945 results about "Cell typing" patented technology

The invention discloses a space transcriptomics cell type deconvolution method based on a generative adversarial network and a comparative learning framework. The method comprises the following steps: firstly, enhancing a gene expression matrix by using a histological image and spatial position information; then, in combination with GraphSAGE and self-supervised comparative learning, optimizing feature representation of space transcriptomics data, and reconstructing high-quality space gene expression data; single-cell RNA sequencing data gene expression features are extracted by using a variational automatic encoder, and meanwhile, the quality of a generated sample is optimized by using a discriminator, so that efficient feature extraction and accurate gene expression reconstruction are realized; and finally, mapping single-cell RNA sequencing data into spatial points through comparative learning and mapping matrix learning based on low-rank representation, thereby predicting the cell type of each point. According to the model, multi-modal information in single-cell RNA sequencing and space transcriptomics data is comprehensively considered, and a mapping matrix is constructed and optimized, so that the cell type of each space point is predicted.

The invention provides a cell type and cell abundance identification method and system based on cross-modal training, relates to the field of image processing, and aims to solve the problems that the existing identification technology mostly depends on non-standard factors such as manual labeling position annotation information and the like, abundant morphological modes in a tissue pathological image are not fully utilized, and the identification accuracy is poor. And the identification reliability and accuracy are influenced. The method comprises the following steps: acquiring spatial transcriptomics data matched with pathological image-gene expression, and preprocessing the spatial transcriptomics data to obtain high-expression gene expression data, local image blocks, cell types and abundance tags; constructing a cross-modal joint representation learning model, and inputting high-expression gene expression data and local image blocks into the model for training; and predicting a to-be-predicted histological image based on the trained model. According to the method, the problems in the prior art are solved, the capability of predicting the cell abundance from the histological image is improved, and the spatial distribution of fine-grained cell types is fully revealed.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

Ionizable cationic lipids, methods for synthesizing the same, intermediates useful in synthesis of the ionizable cationic lipids, and methods of synthesizing the intermediates are disclosed. The ionizable cationic lipids are useful as a component of lipid nanoparticles (LNP), which in turn can be used for delivering nucleic acids into cells in vivo or ex vivo. LNP compositions are also disclosed, including LNP comprising a functionalized lipid to enable conjugation of a binding moiety, and targeted LNP (tLNP), that is a LNP in which a binding moiety has been conjugated to the functionalized lipid and can serve as a targeting moiety to direct the tLNP to a desired tissue or cell type.

The invention provides a biological microenvironment image analysis and classification method, which relates to the technical field of cell image analysis and comprises the following steps: processing an input biological microenvironment image, segmenting cell components and extracting multi-parameter initial features of the cell components; a first machine learning model is adopted to deduce a refined functional sub-state exceeding a traditional cell type based on the internal characteristics and local microenvironment information of the cells; in combination with biomolecule interaction knowledge, calculating and generating a spatially resolved biomolecule interaction potential field map so as to quantify intercellular communication potential; forming a comprehensive state descriptor set; and inputting the descriptor set into a second machine learning classification model to generate a precise classification result of the biological microenvironment. According to the method, functional, quantitative and interpretable analysis and classification of the biological microenvironment are realized, and the evaluation depth, objectivity and accuracy are remarkably improved.

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 discloses an intercellular signal transduction inference method and device based on optimal transmission of a knowledge graph, equipment and a medium, and relates to the technical field of genetic engineering. The source gene expression data of different cells in the source cell type and the target gene table data of different cells in the target cell type are found according to the cell type database. On the basis of the ligand-receptor interaction knowledge graph, obtaining various pathways with an interaction relationship between the source cell type and the target cell type, and constructing a pathway set; and with the aim of minimizing the total transmission cost, determining an optimal transmission cost matrix of each path in the path set by adopting an scKGOT algorithm. According to the method, the intercellular channel can be more conveniently deduced, and the intercellular communication can be deduced by accurately simulating the intercellular ligand-receptor signal channel.

