134 results about "Single cell sequencing" patented technology

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 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 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 invention provides a Web-based single-cell RNA sequencing data intelligent analysis system and method. The method comprises the following steps: receiving a cell group through a Web interface; performing differential gene analysis on the single-cell RNA sequencing data contained in the cell group to obtain an original differential gene list, and filtering the original differential gene list by adopting a multi-threshold screening algorithm to obtain a target differential gene list; performing species automatic identification processing on the target differential gene list, calling a target local gene set database based on a result of the species automatic identification processing, and performing parallel enrichment analysis independent of network connection on the target differential gene list according to the target local gene set database to obtain a gene enrichment analysis result; and generating an interactive chart by adopting an intelligent label anti-overlapping algorithm so as to visualize the interactive chart. According to the method, the problems of high operation threshold, low batch analysis efficiency, unstable result and poor interactivity in the prior art are solved.

An assay for autologous primary immune cells is described, in which individual blood cells can be functionally screened simultaneously for individual antigens of interest, such as T-cell epitopes, without the need for HLA haplotype-specific reagents. An oligonucleotide-labeled hash-tracking system, followed by deconvolution via single-cell sequencing, correlates antigen reactivity with individual T cells.

The invention discloses a method and a system for analyzing immune cell change of an HER2 variant lung cancer patient. The method comprises the following steps: collecting a peripheral blood sample and carrying out single-cell RNA (Ribonucleic Acid) sequencing; performing quality control, standardization processing, dimensionality reduction and clustering analysis on the sequencing data; carrying out immune cell subset annotation on the clustering result based on a preset immune cell marker; analyzing cell communication modes among different immune cell subpopulations by using a ligand-receptor database; performing quantitative statistics on the proportion of various immune cells based on sample grouping; and further performing subpopulation fine analysis, quasi-time sequence trajectory reconstruction and metabolic pathway activity evaluation on the CD8 + T cells to construct a comprehensive immune cell map. According to the method, the multi-dimensional characteristics of the peripheral blood immune cells can be presented at high resolution, and the comprehensive analysis of the functional state, the communication network and the metabolic activity of the immune cells is realized. According to the system and the device, automatic operation and visual display of the method can be realized. According to the invention, the immune monitoring efficiency can be improved, and an effective tool is provided for immune treatment effect evaluation and disease mechanism research.

The invention discloses a gene regulatory network prediction method and system based on explicit correlation modeling, and the method comprises the steps: obtaining a gene expression matrix of single-cell RNA sequencing data and an adjacent matrix of a prior regulatory graph constructed based on prior knowledge, and inputting the matrixes into a graph neural network model; wherein the graph neural network model is configured to perform explicit modeling on a link in the prior regulation and control graph through an intra-layer message passing space and an inter-layer message passing space so as to obtain link representation; predicting whether a regulation relation exists between the gene pairs through a classifier on the basis of link characterization so as to deduce a gene regulation network; wherein the architecture of the graph neural network model is adaptively determined through an automatic architecture search algorithm according to input data. According to the framework provided by the scheme, modeling link representation is displayed in the message passing process, the regulation and control relation of the gene pair is inferred by MLP based on link embedding, utilization and organization of complex connection information of the prior regulation and control graph are enhanced from the source, and the inference accuracy is effectively improved.

The present invention relates to a biomarker for diagnosing and prognostically predicting diabetes mellitus associated with pancreatic cancer and a use thereof, and more particularly, to a biomarker composition for diagnosing and prognostically predicting diabetes mellitus associated with pancreatic cancer, a kit and an information providing method, which comprise a preparation for determining the expression level of REG4 (REGEN 4) protein. Furthermore, the present invention relates to an information providing method for providing necessary information for the diagnosis and prognosis of diabetes-associated pancreatic cancer by measuring the level of REG4 in a biological sample isolated from a subject suffering from diabetes-associated pancreatic cancer, and for determining the prognosis of diabetes-associated pancreatic cancer by patient queue analysis, single-cell RNA sequencing (scRNAseq) analysis, and organoid methods. Provided is a complex prognostic prediction of REG4 against diabetes accompanied by pancreatic cancer. Therefore, an individualized treatment method can be provided for a patient, and prognosis prediction and treatment method determination can be more reasonably carried out on an object with diabetes mellitus accompanied by pancreatic cancer.

