75 results about "Cellular transcription" patented technology

The invention provides a cell development process dynamic modeling method and device based on time sequence single cell transcriptome data and a medium, and relates to the crossing field of bioinformatics and computational biology. The method comprises the following steps: constructing a Shenchang differential equation learning framework; adjusting parameters of the single cell development state change model based on the Shenxuan differential equation learning framework so as to construct a population cell development state change model; obtaining a cell specific gene regulation network and a population cell gene regulation network based on the population cell development state change model so as to predict occurrence opportunity of cell lineage differentiation and a molecular decision mechanism of cell differentiation; therefore, the problems of incomplete modeling mechanism, insufficient noise processing and lack of energy principle in the existing cell development process are solved.

The invention discloses a preparation method of fresh taxus chinensis stem and leaf protoplasts. The preparation method comprises a preparation process and a purification process. The preparation method of the taxus chinensis stem and leaf protoplast is simple and easy to operate, raw materials are easy to obtain and low in price, reagent components have good biocompatibility, and the taxus chinensis stem and leaf protoplast is free of harmful components, safe and environmentally friendly. The invention provides a simple and rapid taxus chinensis stem and leaf protoplast enzymolysis preparation method, which adopts vacuum filtration, accelerates the permeation of enzymatic hydrolysate and improves the enzymolysis efficiency, so that the protoplast can be rapidly obtained. Meanwhile, high-purity separation of the protoplast is carried out in combination with density gradient sedimentation of the iodixanol solution, so that the protoplast with uniform size and complete form is obtained, and convenience is provided for scientific researches such as subsequent conversion and single cell transcriptome sequencing.

The invention discloses an empty transgene expression filling method based on a conditional variation auto-encoder, which comprises the following steps: designing a unified framework for conjoint analysis of single cell transcriptome data and spatial transcriptome data, obtaining single cell transcriptome sequencing expression profile data, spatial transcriptome expression data and a COVET matrix used for coding local neighborhood covariance in a tissue; projecting single cell transcriptome sequencing expression profile data and spatial transcriptome expression data into a shared potential space through an attention enhancement encoder to obtain potential variables; and decoding gene expression from the potential variables by using a decoder network, filling up missing gene expression information in spatial data, predicting a COVET matrix of single cell transcriptome data, and deducing a spatial context. According to the method, gene expression and spatial information can be coded at the same time, so that spatial context prediction of single cell data and filling of missing genes in spatial data are realized.

The invention belongs to the field of biotechnology and medical technology, and discloses application of tumor-associated macrophages with high expression of SLC16A10 in prognosis diagnosis and treatment of colorectal cancer. According to the invention, colorectal cancer single-cell transcriptome sequencing data analysis before and after anti-PD-1 treatment is carried out in the earlier stage; the tumor-associated macrophage subgroup with high expression of the SLC16A10 is enriched in a response group after colorectal cancer anti-PD-1 treatment, and the prognosis of a colorectal cancer patient with high expression of the SLC16A10 is good. Knock-down of the SLC16A10 leads to reduction of expression of the macrophage M1 type marker, and activation and toxicity of co-cultured T cells are reduced. The SLC16A10 promotes T cell activation and weakens immunosuppression on T cells, so that colorectal cancer anti-PD-1 treatment response is caused. The research explains the influence and mechanism of the macrophage SLC16A10 on colorectal cancer anti-PD-1 treatment, and provides a new strategy and theoretical basis for immunotherapy of colorectal cancer.

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 application discloses a single-cell transcriptome data pseudo-time trajectory analysis method and a computer system, which comprises the following steps: 1) calculating a gene explicit comparison advantage matrix; 2) obtaining a gene similarity matrix by similarity and constructing a gene network; 3) taking an initial node in the gene network, starting random walking from the initial node, recording each gene walked through to form a gene text composed of gene sequences; 4) converting the gene text into a gene word vector; 5) adding all single-cell expressed gene vectors with expression as weight to form a sum vector as a word vector representation of the single cell in the gene space; and 6) visualizing all cell vector representations to obtain an embryo cell development pseudo-time trajectory result. The application provides an analysis basis for identifying different rare cell subtypes in tissues and variant genes of different cell subtypes and has a wide and important application prospect in the fields of tumors, developmental biology and life science.

The application discloses a preparation method of fresh taxus chinensis stem and leaf protoplasts, which comprises a preparation process and a purification process. The preparation method of the taxus chinensis stem and leaf protoplasts is simple and easy to operate, raw materials are easy to obtain, the price is low, reagent components have good biocompatibility, have no harmful components, and are safe and environmentally friendly. The application provides a simple and rapid enzyme hydrolysis preparation method of the taxus chinensis stem and leaf protoplasts, vacuum filtration is adopted to accelerate the permeation of the enzyme hydrolysate and improve the enzyme hydrolysis efficiency, so that the protoplasts can be quickly obtained. Meanwhile, the high-purity separation of the protoplasts is carried out by combining with the density gradient sedimentation of iodixanol solution, and the uniform size and complete morphology of the protoplasts are obtained, which provides convenience for subsequent scientific researches such as transformation and single-cell transcriptome sequencing.

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.

Method and systems for generating a single cell gene expression and / or clonality status profile for a subject from only flow cytometry data. Methods and systems for training a machine learning model to predict single cell gene expression and / or clonality status from flow cytometry and methods and systems to use the trained machine learning model to generate a single cell transcriptomic profile for a subject.

