92 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 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 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.

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.

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 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.

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 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.

The present invention discloses a mutual nearest neighbor-based single-cell transcriptome batch correction method, which mainly solves the problems of few MNN pairs found by the existing mutual nearest neighbor-based batch correction method and poor correction effect. The implementation solution is as follows: screening the gene features of single-cell transcriptome data to select highly expressed genes; constructing an autoencoder composed of an encoder and a decoder, and using the highly expressed genes selected from the single-cell transcriptome data to cross-train the encoder and the decoder; using the trained autoencoder to extract the features of the highly expressed genes selected from the single-cell transcriptome data to obtain single-cell low-dimensional embedded batch-free information data; searching for mutual nearest neighbor pairs in the single-cell low-dimensional embedded batch-free information data, and calculating a correction vector using the same; using the correction vector to perform batch correction on the data; the present invention finds many MNN pairs and has a good batch correction effect, and can be used for the preprocessing of single-cell transcriptome data in bioinformatics experiments.

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 invention discloses an application of an IRS1 gene promoter region SNP (Single Nucleotide Polymorphism) site as a porcine sexual maturity molecular genetic marker. According to the invention, an IRS1 gene promoter region is taken as a research object, and the relevance between the IRS1 gene promoter region and the sexual maturity of the sow is researched by adopting molecular and cell biological methods: g.128307611Cgt is found; a and g.128307308Cgt, and g.128307308C T is obviously related to group mature day age of Dupin black pigs; then different vectors are transfected, and different influences of different genotypes of the sites on the activity of the IRS1 gene promoter are studied. Furthermore, 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 to research the influence of the IRS1 gene on the transcriptome level and ferroptosis of the sow ovarian granular cells, and the result shows that the IRS1 gene influences the signal paths that cell transcriptome differential genes are mainly enriched to iron ion homeostasis and the like; the ferroptosis of the cells is inhibited.

The invention discloses application of irbesartan in preparation of a medicine for preventing or treating osteoarthritis, and belongs to the technical field of biological medicine. Osteoarthritis single cell transcriptome data are integrated and analyzed, differential expression genes are screened, and irbesartan is screened by using a relocation method and Mendel randomization analysis to serve as a medicine for treating osteoarthritis; analysis results show that irbesartan can significantly reduce the risk of osteoarthritis. An irbesartan administration experiment is carried out by using a zebra fish bone arthritis model, and the experiment result shows that irbesartan obviously increases the joint gap of anal fins of zebra fish and has an obvious protection effect on articular cartilage; a transcriptome sequencing result shows that irbesartan plays the role by inhibiting the MAPK signal channel. The invention provides the effect of irbesartan in the development of osteoarthritis for the first time, provides a new strategy for prevention and treatment of osteoarthritis, and has a wide application prospect.

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 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 present invention discloses an intelligent method for identifying cell - cell communication in spatial transcriptome data. The method includes: preprocessing single - cell transcriptome and spatial transcriptome data; exploring the deep connections existing in omics data by using the attention mechanism to integrate single - cell transcriptome and spatial transcriptome data; mining the local and global features of each cell through a sub - graph - based mechanism; and combining with a graph attention neural network to propose an intelligent algorithm for identifying cell - cell communication at the single - cell resolution. The present invention overcomes the limitations of single - cell transcriptome and spatial transcriptome data, solves the problem of accurate identification and analysis of cell - cell communication, helps to more comprehensively reveal the co - regulation between cells, and further deepens the understanding of complex biological processes.

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.