative learning framework. The method comprises the following steps: firstly, enhancing a gene expression matrix by using a histological image and spatial position information; then, in combination with GraphSAGE and self-supervised comparative learning, optimizing feature representation of space transcriptomics data, and reconstructing high-quality space gene expression data; single-cell RNA sequencing data gene expression features are extracted by using a variational automatic encoder, and meanwhile, the quality of a generated sample is optimized by using a discriminator, so that efficient feature extraction and accurate gene expression reconstruction are realized; and finally, mapping single-cell RNA sequencing data into spatial points through comparative learning and mapping matrix learning based on low-rank representation, thereby predicting the cell type of each point. According to the model, multi-modal information in single-cell RNA sequencing and space transcriptomics data is comprehensively considered, and a mapping matrix is constructed and optimized, so that the cell type of each space point is predicted.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

A single cell sequencing data interpolation method based on a biclustering and graph convolutional neural network comprises the following steps: 1) respectively downloading matrix-form gene expression data of human and mouse from a gene database, and screening out cells of which the total number of gene expressions is less than 3 and genes of which the total number of cell expressions is less than 200; 2) performing feature selection on the gene expression matrix, finding out cells with consistent growth trend, forming a new two-dimensional matrix, and constructing a cell connection diagram; 3) constructing a BiGCN single-cell interpolation model by using a graph convolutional neural network, learning a nonlinear relationship between cells, dividing a data set into a training set and a test set, training the model by using the training set, and reserving the trained BiGCN single-cell interpolation model; 4) inputting the test set into the trained BiGCN single cell interpolation model to obtain an interpolation result, and evaluating the interpolation result by using an evaluation index; the method has the characteristics that the feature extraction capability is improved, interference of redundant zero expression is avoided, data sparsity is effectively relieved, and the interpolation accuracy is improved.

The invention discloses a combination of different nucleic acid aptamers with known 291 targets, sequences of the nucleic acid aptamers of the combination are subjected to sequencing analysis through a single-cell high-throughput sequencing technology, and a cell map aiming at peripheral blood-derived mononuclear cells of a biliary tract cancer patient can be constructed. The nucleic acid aptamers covered by the combination have high specific affinity to molecular targets of the nucleic acid aptamers, and after the two ends of the nucleic acid aptamer sequences extend to adapt to joints of a commercial sequencing platform, high-throughput sequencing is carried out on the nucleic acid aptamer sequences which are combined with the surfaces of mononuclear cells and are provided with the sequencing joints; high-throughput analysis of the biomarkers on the surface of the peripheral blood-derived mononuclear cell of a biliary tract cancer patient can be realized, so that the cell map of the peripheral blood-derived mononuclear cell of the biliary tract cancer is constructed.

The embodiment of the invention provides an annotation model training method, a cell type annotation method and related equipment, and belongs to the technical field of biological information analysis. The method comprises the following steps: inputting acquired spatial transcriptome data and single cell sequencing data into an initial annotation model; determining reference annotation information corresponding to each target single cell in each spatial site of the sample tissue data according to the single cell sequencing data by taking the spatial transcriptome data as a constraint condition; determining prediction annotation information corresponding to each target single cell based on the reference annotation information and the spatial transcriptome data; performing joint annotation processing on each target single cell according to the reference annotation information and the predicted annotation information to obtain a sample annotation result corresponding to the sample tissue data; and adjusting model parameters of the initial annotation model according to a sample annotation result to obtain a trained annotation model. The cell type annotation accuracy of the biological sample tissue can be improved.

The invention discloses a treatment response prediction method and system, a storage medium and a terminal, and the method comprises the steps: carrying out the heterogeneity analysis and cell division of first single cell sequencing data of a patient through employing a clustering algorithm, and obtaining a plurality of corresponding cell subgroups; performing feature extraction and biomarker recognition on all the cell subgroups to obtain corresponding cell subgroup features; wherein the cell subset characteristics comprise a cell development trajectory and a plurality of cell markers; and inputting the clinical information and the cell subset characteristics of the patient into the treatment response prediction model, so that the model performs multi-factor analysis and outputs a treatment response prediction result of the patient for each treatment scheme. By adopting the embodiment of the invention, the tiny key difference between the cells is captured, the cells are divided based on the difference between the cells, and then feature extraction and biomarker recognition are performed on each cell subset, so that the specific gene expression modes of different cell subpopulations corresponding to diseases can be recognized, and the treatment response prediction accuracy is further improved.

