162 results about "Single cell transcriptome" patented technology

The invention provides a cell development process dynamic modeling method and device based on time sequence single cell transcriptome data and a medium, and relates to the crossing field of bioinformatics and computational biology. The method comprises the following steps: constructing a Shenchang differential equation learning framework; adjusting parameters of the single cell development state change model based on the Shenxuan differential equation learning framework so as to construct a population cell development state change model; obtaining a cell specific gene regulation network and a population cell gene regulation network based on the population cell development state change model so as to predict occurrence opportunity of cell lineage differentiation and a molecular decision mechanism of cell differentiation; therefore, the problems of incomplete modeling mechanism, insufficient noise processing and lack of energy principle in the existing cell development process are solved.

The invention discloses a method and system for screening drug targets with space-time specificity and computer equipment, and relates to the technical field of bioinformatics and computational biology. The screening method is based on single cell transcriptome sequencing data, and comprises the following steps: (1) quantitatively reconstructing spatial positioning and functional modes of cells in tissues, namely 1.1) carrying out data preprocessing on the single cell transcriptome sequencing data; 1.2) reconstructing the spatial positioning of the single cell; 1.3) reconstructing a single cell biological function mode; (2) screening a drug target with space-time specificity, wherein the screening comprises the following steps: 2.1) cell-cell communication analysis; the invention discloses a single-cell data analysis method based on a GRN (Gene Regulatory Network), which is characterized by comprising the following steps of (1) establishing a single-cell data analysis method, (2) establishing a GRN (Gene Regulatory Network) taking a specific tissue microenvironment state as a core, and (3) discovering a target spot. The single-cell data analysis method is innovative, provides a new thought and a technical path for research and development of drugs for metabolic diseases and other systemic diseases, and has a popularization and application basis.

The invention discloses a preparation method of fresh taxus chinensis stem and leaf protoplasts. The preparation method comprises a preparation process and a purification process. The preparation method of the taxus chinensis stem and leaf protoplast is simple and easy to operate, raw materials are easy to obtain and low in price, reagent components have good biocompatibility, and the taxus chinensis stem and leaf protoplast is free of harmful components, safe and environmentally friendly. The invention provides a simple and rapid taxus chinensis stem and leaf protoplast enzymolysis preparation method, which adopts vacuum filtration, accelerates the permeation of enzymatic hydrolysate and improves the enzymolysis efficiency, so that the protoplast can be rapidly obtained. Meanwhile, high-purity separation of the protoplast is carried out in combination with density gradient sedimentation of the iodixanol solution, so that the protoplast with uniform size and complete form is obtained, and convenience is provided for scientific researches such as subsequent conversion and single cell transcriptome sequencing.

The present application relates to the field of bioinformatics. In particular, the present application relates to methods and apparatus for speculating variable splicing functionality based on single cell transcriptome data. The method comprises the following steps: determining a variable splicing mode of each gene in a data set in a cell; determining the incidence relation between the variable splicing mode and the gene expression of each gene; a variable splicing mode module is determined according to the incidence relation between the variable splicing modes and the gene expression, and the variable splicing mode module is a variable splicing mode set obtained through clustering according to the correlation between the variable splicing modes and the cell phenotypes; displaying the cell splicing heterogeneity according to the variable splicing mode module; and / or determining a potential regulatory mechanism between the variable splicing mode and the gene expression according to the variable splicing mode module, the potential regulatory mechanism being used for embodying key splicing factors in the gene expression, and a biological approach in which the variable splicing mode affects the cell phenotype.

The invention discloses a single-cell multi-modal data integration method based on an attention mechanism and a graph variation auto-encoder. The method comprises the following steps: step 1, pre-processing multi-modal data and constructing a cell relation graph; step 2, cross-modal adjacency matrix fusion based on multi-head attention; step 3, performing graph variation auto-encoder training and multi-objective optimization; 4, performing multi-target loss calculation and model joint optimization; and 5, carrying out low-dimensional embedding extraction and clustering analysis on the cells. According to the method, single-cell transcriptome and epigenetic group data are fused through a multi-head attention mechanism, and low-dimensional embedding representation of cells is learned by using a graph variation auto-encoder, so that efficient integration and clustering analysis of single-cell multi-modal data are realized.

The application provides a single-cell transcriptome cell annotation method and system of a fusion large language model, cell type annotation is performed through construction of special prompt words and use of a large language model, and the accuracy and universality of cell annotation are improved. The application has a significant advantage for cell annotation of non-model species, and realizes an automatic and intelligent cell annotation process.