The application belongs to the technical field of biological medicine, and particularly relates to application of long-chain non-coding RNA expression inhibition in esophageal cancer ferroptosis inducer sensitization. The application first reveals dynamic change of long-chain non-coding RNA ENSG00000250658 in esophageal squamous cell carcinoma metastasis based on single-cell sequencing data, detects expression of ENSG00000250658 in esophageal squamous cell carcinoma tissue and correlation with prognosis by using LNA probe-based RNA in-situ hybridization technology, and first finds that knocking down ENSG00000250658 can increase killing effect of ferroptosis inducer on esophageal squamous cell carcinoma cells. Therefore, the application provides a new technical scheme and thought for clinical diagnosis and treatment of esophageal cancer.

This invention relates to a method for assessing the quality of single-cell sequencing data, which addresses the current difficulty in evaluating the differences in data quality after applying different single-cell sequencing data imputation algorithms without the participation of real samples. The method includes the following steps: First, two single-cell sequencing data imputation algorithms are prepared. Then, a synthesis matrix based on the statistical characteristics of real data is created and normalized preprocessed. The normalized gene expression matrix is ​​input into the two imputation algorithms to be evaluated, and the output feature vectors are extracted to set an optimization function. The gene expression matrix is ​​optimized using the optimization function, and then the optimized matrix is ​​denormalized to obtain the imputed gene expression profile. Finally, the obtained gene expression profile is input into the two algorithms to be evaluated to obtain two sets of predicted feature vectors. The data quality difference value can be calculated using these feature vectors. This invention can accurately assess the data quality difference between two data imputation algorithms without the participation of real samples.

The invention provides a construction method and a sequencing method of a single cell DNA library. The construction method comprises the following steps: a) cracking a single cell, mixing the cracked single cell with a first primer, and carrying out first amplification to obtain a first amplification product; b) mixing the first amplification product with a second primer, and performing second amplification to obtain a single-cell DNA library; wherein the first primer comprises a universal amplification fragment and a random amplification fragment; the second primer comprises an index fragment and a complementary fragment; the single cell DNA library is a library suitable for BGI platform or Illumina platform sequencing, and correspondingly, the universal amplification fragment is a fragment suitable for BGI platform or Illumina platform; the fragment suitable for a BGI platform is 8-9 nt, and the fragment suitable for an Illumina platform is 10 nt. The problem that in the prior art, a single-cell DNA library construction method is complex can be solved, and the method is suitable for the field of single-cell sequencing.

A method for identifying cell subpopulations associated with disease phenotypes, belonging to the biomedical field. To identify cell subpopulations associated with disease phenotypes, this invention collects single-cell RNA sequencing data of the disease to obtain a single-cell expression matrix, collects the bulk expression matrix of the disease and corresponding phenotypic tags, and downloads human protein-protein interaction data to construct a protein-protein interaction network; extracts gene signature features of cells and samples and maps them to the protein-protein interaction network to form corresponding cell modules and sample modules; calculates the distance between each cell module and each sample module, and determines a set of multiple sample modules as the sample module set of the disease phenotype; calculates the distance between cell modules and the sample module set of the disease phenotype; creates a background distance distribution to evaluate the statistical significance of the distance between cell modules and the sample module set of the disease phenotype, and identifies cells whose distance to the sample module set of the disease phenotype is significantly smaller than the background distance distribution.

The application belongs to the technical field of medical information, and discloses a tumor chemotherapy scheme individualized adjustment and curative effect evaluation method and system. Static basic data, dynamic treatment data, single cell sequencing data and structured data of doctor-patient conversation are comprehensively collected in the data fusion quality control stage. Format adaptation and semantic alignment of cross-modal data are realized through a unified standardized interface. Data quality control is carried out in combination with an isolated forest algorithm, and only high-quality data with a score of 80 or more are included to provide a reliable basis for scheme development. In the multi-module collaborative prediction stage, a medical knowledge graph and a hybrid modeling framework are fused, multi-center data is jointly trained, and drug sensitivity scores are accurately output. When the scheme is generated, the clinical guidelines and patient needs are linked, multi-constraint rule reasoning and multi-objective optimization are used to develop individualized schemes that adapt to the patient's genetic characteristics, pathological types and quality of life demands, and reduce the risk of poor efficacy and serious adverse reactions caused by individual differences.

