302 results about "Cell typing" patented technology

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 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.

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.

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

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

The present invention relates to lipid nanoparticle (LNP) compositions, as well as diagnostic or therapeutic polynucleotides, such as TERT mRNA, that can be delivered in a formulation together with the LNP compositions to various tissue and cell types in the whole body of a mammal, such as, for example, TNP mRNA. Comprising stem cells, progenitor cells, germ cells, differentiated cells or terminally differentiated cells, cancer cells, endothelial cells, epithelial cells, splenic cells, hepatocells, kidney cells and / or osteoblasts, for example, for use in the diagnosis, prevention and / or treatment of a condition or disease.

The invention relates to a method and system for detecting cells of a first cell type (1) in a biopsy sample in which a plurality of cells of different cell types (2, 3) are present. The invention further relates to a device for carrying out a magnetic field measurement on cells of a biopsy sample that are labeled with magnetic nanoparticles. The method comprises the following method steps: carrying out tissue dissociation of the biopsy sample in order to singulate the cells present in the biopsy sample; supplying magnetic nanoparticles (11) to the singulated cells in order to selectively label the cells of the first cell type (1); removing the excess magnetic nanoparticles (11); introducing the cells into a microchannel (5), the microchannel (5) having a cross section which allows only one labeled cell (1) to pass through at a time; carrying out a magnetic field measurement in the microchannel (5) by means of a quantum magnetic field sensor (6) based on the F-center principle; and sorting out the labeled cells (1) after they have passed through the microchannel (5).

Provided herein are compositions and methods for identifying the cell type origin of cells based on RNA sequencing (RNA-seq) data.The method provided herein may include: receiving a plurality of sequencing read counts; providing a set of detected cell types and a related gene set G for each cell type, such that each gene set G comprises a plurality of genes g; scoring the sequencing read counts to generate an assignment score; and assigning each cell among the plurality of cells to the cell type with the highest assignment score, thereby identifying the cell type origin of each cell.This method can be implemented, for example, using a computer system.

1. The name of the design product: graphical user interface for computer cell marker gene statistics. 2. The use of the design product: the design product is used in electronic equipment to count cell marker genes. 3. The design points of the design product: the interface content of the graphical user interface. 4. The picture or photo that best shows the design points: front view. 5. The product involved in the design patent application is a graphical user interface, and the rear view, left view, right view, top view and bottom view are omitted. 6. Other circumstances that need to be explained: the interface of the product is a graphical user interface for cell marker gene statistics, which is a structured design of species, system, tissue, cell type and visualization design of species structure. The first column is species, the second column is system and tissue, the third column is cell type, and the fourth column is typical marker statistics of cells. Users can filter species, system, tissue and cell type through this interface, and finally view the typical marker statistics chart of cells.

The application provides a pulmonary artery tissue-derived cell, an organoid and a construction method and application thereof. The pulmonary artery intimal stripping tissue organoid structure obtained by the preparation method is clear, has consistent cell types as the source tissue, is close to the original tissue, is convenient for researchers to operate, and thus has good practical application value.

The invention discloses a synchronous imaging method for a multicellular structure in a multicellular type. The synchronous imaging method comprises the following steps: S1, marking the cellular structure in the multicellular type by using fluorescent protein; s2, forming a spectrum-spatial feature joint coding set according to the spectrum and spatial features; s3, performing spectral unmixing on the spectral image to obtain an abundance image of each fluorescent component; s4, performing cell segmentation and cell structure positioning, and extracting spatial features; and S5, carrying out joint decoding on the spectral features and the spatial features to distinguish different cell types, and carrying out synchronous imaging on the multi-cell structure. The method has the beneficial effects that different cell types are marked by using corresponding fluorescent proteins, and a spectrum-space coupling coding strategy is formed in combination with spatial position characteristics, so that simultaneous observation and dynamic analysis of different cell types and a plurality of internal structures in the same imaging view field are realized; and the limitation that traditional spectral imaging can only work in a single cell type is broken through.

