391 results about "Single cell sequencing" patented technology

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.

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 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 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 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 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 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 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.

Provided is a method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing, which method comprises the following steps: S1, sample preparation; S2, construction of a single-cell expression matrix; S3, cell quality control; S4, cell type annotation; S5, cell communication analysis; and S6, co-expression network analysis. The provided method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing comprises performing single-cell sequencing on hearts of healthy mice and IHF mice, screening for cell types with significant differences in cardiac transcriptional profiles of the healthy mice and IHF mice, then exploring interaction characteristics of various types of cells in malignant fibrotic IHF hearts, revealing potential regulatory modules and pathways related to malignant myocardial fibrosis in single-cell expression data of IHF hearts, and performing screening to obtain Pdgfb and Tnfsf12 genes which can be used as therapeutic targets for treating myocardial fibrosis in ischemic heart failure.

The application discloses a cell heterogeneity analysis method, device and computer readable storage medium for large-scale single cell sequencing data. The application combines kernel non-negative matrix factorization with deep neural network to construct a clustering method called KNMF-DNN. The method selects some representative subsets from the overall data, and uses the kernel non-negative matrix factorization method to cluster the subsets; then, the representative subsets are used as a training set, the remaining data is used as a test set, the labels obtained by clustering are used to classify the remaining data through the deep neural network, so that the clustering of the whole data is realized. In addition, the application also uses the idea of stratified sampling to select the most representative samples, and the best division of the clustering samples and the classification samples is determined by the KL divergence. Experimental results show that, compared with other latest methods, the KNMF-DNN has more excellent results on three real scRNA sequencing data sets.

The invention provides a new bunyavirus neutralizing antibody screening method based on single cell BCR sequencing. The new bunyavirus neutralizing antibody screening method comprises the following steps: S1, collecting peripheral blood B cells of acute stage and recovery stage patients and healthy volunteers of new bunyavirus infected persons; s2, carrying out CD19 < + > CD38 < + > CD138 < + > magnetic bead separation to obtain a single-cell suspension; s3, constructing a single cell transcriptome and a BCR library, separating single cells by using a microfluidic system, and performing high-throughput sequencing; s4, analyzing a B cell subset, a BCR cloning amplification mode and a VJ gene pair enrichment characteristic through bioinformatics analysis; and S5, screening a specific BCR clone type related to the severity of the disease as a neutralizing antibody candidate target. The method provided by the invention provides powerful support for immune monitoring and vaccine design of new bunyavirus infection, and is expected to provide new strategies and methods for clinical treatment and prevention.

The invention belongs to the technical field of bioinformatics and medical data analysis, and discloses a neurodegenerative disease comprehensive analysis platform based on multi-source data fusion, and the platform comprises a data integration module; a gene name conversion module; a core analysis module; a network analysis module; a drug screening module; the omics visualization module systematically catalogs data of genes related to at least 10 main neurodegenerative diseases, 486 drug-derived 3, 957 bioactive components and 18 lifestyle factors through the data integration module, and the data source is wide; according to the method, artificially sorted literature evidence, reanalyzed batch transcriptome data, single-cell RNA sequencing data and standardized data from a public database are covered, new disease targets and potential treatment strategies can be found easily, and the neurodegenerative disease research efficiency and comprehensiveness are improved.

The invention relates to the technical field of biomarkers, in particular to a biomarker composition for diabetic foot ulcer and application of the biomarker composition. The invention provides a biomarker combination for diabetic foot ulcer. The biomarker combination comprises a DDIT4 gene and a PKM gene. According to the invention, by integrating transcriptome sequencing data and single cell sequencing data, biomarkers DDIT4 and PKM closely related to diabetic foot ulcer (DFU) are successfully screened out. The expression of the two genes in a disease group is remarkably up-regulated, and the two genes are stably expressed in different data sets, so that a reliable molecular target is provided for early diagnosis of DFU.