The invention relates to the technical field of bioinformatics, in particular to a method for identifying an elncRNAs regulation target gene in a single cell based on single cell ATAC-seq. According to the method provided by the invention, a regulatory network of an elncRNAs-target gene on a single cell whole genome is identified by utilizing chromatin co-accessibility based on scATAC-seq data. Meanwhile, the method is applied to immune cell differentiation, and the elncRNAs-target gene regulation dynamic state of immune cell differentiation is accurately recognized. In addition, the biological functions of the elncRNAs in different cell types are further analyzed. The method provided by the invention can help related researchers in the field of biomedicine to more elaborately and accurately predict the regulation effect and function of the elncRNAs under the single cell resolution.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention belongs to the technical field of biology, and particularly relates to a nano antibody-proximity marker enzyme fusion protein and application thereof. Specifically, the present disclosure provides a fusion protein for proximity labeling wherein the fusion protein is operably linked together by (a) a nanobody and (b) a proximity labeling enzyme; optionally, a peptide linker is also included between the nano antibody and the proximity marker enzyme. The nano antibody-proximity marker enzyme fusion protein disclosed by the invention is obtained through in-vitro expression and purification, target protein is targeted by using an antibody, and the nano antibody-proximity marker enzyme fusion protein does not depend on an overexpression system and has compatibility to various cell types, tissues and clinical fixed samples; proteins with post-translational modification and various organelles, such as membraneless organelles, can be targeted; and the positioning accuracy rate reaches 100%.

The invention discloses a spatial transcriptomics cell clustering method based on multi-scale contrast learning. The method comprises the following steps: S1, carrying out data preprocessing; s2, performing graph construction by using the processed data; s3, performing data enhancement; s4, extracting cell gene expression information by using GCN; s5, enriching node information by using a ContraNorm layer; and S6, further learning information by using multi-scale image comparison learning, and performing biological analysis by using final information obtained by learning. According to the method, computer-aided cell type analysis is utilized, a large amount of high-quality data is not needed, only gene expression data and cell space position information data are utilized, which cells belong to the same category can be predicted, and more effective information is provided to help researchers to identify the onset of cancer and the development of diseases.

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.

The invention provides a cell type annotation method and device, equipment and a storage medium, and the method comprises the steps: clustering cell sequencing data into a plurality of cell populations; analyzing a differential high-expression component list of each cell population, wherein the differential high-expression component list comprises differential high-expression components (components refer to genes or proteins) which are ranked from high to low according to specificity scores in the cell populations; for each cell population, determining the probability that the cell population belongs to each cell type according to the sequence of the preset marking component corresponding to each cell type in the difference high expression component list of the cell population, and determining the cell type with the maximum corresponding probability as the cell type to which the cell population belongs; the sorting of the marked components corresponding to the cell types in the differential high-expression component list of the cell population is positively correlated with the probability that the cell population belongs to the cell types. According to the scheme, the influence of the marking component on the cell type to which the cell population belongs is correspondingly enhanced or weakened according to the specificity of the marking component in the cell population, and the accuracy of the annotation result is improved.

The invention discloses a 3D intestinal organ differentiation method based on human pluripotent stem cells and an induction culture medium and application thereof, and relates to the technical field of stem cells. According to a culture medium formula combination, intestinal organs can be differentiated into various cell types such as epithelial cells, neuroendocrine cells and endothelial cells; the method is a key mark for successful differentiation and functional maturation of intestinal organs. According to the 3D intestinal organ differentiation method disclosed by the invention, histological structures such as intestinal crypts are differentiated from intestinal organs generated by differentiation, and the intestinal organs can creep in a maintenance stage, so that the intestinal organs are changed from structural bionics to functional simulation, and the significance of the 3D intestinal organ differentiation method is far better than that of pure morphological simulation. Through a systematic culture medium formula, a clear operation process and a multi-stage induction strategy, the 3D intestinal organ with structural integrity, cell diversity and functional activity is successfully constructed, and the system provides an efficient, reliable and extensible in-vitro model platform for intestinal biological research and related application.

According to the single-cell transcriptome cell annotation method and system fused with the large language model, cell type annotation is carried out by constructing the proprietary cue word and utilizing the large language model, and the accuracy and universality of cell annotation are improved. The method has remarkable advantages on cell annotation of non-mode species, and an automatic and intelligent cell annotation process is realized.

