527 results about "Cell typing" 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 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 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 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 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 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 present invention relates to an integrated system for automating cell culture processes. The system combines artificial intelligence (AI) with a robotic apparatus to execute tasks typically performed manually in cell culture laboratories. The AI module employs machine learning algorithms trained on extensive datasets, enabling it to make informed decisions regarding cell culture conditions and protocols for a variety of cell types. An accompanying robotic system performs liquid handling tasks such as media changes and cell passaging with precision. The system is compatible with multiple types of cell culture vessels, facilitating bulk processing. Additionally, an enclosed sterile environment is maintained to prevent contamination. A user interface allows for the customization of protocols and remote monitoring, enhancing operational efficiency. This invention streamlines cell culture workflows, reduces manual labor, and increases the reproducibility and scalability of cell culture.

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.

Described herein are compositions, kits, and methods for potent delivery to a cell of a subject. The cell can be of a particular cell type, such as a basal cell, a ciliated cell, or a secretory cell. In some cases, the cell can be a lung cell of a particular cell type. Also described herein are pharmaceutical compositions comprising a therapeutic or prophylactic agent assembled with a lipid composition. The lipid composition can comprise an ionizable cationic lipid, a phospholipid, and a selective organ targeting lipid. Further described herein are high-potency dosage forms of a therapeutic or prophylactic agent formulated with a lipid composition.

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.

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.

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.

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.

Disclosed is an analysis method for a specimen using an analyzer connected to a host computer, the analysis method including: obtaining, with respect to each of a plurality of cells contained in the specimen, feature data of the cell; generating classification information in which each of the cells is classified into a plurality of cell types, by analyzing the feature data with use of an artificial intelligence algorithm and performing classifying; generating a measurement result of the specimen on the basis of the classification information; displaying, on a display part of the analyzer, the measurement result and at least a part of the classification information; and transmitting, to the host computer, output data that includes the measurement result and in which at least a part of the classification information has been removed.

Disclosed are cell penetrating peptides and compositions comprising such peptides that can be used to deliver agents to various cell types.

The invention discloses a method, a device and equipment for identifying a communication relationship between cells, and a storage medium, and relates to the technical field of bioinformatics, direct causal contribution between the cells is quantified by using a causal association relationship of a source cell gene to a target cell gene, the communication relationship between the cells can have explanatory force at a causal level, and the identification accuracy of the communication relationship between the cells is improved. Further, the overall regulation effect among the cell populations is accurately evaluated. The method comprises the following steps: acquiring gene expression data of different cell types, wherein the different cell types at least comprise source cells and target cells; aiming at the candidate genes of the target cell, constructing different hypothesis models by using the covariable gene set, and determining a causal association relationship between the source cell gene and the target cell gene through the different hypothesis models; and calculating the communication intensity of the source cell to the target cell according to the causal association relationship of the source cell gene to the target cell gene, so as to identify the causal regulation relationship between the cells through the communication intensity.

The invention discloses a cell interaction identification method based on cell space transcriptome data, which comprises the following steps: constructing cell pairs according to cell space proximity, and calculating an interaction signal matrix and space information of each pair of cells by combining a ligand-receptor database; then generating cell pair embedding representation by using a graph neural network, calculating the similarity between cell pairs, and calculating the similarity between cells based on a gene expression matrix; updating the similarity matrix through iteration until convergence to obtain a final similarity matrix between cells and between cell pairs; and clustering the similarity matrix, and constructing a cell type and cell pair type model, thereby realizing accurate cell interaction identification. According to the method, space and expression information can be fully utilized, inter-cell heterogeneity and context specificity interaction can be captured, the analysis precision and biological authenticity of a cell communication network are improved, and an effective tool is provided for research on tissue development, disease mechanisms, immune response and the like.

The invention concerns methods for predicting changes in a tissue, comprising obtaining snap shot information on individual cells of at least one cell type in a section of a tissue biopsy, wherein the information comprises: (i) spatial localization, (ii) cell type and (iii) cell division state at a specific point in time; calculating cell division probabilities for each of said individual cells; determining cell population dynamics based on the cell division probabilities and predicting future changes in the tissue based thereon. Particularly, the method concerns predicting tissue dynamics in cancer biopsies relevant for prognosis, and assessment of response to therapy.

The invention discloses a cell type annotation method and annotation system. The annotation method comprises the following steps: confirming a common gene of single cell data and spatial transcriptome data, and extracting first gene expression corresponding to the single cell data and the common gene and second gene expression corresponding to the spatial transcriptome data and the common gene; performing feature extraction on the first gene expression by using a common encoder to obtain a first potential feature, and performing feature extraction on the second gene expression to obtain a second potential feature; aligning a distribution of the first potential feature and the second potential feature over a common space; and performing cell type annotation on the spatial transcriptome data based on a preset distance function in combination with the first potential feature and the second potential feature. According to the technical scheme, the accuracy of annotating the cell types can be effectively improved.

The invention belongs to the technical field of medical instruments, and particularly relates to an esophageal mucosa detector which comprises a handle, one side of the upper end of the handle is fixedly connected with an air pump, the air outlet end of the air pump is communicated with an air pipe, one end of the air pipe is communicated with an air cylinder, the lower end of the air cylinder is fixedly connected with a shell, and the upper end of the shell is fixedly connected with an air bag. An air inlet of the air bag is communicated with an air outlet of the inflator, and a mucous membrane scraping assembly is further arranged on the shell; the mucous membrane scraping assembly comprises second sliding rods fixedly connected to the two ends of the inner cavity of the shell, and the second sliding rods are slidably connected with a supporting plate. The esophageal mucosa of a patient can be collected by utilizing the mucosa scraping assembly, so that subsequent pathological histological analysis is facilitated, key information such as the cell type, differentiation degree and heterotype of a lesion can be determined conveniently, whether the lesion is benign or malignant can be accurately judged, a basis is provided for formulating a treatment scheme, and the treatment efficiency is improved. And the condition that confusion is easy to occur when the lesion property is judged only by naked eyes is avoided.

Methods are provided for the step-wise generation of endothelial cells. Also provided are methods for generating pure populations of such cell types and derivatives thereof. The instant disclosure also provides methods of screening for cellular responses of the generated cell types and derivatives thereof. Treatment methods making use of the generated cell types and derivatives thereof are also provided. The instant disclosure also provides systems, compositions, and kits for practicing the methods of the disclosure.