The invention provides a multi-element machine learning model-based cross-species lung disease characteristic gene screening method and system, an electronic system and a storage device. The method comprises the following steps of: acquiring single cell / transcriptome data related to mouse lung diseases from a public database and preprocessing the single cell / transcriptome data; training the model by adopting six machine learning algorithms and outputting a gene importance score; calculating the weight according to the model performance and normalizing the score; and integrating the cross-species scores through a weighted fusion formula, and outputting a feature gene list and a visual report. The system comprises a data acquisition and preprocessing module, a multi-element machine learning model training module, a weight calculation and normalization module, a cross-species comprehensive scoring module and a result output module. The screening accuracy, stability and generalization ability are improved through multi-algorithm integration and cross-species fusion, and the method can be widely applied to the fields of mechanism research of lung diseases, diagnosis marker development and drug target verification.

The invention discloses an empty transgene expression filling method based on a conditional variation auto-encoder, which comprises the following steps: designing a unified framework for conjoint analysis of single cell transcriptome data and spatial transcriptome data, obtaining single cell transcriptome sequencing expression profile data, spatial transcriptome expression data and a COVET matrix used for coding local neighborhood covariance in a tissue; projecting single cell transcriptome sequencing expression profile data and spatial transcriptome expression data into a shared potential space through an attention enhancement encoder to obtain potential variables; and decoding gene expression from the potential variables by using a decoder network, filling up missing gene expression information in spatial data, predicting a COVET matrix of single cell transcriptome data, and deducing a spatial context. According to the method, gene expression and spatial information can be coded at the same time, so that spatial context prediction of single cell data and filling of missing genes in spatial data are realized.

The invention discloses a high-yield and high-activity pear protoplast extraction method based on light shielding and enzymolysis optimization, which comprises the following steps: by taking pear tissue culture seedling leaves growing for 30-40 days or field pear tender leaves as an extraction material, carrying out enzymolysis treatment and protoplast purification by adopting enzymatic hydrolysate to obtain protoplast; before enzymolysis of the pear tissue culture seedlings, the whole pear tissue culture seedlings are placed in a dark environment and subjected to dark pretreatment at 25 + / -2 DEG C for 48-72 hours. According to the method, the yield of the field pear leaf and tissue culture seedling pear leaf protoplast can be increased by more than or equal to 2 times (the highest protoplast yield is greater than or equal to 3.0 * 10 / g FW), the activity is increased to be greater than or equal to 95%, and the technical bottleneck of the existing patent is remarkably broken through. The method has the advantages of high yield, high activity, wide application range and the like, can provide a high-quality protoplast material for pear genetic regeneration, gene editing, single cell transcriptome analysis and metabonomics research, and has wide scientific research and industrial application potential.

The invention provides an ischemic stroke auxiliary diagnosis system based on a peripheral blood T cell single cell transcriptome and application thereof, and the auxiliary diagnosis system comprises a peripheral blood mononuclear cell acquisition module, a single cell RNA sequencing module, a data processing module, a data analysis module and a result discrimination module. With a coding gene of a protein molecule significantly related to the occurrence of ischemic stroke as a target gene, a data processing module obtains the expression level of the target gene in each T cell; the data analysis module obtains a risk score of each T cell of the subject by using a built-in single cell risk scoring model, and weights the risk scores of all the T cells through distributed perception identification to obtain an individual risk score of the subject; and the result judgment module is used for classifying the subjects into ischemic stroke negative and ischemic stroke positive according to the individualized risk scores of the subjects. The auxiliary diagnosis system disclosed by the invention is high in ischemic stroke discrimination capability, and high in sensitivity and specificity.

The invention discloses a regulation mechanism research method of depleted precursor CD8T cells, and relates to the field of immunology and molecular biology. Comprising the following steps: detecting the expression level of the ARHGAP9 gene in a chronic virus infection model; constructing a T cell specific ARHGAP9 gene knockout animal model; the influence of ARHGAP9 deletion on the frequency, phenotype and function of the Tpex cell is analyzed; a single cell transcriptome sequencing technology reveals that ARHGAP9 regulates and controls a downstream signal channel of a Tpex cell. By constructing a chronic virus infection model, detecting the expression dynamic state of the ARHGAP9 gene, knocking out an animal model by utilizing T cell specificity ARHGAP9, and combining flow cytometry, qPCR, single cell transcriptome sequencing and other technologies, the invention discloses the effect of the ARHGAP9 as a novel immune checkpoint molecule, and provides a theoretical basis for developing Tpex cell targeting immunotherapy.