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.

The invention discloses a non-paired single cell multi-omics data gene regulatory network inference method, which comprises the following steps: collecting single cell sequencing non-paired data, and preprocessing the data; inputting the pre-processed single cell sequencing non-paired data into a multi-layer variational auto-encoder structure to generate advanced potential variables and reconstructed single cell sequencing non-paired data; training a layered GAN by adopting a layered adversarial alignment mechanism according to the advanced potential variables and the reconstructed single cell sequencing non-paired data; according to the advanced potential variables, adopting a mutual nearest neighbor strategy to train a mutual nearest neighbor module; fusing the advanced potential variables of different modes by adopting a gating fusion mechanism, constructing a unified advanced potential variable, and completing adaptive feature integration of the multi-mode advanced potential variables; according to prior information, an initial adjacency matrix is established, a gene regulation network is constructed in combination with unified advanced potential variables, and the gene regulation network with biological rationality is directly deduced from non-pairing input.

The invention discloses a gastric cancer multi-omics marker detection method, system and device, and the method comprises the following steps: S1, collecting a public database open-source space transcriptome, a single cell sequencing sample and bulk-RNAseq data for pre-processing, and collecting a primary tissue sample of a gastric cancer patient in the center for data processing; s2, integrating different modal data samples to obtain a patient label of an input end, and constructing a marker detection model and training the marker detection model; and S3, extracting a target feature value from the input external pathological section by using the trained model, and generating a diagnosis prediction result. Through multi-modal data chimerism, algorithm optimization and AI system development, subpopulation cell marker proportion prediction and prognosis diagnosis and marker evaluation with population prognosis information are realized, and an integrated diagnosis scheme for breaking through molecule-space-prognosis information is constructed.

The present invention relates to the field of tissue-derived cell identification, in particular to a method for identifying tissue-derived cells in the body fluid based on single-cell sequencing technology. In the present invention, on the basis of high-throughput single-cell RNA sequencing data of cell samples obtained from body fluids, by means of reference component analysis (RCA), expression of known tissue-related marker genes, and a tissue-derived cell prediction model constructed based on logistic regression, the specific identification of tissue-derived cells in body fluids is achieved.

The invention relates to the technical field of cell data analysis, and discloses a single-cell transcriptome data and text description conjoint analysis method based on a multi-modal language model, which comprises the following steps: acquiring a single-cell RNA sequencing expression matrix and a corresponding cell text description, preprocessing the single-cell RNA sequencing expression matrix and the corresponding cell text description, and analyzing the single-cell transcriptome data and the corresponding cell text description; according to the method, a multi-modal data set is constructed, deep fusion of gene expression data and text knowledge is realized by constructing a double-model and cross-modal projection module, limitation of a single mode is avoided, a gene expression value and an index sequence are reserved during preprocessing, a rough coding mode is changed, and the cell type identification accuracy is improved; based on a pre-training strategy of comparative learning, matching learning and a cross-modal projection module, fine-grained cross-modal information interaction and sharing are realized, and cross-modal task effects of text generation cells or cell generation texts and the like are optimized.

The invention relates to the technical field of bioinformatics, in particular to a data classification method and system for single cell sequencing. The method comprises the following steps: acquiring an original gene expression matrix; performing gene data preprocessing on the original gene expression matrix to obtain a preprocessed gene expression matrix; constructing a cell topological graph according to the preprocessed gene expression matrix to obtain the cell topological graph; performing cell division rate calculation according to the cell topological graph and the preprocessed gene expression matrix, and performing RNA rate calculation to obtain RNA rate data; adding node rates to the cell topological graph according to the cell division rate data and the RNA rate data to obtain an enhanced cell topological graph. According to the method, the dynamic graph is constructed and the change rule of the dynamic graph is analyzed, so that the dynamic change of the cell state is better captured, the sensitivity to parameters is reduced, a complex cell population structure is processed, and a quantitative cell heterogeneity evaluation index is provided.