The invention provides a single-cell multi-omics cell type annotation method based on distribution and knowledge alignment, and belongs to the technical field of single-cell type annotation, the method comprises the following steps: obtaining single-cell transcriptome data and single-cell chromatin accessibility sequencing data, and pre-training and training a multi-omics variation auto-encoder model, the multi-omics variational auto-encoder model is combined with a variational auto-encoder and a knowledge distillation technology, and multi-omics single cell data is integrated and annotated through distribution and knowledge alignment. And performing cell type prediction on the single cell transcriptome data and the single cell chromatin accessibility sequencing data which are input at the same time by using the trained multi-omics variational auto-encoder model. According to the method, the problem of limitation of a method only depending on single omics is solved, the synergistic effect between the omics is enhanced, the accuracy of annotation is improved, and the calculation overhead is reduced through knowledge distillation.

The invention relates to the technical field of cell data analysis, and discloses a single-cell transcriptome data and text description conjoint analysis method based on a multi-modal language model, which comprises the following steps: acquiring a single-cell RNA sequencing expression matrix and a corresponding cell text description, preprocessing the single-cell RNA sequencing expression matrix and the corresponding cell text description, and analyzing the single-cell transcriptome data and the corresponding cell text description; according to the method, a multi-modal data set is constructed, deep fusion of gene expression data and text knowledge is realized by constructing a double-model and cross-modal projection module, limitation of a single mode is avoided, a gene expression value and an index sequence are reserved during preprocessing, a rough coding mode is changed, and the cell type identification accuracy is improved; based on a pre-training strategy of comparative learning, matching learning and a cross-modal projection module, fine-grained cross-modal information interaction and sharing are realized, and cross-modal task effects of text generation cells or cell generation texts and the like are optimized.

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.

The invention discloses a construction method and application of a mouse airway organoid inflammation model, and relates to the technical field of biology. The construction method comprises the following steps: (1) taking a mouse airway tissue, digesting with digestive juice, and centrifuging to obtain a cell suspension; (2) centrifuging the cell suspension, discarding the supernatant, adding matrigel, uniformly mixing, incubating, and culturing by using an airway organoid complete medium; (3) adding a cell recovery solution into the cells cultured in the step (2), incubating, degumming, centrifuging, discarding supernatant, adding matrigel, uniformly mixing, incubating, and adding an airway organ complete culture medium for continuous culture; and (4) adding 1-20 [mu] g / ml of LPS into the airway organoid complete culture medium, and incubating. The AO constructed by the invention covers four main cell types of cilia cells, goblet cells, basal cells and rod-like cells, causes epithelial injury in the AO and expression of AO inflammatory factors, and can provide reference for development of CRD new drugs.

The invention discloses a key marker gene-based intelligent cell type annotation method, which comprises the following steps of: constructing a static knowledge base by using known marker genes in a reference database, and endowing the marker genes with cell specific weights by using a TF-IDF method, so that the annotation accuracy and interpretability are improved. Meanwhile, under the condition that static matching is insufficient, the literature is understood through a large language model, mark information is extracted, dynamic completion of the knowledge base is achieved, the defect that updating of a traditional knowledge base is lagged is overcome, and good adaptability and expansibility are achieved. Besides, static and dynamic matching scores are fused in the annotation process, so that more robust cell type identification is realized, annotation requirements of multi-tissue, multi-species and novel cell states are adapted, high-precision and extensible cell type annotation can be realized in a scene with insufficient reference knowledge or a fuzzy sample, and the annotation efficiency is improved. And the method has good universality and practicability.

The embodiment of the invention relates to the technical field of bioinformatics, and provides a bulk data cell analysis method, device and equipment, and the method comprises the following steps: constructing an initial reference matrix according to a single cell data set and a cell type annotation template, each element in the initial reference matrix represents the gene expression quantity of each cell state under each characteristic gene; performing deconvolution on the bulk data to be analyzed according to the initial reference matrix to obtain a first deconvolution result; updating the initial reference matrix according to the first deconvolution result to obtain a first reference matrix; and according to the first reference matrix, performing deconvolution on the bulk data to be analyzed to obtain a second proportion and a second gene expression quantity of each cell type in the bulk data to be analyzed. According to the embodiment of the invention, the accuracy of cell analysis in bulk data can be improved.