The invention relates to a PDC (Polycrystalline Diamond Compact) cell carrying a bar code DNA (Deoxyribose Nucleic Acid) tag convenient to track as well as a preparation method and application thereof, and belongs to the technical field of The invention provides an isolated culture method of PDC primary cells, and provides a method for constructing recombinant PDC cells containing bar code DNA tags. According to the PDC primary cell culture system, the cell caking rate is reduced, the cell viability and the adherence rate are improved, and continuous passage to 20 generations or above can be achieved. The PDC primary cells faithfully retain the heterogeneity of clinical tumor samples in the aspects of mutation types, tumor generation and development mechanisms and the like. The bar code DNA tag introduced into the PDC cell can be detected by carrying out targeted sequencing and single cell RNA sequencing on a bar code DNA tag sequence in cell genome DNA, so that different clone groups reserved from PDX of the PDC cell line are conveniently distinguished and tracked in a high-throughput manner.

The invention discloses a cell lineage tracking system based on synNotch and CRISPR / Cas9 bar code technology and application thereof, the cell lineage tracking system comprises a system mGFP ligand vector for constructing Sender ligand cells and a system for constructing Receiver recipient cells, and the system comprises a synNotch receptor-rtTA fusion vector, a TetO-Cas9 expression vector, a gRNA expression vector and a Target vector library containing a Barcode sequence. A synNotch system and a CRISPR / Cas9 gene editing technology are combined with a bar code strategy, a unique bar code is generated, the contact sequence between cells is recorded, the method is compatible with a single-cell sequencing technology, transcription information of the cells is provided, long-term tracking of the contact history between the cells is achieved, the method can be used for tracking interaction and functions between the cells in a tumor microenvironment, and the application prospect is wide. Through long-term tracking and functional analysis of interaction between macrophages, especially macrophages and tumor cells, the accuracy of interaction analysis is remarkably improved, and a more accurate target spot is provided for targeted therapy.

The invention discloses a sparse auto-encoder-based cell type deconvolution modeling method and system, and relates to the technical field of artificial intelligence technology and bioinformatics. Single cell RNA sequencing data is input into a trained sparse auto-encoder model to obtain a predicted cell type; the sparse auto-encoder model training process comprises the following steps: generating simulated bulk transcriptome data by using single-cell RNA sequencing data to construct a training set; constructing a sparse auto-encoder model, simulating bulk transcriptome data, generating a predicted cell type proportion through an encoder, and generating reconstructed bulk transcriptome data through a decoder according to the predicted cell type proportion; constructing a total loss function based on the KL divergence function, the reconstruction error and the prediction error so as to optimize trainable parameters in the sparse auto-encoder model; according to the deconvolution modeling method and system, the problems of low cell type prediction accuracy and low algorithm efficiency in the prior art are solved, and rapid and efficient bioinformatics analysis is realized.

The invention provides a preparation method of a single-cell sequencing suspension fixedly preserved in situ. The invention provides digestive juice for preparing a single-cell suspension. The digestive juice is a solution composed of collagenase II and neutral protease. The invention also provides a kit containing the digestive juice. The invention provides application of the digestive juice to preparation of a kit and application of the kit to in-situ fixed preservation of cells and preparation of a single-cell suspension. The invention also provides a preparation method of the in-situ immobilized preserved cell and the single-cell suspension, and the single-cell suspension prepared by the preparation method.

Injectable compositions and methods of preparation, as well as therapeutic uses, of induced pluripotent stem cell (iPSC)-derived notochordal cell (iNC)-loaded microgels are provided. Microfluidic on-chip platform can be utilized to prepare microgels (or microgel particles / spheres) formed from block copolymers that exhibit reverse thermal gelation, so as to encapsulate iNCs. Also provided are preconditioned iNC-loaded microgels and iNCs in bulk hydrogel. Cell purity, identity, viability, sterility, and the stability of microencapsulated iNCs have been evaluated. Safety and efficacy of the compositions as therapeutic candidates has been tested via intradiscal injection in animal models of intervertebral disc (IVD) degeneration and discogenic low back pain. Biobehavioral testing, MRI, and immunohistochemical analyses were utilized to evaluate the regenerative potential and reproducibility of the compositions as therapeutic candidate. Single cell RNA sequencing of the treated IVDs may also reveal mechanism of action of the compositions.