The invention relates to the technical field of biomedicine, in particular to a method for identifying fibroblast subsets in colorectal cancer tissue based on single cell sequencing, and provides a method for identifying fibroblast subsets in colorectal cancer tissue based on single cell sequencing. The method comprises the following steps: S1, extracting colorectal cancer tissues: obtaining tumor tissues removed from the colorectal cancer tissues, and collecting a suspension to prepare a single-cell suspension; s2, single cell sequencing analysis: constructing a high-throughput sequencing library; s3, data analysis: processing the sequencing data by using a CellRanger software pipeline, and mapping readings to a genome and a transcriptome; and carrying out data dimension reduction and visualization. S4, identification of the tumor-associated fibroblast subpopulation: after the total cell type in the colorectal cancer tumor tissue is identified, annotating the cells; and identifying differentially expressed genes by using a Seurt software package, and carrying out function enrichment analysis, so as to identify various cell types.

The invention relates to a single-cell RNA (Ribonucleic Acid) sequencing data clustering method, device and equipment based on graph structure information fusion and a medium, and the method comprises the following steps: respectively calculating soft distribution matrixes corresponding to cell attribute representation, cell structure representation and consensus embedding representation, and deriving target distribution based on the soft distribution matrixes of the cell consensus embedding representation, all parameters of a zero-expansion negative binomial autoencoder, a graph autoencoder and a cross-modal fusion module are jointly optimized by minimizing the KL divergence between target distribution and each soft distribution matrix, iterative training is performed until the model reaches a preset convergence condition, and optimized cell consensus embedding representation is determined; and carrying out clustering analysis on the optimized cell consensus embedded representation by adopting a preset K-means clustering algorithm to obtain a cell type tag corresponding to the gene expression data of single cell RNA sequencing. According to the method, the problems of zero expansion, sparsity and over-dispersion characteristics of single-cell RNA sequencing data can be solved.

This invention discloses DLL3 as a therapeutic target for cervical neuroendocrine carcinoma and its application, belonging to the field of biotechnology. This invention discloses the specific expression of the DLL3 gene in cervical neuroendocrine carcinoma (NECC) and its application as a therapeutic target. Single-cell sequencing and immunohistochemistry revealed activation of the DLL3-NOTCH1 / 2 signaling axis in NECC, and functional experiments confirmed that it drives malignant progression by promoting tumor proliferation and inducing T cell exhaustion. Furthermore, this invention constructed an NECC organoid-TIL co-culture model to verify the synergistic efficacy of the DLL3-targeting drug AMG757 combined with EP chemotherapy. This invention provides new target selection and theoretical basis for the precision diagnosis and treatment of NECC, and has significant clinical translational value.

The application relates to the technical field of gene sequencing, in particular to a CNV detection method based on an isolated forest algorithm, a medium and equipment. The method comprises the following steps: obtaining genomic sequencing data of a CNV event introduced in a reference genome from a Bam file of single-cell DNA sequencing; adopting a variable window strategy to divide the reference genome into windows, and adjusting the windows through a consistent read quantity to obtain a segmentation site information file; calculating RD signal values in each window according to the segmentation site information file and the genomic sequencing data, and extracting PEM signals to obtain PEM signal values; performing multi-feature calculation and analysis on the RD signal values and the PEM signal values based on the isolated forest algorithm; and identifying CNV events in each window according to an analysis result. The application can effectively identify CNV events in single-cell DNA sequencing data, not only overcomes the limitations of traditional methods in single-cell DNA sequencing data, but also improves the accuracy and reliability of CNV event detection.

According to the metabolism-oriented clustering-based tumor-associated macrophage metabolism typing method provided by the invention, the single-cell RNA sequencing data set for analysis is collected and arranged, and then the tumor-associated macrophage population is identified and extracted from the single-cell RNA sequencing data set, so that the technical deviation is eliminated; a core metabolism gene set is constructed based on a tumor-associated macrophage population, then weight distribution and optimization of core metabolism genes are carried out, contribution of the metabolism genes in clustering analysis is enhanced, metabolism-oriented clustering is carried out based on the optimized weight, and a metabolism typing result is generated. Therefore, enhancement of metabolic gene expression characteristics and more accurate distinguishing of TAMs subgroups are achieved, typing results are analyzed and verified through GSEA enrichment analysis or GO enrichment analysis, typing categories are output, challenges confronted when high-dimensional single-cell data are processed through a traditional method are effectively overcome, the method is not only suitable for TAMs, but also can be expanded to other tumor-related cell types, and the method has a good application prospect. And the blank in the prior art is filled.