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 construction method of the digital cell basic model disclosed by the invention comprises the following steps: inputting single cell transcription sequencing data and related biological characteristics, and respectively encoding and integrating the data into node characteristics and edge characteristics of a cell map; inputting the formed cell graph into GNN, and adopting a message passing mechanism to jointly learn feature representation of nodes and edges; learning a global relationship among genes in the cell map through an attention mechanism, and outputting feature representation of the genes; and coding based on the feature representation of the gene to obtain a cell feature vector. According to the digital cell basic model CGCompass provided by the invention, pre-training is carried out on five million pieces of human single cell sequencing data, so that information of biological significance of genes and information of interaction between the genes can be learned; biological cell downstream tasks such as cell clustering, cell classification, single-gene disturbance prediction and bulk gene knockout prediction can be effectively completed through two modes of fine tuning and zero sample reasoning.

The application discloses a kind of multi-task cell analysis method and system based on residual graph neural network, comprising: normalizing single-cell transcriptome data, selecting the top 2000 genes with the highest transcription level from the normalized data, obtaining new single-cell transcriptome data;According to the new single-cell transcriptome data, construct and train denoising auto-encoder, reduce the dimension of original single-cell transcriptome data, obtain the feature representation after dimension reduction;Using the feature representation after dimension reduction constructs adjacency matrix, constructs residual graph neural network model;Connecting the graph neural network model with the denoising auto-encoder, construct double self-supervised model and train;According to the double self-supervised model, output the clustering result, interpolation result and low-dimensional representation of single-cell transcriptome data.The method and system provided by the application greatly improve the feature discrimination of network extraction, improve the performance of each single-cell analysis task.

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 provides an application of a TRIM44 expression inhibitor in preparation of a cytarabine-resistant leukemia drug, and belongs to the technical field of bioengineering.The application provides the application of the TRIM44 expression inhibitor in preparation of the cytarabine-resistant leukemia drug.In the application, a key gene TRIM44 affecting AML drug resistance is identified by analyzing a single cell transcriptome data set, the expression of the TRIM44 can be lowered by using sinomenine, the function of regulatory T cells (T-reg cells) is inhibited, the sensitivity of AML cells to a chemotherapy drug is enhanced, a good synergistic treatment effect with cytarabine is shown, the cytarabine resistance of leukemia is reversed, and a new target site and a molecular marker are provided for predicting and improving the treatment effect of patients in clinic.

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.

To provide a novel method that forms a complex linking a peptide with DNA encoding the peptide to establish correspondence therebetween, and to provide a method for readily selecting DNA encoding a desired peptide from a complex library in which peptides correspond to DNA encoding the peptides.SOLUTION: A method for creating a complex in which a peptide is linked to a nucleotide sequence encoding the peptide, the method comprising: (1) preparing circular DNA having a nucleotide sequence encoding a peptide; and (2) transcribing and translating the nucleotide sequence from the circular DNA using a cell-free transcription-translation reaction to express a peptide, wherein the expressed peptide forms a complex linked to the circular DNA.SELECTED DRAWING: None

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.

The present disclosure relates to a single-cell transcriptome and proteome co-analysis method based on double microspheres. The present disclosure provides a single-cell transcriptome and proteome co-analysis method, comprising: providing, in a single compartment in a separated manner, first particles comprising capture reagents, a single cell from a sample, and a protein detection reagent with a label, wherein the capture reagents include a nucleic acid capture reagent and a protein capture reagent that can respectively directly or indirectly capture a nucleic acid and a protein from the single cell; enabling the nucleic acid and the protein from the single cell to be in contact with the first particles and the protein detection reagent; and enabling the first particles to capture protein information by means of the protein capture reagent and the protein detection reagent, and to capture nucleic acid information by means of the nucleic acid capture reagent. The present disclosure can acquire a secreted protein from a single cell and transcriptome information of the single cell, and also can co-capture a single-cell intracellular protein and the transcriptome information.

The invention relates to the technical field of biological information, in particular to a gene regulatory network inference method, model and system for single-cell transcription genes and a storage medium. Determining a correlation vector of each gene pair in the transcriptional gene data of the single cell to be detected, and representing each gene pair in a histogram form; capturing time features in the correlation vectors through a time sequence convolutional network, and capturing spatial features in the histogram through a convolutional network; performing weighted fusion on the time features and the space features to obtain fusion features; and predicting a gene regulation network inference result of the transcriptional gene data of the single cell to be detected based on the fusion features. The method aims at solving the problem of how to predict single cell transcriptome data in a gene regulation and control network.

The invention relates to the technical field of biology, in particular to a racoon ussuriensis dog skin hair follicle single cell transcriptome map and a construction method thereof. The construction method comprises the following steps: collecting a skin tissue sample in the middle of the back of a male racoon ussuriensis dog in a winter hair period, dissociating and preparing a single-cell suspension, constructing a cDNA library and sequencing; comparing original sequencing data to a reference genome, and quantifying high-throughput single cell transcriptome data; further quality control is carried out, low-quality cells and double cells are removed, and standardization processing is carried out on data; screening hypervariable genes, and performing dimensionality reduction and visualization on a gene expression profile to obtain marker genes of a cell population; and calculating the correlation between the to-be-identified cell expression profile and the reference data set, and identifying the cell type. Based on a single cell transcriptome sequencing technology, the cell heterogeneity problem which cannot be solved by tissue sample sequencing is solved, a new way is provided for exploring a hair follicle development heterogeneity mechanism, and a new direction is also provided for biological research of racoon dog hair follicles.

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.

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.