42 results about "Cellular transcription" patented technology

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.

Microglial spatial heterogeneity remains a crucial yet poorly studied question in light of potential cell-directed therapies for Alzheimer`s disease (AD). Little is known about the dynamics of spatially distinct microglia states, which are either adjacent or non-associated with the plaque site, and their selective contributions to neurodegeneration in vivo. So far, research has essentially focused on pathology-associated microglia. Here, we combined novel multicolor fluorescence fate mapping, single-cell transcriptional analysis, epigenetic profiling, advanced immunohistochemistry and computational modelling to comprehensively characterize the relation of plaque-associated and non-plaque- associated microglia during neurodegeneration. This approach enabled us to identify and characterize non-plaque-associated microglia as a unique and highly dynamic microglial state in a mouse model of AD. Non-plaque-associated microglia modulate network expansion, quickly adapt to environmental cues and their transition to plaque-associated microglia can be specifically modulated during disease, contrary to their reputation as a passive bystander subpopulation. This description of the dynamics of spatially segregated microglial states and their distinct molecular features may therefore open promising new avenues for state-specific therapeutic interventions during neurodegeneration.

Provided are a transcription factor activity inferring method, an apparatus, a storage medium, and a computer device, relating to the field of transcription factor activity inference. Provided is a method for performing transcription factor activity inference for spatial transcriptome sequencing data. The method specifically comprises: combining spatial position information of genes in the spatial transcriptome sequencing data with a single-cell transcription factor activity analysis method to perform transcription factor activity inference on the spatial transcriptome sequencing data. The method can improve the accuracy of performing transcription factor activity inference on the basis of spatial transcriptome sequencing technology.

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

The present invention relates to a method for recording a transcriptome of a cell by: providing a test cell that includes a first transgene nucleic acid sequence encoding a fusion protein that is a reverse transcriptase polypeptide and a Cas1 polypeptide and a second transgene nucleic acid sequence encoding a Cas2 polypeptide, wherein the first transgene nucleic acid sequence and the second transgene nucleic acid sequence are under transcriptional control of an inducible promoter sequence, and a third transgene nucleic acid sequence including a CRISPR direct repeat (DR) sequence; wherein the CRISPR direct repeat sequence is specifically recognizable by a RT-Cas1-Cas2 complex formed by the expression products of the first transgene nucleic acid sequence and the second transgene nucleic acid sequence.

The present invention relates to a cell-free transcriptional electrochemical biosensor and to the use of the same for detecting specific molecular analytes, such as specific antibodies, proteins, small molecules, nucleic acids, and derivatives thereof, in complex arrays of biological samples, such as plasma, serum, blood, saliva, sweat, and the like, wherein said biosensor is based on the activation of the transcription of a specific RNA strand, induced by recognition with the analyte. The invention further relates to a method for the detection of specific molecular analytes in complex arrays of biological samples, said method being based on the use of said cell-free transcriptional electrochemical biosensor.

The application discloses a kind of idle gene expression filling methods based on conditional variation auto-encoder, comprising: design is used for the unified framework of single-cell transcriptome data and spatial transcriptome data joint analysis, obtains single-cell transcriptome sequencing expression profile data, spatial transcriptome expression data and the COVET matrix for encoding local neighborhood covariance in tissue;By attention enhanced encoder, single-cell transcriptome sequencing expression profile data and spatial transcriptome expression data are projected into shared latent space, obtain latent variable;Gene expression is decoded from latent variable using decoder network, fill in the missing gene expression information in spatial data, and predict the COVET matrix of single-cell transcriptome data, deduce spatial context.The application can simultaneously encode gene expression and spatial information, so as to realize the spatial context prediction of single-cell data and the filling of missing gene in spatial data.

The application discloses application of an IRS1 gene promoter region SNP site as a pig sexual maturity molecular genetic marker. The present application takes the IRS1 gene promoter region as a research object, and studies the correlation between the IRS1 gene promoter region and sow sexual maturity by using molecular and cell biology methods: it is found that g.128307611C>A and g.128307308C>T are significantly correlated with the age of sexual maturity of Duhei pig populations; then, the influence of different genotypes of the above sites on the activity of the IRS1 gene promoter is studied by transfecting different vectors. Further, the sow ovarian granulosa cells are taken as experimental materials, and experimental techniques such as transcriptome sequencing, total iron colorimetry and malondialdehyde colorimetry are used to study the influence of the IRS1 gene on the transcription level and ferroptosis of the sow ovarian granulosa cells, and it is found that the IRS1 gene influences the different genes of the cell transcriptome, and is mainly enriched in signal pathways such as iron ion homeostasis, and inhibits the ferroptosis of the cells.