The present application relates to the technical field of single cell sequencing, and particularly relates to a method for constructing a full-length transcriptome library of a single cell or a trace sample for nanopore sequencing. The method for constructing a library provided by the present application comprises: after lysing a single cell or a trace sample, mRNA is reversely transcribed to prepare cDNA by using a Smart-seq2 method, PCR amplification is performed by taking the cDNA as a template, the PCR amplification product is subjected to FFPE and end repair, and then a nanopore sequencing adaptor is connected. The method can realize true single cell sequencing, and has the advantages of high transcript coverage, high resolution and strong flexibility, and can be applied to differential gene expression analysis of a single cell, transcript structure analysis, full-length isoform identification and the like.

The application discloses a PIP-seq technology-based adjustable osmotic pressure single-cell sequencing kit and a detection method, and through a wrapping buffer with adjustable osmotic pressure and a non-toxic digestion system, high-throughput sequencing of marine high-osmotic living cells and freshwater low-osmotic living cells without chemical fixation is realized for the first time, and the retention rate of sensitive cell groups (such as stinging cells and freshwater ion-regulating cells) is doubled. By only changing the buffer formula, marine and freshwater samples can be seamlessly switched without changing the core reaction process, and the kit has strong versatility, and successfully solves the osmotic pressure barrier problem that has long plagued the field of aquatic biology.

The invention discloses a federated learning classification system and method for protecting privacy of single-cell RNA sequencing data, and relates to the technical field of data privacy classification. The scRNA-seq data are standardized, and features are enhanced through comparative learning and an auto-encoder; the multi-model dynamic collaborative training module supports dynamic switching of models such as a graph convolutional network and a cross-modal Transform according to data features; the causal inference intelligent evaluation module recommends an optimal model through a three-level evaluation system and a deep Q network; the layered compression communication optimization module adopts a three-stage compression strategy to reduce communication traffic; and the security multi-party computing privacy protection module integrates encryption and differential privacy mechanisms to guarantee data security. The single-cell RNA sequencing data federal learning classification method supports multi-model dynamic adaptation, improves classification precision and system universality, reduces communication overhead, adapts to heterogeneous clients, guarantees data privacy, and achieves efficient and safe single-cell RNA sequencing data federal learning classification.

According to the invention, a human renal cortex proximal tubule epithelial cell HK2-CaOx model and a rat kidney stone animal model are adopted, differential modified proteins are analyzed and verified through a protein modification omics technology, and key factors and key sites of lactic acid modification participating in regulation and control of kidney stone generation are identified; single cell sequencing is combined with space transcriptome analysis, identification and subdivision of kidney cell subgroups, and differential expression ligand and receptor pairs and key signal channels of macrophages and renal tubular epithelial cells are analyzed through cell communication. On the basis, the key function of the LDHB protein lactic acid modification site K156 in the kidney stone progress process is identified, and a new molecular marker and a treatment target are provided for early diagnosis of kidney stone and intervention of kidney stone.

The application provides a single-cell RNA sequencing annotation method and device based on a dynamic hypergraph, relates to the technical field of bioinformatics, and comprises the following steps: obtaining a data set, extracting a low-dimensional embedding vector of each cell from a gene expression vector, and constructing a dynamic hypergraph with cells as nodes and gene pathways as hyperedges; extracting a pathway feature of each hyperedge from the dynamic hypergraph; calculating the importance weight of each cell in each hyperedge based on the low-dimensional embedding vector and the pathway feature of the hyperedge; inputting the dynamic hypergraph, the pathway feature and the importance weight into a preset hypergraph neural network for message aggregation and feature learning to generate a prediction label of a cell type; and training the hypergraph neural network through an optimization algorithm to obtain a trained cell annotation model. The application realizes more accurate and more biologically interpretable cell type and function annotation by introducing a hypergraph structure and a metabolic pathway activity dynamic modeling mechanism.

The invention relates to the technical field of biology, and discloses a method for discovering a new stomach-tumor target spot and a system establishment method. By integrating single cell second-generation sequencing and third-generation full-length transcriptome technologies and adopting a CopyKat and Infercnv dual algorithm model, the technical limitations that tumor heterogeneity cannot be analyzed by traditional batch sequencing and full-length new transcripts and malignant cells are difficult to accurately identify by conventional single cell sequencing are overcome. The method can systematically and precisely complete the whole process from cell subset analysis, malignant cell identification to brand new membrane protein target discovery, and further improves the reliability and transformation application potential of the discovered target through cross-platform verification and function correlation analysis, and has a wide application prospect. And a more efficient and reliable target spot discovery scheme is provided for gastric cancer targeted therapy and prognosis judgment.