The present invention provides targeting polyplexes comprised of (i) nucleic acids, in particular nucleic acids encoding pharmaceutically active peptides or proteins, and (ii) targeting conjugates comprising LPEI and PEG fragments that are connected by discrete linkages formed through defined, chemoselective reactions. Thus, the LPEI fragment is bonded in a linear end- to-end fashion to a single PEG fragment. The linear conjugates are further conjugated to a targeting fragment to enable selective interaction with a particular cell type. The polyplexes selectively deliver the nucleic acids to the targeted cells resulting in high expression and efficient protein translation as well as secretion of the encoded pharmaceutically active proteins.

The invention provides a brain-computer interface-based cell culture optimization method and system, and belongs to the technical field of cell culture automation and intelligent control. The method comprises the following steps: inputting a cell type and a culture target, and calling database parameters and data; the artificial neural network learns and outputs a culture parameter range; a medicament proportioning scheme and a physical environment regulation and control strategy are generated through multi-algorithm optimization; the medicament precise proportioning module and the closed-loop optical-electric multi-mode regulation and control module are used for executing a scheme and a strategy respectively; and the result verification and feedback module is used for detecting the cell culture effect, feeding back data, optimizing model parameters and forming closed-loop iterative optimization. The system is composed of an input and data unit, an intelligent decision-making unit, an execution unit and a result verification and feedback unit. The device can accurately control a chemical microenvironment and a physical illumination environment, solves related problems in the prior art, stably produces high-quality cells, is especially suitable for brain-computer interface research, realizes culture process standardization and intelligence, and provides reliable cell materials for downstream applications.

The present invention relates to a computer-implemented method for producing a trained model for detecting one or more defined cell types and / or cellular indicators, such as biomarkers or cellular abnormalities, in particular genomic aberrations, from single-cell images obtained from a sample of a body liquid, bone marrow, cytology smear or slide preparations, in particular a blood smear sample. It relates also to a computer-implemented method for detecting one or more defined cell types and / or cellular indicators, such as biomarkers or cellular abnormalities, in particular genomic aberrations, from single-cell images obtained from a sample of a body liquid, bone marrow or cytology smear, in particular a blood smear sample.

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. 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 to a lipid composition. The lipid composition can comprise an ionizable cationic lipid, and a selective organ targeting lipid. The lipid composition can further comprise a phospholipid. Further described herein are high-potency intravenous dosage forms of a therapeutic or prophylactic agent formulated with a lipid composition.

Methods for determining the fraction of B lymphocytes of a particular class in a sample comprising genomic materials from multiple cell types are described. The method comprises: obtaining a read depth spectrum of the sample, the read depth spectrum comprising a read depth derived from sequence data of the sample in a predetermined genomic region, the predetermined genomic region comprising at least a region in the IGH locus that undergoes category conversion recombination and a region in the IGH locus that does not undergo category conversion recombination; obtaining a plurality of read depth ratios (ri) by normalizing read depths in the predetermined genomic region with reference to a baseline read depth derived from a subset of the predetermined genomic region that is not subjected to category conversion recombination; obtaining one or more summarized read depth ratios (rB) of portions of regions in the IGH locus that may be deleted by category conversion recombination; and determining a fraction of B lymphocytes of a particular category in the sample as a function of the one or more summarized read depth ratios (rB).

This document provides a high throughput method for the creation of AAV vectors and / or promoter sequences with high efficiency and / or specificity for multiple cell types.

This invention provides a method and system for detecting the cellular state in biological samples. [Solution] A method for determining cell type or cell state is provided. The method includes: providing, or already providing, a sample containing DNA or RNA, and creating, or providing, or already providing, a methylation profile of the DNA or RNA in the sample.