The application discloses a method for constructing a single-cell transcriptome and chromatin accessibility double-omics single-cell sequencing library, which comprises the following steps: a) preparing a single-cell suspension; b) obtaining chromatin open sites; c) performing a reverse transcription reaction on the transcriptome of the cell by using a reverse transcriptase and a reverse transcription primer, so as to obtain the transcriptome information of the single cell; d) performing a template switching reaction by using a template switching oligo (TSO); e) subsequent coding; f) initial library amplification; and g) preparing a sequencing chromatin open site library and a transcriptome library. The application also discloses a method for sequencing a single-cell transcriptome and chromatin accessibility double-omics sequencing library, which comprises the steps of respectively sequencing the chromatin open site library and the transcriptome library prepared by the method.

The application discloses a single-cell transcriptome analysis method, system and storage medium, relates to the technical field of biological statistical data analysis, and comprises the following steps: receiving single-cell transcriptome sequencing data and analysis parameters corresponding to a current analysis task, and determining a hash value corresponding to the analysis parameters; comparing the hash value with a preset hash value, and determining whether a target preset hash value matching the hash value exists; if yes, taking an analysis result associated with the target hash value as a target analysis result of the current analysis task; if no, analyzing the single-cell transcriptome sequencing data based on the analysis parameters, generating the target analysis result, associating the target analysis result with the hash value, and outputting the target analysis result. The application realizes cache reuse and version isolation of single-cell transcriptome analysis results by calculating and comparing the hash value of the analysis parameters, and solves the technical problem that researchers need to repeatedly calculate due to result coverage in traditional analysis.

The invention relates to the technical field of gene engineering, in particular to a Cilp gene enhancer and application thereof. Through integration and verification of an ATAC-seq map, a single cell transcriptome data set and multi-omics data, the Cilp gene enhancer is obtained through screening, and a mouse inner ear microinjection system is utilized to further prove that the Cilp gene enhancer can significantly enhance the transcriptional activity of an EGFP protein gene in cochlea type 2 neuronal cells. Compared with other sequences with similar sizes, the Cilp gene enhancer provided by the invention can drive specific expression of a reporter gene or a Cilp gene in cochlear neurons, especially type 2 neurons, and the gene expression level is remarkably improved. The Cilp gene enhancer provided by the invention is suitable for biological materials such as recombinant DNA (deoxyribonucleic acid), vectors or adenoviruses, can be used for promoting transcription of EGFP (enhanced green fluorescent protein) genes or other genes in cochlea neurons, and provides a new targeting tool for gene therapy of hereditary hearing loss.

The application belongs to the technical field of biological medicine, and particularly relates to application of VSIG4 as a biomarker in evaluation of risk of lung fibrosis progression. The application first discovers and verifies that VSIG4 is significantly highly expressed in ILD patients with active inflammatory response, and the high expression is closely related to a signal of continuous progression of the disease by integrating peripheral blood exosome proteomics data and lung tissue single-cell transcriptome data. Based on the discovery, the application provides a kit and a detection method for detecting a VSIG4 protein level in peripheral blood. Data show that the PPF patients can be effectively identified by detecting the content or level of the exosome protein VSIG4 in peripheral blood, and the method has high sensitivity and specificity. The application provides a new non-invasive solution for early risk prediction and disease progression monitoring of lung fibrosis, and has important clinical value.

The invention belongs to the field of bioinformatics, and particularly relates to a chromatin accessibility and transcription factor interaction deep learning method. The method comprises the following steps: firstly, providing a gene expression prediction framework based on deep learning, and simulating a cis-regulation effect by constructing a three-dimensional interaction tensor of a cell * transcription factor * chromatin region; secondly, designing a neural network containing a learnable interaction weight matrix, dynamically modeling specific combination of transcription factors and a regulation and control region by utilizing an attention mechanism, and synchronously optimizing prediction precision and correlation by adopting a joint loss function; and finally, introducing a gene specificity training and data enhancement strategy to realize personalized modeling and robust prediction of different gene regulation and control modes. According to the method, an interpretable deep learning system is established, potential interaction of transcription factors and chromatin can be deduced from multiple omics data, and a new calculation tool is provided for analyzing a gene regulation mechanism and screening key regulation elements.