The invention relates to an mRNA sequence ribosome occupancy prediction method, a model training method and a related device. The method comprises the following steps: acquiring an mRNA sequence and a secondary structure of the mRNA sequence in a target cell type; inputting the mRNA sequence and the secondary structure thereof into a pre-trained prediction model, respectively obtaining a first embedded representation of the mRNA sequence and a second embedded representation of the secondary structure through different encoders in the prediction model, fusing the first embedded representation and the second embedded representation based on a multi-head self-attention mechanism to obtain a fused embedded representation, and obtaining a multi-head self-attention prediction model; classification is carried out based on the fused embedded representation; and based on classified output of the prediction model, obtaining a prediction result of the ribosome occupancy of the mRNA sequence in the target cell type. Compared with the prior art, after the prediction model is trained in any cell type, ribosome occupancy prediction is carried out on any other cell type, the prediction precision is high, and the prediction model is high in mobility.

The invention discloses a method for constructing a differential diagnosis model for lupus nephritis and membranous nephropathy, and belongs to the technical field of intelligent medical treatment. The modeling method comprises the following steps: S1, respectively collecting flow cytometry detection data of lupus nephritis patients and healthy control personnel; s2, performing data cleaning and conversion on the flow cytometry detection data, and converting non-numerical features into digits; carrying out implication on the missing value by adopting a k nearest neighbor algorithm from an implication packet; s3, random sampling is carried out on the cleaned and converted data set, and samples are divided into a training set and a verification set according to the proportion of 7: 3; s4, dividing a training subset and a test set from the training set, and iteratively selecting the types of cells incorporated into the constructed model as pDC, CD4T, effector CD4T, Th2, CD8T, CD38 + HLA-DR + CD8T, and CD38 + PD-1 + CD8T by adopting an RFE method, wherein the types of the cells incorporated into the constructed model are pDC, CD4T, effector CD4T, Th2, CD8T, CD38 + HLA-DR + CD8T and CD38 + PD-1 + CD8T; and S5, performing a classification task by adopting TabPFNClassifier, performing training from features selected from the training data set, then evaluating the performance of the model, performing model training by utilizing detection data, and performing evaluation to construct a lupus nephritis prediction model with high accuracy.

Provided herein are methods, devices, and systems comprising identification of a panel of gene markers useful for immune cell typing and identifying, detecting, and / or monitoring disease conditions in a subject in need thereof, including infections, cancer and autoimmune conditions. The methods also provide for assays for determining the levels of said markers prior to and after treatment of a condition in the subject, wherein the condition is impacted by one or more factors including age, occupation or environment of the subject.

The invention belongs to the technical field of gene editing, and discloses a gene editing system and application. The invention provides a gene editing system. The gene editing system comprises Cas9 nickase, sgRNA (small guide ribonucleic acid) and annular petRNA. According to the present invention, the target chain cutting mediated reverse pilot editing (Split Reverse Prime Editing, srPE) realizes the precise editing of the non-target chain cutting site upstream sequence, such that the gene editing coverage range is expanded from the specific region only limited to the non-target chain cutting site downstream to the whole genome range, the stable and efficient editing ability is maintained in different cell types, and the target chain cutting mediated reverse pilot editing can be provided for the target chain cutting site. And meanwhile, the off-target risk is reduced. The srPE gene editing technology breaks through the fundamental limitation of the prior art, and provides a more comprehensive and more efficient technical tool for precise medicine and gene therapy.

