721 results about "Cell type" patented technology

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.

The invention belongs to the technical field of biology, and discloses a reagent combination or kit for constructing intestinal organs and application of the reagent combination or kit. According to the reagent combination or the kit, intestinal organs can be obtained from cell-derived epithelial cells obtained from a donor in a non-invasive manner, and the obtained intestinal organs can be cryopreserved and recovered and can be amplified in vitro for a long time; transcriptome characteristics are similar to those of real human small intestine tissues, and typical marker genes of various small intestine pedigree cell types are highly expressed; compared with intestinal organs obtained through induction of pluripotent stem cells, the intestinal organs have more intestinal pedigree characteristics, and the intestinal function development is more mature; after being promoted to be mature, the intestinal organ also highly expresses genes related to drug absorption and metabolism, has drug absorption capability similar to that of an immortalized intestinal cell line, but more prominently shows intestinal cell lineage characteristics, is closer to an intestinal environment in a real human body, and can be used for screening intestinal disease drugs; the intestinal barrier function is realized.

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 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.

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 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 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.

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 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 discloses a construction method of a homogenized self-assembled heart organoid, and belongs to the field of organoid. The preparation scheme of the heart organoid provided by the invention is controllable in method and simple in procedure, emphasizes the transition of differentiation from an iPSC 2D level to a 3D self-assembly heart organoid, overcomes the heterogeneity, including size, structure, function and even gene difference, of cross-batch or even same-batch organoid caused by an existing differentiation method, and has good cross-batch reproducibility. The constructed heart organoid contains various cell types and can continuously beat for at least 180 days in vitro, and the heart organoid in the same batch is uniform in height and stable in function. The preparation scheme does not need an engineering scaffold, matrigel and the like, emphasizes self-assembly of heart organs, and better accords with physiological characteristics of a human body.

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.

The invention discloses a senescent cell recognition system and method based on mitochondrial morphology. The system is a classification network, and the input data of the system is a distribution image of mitochondria in subcellular level cells, the distribution image of mitochondria in cells, the shape and / or number of mitochondria distributed at different positions in a three-dimensional structure for displaying cells, and preferably the shape and number of mitochondria distributed at different positions in the three-dimensional structure for displaying cells. According to the senescence cell recognition system based on the mitochondrial morphology, the senescence cells jointly show mitochondrial morphological changes including mitochondrial distribution, mitochondrial shapes and mitochondrial number changes, the senescence cell recognition method is easy to conduct through computer vision, good robustness is achieved, and the senescence cell recognition system based on the mitochondrial morphology is suitable for being applied to senescence cell recognition. Compared with other senescence cell identification methods based on cell nucleus morphological characteristics, the senescence cell identification method based on the general characteristics of the senescence cells has good universality for different cell types.

The invention discloses a method for automatically constructing a pathological image data set and training cell nucleus detection and classification based on a space transcriptome technology, and belongs to the field of image processing and artificial intelligence auxiliary pathological diagnosis. According to the method, a spatial transcriptome public data set is obtained, and a data set containing image blocks, weak supervision / semi-supervision labels and cell nucleus boundary information is automatically constructed through preprocessing, deconvolution cell type annotation and cell nucleus instance segmentation, so that the dependence on manual annotation is reduced. Furthermore, a detection and classification model is designed, a multi-scale deformable attention encoder and a decoupled detection and classification decoder are adopted, a limited deformable cross attention mechanism is introduced into the classification decoder, KL divergence classification loss is combined, and instance-level cell nucleus categories are learned from region-level proportion labels. According to the method, end-to-end automation is realized, the cell nucleus detection and classification precision and efficiency are improved, and a high-quality pre-training model basis is provided for downstream pathological analysis.

The application provides a single-cell transcriptome cell annotation method and system of a fusion large language model, cell type annotation is performed through construction of special prompt words and use of a large language model, and the accuracy and universality of cell annotation are improved. The application has a significant advantage for cell annotation of non-model species, and realizes an automatic and intelligent cell annotation process.

Provided is a method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing, which method comprises the following steps: S1, sample preparation; S2, construction of a single-cell expression matrix; S3, cell quality control; S4, cell type annotation; S5, cell communication analysis; and S6, co-expression network analysis. The provided method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing comprises performing single-cell sequencing on hearts of healthy mice and IHF mice, screening for cell types with significant differences in cardiac transcriptional profiles of the healthy mice and IHF mice, then exploring interaction characteristics of various types of cells in malignant fibrotic IHF hearts, revealing potential regulatory modules and pathways related to malignant myocardial fibrosis in single-cell expression data of IHF hearts, and performing screening to obtain Pdgfb and Tnfsf12 genes which can be used as therapeutic targets for treating myocardial fibrosis in ischemic heart failure.

The invention discloses a method and system for evaluating the treatment effect of traditional Chinese medicine based on single cell and spatial transcriptome data, and the method comprises the following steps: respectively obtaining single cell data and spatial transcriptome data of a tissue sample before and after administration, and carrying out the preprocessing; classifying the cells and identifying cell types; the expressed ligand and receptor genes are paired to obtain ligand-receptor pairs, and the ligand-receptor pairs which are differentially expressed before and after administration are screened out; acquiring space coordinate information of a single cell, and constructing a cell interaction network and a differential gene network according to the cell type, the ligand-receptor pair and the space coordinate information; weighting processing is conducted on the cell interaction network and the differential gene network, and comprehensive indexes for evaluating the effect of the traditional Chinese medicine are obtained.The brand-new method for evaluating the disease treatment effect of the traditional Chinese medicine is provided, the method is scientific and reliable, and the treatment effect of the traditional Chinese medicine on complex diseases can be accurately reflected.