The invention is a method of single cell transcriptome analysis. The method comprises detecting multiple transcripts in each individual cell of the plurality of cells by barcoding the transcripts with a cell-specific compound barcode formed using a DNA polymerase and a terminal transferase, optionally in a single enzyme such as a reverse transcriptase.

This invention discloses a spatial single-cell transcriptome sequencing method based on light-controlled cell labeling. The method uses ONPF-biotin as a probe and includes labeling the cells to be sequenced with ONPF-biotin; sorting the ONPF-biotin-labeled cells; and performing single-cell sequencing. This invention achieves efficient light-controlled cell labeling and spatial single-cell transcriptome sequencing, solving the problems of low labeling efficiency, limited number of labeling sites, high background signal, and dependence on genetic manipulation in existing methods. It provides a new generation solution for studying the spatial distribution and regulation of cells in different biological systems.

The present invention is a method for single-cell transcriptome analysis, which involves detecting multiple transcripts in each individual cell of a plurality of cells by barcoding the transcripts with cell-specific compound barcodes formed using DNA polymerase and terminal transferase, optionally in a single enzyme such as reverse transcriptase.

The invention provides a specific marker gene lhh2a. 2.2 of spermatogonium of larimichthys crocea as well as a screening method and application of the specific marker gene lhh2a. 2.2, and belongs to the field of molecular markers and the field of germline stem cell research. According to the invention, the nucleotide sequence of the lhh2a. 2.2 gene is as shown in SEQ ID NO: 1; through screening and analysis of a single cell transcription sequencing technology, and through in-situ hybridization, Vasa gene and PCNA gene co-localization, it is determined that the lhh2a. 2.2 gene can specifically identify spermatogonium in a proliferation state; a core tool and a prerequisite condition can be provided for establishment of a high-efficiency germ cell transplantation technology, purification of spermatogonial stem cells for in-vitro culture and construction of a transgenic pseudosciaena crocea strain.

The application discloses a single cell transcriptome low-quality cell filtering method, and relates to a biological data processing method. The method comprises the following steps: clustering cells; taking the expression value average of genes to generate a characteristic expression profile of the cell group; randomly combining the characteristic expression profile of the cell group in pairs to generate artificial multicells; merging the artificial multicell expression profile and the real cell expression profile to calculate the distance between each cell; setting a plurality of equidistant neighborhoods within a specified range, calculating the proportion of artificial multicells of each real cell within the neighborhood under each neighborhood; counting the distribution of the proportion of artificial multicells under each neighborhood, calculating the bimodal coefficient, and taking the neighborhood with the maximum bimodal coefficient as the optimal neighborhood; in the optimal neighborhood, a specified number of real cells with the maximum proportion of artificial multicells are identified as multicells, and the multicells are deleted from the real cell expression profile. The filtering standard and precision of the single cell transcriptome data are improved, and the reliability of the data is enhanced.

The present application belongs to the field of bioinformatics, and particularly relates to a chromatin accessibility and transcription factor interaction deep learning method. First, a gene expression prediction framework based on deep learning is proposed, and a three-dimensional interaction tensor of cell x transcription factor x chromatin region is constructed to simulate cis-regulation; second, a neural network containing a learnable interaction weight matrix is designed, an attention mechanism is used to dynamically model the specific binding of transcription factors and regulatory regions, and a joint loss function is used to simultaneously optimize the prediction accuracy and correlation; finally, gene-specific training and data enhancement strategies are introduced to realize personalized modeling and robust prediction of different gene regulation modes. The present application establishes an interpretable deep learning system that can infer potential transcription factor and chromatin interactions from multi-omics data, providing a new computational tool for analyzing gene regulation mechanisms and screening key regulatory elements.