The invention discloses siRNA (small interfering Ribonucleic Acid) targeting TMEM86A and application thereof, and relates to the technical field of biology, the siRNA comprises the following positive-sense strands and antisense strands: siRNA-001: a positive-sense strand: 5 '-GAAGAGCGAAGGACCCAAATT-3'; according to the tumor-associated macrophage, TMEM86A has a positive-sense strand of 5 '-TTTGGGTCCTTCGCTTCT-3', an antisense strand of 5 '-TTTGGGTCCTGTCGCTTCT-3', siRNA-002 has a positive-sense strand of 5 '-GGCTCATGGTTCGGTTTT-3', and an antisense strand of 5 '-AAACCGAACCCATGAGCCT-3'. According to the tumor-associated macrophage, single cell sequencing analysis finds that TMEM86A is highly expressed in tumor-associated macrophages, and the development of tumors is inhibited by designing siRNA of TMEM86A to target the tumor-associated macrophages in a colon cancer tumor microenvironment.

The present invention relates to methods of detecting region(s) of interest in a gene comprising a polyA tail. The region(s) of interest can include gene(s), region(s), mutation(s), deletion(s), insertion(s), indel(s), and / or translocation(s). The region(s) can be greater than or less than 1 kilobases from the polyA tail. Methods can include forming a library of single cell transcripts comprising the region(s) in close proximity to a cell barcode and a unique molecular identifier (UMI). Methods for distinguishing cells by genotype can include amplifying the transcripts using PCR methods and detecting the cell barcode and UMI using single cell sequencing methods. Transcripts can be enriched using tagged region-specific PCR primers. Cell barcodes can be brought into close proximity to the region(s) by circularizing the transcripts. Sequencing of the transcripts can include using primer binding sites added during PCR amplification and library indexes for multiplexed sequencing.

The invention belongs to the technical field of single cell sequencing, and relates to a full-automatic processing method and system for full-process single cell sequencing data. The system comprises a data loading and shallow copying module which is used for loading single cell data sets in different formats and is internally provided with example data; the preprocessing process standardization module is used for preprocessing the data loaded by the data loading and shallow copying module by using a Scanpy built-in method; the key parameter automatic search module is used for optimizing key parameters by adopting two optimization strategies of integrating Optuna and CMA-ES in sequence; and the dimension reduction and clustering analysis module is used for reducing dimensions by adopting a PCA (Principal Component Analysis) technology, constructing a cell proximity graph by adopting KNN (Kernel Nearest Neighbor) composition, realizing multi-granularity division by adopting Leiden clustering and adjusting a Leiden clustering granularity parameter reduction, and realizing visualization by adopting UMAP (Uniform Mode Access Point) to generate a clustering graph. The system provided by the invention provides a more intelligent, reliable and efficient solution for single-cell RNA sequencing data analysis, and has wide technical popularization value and application prospect.

The application discloses a single-cell biopsy and dynamic transcriptome tracking system and method, and belongs to the technical field of single-cell sequencing. The system comprises the following steps: culturing single cells to be detected in a cell culture dish; puncturing the single cells by using an amino-modified quartz nanocapillary to extract trace cytoplasm samples; recovering the single cells in a culture environment; applying specific stimulation to the recovered single cells; at one or more time points after the stimulation is applied, puncturing the same single cells again by using the quartz nanocapillary to extract trace cytoplasm samples, and recovering the single cells after each extraction; and respectively performing reverse transcription, amplification, library construction and sequencing on the cytoplasm samples extracted at different time points to obtain dynamic transcriptome data of the single cells at different time points. The application can perform low-damage multiple longitudinal biopsies on the same single living cell, and can construct a dynamic transcriptome atlas of the single cell on a time axis in combination with downstream sequencing.

The present application relates to the technical field of bioinformatics, and particularly relates to a method for constructing a biological age prediction model based on DNA methylation. The method comprises the following steps: obtaining whole genome methylation sequencing data of a human peripheral blood sample; classifying cell subpopulations of the human peripheral blood sample, and performing single-cell RNA sequencing processing on each cell subpopulation to obtain single-cell sequencing data including lymphocytes, neutrophils and monocytes; performing tissue-specific analysis on CpG sites within a range of 2000 base pairs upstream and downstream of each cell subpopulation-specific transcription factor binding site according to the single-cell sequencing data and the whole genome methylation sequencing data to obtain candidate marker site data. The present application can provide strong support for early detection of accelerated aging, prediction of related disease risks and guidance of precision medicine.