The invention relates to a cell differentiation trajectory inference method based on deep hyperbolic manifold embedding. The method comprises the following steps: acquiring single-cell RNA sequencing data; introducing hyperbolic space embedding with negative curvature, performing feature extraction on the single-cell RNA sequencing data through a deep feature extraction network, and mapping extracted high-dimensional features to a low-dimensional hyperbolic space; through track inference in a low-dimensional hyperbolic space, transformation paths of cells from progenitor cells to various differentiation end cell types are constructed, and key genes and pathways are evaluated by utilizing a sensitivity analysis method. The method has the beneficial effects that by introducing hyperbolic space embedding with negative curvature, the model can better capture the hierarchical structure of data, and the accuracy and consistency of differentiation tracks are enhanced. Moreover, high-dimensional single-cell sequencing data can be processed, the limitation of a traditional method under the high-dimensional condition is overcome, and the applicability of the model is improved.

The invention provides a multi-modal medical image real-time labeling and collaborative browsing method and device based on adaptive DeepZoom and a readable storage medium thereof. A multi-resolution pyramid is constructed, a medical image is downsampled into multiple resolution levels layer by layer, each layer is divided into standard size blocks, dynamic loading is carried out based on a window, and cache management is carried out through an LRU algorithm. The problems of loading lagging and memory overflow of the GB-level image are solved; real-time synchronization of multi-user labeling operation is achieved through a WebSocket protocol, a three-level semantic label system containing lesion areas, cell types and tissue grading is supported, labeling conflicts are solved in combination with a timestamp priority strategy, and version backtracking is supported; a medical compliance log system is designed, information such as cases is recorded, and the whole operation process can be traced through synchronization of local Sqlite cache and cloud HIPAA encryption. According to the method, the medical image browsing efficiency and the labeling collaboration are remarkably improved, and the medical data compliance requirement is met.

The invention discloses an animal single cell data cell type annotation method, which is characterized by comprising the following steps: collecting Bulk RNA-seq data of purified cell types in various tissues and organs of a specific animal, and integrating the Bulk RNA-seq data into a reference data set after preprocessing; the method comprises the following steps: acquiring single-cell RNA-seq original data, screening the single-cell RNA-seq original data to obtain high-quality cells, screening high-variation genes from the high-quality cells, processing the high-variation genes, extracting principal components, performing dimensionality reduction on the principal components, and performing cell clustering based on a dimensionality reduction result to obtain cell clusters; and calculating expression similarity between the cell clusters and the reference data set based on the high-variation genes, determining initial cell types of the cell clusters according to a similarity result, and carrying out iterative tuning on the initial cell types with similar scores to obtain a final cell type annotation result. The method provides efficient and accurate technical support for animal single cell research.

The invention relates to a genome regulatory element analysis method based on deep learning. The method comprises the following steps: acquiring a genome DNA sequence and DNA shape information; on the basis of a shared hybrid neural network, transcription factor binding site features are extracted from a genome sequence, and binding specificity prediction is carried out aiming at the sequence level; aiming at the specificity of transcription factor binding, accurately positioning a transcription factor binding site on a nucleotide level by adopting a full convolutional neural network coupled with different models, and identifying a motif; for a chromosome three-dimensional genome, a chromatin ring is recognized in the three-dimensional whole genome based on a deep learning model, the relationship between the chromatin ring and regulatory elements in all cell lines is analyzed, and the chromatin ring represents cell type specificity and co-enrichment of transcription factor motifs among different cell lines and species. Compared with the prior art, the method has the advantages that the regulatory elements of the regulatory mechanism can be identified and analyzed by utilizing multiple information sources, and the accuracy of predicting the whole genome chromatin ring is effectively improved.

The present invention relates to the intersection of bioinformatics and computational biology, and discloses a transcriptome annotation method and system based on a large language model, including: fusing single-cell spatial coordinates and gene expression values ​​into pseudo-image modal data, extracting spatial topological features of the pseudo-image modal data; performing cross-modal alignment between the spatial topological features and a preset medical database, and analyzing the cell type probability of the cross-modal embedding vector; constructing a functional semantic space of functional description texts, and projecting non-model species into the functional semantic space; calculating the semantic similarity between gene expression embeddings and homologous genes of reference species, and using a large language model to convert gene expression embeddings into semantic mapping relationships. The present invention reduces the core problems faced in single-cell spatial transcriptome annotation, such as extensive spatial topological modeling, low cross-modal alignment accuracy, limited non-model species annotation, and rigid semantic mapping.