The invention belongs to the technical field related to single cells, and provides a single cell transcriptome batch effect identification method and system, and the method comprises the steps: screening metadata information most valuable for batch effect identification from clinical metadata based on a PERMANOVA and confusion detection mechanism clinical metadata importance evaluation method; the noise is reduced by aggregating the cells into metacells, the calculation efficiency is improved, and a distribution characteristic matrix between batches is constructed; self-adaptive weighting is carried out according to the importance of different clinical metadata, and the real difference between batches is accurately measured; based on a batch grouping strategy of dynamic tree cutting, outlier samples and sample groups are automatically identified, and a new batch system is constructed for subsequent batch effect correction. According to the method, the technical batch effect and the biological difference can be effectively distinguished, biological signal loss caused by excessive correction is avoided, and the real biological heterogeneity between samples can also be revealed.

Multiplexed fluorescent imaging methods are essential for resolving cellular transcriptional state with high resolution spatial information, but multiplexity is mostly limited to low numbers because of spectral overlap between the fluorophores that can be used. Although sequential fluidic exchange of DNA imagers expands its multiplexity, it requires time-consuming workflows and complex instrumentation. To eliminate such problems, here the toehold-FISH method as well as pre-programmed DNA probes is introduced, which is a novel imaging method that uses rapid and orthogonal DNA strand displacement reactions to enable highly multiplexed RNA imaging without buffer exchange steps and necessary accessories. In toehold-FISH, signal switching from one RNA target to the next is achieved by strand displacement reactions with sequentially added non- fluorescent DNA displacer strands, which take less than 30 seconds to complete signal switching in fixed cells. Because of vast sequence design space of DNA probes, unlimited multiplexity can be achieved.

The embodiment of the invention relates to the crossing field of generating drug molecules by using a large language model, in particular to a phenotypic drug molecule discovery method, device and equipment based on cell transcriptomics and a computer readable storage medium. The method comprises the following steps: acquiring transcriptome sequencing result data to be analyzed, and extracting a differential expression gene list from the transcriptome sequencing result data; obtaining a differential gene list text description corresponding to the differential expression gene list; inputting the differential gene list text description into a trained molecular generation model to obtain at least one candidate molecular structure representation; the molecular generation model is used for converting the differential gene text description serving as gene expression information into a molecular reasoning result causing the change. The method can break through the dependence of traditional drug research and development on a known compound library, brand new molecules meeting functional requirements are generated, and the chemical space of drug discovery is expanded.

The invention discloses a method for screening specific cells and characteristic genes of rheumatoid arthritis based on peripheral blood single cell transcriptome sequencing and a gene combination. The method comprises the following steps: carrying out single cell transcriptome sequencing on peripheral blood mononuclear cells (PBMC) of a patient with rheumatoid arthritis, carrying out multi-dimensional analysis to obtain a disease-related cell population, and further obtaining a characteristic expression gene of the cell population, according to the method, the specific genes CREB5, GLIPR1 and ISGF6 of the rheumatoid arthritis are screened out by combining single-cell transcriptome sequencing data and whole transcriptome sequencing data of various autoimmune diseases PBMC (peripheral blood mononuclear cells), the obtained gene combination is used for constructing a column graph model, and the model is proved to have relatively high accuracy when being used for diagnosing the rheumatoid arthritis. The invention provides a method for screening and identifying specific cells and characteristic genes with more biological significance, which is beneficial to obtaining diagnostic markers or therapeutic targets with higher reliability and provides a new thought for diagnosis and treatment of autoimmune diseases.

The invention provides a single cell transcriptome transient change vector prediction method, system and device. The method comprises the steps of obtaining single cell transcriptome data and a corresponding time point label; generating a supervision label for model training based on the time point label and the single cell transcriptome data; constructing a semi-supervised learning model; and predicting a gene expression transient change vector of each cell by using the trained model. A semi-supervised learning framework is provided, a single cell transcriptome is used as input, and a limited real time point label is used for predicting a single cell transcriptome transient change vector; the method for deducing the transient change vector of the single cell transcriptome by using real time point information is provided for the first time, and the blank in the field is filled; meanwhile, coherent time characterization can be deduced from discrete real time points, and another technical blank in the field is filled.

According to the cell subset classification method based on the plant single cell transcriptome marker gene, the electronic equipment and the storage medium, original genes with low-quality expression are removed based on the gene expression matrix, the marker gene set is obtained, redundant information is effectively removed, key cell markers are accurately recognized, and the classification efficiency is improved. Then, low-dimensional cell expression vectors corresponding to the plant single cells are determined on the basis of the marker gene set, and training is carried out on the basis of the low-dimensional cell expression vectors corresponding to the plant single cells and the cell subset categories to which the plant single cells belong, so that a cell subset classification model is obtained; and acquiring the low-dimensional cell expression vector of the to-be-classified plant cells based on the marker gene set, and determining the cell subpopulation to which the to-be-classified plant cells belong according to the low-dimensional cell expression vector of the to-be-classified plant cells and the cell subpopulation classification model, thereby realizing accurate classification of the to-be-classified plant cells.

