274 results about "Single cell sequencing" patented technology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

The invention discloses a multi-modal biophysical characterization device, a multi-modal biophysical characterization system, a multi-modal biophysical characterization method and application thereof. The multi-modal, high-resolution, high-throughput, high-sensitivity, low-cost, low-damage and configurable physical characterization of single cells or multi-cell polymers can be realized, stimulation (such as mechanical stimulation, electrical stimulation and light stimulation) of different types and intensities can be performed on a to-be-detected sample, and the in-vivo microenvironment can be better simulated; and carrying out operations (such as marking, curing, ablation, cutting, extraction, sorting and the like) on samples at specific positions or regions, and carrying out comparative analysis in combination with other characterization methods (such as protein staining, histochemical staining, single cell sequencing and the like). The multi-mode biophysical characterization device is suitable for the fields of synthetic biology, diagnosis, drug discovery, tumor early screening, cell therapy, precision medical treatment and the like.

Embodiments disclosed herein provide for spatially tagged nuclei that are compatible with any genomic or multiomic single cell / nuclei assay to allow generation of a spatially resolved single cell sequencing library with single cell resolution.

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.

Disclosed are methods and microfluidic devices for successfully co-encapsulating single cells and single beads in single droplets in a high-throughput, high efficiency manner. Cells and beads are organized into two or more ordered streams flowing through separate microchannels of the microfluidic device, which can include non-rectangular microchannels. Cells and beads in the ordered streams are sufficiently spaced such that at a junction of the microfluidic device, single droplets are generated at higher co-encapsulation efficiencies that beat Poisson statistics. Single droplets including a cell and a bead can undergo single-cell sequencing.

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 discloses application of a reagent for detecting chemokine ligand 9 in preparation of a myocarditis early detection product. The application identifies immune cell characteristics in a mouse myocarditis early stage by using single cell RNA sequencing technology, and finds that compared with healthy mice, myocarditis mice have more macrophages infiltrating myocardial tissue and expressing chemokine ligand 9, which reveals that the chemokine ligand 9 can be used as a specific target for detecting acute myocarditis. Further, the application uses BPBBT, DSPE-mPEG 2000 and DSPE-PEG 2000 -MAL to prepare nanoparticles, designs a targeting peptide capable of specifically combining with the chemokine ligand 9 and connects the targeting peptide to a surface of the nano material, and thus myocarditis targeted nano probes are prepared. The myocarditis targeted nano probes can specifically gather in an inflammation area, and thus myocarditis can be accurately detected.

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.

The present invention provides a method for analyzing molecular differences in glioblastoma and its application, belonging to the field of biotechnology. Using differential gene expression analysis, spatial transcriptomics analysis, deconvolution analysis, single-cell RNA sequencing data, and immunofluorescence, this application identified specific molecular signatures associated with pseudopalisading necrosis (PAN) and microvascular proliferation (MVP), revealing differences between these tissue structures. The results showed that PAN and MVP shared spatial expression of specific marker genes and were enriched for endothelial cells of distinct phenotypes. Single-cell trajectories inferred pseudotemporal trajectories of glioblastoma stem cells (GSCs) to EC differentiation. This study reveals distinct roles for endothelial cells in the PAN and MVP regions of GBM, highlighting the complexity and diversity of the tumor microenvironment and providing new insights into endothelial cell subpopulations and GSC differentiation into endothelial cells in GBM.

This invention discloses a kit and method for extracting cell nuclei from plant tissues, belonging to the field of biotechnology. The kit includes a nuclear lysis buffer and a nuclear washing buffer. The nuclear lysis buffer is prepared using nuclease-free water with sucrose, Tris-HCl (pH 7-8), NaCl, MgCl2, a surfactant, DTT, an RNase inhibitor, and spermidine. The nuclear washing buffer is prepared using nuclease-free water with sucrose, Tris-HCl (pH 7-8), NaCl, MgCl2, DTT, an RNase inhibitor, and spermidine. This kit is suitable for extracting cell nuclei from fresh yew stem tissue, yielding cell nuclei with intact nuclear membranes, stable gene expression, high purity, and few impurities, suitable for single-cell sequencing.