The present invention discloses a combination of different nucleic acid aptamers with 291 known targets. By performing sequencing analysis on the nucleic acid aptamer sequences of this combination through single-cell high-throughput sequencing technology, a cell atlas targeting mononuclear cells derived from the peripheral blood of patients with cholangiocarcinoma can be constructed. The nucleic acid aptamers covered by this combination have high specific affinity for their molecular targets. After extending adapters commercialized for sequencing platforms at both ends of the nucleic acid aptamer sequences respectively, by performing high-throughput sequencing on the nucleic acid aptamer sequences with sequencing adapters bound to the surface of mononuclear cells, high-throughput analysis of biomarkers on the surface of mononuclear cells derived from the peripheral blood of patients with cholangiocarcinoma can be achieved, thereby constructing a cell atlas of mononuclear cells derived from the peripheral blood of cholangiocarcinoma.

The invention provides an immunotherapy reaction marker identification method and system based on a cooperative game theory, and relates to the technical field of intelligent medical treatment, and the method comprises the following steps: obtaining immunotherapy single cell sequencing data, and determining a candidate gene set through differential expression analysis; extracting regulation and control relation pairs of the candidate gene set to obtain a multi-level information gene regulation and control network; embedding the multilevel information gene regulation and control network by adopting a Node2vec algorithm to obtain a network module containing potential biological functions, calculating the contribution degree of each gene feature to model prediction based on a Myerson value, and obtaining a feature importance sequence; combining the optimal features of the modules into a global candidate set; and iteratively optimizing the global candidate set, and outputting a target immunotherapy reaction marker set. Through a Myerson value principle in a cooperative game, a biomarker set related to immunotherapy response is efficiently searched, deep analysis of a drug resistance mechanism is realized, the accuracy and robustness of marker screening are improved, and the method serves for cancer clinical scheme customization.

The invention discloses a single cell sequencing data clustering method and system based on similarity constraint, and the method comprises the steps: obtaining a Fastq sequencing file, carrying out the quantitative analysis and data preprocessing, and obtaining a preprocessed gene expression matrix; performing similarity priori knowledge and non-similarity priori knowledge extraction processing on the preprocessed gene expression matrix, and constructing a similarity matrix and a non-similarity matrix; constructing a joint non-negative matrix factorization model according to the similarity matrix and the non-similarity matrix, and performing dimension reduction processing to obtain a common coefficient matrix; and carrying out hierarchical clustering and visualization processing to obtain a single cell sequencing data clustering result. According to the method, similarity and non-similarity constraints of a plurality of gene expression matrixes are mined, and the feature extraction capability of the model is improved, so that a better single cell sequencing data clustering effect is realized. The single cell sequencing data clustering method and system based on similarity constraint can be widely applied to the technical field of single cell sequencing data clustering.

The invention discloses a single-cell RNA sequencing data classification method and system based on deep learning. The invention provides a multi-scale neural network based on an improved Transform. The multi-scale neural network is used for classifying preprocessed single-cell RNA (Ribonucleic Acid) sequencing data. The multi-scale neural network integrates a multi-scale attention mechanism, relative position coding and a gating feedforward network, not only can the multi-level dependency relationship of data be captured, but also noise interference can be effectively reduced through feature screening. Compared with a traditional convolutional neural network or a classical Transform, the multi-scale neural network provided by the invention shows stronger robustness and generalization ability in sequence modeling and classification tasks. Through wide tests on a plurality of public data sets, the proposed multi-scale neural network shows better performance in a cell category classification task.