The invention discloses a self-supervised cross-modal alignment and joint representation learning method oriented to single-cell multi-modal data, and relates to a self-supervised cross-modal alignment and joint representation learning method. The invention aims to solve the problems that in the prior art, single-modal analysis cannot correlate T cell antigen specificity and functional phenotype, and a multi-modal tool is insufficient in characterization granularity and weak in generalization ability. The method comprises the following steps: collecting and preprocessing single cell transcriptome and single cell TCR sequencing pairing data, and constructing a training set and a test set; constructing a gene expression single-mode module and a TCR single-mode module; alignment and fusion of two modes are realized through a multi-mode fusion module. Meanwhile, a multi-mode and single-mode loss function is designed for self-supervised training, and GEXunimodal, TCRunimodal and multi-mode combined representation are finally output, and downstream tasks such as cell clustering annotation, antigen specificity prediction and TCR-to-gene expression generation are adapted. The invention belongs to the technical field of bioinformatics and single-cell multi-omics analysis.

The invention relates to application of SS-31 in preparation of a medicine for improving fertility, and belongs to the technical field of medicines. The invention relates to application of SS-31 in preparation of drugs for improving fertility, in particular to application of SS-31 in preparation of drugs for improving mitochondrial functions and metabolism and improving fertility of mammals and / or humans. By intraperitoneal injection of SS-31, ovary mitochondrial dysfunction and oxidative stress of an aged mouse can be accurately reversed, a follicle microenvironment is remodeled, and the fertility potential of the mouse is remarkably recovered; in addition, a molecular route map of the SS-31 for rescuing the fertility of the senescent mouse is drawn for the first time, wherein dynamic imaging of an ovary single cell transcriptome and living cells is taken as an entry point, and the SS-31 exerts the protection effect through a mitochondrial-VEGF axis. Not only is a theoretical foundation laid for clinical transformation of SS-31, but also a verifiable new normal form is provided for delaying reproductive aging by targeting mitochondrial-VEGF interaction regulation.

The present application provides a single-cell transcriptome data preprocessing method, an electronic device, and a storage medium. The method comprises: acquiring a first data set uploaded by a user and generated based on single-cell omics; filtering the first data set on the basis of metric data used for filtering cells, so as to obtain a second data set, wherein the metric data comprises threshold ranges respectively corresponding to UMI count, feature gene count, mitochondrial gene percentage, and β-actin expression; calling a batch effect removal tool to perform batch effect removal on the second data set to obtain a third data set; and performing doublet removal on the third data set by means of a DoubletFinder tool so as to obtain a result data set obtained by preprocessing. The present solution supports uploading and preprocessing of data in various formats such as tsv, txt, csv, RDS, and HDF5, and can improve the efficiency of preprocessing and the validity of processed data sets.

Disclosed herein are methods for preparing a biological sample for single cell analysis that will simultaneously provide single cell RNA, single cell extracellular protein, single cell cytoplasmic protein, and single cell transcription factor readings while retaining cell heterogeneity. Some methods disclosed herein allow for detection of proteins, including detection of protein levels and / or detection of protein post-translational modifications.

The invention discloses a pig immune state evaluation method based on a cell specific gene co-expression module. The method comprises the following steps: S1, key module determination: pre-determining a key gene co-expression module significantly associated with pig core immune traits; the key gene co-expression module comprises at least one first module which is selected from a neutrophil transcriptome and is remarkably and positively correlated with the percentage of neutrophil, and at least one second module which is selected from a peripheral blood mononuclear cell transcriptome and is remarkably and positively correlated with the percentage of lymphocyte; and S2, an immune state evaluation step: evaluating the immune state of the to-be-tested pig. Compared with the prior art, the invention has the following beneficial effects: cell specificity: specific immune cells are separated for transcriptome analysis, specific expression signals of neutrophils and PBMCs are accurately captured, the problem of mixed signals of whole blood sequencing is overcome, and evaluation is more targeted and accurate;