The invention discloses a method for accurately identifying benign and malignant cells based on multi-dimensional characteristics of a single cell transcriptome, and belongs to the technical fields of bioinformatics, tumor molecular biology and cell identification. On the basis of single cell transcriptome sequencing data of tumor tissues and para-carcinoma tissues, three types of information including copy number variation, allele specific copy number variation and tumor-related transcriptional characteristics are synthesized, and final benign and malignant identification is performed on each EpCAM positive epithelial cell through a multi-evidence voting strategy. The method disclosed by the invention has relatively high stability and accuracy in a multi-patient, multi-sample and early tumor scene, particularly improves the recognition capability of early lesion and malignant cells in benign and malignant boundary transition state cells, and provides a reliable technical means for precise diagnosis and individualized treatment of tumors.

The invention discloses an application of TRBC2 in diagnosis of gout. According to the application, a recognized moving meniscus tearing patient (equivalent to a completely healthy synovial tissue) without gout medical history is taken as a contrast, single cell transcriptome analysis is carried out on the basis of a clinical biological sample, and a marker TRBC2 of a T cell subset is found to have significant difference in gout patients; a Raman spectrum technology is further adopted to find that the TRBC2 protein is specifically and highly expressed in synovial tissues of gouty arthritis patients and has relatively high diagnosis efficiency, and the TRBC2 is relatively high in clinical value and wide in application prospect when being used as a marker for diagnosing gout.

The application provides an analysis method, device and equipment based on single-cell transcriptome sequencing data, which comprises the following steps: performing quality control, downstream analysis and visual display on single-cell transcriptome sequencing expression quantitative data, performing cell filtering by using Grubbs test method to obtain effective single-cell transcriptome sequencing quantitative data, performing initialization clustering analysis on the data to obtain single-cell subgroup classification results; performing screening on the single-cell subgroup classification results to obtain target single-cell subgroups, and performing re-clustering analysis to obtain single-cell sub-subgroup classification results; performing significant difference gene screening analysis on the single-cell transcriptome sequencing quantitative data between single-cell subgroups; and performing regression analysis based on characteristic genes of cell cycles to predict cell division periods corresponding to different cell types. The application effectively solves the technical complex problems of existing single-cell transcriptome sequencing quantitative data analysis, makes data analysis more simple and reliable, and reduces the difficulty of data analysis.

The invention discloses a cell communication analysis method and system based on single cell transcriptome data. The cell communication analysis method based on the single cell transcriptome data comprises the following steps: data input, standardized screening, stratified analysis and visual output. By means of CellPhoneDB and CellChat software, a cell-cell communication network is established by integrating gene expression with a signal ligand and a receptor according to a single-cell gene expression quantity matrix, and a visual analysis result is provided.

This document provides a high throughput method for the creation of AAV vectors and / or promoter sequences with high efficiency and / or specificity for multiple cell types.

The invention provides a method for sorting cell units and application thereof. The method for sorting the cell units comprises the following steps: specifically binding a first probe and a second probe with a target nucleic acid sequence, wherein the interval between binding sites of the first probe and binding sites of the second probe does not exceed a preset length; providing an insertion fragment, a skeleton fragment and a ligase, and setting the nucleic acid sequences of the insertion fragment and the skeleton fragment to be that when the first probe and the second probe are specifically combined with a target nucleic acid sequence, the insertion fragment and the skeleton fragment can be connected into a single-chain loop; performing rolling circle replication on the single-chain loop to obtain a long single-chain molecule, the long single-chain molecule comprising a plurality of repetitive units; combining a third probe with the repeating unit, the third probe carrying a detectable signal; and sorting the cellular units based on the detectable signal. According to the method, the target cell group can be efficiently selected, so that high-quality single cell transcriptome sequencing data can be obtained.

A single-cell transcriptome-guided multimodal synergistic injectable magnetically responsive biomimetic hydrogel system, its preparation method, and its application are described. This hydrogel platform is a three-level composite system of "matrix-microsphere-nanoparticle": the primary structure is a photocrosslinked methacryloyl hyaluronic acid three-dimensional network matrix; the secondary structure is GelMA microspheres loaded with MTPT nanoparticles prepared by microfluidic control; and the tertiary structure consists of bevacizumab and magnetite nanoparticles dispersed in the HAMA matrix. The MTPT nanoparticles have a mesoporous silica core, which is sequentially coated with temozolomide, a polydopamine coating, and a T7 targeting peptide. Under the activation of an alternating magnetic field, Fe3O4 generates a magnetothermal effect, which not only achieves local hyperthermia but also accelerates the time-sequential release of bevacizumab and MTPT, synergistically exerting the effects of chemotherapy, anti-angiogenic therapy, and hyperthermia.