The invention relates to a cell differentiation trajectory inference method based on deep hyperbolic manifold embedding. The method comprises the following steps: acquiring single-cell RNA sequencing data; introducing hyperbolic space embedding with negative curvature, performing feature extraction on the single-cell RNA sequencing data through a deep feature extraction network, and mapping extracted high-dimensional features to a low-dimensional hyperbolic space; through track inference in a low-dimensional hyperbolic space, transformation paths of cells from progenitor cells to various differentiation end cell types are constructed, and key genes and pathways are evaluated by utilizing a sensitivity analysis method. The method has the beneficial effects that by introducing hyperbolic space embedding with negative curvature, the model can better capture the hierarchical structure of data, and the accuracy and consistency of differentiation tracks are enhanced. Moreover, high-dimensional single-cell sequencing data can be processed, the limitation of a traditional method under the high-dimensional condition is overcome, and the applicability of the model is improved.

The invention discloses a self-supervised clustering method for massive single cell data. The method comprises the following steps: 1) downloading single cell RNA sequencing data from a disclosed gene expression database; 2) preprocessing the single-cell RNA-seq data, wherein the preprocessing comprises filtering, standardization and feature selection; (3) carrying out preliminary clustering on the preprocessed single cell RNA sequencing data by utilizing a rapid Louvain algorithm; 4) screening representative cell points from the preliminary clustering result by using an index of clustering representativeness; 5), after representative cell points are screened out from each preliminary cluster, a Transform model is used, and a final clustering result is obtained.According to the method, the rapid Louvain algorithm and the Transform neural network are combined, the efficiency and accuracy of clustering analysis are remarkably improved, and an efficient and accurate solution is provided for analysis of mass single cell data.

The invention discloses application of a cell surface adhesion molecule CD22 as a target spot in preparation of drugs for treating spinal cord injury related diseases. The change of gene expression in the glial scar formation process is represented by space transcriptome sequencing, and the specific expression of CD22 in the glial scar region is up-regulated. According to single cell sequencing, in-situ hybridization and immunohistochemistry, specific high expression CD22 of part of microglial cells in a glial scar area is found. CD22 is knocked out through a genetic means, and it is found that formation of glial scars after spinal cord injury is remarkably reduced through inhibition of CD22. Behavioral detection finds that the error rate of irregular horizontal ladders is remarkably reduced by inhibiting CD22, and fine movement recovery of hind limbs of mice after spinal cord injury is promoted. The siRNA specifically targeting CD22 is injected into the sheath, so that the expression of microglial cells CD22 is inhibited, the error rate of irregular horizontal ladders of hind limbs can be reduced, and the recovery of the fine movement function of the hind limbs of the mouse after spinal cord injury is promoted. The invention provides a new possibility for development of drugs for spinal cord injury and treatment of spinal cord injury.

The invention relates to the technical field of biological computing, in particular to a single-cell Hi-C atlas interpolation method and device, and the method comprises the steps: obtaining single-cell sequencing library data and a reference genome, determining the position of the sequencing data in the genome, and carrying out the statistics of the interaction frequency to obtain a Hi-C contact matrix; performing interpolation on the Hi-C contact matrix of any chromatin of the single cell according to similarity of adjacent nodes to obtain an adjacent node interpolation map of the single chromatin of the single cell; calculating an interpolation map of each chromatin of each cell, performing representation learning by taking the interpolation map as an adjacent matrix, splicing vectors to obtain a cell pre-embedded vector, and performing dimensionality reduction to obtain a whole cell embedded vector matrix; a cellular level similarity matrix is calculated by using a single cell embedded vector matrix and converted into a Markov probability transfer matrix, and the Markov probability transfer matrix is used for correcting an adjacent node interpolation map to obtain a Hi-C interpolation map, so that the interpolation accuracy is improved.

The invention provides a full-length gene sequence modeling method and system based on a neural network, and the method comprises the steps: constructing a first expression matrix for initial single-cell RNA sequencing data, and carrying out the quality control transformation of the first expression matrix to obtain a second expression matrix; inputting the second expression matrix into a preset binning embedding module to obtain a binning embedding matrix; maintaining and loading a gene pathway set through a knowledge base and a mapping module to obtain a binary mask matrix, and performing mask processing on the binning embedded matrix based on the binary mask matrix to obtain a pathway mask matrix; the path mask matrix is input into a preset attention state space model, the attention state space model comprises an encoder module, a jump connection module and a decoder module which are arranged in sequence, and a reconstruction tensor is output through the decoder module. According to the scheme, an efficient and extensible whole-gene annotation method is provided, and whole-gene expression input can be processed while the calculation efficiency is kept.

