228 results about "Single cell transcriptome" patented technology

This disclosure provides a novel method and system for characterizing morphodynamic profiles of objects, such as biological entities. This disclosure provides a shape, appearance, and motion (SAM) phenotype Observation Tool (SPOT). SPOT establishes a standardized SAM “phenome,” image descriptors resembling single-cell transcriptomes, to comprehensively quantify a cell's instantaneous state without prior knowledge. SPOT also establishes a standardized workflow for temporal analysis. SPOT is a generalist tool, applicable to any live-cell imaging and advances biomedical discovery through its standardized, unbiased, streamlined workflow to quantify phenotypic heterogeneity and predict phenotype-genotype-function coupling.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention provides a single-cell multi-omics cell type annotation method based on distribution and knowledge alignment, and belongs to the technical field of single-cell type annotation, the method comprises the following steps: obtaining single-cell transcriptome data and single-cell chromatin accessibility sequencing data, and pre-training and training a multi-omics variation auto-encoder model, the multi-omics variational auto-encoder model is combined with a variational auto-encoder and a knowledge distillation technology, and multi-omics single cell data is integrated and annotated through distribution and knowledge alignment. And performing cell type prediction on the single cell transcriptome data and the single cell chromatin accessibility sequencing data which are input at the same time by using the trained multi-omics variational auto-encoder model. According to the method, the problem of limitation of a method only depending on single omics is solved, the synergistic effect between the omics is enhanced, the accuracy of annotation is improved, and the calculation overhead is reduced through knowledge distillation.

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 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 cell type annotation method and device based on plant single cell transcriptome data and a readable storage medium. Annotation is achieved through multi-level data integration, wherein preliminary annotation is expressed based on cell type marker genes or homologous genes; calculating expression correlation auxiliary annotations of the to-be-analyzed data and the known transcriptome data set; performing function enrichment on the cell cluster differential genes to deduce cell types; carrying out quasi-timing analysis on the heterogeneous cell clusters and annotating subgroups; and finally integrating and generating a comprehensive annotation. The method solves the problem that the prior art depends on artificial experience and is insufficient in basic data set, and is suitable for mode and non-mode plants.

The invention provides a deconvolution model construction method and device, a terminal and a medium, and the method comprises the steps: generating simulated space transcriptome data with a cell type proportion label based on single cell transcriptome data, and obtaining real space transcriptome data obtained through sampling based on a 3D concentric sphere sampling strategy, and based on a comparative learning strategy and a domain adversarial network, training an encoder and an initial prediction model by using target space transcriptome data containing differential expression genes obtained by screening from simulation data and real data, and obtaining a feature extraction model for extracting embedded information and a prediction model for predicting a cell type proportion. According to the method, deep learning is combined, target space transcriptome data is used as encoder input, encoder output is used as prediction model input, the model is trained based on domain adversarial learning and contrast learning strategies, a model with good generalization ability is obtained, and high-precision deconvolution and cell type proportion prediction of 3D space transcriptome data are realized.

The present invention relates to a method for constructing a gene network through a single-cell transcriptome and a method for discovering key genes in differentiation using same, and a composition for the prevention, alleviation, or treatment of colon cancer using the target discovered through the method. The method for constructing a gene network of key genes according to the present invention employs single-cell transcriptome data and thus can be applied to all single-cell transcriptome data. The combination of MYB / HDAC2 / FOXA2 discovered upon application to colon cells can serve as a cancer treatment target that promotes the differentiation of colon cancer cells to revert same into differentiated normal cells.

The embodiment of the invention provides a gene regulation network inference method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a first time sequence corresponding to a target cell type; the first time sequence comprises accessible chromatin sequencing data and single cell transcriptome sequencing data at different first time points; constructing a corresponding first gene regulation network according to the accessible chromatin sequencing data at each first time point; pruning the first gene regulatory network based on single cell transcriptome sequencing data to obtain a second gene regulatory network corresponding to each first time point; and deducing a plurality of second gene regulatory networks corresponding to the first time sequence based on a pre-constructed gene regulatory network prediction model to obtain a target gene regulatory network corresponding to the target cell type at a second time point, the second time point at least comprising a future time point and / or a missing time point in the first time sequence. The method can improve the inference accuracy of the gene regulatory network.

The invention provides a method and system for analyzing lung cancer brain metastasis organoid cell subpopulation and functional characteristics, and the method comprises a series of steps: data quality control, batch effect removal, cell grouping, copy number variation inference, quasi-time sequence analysis, function enrichment analysis, and drug sensitivity analysis before and after radiotherapy. Single cell transcriptome sequencing data obtained after organoid culture is subjected to personalized analysis, heterogeneity of tumor cells and sensitivity of the tumor cells to drugs after radiotherapy are described, and the characteristics of all cell subgroups in the lung cancer brain metastasis organoid and the effect of the cell subgroups in the tumor microenvironment can be accurately analyzed; powerful support is provided for mechanism research and precise medicine of lung cancer brain metastasis.

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 belongs to the field of single cell level nucleic acid detection, and particularly relates to a construction method and application of a high-throughput single cell transcriptome library based on probe targeted enrichment. According to the method, single cell fixed punching and hybridization enrichment of a specific targeting double probe designed by a target nucleic acid sequence are combined, the nucleic acid sequence comprises mRNA and non-coding RNA, the probe is connected with beads with barcodes through a connecting sequence on the probe, cell labeling is carried out, and library building steps such as extension and amplification are completed; therefore, the problems of insufficient transcriptome integrity, single detection target and low number of detected genes are solved.

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 application of a TGM2 inhibitor in treatment of chronic sinusitis with nasal polyp, asthma and fibrosis complications thereof. Through single cell transcriptome analysis, it is found that the TGM2 is specifically and highly expressed in a macrophage subset (Tgm < 2 + > macrophage) in a CRSwNP sample with asthma, and the TGM2 protein expression level is significantly and positively correlated with the clinical severity of diseases such as CRSwNP and asthma; animal experiments verify that pathological injuries of the nasal cavity and the lung of a Tgm2-knocked-out mouse type 2 inflammation model are relieved, the inflammation of the upper and lower respiratory tracts of the mouse and the fibrosis level of the lung tissue can be remarkably relieved by adopting a small molecule drug ERW1041E for nasal drop administration, and the safety is good; the invention provides a brand-new treatment strategy for patients with diseases related to type 2 inflammation, and has remarkable clinical transformation value and market prospect.

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.