The application provides a single-cell transcriptome cell type automatic annotation method and device based on consensus voting, electronic equipment and storage medium, relates to the field of medical biotechnology, and integrates a plurality of initial annotation results obtained by a plurality of cell type annotation methods by applying an ensemble learning strategy, so as to reduce errors that may exist in a single annotation method, and improve the accuracy and robustness of cell type annotation. In addition, the method uses single-cell transcriptome data of a sample, combines rich prior knowledge and strong reasoning ability of a large language model, and has the ability to discover rare cell types, so as to effectively identify rare cell types, widen the application range of cell type annotation, and improve the general tissue annotation capability of cell types.

The embodiment of the invention discloses a single cell transcriptome data processing method and device, a parameter library and electronic equipment, and the method comprises the steps: obtaining a common parameter, the common parameter comprises a reference feature gene set and a reference association relationship between an original feature and an extracted feature, the reference feature gene set comprises a plurality of feature genes, and the reference association relationship comprises a reference association relationship between the original feature and the extracted feature; the reference association relationship is used for dimension reduction processing of a gene expression condition; based on the reference feature gene set and the single cell transcriptome data of the to-be-detected sample, determining the gene expression condition of the feature gene in the to-be-detected sample; on the basis of the gene expression condition of the feature gene in the to-be-detected sample and the reference association relationship, performing first data dimension reduction processing to obtain a first dimension reduction result; wherein the to-be-detected sample and the common parameters belong to the same biological tissue type. By adopting the embodiment of the invention, the computing resource demand can be effectively reduced, and the data processing efficiency is improved.

The invention provides a plasma proteome data difference evaluation method and device based on two-group comparison and a medium, and relates to the technical field of biological information analysis. Comprising the following steps: S101, sorting omics data of two single cell transcriptomes, and annotating the cell type of each cell to obtain a first single cell gene expression profile; s102, splitting the first single-cell gene expression profile according to cell types to obtain a plurality of second single-cell gene expression profiles, wherein each second single-cell gene expression profile corresponds to one cell type; s103, calculating a state difference score of each cell type based on logistic regression and ten-fold cross validation; and S104, sorting according to the state difference scores from high to low to obtain a difference evaluation result of the corresponding cell type. The method is not limited to the difference of a single feature (such as a gene) and is not limited by specific omics, the cell type difference among multiple groups can be evaluated from the overall level, and the comparability among omics data is improved.

The embodiment of the invention provides a TPD52 immunomodulatory effect research method and system, and the method comprises the steps: obtaining single cell transcriptome data of a breast cancer tumor microenvironment; on the basis of the data, identifying the TPD52 as the immune regulation key dangerous gene by utilizing a machine learning algorithm; verifying the association of TPD52 expression and patient prognosis in a multi-center queue; and based on the function annotation and the body appearance type experiment of the TPD52, confirming the immunomodulatory effect of the TPD52, and outputting a comprehensive evaluation report of the immunomodulatory effect of the TPD52. According to the invention, a machine learning method and scRNA-seq are utilized to explore the effect of TPD52 as a key immunomodulatory factor in BRCA, and the application has important significance on tumor behavior and patient prognosis.

The invention provides a determination method of signal channels of a cell subset and a cell model, a prescription efficacy prediction method and a related device, and relates to the technical field of traditional Chinese medicines. The method comprises the following steps: performing low-depth single cell transcriptome sequencing and cell grouping and annotation analysis on a disease sample and a normal control sample, screening cell subgroups of which the quantity proportion is remarkably changed in the disease sample, and performing gene expression data aggregation analysis and signal path enrichment analysis, thereby obtaining the disorder signal path of the disease-related cell subgroups. Based on the corresponding relation between the disease-related cell subpopulation and the cell model of the traditional Chinese medicine effect, the cell model and the imbalance signal channel for predicting the efficacy of the prescription are determined, and applications such as quantitative prediction of the efficacy of the prescription with the reversal of the disease signal channel as the core are further developed.