The invention provides a spatial omics data completion method and system based on a variational graph auto-encoder, and relates to the technical field of bioinformatics. Extracting partial common genes in the data pair to obtain a feature matrix; based on the spatial position information, obtaining a first sub-adjacency matrix of cells in the spatial transcriptomics sequencing data; respectively obtaining a second sub-adjacency matrix of the cells in the single-cell RNA sequencing data and a third sub-adjacency matrix of the cells in the data pair based on gene expression similarity; combining the first sub-adjacency matrix, the second sub-adjacency matrix and the third sub-adjacency matrix to obtain a final adjacency matrix; and inputting the feature matrix and the final adjacent matrix into a pre-trained variational graph auto-encoder network to complete the deletion gene of the space transcriptomics. According to the method, the position information and gene expression characteristics between idle data cells can be effectively utilized, errors can be reduced, and meanwhile, the similarity between complementation genes and true values can be improved.

The invention provides a method for constructing a chronic unpredictable negative stress model, the chronic unpredictable negative stress model and application of the chronic unpredictable negative stress model in mental disease research, and belongs to the field of brain organ in-vitro model construction. Through dual verification of a microelectrode array and single cell sequencing, an in-vitro brain organ model capable of simulating chronic unpredictable negative stress core characteristics is successfully constructed, and a potential neural network function reconstruction mechanism and a cell molecule basis of the in-vitro brain organ model are disclosed. The comprehensive research normal form breaks through the limitation of a traditional single technology platform, and an accurate and efficient innovative research platform highly related to human is provided for pathogenesis research of mental diseases, especially chronic stress related diseases such as depression and anxiety and development of novel treatment strategies.

The invention provides a multi-scale causal discovery method and system based on collective behavior modeling, and relates to the technical field of causal discovery, and the method comprises the steps: obtaining single-cell RNA sequencing data, the single-cell RNA sequencing data being a gene expression matrix of a plurality of cells; single-cell RNA sequencing data is input into a multi-scale causal model for reasoning, a multi-scale causal diagram is obtained, and the multi-scale causal diagram is used for describing the multi-scale causal relationship between genes and between genes and gene programs; wherein the multi-scale causal model takes a single gene as an individual variable, through collective behavior modeling, the single gene is aggregated into a collective variable representing a gene program, and a multi-scale collaborative learning strategy is adopted to collaboratively learn the multi-scale causal relationship between individuals and between individuals and collective. According to the method, a collective behavior modeling framework is innovatively constructed, key collective behaviors driving state changes can be effectively identified, and the causal discovery accuracy, rationality and robustness are improved.

The invention discloses a genetic state cancer clustering analysis method based on deep learning, which comprises the following steps: S1, constructing a single cell sequencing data set which comprises a gene expression matrix; s2, constructing a deep neural network composed of an input layer, a hidden layer and an output layer to obtain converted low-dimensional high-variation gene expression features; s3, constructing a gridding preprocessing model, and performing networked preprocessing including grid segmentation and isolated point removal on the low-dimensional high-variation gene expression features; constructing a clustering algorithm model, inputting the preprocessed low-dimensional high-variation gene expression features into the clustering algorithm model, and designing an optimization strategy based on a target function by the clustering algorithm model to enhance label entropy on a KL divergence index and amplify the ratio weight of auxiliary probability distribution and clustering distribution of the gene features; and iteratively realizing clustering result optimization as the output of the clustering algorithm model. Compared with the prior art, the clustering precision can be improved.

