108 results about "Cellular transcription" patented technology

The embodiment of the invention discloses a single cell transcriptome sequencing data annotation method. The method comprises the following steps: acquiring single cell transcriptome sequencing data associated with a target disease, and clustering a plurality of cells corresponding to the single cell transcriptome sequencing data to obtain a plurality of cell clusters; aiming at each marker gene corresponding to the target disease, according to single cell transcriptome sequencing data, obtaining the cell cluster gene expression quantity of the marker gene in each cell cluster; for each cell cluster and each cell type corresponding to the target disease, accumulating the cell cluster gene expression quantity of each marker gene corresponding to the cell type in all the marker genes in the cell cluster to obtain the total gene expression quantity; and aiming at each cell cluster, according to the total gene expression quantity corresponding to the cell cluster under each cell type, annotating a target cell type for the cell cluster from all the cell types. According to the technical scheme provided by the embodiment of the invention, the accuracy of cell type annotation can be ensured.

The invention relates to a single-cell transcriptome data clustering method based on graph contrast representation learning, and belongs to the technical field of single-cell transcriptome data clustering. Comprising the following steps: acquiring single-cell transcription data; preprocessing the acquired single cell transcription data; obtaining a first enhanced view and a second enhanced view based on the preprocessed single cell transcription data; constructing a clustering model, wherein the clustering model comprises a first encoder, a second encoder, a first decoder and a second decoder; sequentially inputting the first enhanced view into a first encoder and a first decoder for training, and sequentially inputting the second enhanced view into a second encoder and a second decoder for training; and carrying out single cell transcriptome data clustering by using the first encoder and the first decoder of the trained clustering model. According to the method, the influence of the characteristics of the single cell transcriptome data on the clustering effect is weakened, so that a better clustering effect is achieved.

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 provides a cell type annotation method and device based on plant single cell transcriptome data and a readable storage medium. Annotation is achieved through multi-level data integration, wherein preliminary annotation is expressed based on cell type marker genes or homologous genes; calculating expression correlation auxiliary annotations of the to-be-analyzed data and the known transcriptome data set; performing function enrichment on the cell cluster differential genes to deduce cell types; carrying out quasi-timing analysis on the heterogeneous cell clusters and annotating subgroups; and finally integrating and generating a comprehensive annotation. The method solves the problem that the prior art depends on artificial experience and is insufficient in basic data set, and is suitable for mode and non-mode plants.

The invention discloses an MMP2 gene promoter region SNP (Single Nucleotide Polymorphism) site and application thereof in pig breeding. According to the invention, an MMP2 gene promoter region is taken as a research object, and the relevance between the MMP2 gene promoter region and sow sexual maturity is researched by adopting a molecular and cell biology method, so that the situation that g.30088284G > A site and g.30088004C > T site are obviously related to the group maturity day age of Dumet black pigs is found; then different vectors are transfected, and different influences of different genotypes of the sites on the activity of the IRS1 gene promoter are studied. Further, sow ovarian granular cells are used as experimental materials, experimental technologies such as transcriptome sequencing, a total iron colorimetric method and a malondialdehyde colorimetric method are adopted, the influence of the MMP2 gene on the transcriptome level and ferroptosis of the sow ovarian granular cells is researched, and it is found that cell transcriptome differential genes influenced by the MMP2 gene are mainly enriched to signal channels such as cell differentiation; and ferroptosis of cells is promoted.

The invention belongs to the technical field of single cell data analysis, and particularly relates to a single cell pseudo-trajectory identification method based on a gene co-expression network. Comprising the following steps: S1, constructing a single cell transcription data matrix of behavior gene names and cell names; s2, preprocessing the single-cell transcription data matrix, constructing a cell expression matrix, and performing characteristic gene screening on the cell expression matrix to obtain a single-cell gene expression matrix; s3, constructing a gene co-expression network; s4, constructing a gene rank differential expression network in combination with the single-cell gene expression matrix and the gene co-expression network; and S5, carrying out integrated clustering on the gene rank differential expression network, fusing by using a similarity network to obtain a final clustering result, determining an initial cluster, distributing pseudo time for cells in each cluster, and deducing a cell pseudo trajectory. According to the method, the differentiation track of the cell can be identified, and compared with an original gene expression matrix, a more stable identification result can be obtained by utilizing a relative expression sequence.

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 present disclosure provides gene editing methods for modulating the expression of an inhibitor of DNA-binding E-protein transcription factors, namely Id3, and thereby affecting T cell function. First, it provides experimental evidence that Id3 is critical to the persistence and function of tissue-infiltrating GVHD T cells in a mouse model. Id3 reduces chromatin accessibility (ChrAcc) of transcription factors (TFs) that drive T cell PD-1 transcription, differentiation and dysfunction. Id3 loss increases PD-1 expression and impairs tissue-infiltrating Th1 cells. Second, it provides proof-of-concept that targeting ID3 in human T cells using a CRIPSR / Cas9 knockout (KO) prevents xeno-GVHD but preserves the anti-leukemic activity of chimeric antigen receptor (CAR)-T cells. Third, it provides experimental evidence that ectopic expression of Id3 in engineered human CAR-T cells enhances the ability of these cells to eliminate tumors.

The present disclosure provides gene editing methods for modulating the expression of an inhibitor of DNA-binding E-protein transcription factors, namely Id3, and thereby affecting T cell function. First, it provides experimental evidence that Id3 is critical to the persistence and function of tissue-infiltrating GVHD T cells in a mouse model. Id3 reduces chromatin accessibility (ChrAcc) of transcription factors (TFs) that drive T cell PD-1 transcription, differentiation and dysfunction. Id3 loss increases PD-1 expression and impairs tissue-infiltrating Th1 cells. Second, it provides proof-of-concept that targeting ID3 in human T cells using a CRIPSR / Cas9 knockout (KO) prevents xeno-GVHD but preserves the anti-leukemic activity of chimeric antigen receptor (CAR)-T cells. Third, it provides experimental evidence that ectopic expression of Id3 in engineered human CAR-T cells enhances the ability of these cells to eliminate tumors.