The application discloses a single-cell transcriptome data pseudo-time trajectory analysis method and a computer system, which comprises the following steps: 1) calculating a gene explicit comparison advantage matrix; 2) obtaining a gene similarity matrix by similarity and constructing a gene network; 3) taking an initial node in the gene network, starting random walking from the initial node, recording each gene walked through to form a gene text composed of gene sequences; 4) converting the gene text into a gene word vector; 5) adding all single-cell expressed gene vectors with expression as weight to form a sum vector as a word vector representation of the single cell in the gene space; and 6) visualizing all cell vector representations to obtain an embryo cell development pseudo-time trajectory result. The application provides an analysis basis for identifying different rare cell subtypes in tissues and variant genes of different cell subtypes and has a wide and important application prospect in the fields of tumors, developmental biology and life science.

The invention relates to the field of bioinformatics, and provides a single cell pedigree tracing method. According to the technical scheme, single cell transcriptome sequencing is combined with Bulk DNA targeted sequencing, so that the cost of pure single cell transcriptome sequencing is reduced, and the accuracy of single cell labeling is ensured through a subsequent filtering method of reserving a unique bar code.

Methods of tagging cells with unique oligonucleotide “zipcode” constructs are provided. By these methods and associated compositions, cells in a multicellular structure such as a tissue section can be tagged with a construct, the unique composition of which is associated with the cells position in the multicellular structure. Subsequently, the multicellular structure can be dissociated into single cells and a single cell transcriptome analysis performed, as well as other types of single cell analyses. By preserving positional information in the analyzed single cells, biological processes within the tissue can be mapped. By these methods, the effects of the local environment surrounding a cell on its state and various functions can be elucidated, and intra-tissue processes can be mapped and observed. Likewise, coordinated actions by multiple cells within a tissue can be mapped and tracked over time.

This invention discloses a method and system for single-cell transcriptome cell type annotation based on deep learning, belonging to the field of bioinformatics data processing technology. The method includes five steps: data quality control preprocessing, Transformer cell encoder pre-training, graph attention network cell relationship modeling, hierarchical classification annotation, and zero-shot transfer annotation. This invention deeply couples Transformer representation learning with graph attention networks, obtains general representations through masked gene prediction pre-training, enhances rare cell type features using KNN graphs and graph attention message passing, improves recognition accuracy by adopting a hierarchical classification architecture from main lineage to subtype, and supports zero-shot cross-modal annotation based on text description.

The invention discloses an application of a TNFRSF12A inhibitor in preparation of a medicine for treating oral squamous cell carcinoma. Based on single cell transcriptome data, oral squamous cell canceration related tissues are divided into four tissue ecological subtypes, and a tumor sample is divided into two subtypes ET3 and ET4. The ET3 subtype is a lymphocyte infiltration type, and shows CD8 + T cell infiltration, immune activation pathway activity enhancement and high expression of CD3, CD8A and PDCD1; the ET4 subtype is a lymphocyte rejection type, is enriched with COL1A1 + fibroblasts, and is accompanied by high expression of TNFRSF12A, COL1A1 and CD276. According to the application disclosed by the invention, the expression of the TNFRSF12A in the ET4 subtype is obviously improved, in-vivo experiments prove that knockout or blocking of the TNFRSF12A can promote lymphatic T cell infiltration and inhibit tumor growth, and a stronger inhibition effect is shown when the knockout or blocking of the TNFRSF12A is combined with an immune checkpoint inhibitor. The invention provides a system for typing oral squamous cell carcinoma.

The application discloses application of TRBC2 in diagnosis of gout. In the application, patients with torn meniscus (equivalent to completely healthy synovial tissue) without a history of gout are used as a control, single-cell transcriptome analysis is carried out based on clinical biological samples, it is found that the marker TRBC2 of T cell subpopulation presents significant difference in gout patients, further Raman spectrum technology is used to find that TRBC2 protein is specifically highly expressed in synovial tissue of gouty arthritis patients, and has high diagnostic efficiency. The clinical value of TRBC2 as a marker for diagnosing gout is high, and the application prospect is wide.

The application discloses a method for analyzing B lymphocyte BCR characteristics based on single-cell multi-omics sequencing. The method collects samples of patients in different disease states for single-cell sequencing, constructs a single-cell gene expression matrix based on single-cell transcriptome sequencing data and carries out cell type annotation, assembles BCR sequences based on single-cell immunome sequencing data and identifies BCR chain composition genes, then evaluates and identifies the stable BCR expression rate of each cell subpopulation, evaluates the clonal state of each cell subpopulation in different disease states, analyzes the BCR assembly and single-gene use preference of each sample in different disease states, and the BCR assembly and V-J gene pair use preference. The application has important significance for the mechanism analysis of specific viral infection hosts and the research and development of specific vaccines and drugs.