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 an 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 embodiment of the invention provides a gene regulation network inference method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a first time sequence corresponding to a target cell type; the first time sequence comprises accessible chromatin sequencing data and single cell transcriptome sequencing data at different first time points; constructing a corresponding first gene regulation network according to the accessible chromatin sequencing data at each first time point; pruning the first gene regulatory network based on single cell transcriptome sequencing data to obtain a second gene regulatory network corresponding to each first time point; and deducing a plurality of second gene regulatory networks corresponding to the first time sequence based on a pre-constructed gene regulatory network prediction model to obtain a target gene regulatory network corresponding to the target cell type at a second time point, the second time point at least comprising a future time point and / or a missing time point in the first time sequence. The method can improve the inference accuracy of the gene regulatory network.

Provided herein is a DNA analysis method for detecting and quantifying immune cell types from which the DNA originated. Provided herein are also methods for determining the likelihood that a subject has a disease or condition, such as cancer.

The invention discloses a nucleic acid transfection system and method based on automatic control, and relates to the technical field of genetic engineering.The method comprises the steps that after target cells are intelligently cultured to be in a suitable state, an automatic system selects a transfection reagent and prepares a compound according to cell types; the cells and the compound are mixed through low shear force and then incubated; multi-modal monitoring equipment is used for tracking nucleic acid distribution and cell states in real time, and incubation conditions are dynamically adjusted; analyzing the monitoring data based on a machine learning algorithm and optimizing transfection parameters; after transfection, culture and multi-dimensional analysis are automatically executed. The system correspondingly comprises a cell culture module, a reagent preparation module, a mixed incubation module, a real-time monitoring module, a dynamic optimization module and a subsequent analysis module. Through closed-loop automatic control and intelligent optimization, the transfection efficiency and stability are remarkably improved, the cytotoxicity is reduced, manual intervention is reduced, and a standardized solution is provided for recombinant gene expression.

Apparatus and methods are described including preparing a blood sample for analysis by depositing the blood sample within a sample chamber (52), and placing the sample chamber, with the blood sample deposited therein, within a microscopy unit (24). One or more microscopic images of the sample chamber (52) with the blood sample deposited therein are acquired, using a microscope of the microscopy unit. Based upon the one or more images, an amount of one or more cell types within the sample chamber that had already settled within the sample chamber, prior to acquisition of the one or more microscopic images is determined. A characteristic of the sample is determined, at least partially in response thereto. Other applications are also described.

Devices, systems, and methods are provided for in vivo fluorescence imaging. Disclosed herein is an implantable miniature fluorescence imager on a chip having a custom imaging array with angle selective gratings, fiber optics, or microcollimators for image deblurring, and optical filters that can be tuned to image fluorescence from multiple fluorophores simultaneously. Power is supplied by an on-chip power source or transmitted to the chip from an external transducer such as an ultrasound transducer, electromagnetic transducer, inductive transducer, or radiofrequency transducer. Wireless communication may be provided by electromagnetic or ultrasound links to the device. The function of a fluorescence microscope is provided in a millimeter-scale device that can be readily implanted in tissue and used to image fluorescently labeled cells in vivo. The small size of the fluorescence imager makes possible sustained in vivo imaging with real-time monitoring of multiple cell types within Shifting the dynamic diseased tissue or a tumor.

The invention discloses a sparse auto-encoder-based cell type deconvolution modeling method and system, and relates to the technical field of artificial intelligence technology and bioinformatics. Single cell RNA sequencing data is input into a trained sparse auto-encoder model to obtain a predicted cell type; the sparse auto-encoder model training process comprises the following steps: generating simulated bulk transcriptome data by using single-cell RNA sequencing data to construct a training set; constructing a sparse auto-encoder model, simulating bulk transcriptome data, generating a predicted cell type proportion through an encoder, and generating reconstructed bulk transcriptome data through a decoder according to the predicted cell type proportion; constructing a total loss function based on the KL divergence function, the reconstruction error and the prediction error so as to optimize trainable parameters in the sparse auto-encoder model; according to the deconvolution modeling method and system, the problems of low cell type prediction accuracy and low algorithm efficiency in the prior art are solved, and rapid and efficient bioinformatics analysis is realized.

The invention provides a method and a system for predicting a cell type-specific non-coding variation function, and belongs to the technical field of bioinformatics. Comprising the steps that a DINOSNN prediction model is constructed, model training is carried out, and the model is composed of a convolution and attention mixed neural network model and a non-coding variation prediction model; obtaining all non-coding variations corresponding to each brain mental disease, inputting the non-coding variations into a DINOSNN prediction model, predicting the probability that each non-coding variation is a functional non-coding variation through a trained gradient boosting tree model, and predicting a cell type set influenced by the variation, further establishing a corresponding relationship among the brain and mental diseases, the non-coding variation and the cell type set influenced by the non-coding variation; and selecting a cell type set corresponding to the non-coding variation with the highest probability of functional non-coding variation from the non-coding variations corresponding to the brain and mental disease to be analyzed as a targeted cell set corresponding to the brain and mental disease to be analyzed.

A method may include applying a cell classification model to identify, based at least on an image of a biological sample, one or more cell types present in the biological sample. The cell classification model may be trained to differentiate between a plurality of cell types including a first cell type whose likelihood of being a macrophage satisfies a threshold and a second cell type whose likelihood of being the macrophage fails to satisfy the threshold. A composition profile for the biological sample may be generated based on the one or more cell types identified in the biological sample. At least one of a disease diagnosis, a disease progress, a disease burden, and a treatment response for a patient associated with the biological sample may be determined based on the composition profile of the biological sample. Related systems and computer program products are also provided.