52 results about "Single cell sequencing" patented technology

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 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 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 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.

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 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 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.

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.

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.

The present disclosure provides methods, systems, devices, kits, and reagents for performing single cell sequencing (e.g., single cell RNA sequencing) from a low volume, capillary blood (or any low volume blood sample which is not obtained from a vein or by venipuncture).

The invention relates to methods for capturing cellular analytes, particularly for the generation of libraries and for single cell sequencing. The methods comprise using temperature control to convert molten hydrogel mixed with cellular analytes and cellular analyte capture agents to hydrogel beads in which captured cellular analytes are embedded. The invention also relates to methods of single cell sequencing or library generation, and to hydrogel beads, compositions, kits, devices, and chips for use in the methods.

The application belongs to the field of single cell sequencing, and discloses a construction method of a single cell multi-modal sequencing library, comprising the following steps: S1: preparing a cell nucleus suspension; S2: mixing each sample cell nucleus with a Tn5 transposome complex, and obtaining a cell nucleus containing sample barcode and ATAC-seq library fragment in situ labeling after transposition reaction; S3: mixing all sample cell nuclei, and then incubating them with different first antibodies and second antibodies in turn; adding a protein A-Tn5 transposome complex mixture, and obtaining a cell nucleus containing target barcode and CUT&Tag library fragment in situ labeling after transposition reaction; S4: mixing all cell nuclei, and performing reverse transcription reaction, and collecting RNA-seq library in situ reverse transcription cell nuclei; S5: after three rounds of single cell index labeling, the cell nuclei are divided into several sub-libraries; S6: sub-library cell lysis separates DNA and RNA, and constructs a DNA sequencing library and an RNA sequencing library containing ATAC-seq and CUT&Tag-seq information respectively. The single cell multi-modal sequencing library can obtain complete multi-omics data through high-throughput sequencing.

The application discloses a cell subpopulation automatic annotation method and device, and relates to the technical field of single-cell sequencing. The method comprises the following steps: acquiring single-cell transcriptome data; in a preset target resolution range, an initial clustering result of each resolution, i.e., a first target cluster, is obtained by means of a community discovery algorithm based on modularity optimization; if there is a cluster in the first target clustering result at each resolution that does not meet the specificity requirement, a second target clustering result is obtained by using similarity iterative merging; a representative marker of each cluster in the second target clustering result is determined by using a getSpecificityScore function; and a cell subpopulation annotation result under the resolution is recommended by comparing the specificity score. By means of a multi-scale analysis strategy, the application combines the specificity score and similarity merging cell subpopulation automatic annotation, realizes automatic identification and annotation of novel cell subtypes without relying on prior annotation information, can adapt to diversified data characteristics, and improves the accuracy and robustness of cell subpopulation annotation.

The present application relates to the technical field of biology, in particular to buffalo mammary gland tissue digestion solution and application thereof, and provides a preparation method of buffalo mammary gland tissue digestion solution and buffalo mammary gland tissue single-cell suspension, and the preparation method can obtain a primary cell suspension of buffalo mammary gland tissue with high viability, low clumping rate, high nucleated rate and less impurities, and can greatly improve the success rate of single-cell sequencing experiment of buffalo mammary gland tissue.

The present application relates to the technical field of biology, in particular to human thymus tissue digestion solution and application thereof. The present application provides a preparation method of human thymus tissue digestion solution and human thymus tissue single cells, and the preparation method can obtain a human thymus tissue primary cell suspension with high viability, low fine nodule rate, high nucleated rate and less impurities, and can greatly improve the success rate of single cell sequencing experiment of human thymus tissue primary cells.

The embodiment of the application provides a digital microfluidic sequencing chip structure and a preparation method thereof. The digital microfluidic sequencing chip structure comprises a first substrate and a second substrate arranged oppositely, the first substrate and the second substrate enclose a containing cavity, the containing cavity comprises an outlet flow channel layer close to the first substrate and a liquid inlet flow channel layer close to the second substrate, and a sequencing structure layer for performing single-cell sequencing is located between the liquid inlet flow channel layer and the outlet flow channel layer. The sequencing structure layer is provided with one or more than one channel communicating with the liquid inlet flow channel layer and the outlet flow channel layer, one or more than one capture element is arranged in each channel, the capture element is used for capturing DNA or RNA of a single cell, and the inner wall of the channel is provided with a library construction reagent layer. The library construction reagent layer is used for performing targeted library construction on the DNA or the RNA, the automation capability of single-cell sample processing is effectively improved, the operation efficiency of sample processing is improved, and an effective tool is provided for sample processing of molecular diagnosis.