The invention provides a single cell sequencing data autonomous analysis method and system based on a large language model. The method comprises the steps that S1, the large language model, task description and single cell sequencing data are initialized; s2, constructing a planner based on a large language model, and analyzing and decomposing tasks according to the task description; s3, constructing an actuator based on the large language model, and retrieving and acquiring a tool document; step S4, based on the tool document, generating a code solution for the subtask; s5, executing the code solution in the code sandbox; s6, constructing an evaluator based on the large language model, and evaluating the code solution and the execution result thereof; and S7, returning and repeatedly executing the steps S3 to S6 until the execution is finished. Effective cooperative work among the planner, the actuator and the evaluator can be realized, full-process automation and high-quality single cell sequencing data analysis aiming at natural language input are realized, and the labor cost is reduced.

The invention discloses DLL3 serving as a cervical neuroendocrine cancer treatment target and application of the DLL3, and belongs to the technical field of biology. The invention discloses specific expression of a DLL3 gene in cervical neuroendocrine carcinoma (NECC) and application of the DLL3 gene as a therapeutic target. Single cell sequencing and immunohistochemistry find that a DLL3-NOTCH1 / 2 signal axis is activated in NECC, and functional experiments prove that the DLL3-NOTCH1 / 2 signal axis drives malignant progression by promoting tumor proliferation and inducing T cell depletion. In addition, the invention also constructs an NECC organ-TILs co-culture model, and verifies the synergistic effect of the DLL3 targeted drug AMG757 combined with EP chemotherapy. The invention provides new target selection and theoretical basis for precise diagnosis and treatment of NECC, and has important clinical transformation value.

The invention discloses a single-cell multi-omics gene regulation network reasoning method based on comparative learning, and belongs to the technical field of biological information, and the method comprises the following steps: obtaining single-cell RNA sequencing data and single-cell chromatin accessibility data, and carrying out preprocessing to obtain a prior network of multi-omics data; loading the prior regulation and control network into a graph structure, and converting the graph structure into a feature space matrix; calculating a Pearson's correlation coefficient of two nodes on each side in the graph structure, when the Pearson's correlation coefficient is greater than a preset threshold value, taking the Pearson's correlation coefficient as a node pair, and storing the node pair and the corresponding feature vector and hidden vector into a data set; the data set is divided into a plurality of batches to be randomly input into the comparison learning model, and the node type of each node, the pairing degree of node pairs and node feature reconstruction vectors are obtained; and extracting the mapping of two nodes in the node feature reconstruction vector in the feature space by adopting a regulation and control network interpreter, and calculating the structural entropy of the nodes based on the mapping so as to determine whether the nodes are key nodes or not.

The utility model discloses a particle pairing device and a pairing system, the pairing device is used for controlling pairing of a first particle and a second particle in a micro-fluidic chip, and the particle pairing device comprises a detection module and a valve control module electrically connected to the detection module, the detection module is used for identifying first particles when a first particle solution flows through a first particle flow channel of the micro-fluidic chip, is also used for identifying second particles when a second particle solution flows through a second particle flow channel of the micro-fluidic chip, and is electrically connected to the valve control module; the valve control module is used for blocking the first particle flow channel when the detection module recognizes the first particles and blocking the second particle flow channel when the detection module recognizes the second particles. Therefore, pairing of a single first particle and a single second particle can be realized, for example, when the first particle and the second particle are respectively cells and microspheres, the accuracy and efficiency of pairing of the cells and the microspheres can be improved, and then the efficiency of single cell sequencing is improved.

The invention discloses a chemical labeling system (HIM-IP) for detecting interaction between cells. According to the system, tyrosinase and phenol are respectively modified on the surfaces of two to-be-detected cells to construct capturing cells and prey cells, and then biotin or single-stranded DNA with nucleophilic groups is used as a molecular tag for labeling. The system not only can perform detection through traditional flow cytometry, but also can be compatible with a commercialized single cell transcriptome sequencing platform to realize association of intercellular interaction information and transcriptome information on a single cell level. The system does not need to carry out complex gene engineering modification on cells, the whole modification and marking process is simple, convenient and efficient, the detection sensitivity of intercellular interaction is high, and the biological mechanism of intercellular interaction regulation and control can be deeply excavated by integrating the single cell sequencing technology.