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.

The invention belongs to the field of single cell level nucleic acid detection, and particularly relates to a construction method and application of a high-throughput single cell transcriptome library based on probe targeted enrichment. According to the method, single cell fixed punching and hybridization enrichment of a specific targeting double probe designed by a target nucleic acid sequence are combined, the nucleic acid sequence comprises mRNA and non-coding RNA, the probe is connected with beads with barcodes through a connecting sequence on the probe, cell labeling is carried out, and library building steps such as extension and amplification are completed; therefore, the problems of insufficient transcriptome integrity, single detection target and low number of detected genes are solved.

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.

Disclosed herein are methods for preparing a biological sample for single-cell analysis which will simultaneously provide single-cell RNA, single-cell extracellular protein, single-cell cytoplasmic protein, and single-cell transcription factor readouts while preserving cellular heterogeneity. Some methods disclosed herein allow for the detection of proteins including the detection of the level of proteins and / or the detection of post translational modifications of proteins.

The present invention provides a single-cell transcriptome sequencing method and use thereof. The method comprises: preparing a single-cell suspension with a test cell sample, then fixing cells with a fixative solution; and using a reverse transcription primer to perform an in-situ reverse transcription reaction on RNA of the fixed single cell to synthesize a first cDNA strand.

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 invention belongs to the technical field of single cell data analysis, and particularly relates to a single cell pseudo-trajectory topology identification method based on a Reeb graph. Comprising the following steps: S1, preprocessing a single-cell transcription data set to obtain a single-cell gene expression matrix; s2, constructing an optimization model, constructing a single cell connected network, and performing cell population recognition; s3, extracting a single-cell Reeb map in combination with Morse function distribution and equivalence definition of the cells; and S4, identifying key nodes in the Reeb graph by combining the Morse function extreme value, deducing the middle stage and initial and end states of cell differentiation, measuring the distance influence of the cells in the network and the equivalence class, and identifying the cell pseudo trajectory. According to the method, differentiation tracks of cells in multiple directions are extracted on the basis of a topological structure Reeb graph of a cell connected network. The algorithm framework can convert the cellular network into a Reeb graph with fewer equivalent nodes, so that the calculation complexity is reduced; the nature of the Morse function is provided to determine the critical state of cell differentiation as compared to the initial time determination that completely depends on a priori information.

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 invention discloses a high-throughput single cell transcriptome sequencing and library building method based on microfluidics. The method can be expanded to be used for information acquisition of various transcripts. A single cell and RNA, a single-cell molecular tag, a reverse transcription reagent and a PCR reagent carried by the single cell are fixed in a partition through a one-button automatic instrument, labeling of the single cell in the single partition is achieved through the single-cell molecular tag in the partition, reverse transcription of the RNA in the single partition is achieved through the reverse transcription reagent, and cDNA is obtained through PCR. Signal amplification of cDNA of a single cell in a single partition is realized through a PCR reagent. After a cDNA signal in a single partition is amplified, the partitions are broken, cDNA products carrying single-cell molecular tags of the multiple partitions are combined, the cDNA products are subjected to fragmentation and then introduced into a sequencing linker, and library construction is completed. According to the method provided by the invention, the loss of cDNA products is reduced while experimental operation steps are reduced, so that the quantity of gene amplification products is increased by 40% or above.

Provided herein are methods of releasing nucleic acids from a fixed biological sample comprising contacting the fixed tissue sample to an enzyme. The disclosure further provides methods to quantify and deconvolute a population of mRNA spliced variant isoforms from a cellular transcriptome. Additionally provided herein are methods delivering a barcode to a nucleic acid sample using integrases, nucleic acid samples having barcodes, and nucleic acid libraries thereof.

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 invention discloses a nucleic acid aptamer AS2-3 of a human thrombin protein and a screening method and application thereof. The sequence of the nucleic acid aptamer AS2-3 is shown in SEQ ID NO:1. The screening method comprises the following steps: on the basis of a human thrombin protein aptamer obtained by previous screening, retaining its specific sequence for library construction, performing primary screening by using a Capture-SELEX method, and then performing screening again by using a transcription process of a cell-free transcription and translation system. The invention successfully obtains a nucleic acid aptamer sequence AS2-3 which can not only specifically bind to the human thrombin protein, but also can respond to 0.5 μM human thrombin protein in a cell-free transcription process in the form of a linear or plasmid, and inhibit and regulate the transcription of a downstream gene. By combining the two screening methods, it is expected that the nucleic acid aptamer AS2-3 can be applied to the screening of aptamer sequences of other target ligands, thereby having the opportunity to replace transcription regulatory factors as gene elements and be applied to biological circuits for regulation.

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 invention provides a single cell transcriptome sequencing method. Specifically, the method provided by the invention adopts a one-tube method, and the steps of sample lysis, reverse transcription, PCR amplification and product purification are completed in the same reaction container, so that the sensitivity of amplification of low-abundance RNA in a cell transcriptome and RNA in a special structure is improved, the loss of transcriptome information is reduced, and the amplification and sequencing efficiency of a single cell transcriptome is improved. In addition, the method is suitable for transcriptome sequencing of fixed single-cell samples and live fresh single-cell samples at the same time. The invention also provides a kit for sequencing the single-cell transcriptome and a single-cell transcriptome sequencing device, so that the single-cell transcriptome can be more simply, conveniently, sensitively and completely sequenced.

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.