The application discloses a method and device for comprehensively analyzing immune metabolism and AD disease-related genes by multiple omics. The method comprises the following steps: obtaining single-cell sequencing data of AD patients; obtaining GWAS data of immune cells and metabolites of AD patients; screening independent single nucleotide polymorphisms (SNPs); obtaining causally related immune cell characteristics and metabolite factor characteristics through Mendelian randomization causal inference; analyzing the causally related immune cells and metabolites to identify immune cells and metabolites that have an important impact on the occurrence of AD, and then obtaining metabolic pathways that affect AD; obtaining gene expression data of expression differences of metabolic pathways in different cell populations; performing differential gene analysis to obtain gene analysis results; and screening genes related to AD disease. The method disclosed by the application uses advanced machine learning technology to identify key biomarkers related to Alzheimer's disease and accurately predict the risk of individuals in disease development.

The application discloses a kind of based on PBMC single-cell sequencing multi-cancer risk prediction model construction method and its application, belong to biotechnology field.The method is by integrating multi-source single-cell data, obtains high-quality immune cell atlas by quality control, batch correction and cell annotation, finally by self-test single-cell data queue is verified.Pseudo batch analysis and differential expression screening are used to obtain cell characteristic genes consistent across cancer species, and then LASSO regression dimensionality reduction is performed to finally determine a set of 39 key characteristic genes.Based on this, the screening model exhibits high accuracy (92.2%-97.3%) in both training and independent validation, and can evaluate the risk of 15 cancers at once.The model only needs a small amount of peripheral blood, adapts to conventional commercial scRNA-seq platform, has the advantages of non-invasive, high generalization, strong specificity and good interpretability, and is suitable for large-scale early screening in clinical practice, and has clear conversion prospect.

This invention proposes a method for spatial domain identification based on data interpolation and cell type deconvolution, belonging to the field of bioinformatics. This invention addresses the problems of traditional methods failing to utilize spatial gap information between adjacent points and insufficient integration of prior information about cell types at the tissue spatial structure level in low-resolution spatial transcriptome data. The method includes: acquiring spatial transcriptome datasets and single-cell RNA sequencing datasets, and preprocessing the acquired datasets; interpolating the preprocessed spatial transcriptome data and performing cell type deconvolution in conjunction with the single-cell RNA sequencing data; constructing a deep learning model based on a graph convolutional network; training the deep learning model using a self-supervised contrastive learning strategy based on gene expression information, spatial location information, and cell type information from the deconvolved spatial transcriptome data; and performing spatial domain identification on the test data based on the trained model.

The application belongs to the technical field of biotechnology, and discloses a set of biomarkers for predicting the risk of idiopathic pulmonary arterial hypertension and application thereof. The application integrates expression quantitative trait locus (eQTL) data and single-cell RNA sequencing (scRNA-seq) data sets from peripheral blood and lung tissue and whole genome association analysis (GWAS) summary statistics of idiopathic pulmonary arterial hypertension, uses Mendelian randomization algorithm (MR), genome enrichment analysis and immunofluorescence experiments to identify and accurately locate related genes, and for the first time discovers biomarkers related to the risk of idiopathic pulmonary arterial hypertension, which include at least one of CST7, HLA-B, HLA-E, FKBP1A and UBB. By detecting the above biomarkers, the risk of idiopathic pulmonary arterial hypertension can be effectively diagnosed.

This invention discloses a method for constructing and sequencing single-cell multi-omics libraries based on a dual-permeable microarray design, comprising the following steps: S1, performing an in-situ nucleic acid molecular labeling reaction on a single-cell or mononuclear suspension of the tissue to be tested in a multi-well plate to tag it with a known base sequence; S2, mixing the reacted single-cell or mononuclear suspension with microbeads carrying the known base sequence and loading it onto a dual-permeable microarray; after the reaction is complete, sealing the chip with sealing oil; S3, performing nucleic acid molecule polymerization and amplification within the chip, and then collecting the liquid within the chip to construct a single-cell sequencing library; S4, sequencing the single-cell library and processing the data to obtain single-cell multi-omics library data. This invention enables high-throughput analysis of multi-omics genetic information, including transcriptomics and chromatin availability, at the single-cell level at a low cost and with rapid convenience.

The application provides a single-cell sequencing data feature sequence detection method suitable for multiple library structures. The method is adapted to multiple different requirements by defining a library structure and an output structure, and uses prefix tree-based fuzzy matching to improve processing speed.