The application discloses a KEGG pathway constrained dynamic graph structure optimization cross-patient cell annotation method, comprising the following steps: obtaining and preprocessing single-cell RNA sequencing data of a reference patient and a query patient; calculating a functional activity score of cells based on a KEGG pathway classification system, and constructing a KEGG function semantic matrix; constructing an initial sparse kNN cell graph; using the KEGG function semantic vector as a biological constraint, dynamically adjusting the connection weight between cells through a multi-view gated similarity calculation and an iterative optimization mechanism, and generating a patient-specific cell graph containing function semantic information; constructing a shared graph encoder and a classifier, and introducing a Wasserstein discriminator for adversarial training to realize cross-domain alignment of embedding distribution of the reference patient and the query patient; and finally predicting the type of the query patient cell by using the trained model. The application solves the problem of significantly improving the accuracy, robustness and biological interpretability of cross-patient cell annotation.

Embodiments of the present application provide a spleen structure analysis method and device, electronic equipment and storage medium. The method comprises obtaining a spleen structure analysis request, the spleen structure analysis request comprising first single-cell sequencing data and first spatial transcriptome sequencing data of a spleen tissue to be analyzed; determining a plurality of first significantly expressed gene data corresponding to a plurality of cell types according to the first single-cell sequencing data, and determining a plurality of second significantly expressed gene data corresponding to a plurality of spleen partitions according to the first spatial transcriptome sequencing data; determining a first intersection between the second significantly expressed gene data of each spleen partition and the first significantly expressed gene data of each cell type, and constructing an input data set based on the first intersection; determining a spleen structure analysis result according to the similarity between the input data set and a reference data set, the reference data set being a data set corresponding to a spleen sample of a reference spleen structure. The method can improve the accuracy of spleen structure analysis.

This invention provides a monoclonal antibody against CD163, antibody combinations, and related applications. Utilizing single-B cell sequencing technology, this invention identifies 19 anti-CD163 antibodies belonging to multiple different antigen-binding epitope groups, offering greater antibody diversity and providing more possibilities for selecting paired antibodies. It is applicable to various scenarios such as ELISA, flow cytometry, proteoblotting, and immunohistochemistry.

This invention discloses a method, system, and device for gene regulation network inference based on edge-level contrastive learning. The method includes: acquiring single-cell RNA sequencing data from a database and preprocessing it; acquiring prior regulatory edges from the database; using known interacting gene pairs as positive samples and unknown interacting gene pairs as negative samples; and proportionally dividing the dataset into training, validation, and test sets; inputting the processed dataset and training set into a gene regulation network inference model for iterative training; using a weighted joint optimization of edge-level contrastive loss and supervised binary cross-entropy loss; updating parameter weights using the loss between predicted and actual gene pair values; and establishing a user interface where the user inputs prepared transcriptome data into the trained gene regulation network inference model, and the model returns the inferred gene regulation network to the user.

The application discloses a cell microdroplet identification method based on XGBoost, and relates to the technical field of single-cell RNA sequencing, wherein step one is to search for the boundary of cells and empty droplets by using a RankMSE index and to construct a training set, and to solve the class imbalance problem in the training data by using multiple rounds of downsampling; step two is to combine the gene expression of cells and the cell quality control features calculated in advance such as cell entropy, and to use a machine learning method XGBoost to construct a cell-empty droplet binary classification model suitable for the current data; and step three is to iterate the cell-empty droplet binary classification model, add newly predicted empty droplet data to the model for retraining, and obtain an optimized prediction model. The cell microdroplet identification method based on XGBoost has the advantages that cells can be robustly identified in different data sets, and empty droplets and low-quality cells or cell fragments in the data can be effectively excluded, and the method has higher accuracy and stability compared with the prior art.

The present application relates to the field of biotechnology, in particular to human bladder tissue digestion solution and application thereof.The digestion method of the present application can dissociate human bladder tissue cells while keeping the cell viability at more than 90%, and the amount of harvested cells is also obviously improved.Sufficient cell amount is the prerequisite for optimization treatment such as red blood cell lysis and dead cell removal.The single cell suspension obtained by the method has high viability, low clumping rate, high nucleated rate and less impurities, which can provide a basis for obtaining high-quality gene expression data in human bladder tissue single cell sequencing experiment.The present application can quickly obtain human bladder tissue primary cell suspension (sufficient cell amount, high viability, low clumping rate and less impurities) meeting the requirements of single cell experiment within 2-4 hours.

This invention relates to a method for processing, storing, reading, and writing sequencing data, which defines a unique writing structure and reading method. It solves the problems of excessive memory consumption and heavy computer load caused by loading large-scale h5ad files of single-cell RNA sequencing data, while also improving the analysis difficulties caused by high noise and large batch effects in single-cell RNA sequencing data. The method includes the following steps: First, the single-cell RNA sequencing data file is read using the h5py method to access the underlying data structure; the file content is presented in a hierarchical HDF5 structure. Then, the required groups and datasets are extracted and compiled into a database for appropriate processing. Next, gene names are standardized according to the correspondence between gene names in different formats. Finally, sample screening, data dimension unification, and standardization are performed. Simultaneously, to adapt to this new data structure, a custom dataset object with parallel data reading capabilities is designed.