This invention discloses a single-cell spatial annotation method that integrates image and sequencing data. It obtains candidate cue point locations, cell nucleus instance segmentation masks, and cell nucleus type probability vectors by processing full-field H&E stained tissue images. It also obtains a preprocessed spatial transcriptome raw sequencing matrix and corresponding cell type labels by processing the raw spatial transcriptome sequencing matrix. Each capture site and its corresponding integrated data structure are constructed. Based on rigid constraints on the number of cells allocated to each type within each capture site and main type quota constraints, the main type annotation is written. For cells in the intercapture zone, one or more neighboring capture sites with existing annotation results are found. The main type and cell subtype of the intercapture zone cells are inferred through morphological feature similarity calculation, thus obtaining the annotation result. This solves the problem that existing technologies cannot determine the exact type of each cell in ST-sequencing tissue.

Provided is a viral vector having a lipid bilayer envelope, the lipid bilayer envelope comprising an antibody binding domain displayed outside the envelope, the antibody binding domain being cell type specific; a viral envelope protein exhibited outside the envelope, the viral envelope protein being capable of promoting infection of the same cell type; and a nucleic acid molecule comprising a promoter capable of being expressed in the same cell type. Methods of making the viral vectors and methods of modifying cells and treating diseases / conditions using the viral vectors are also provided.

The application provides a single-cell transcriptome cell type automatic annotation method and device based on consensus voting, electronic equipment and storage medium, relates to the field of medical biotechnology, and integrates a plurality of initial annotation results obtained by a plurality of cell type annotation methods by applying an ensemble learning strategy, so as to reduce errors that may exist in a single annotation method, and improve the accuracy and robustness of cell type annotation. In addition, the method uses single-cell transcriptome data of a sample, combines rich prior knowledge and strong reasoning ability of a large language model, and has the ability to discover rare cell types, so as to effectively identify rare cell types, widen the application range of cell type annotation, and improve the general tissue annotation capability of cell types.

A nanoparticle for delivery of mRNA to cell, comprising a core comprising a polyethylenimine polymer having fluorinated groups covalently coupled thereto and with mRNA reversibly associated therewith, and a shell surrounding the core comprising heparin. Ij some embodiments, the nanoparticle comprises a targeting agent associated with the shell, wherein the targeting agent is selected from the group consisting of agents that bind to receptors overexpressed on tumor cells, agents that bind to cell surf ace antigens that are expressed on pluripotent stem cells, agents that bind to cell surface antigens on T cells, and agents that bind to antigens presented by MHO molecules. Pharmaceutical compositions that include the nanoparticle and methods for using the nanoparticle for transfecting cells are provided

The invention relates to the technical field of biology, and provides a guide editing system based on a bidirectional micro homologous arm and application of the guide editing system. Based on a guided editing technology, two pairs of independent pegRNAs are adopted, single-stranded DNA (ssDNA) micro homologous arms with the length of 20-60 nt are generated at the two ends of a genome target and the two ends of a donor carrier insertion fragment respectively, and efficient integration of donor DNA and genome DNA can be promoted. At the two target points of the genome, the ssDNA is homologous with the DNA sequence of the integration site in the donor vector, so that the seamless connection of the donor DNA and the genome DNA is effectively promoted by the micro homologous arm in the integration process. In the donor vector, ssDNA on the two sides of the integration part is homologous with a sequence on the upstream of a targeting nick site in a genome, so that the integration efficiency and accuracy are further improved. According to the method, high editing efficiency can be kept between different target sites and cell types, and large-fragment DNA can be inserted to realize seamless editing.

The application discloses a gene data processing method and device, computer equipment and a storage medium, and belongs to the technical field of computers. Through a gene function query request of a to-be-tested gene, the application can call a gene association model corresponding to a cell type to which the to-be-tested gene belongs, mine a nonlinear association degree between the to-be-tested gene and a known candidate gene, and use the candidate gene with a higher nonlinear association degree to label function annotation information of the to-be-tested gene. The way of calling the gene association model to extract the nonlinear relationship is completely different from the way of extracting the linear relationship in traditional statistics, can deeply mine the candidate gene with a higher similarity to the to-be-tested gene, and the similarity is not a linear similarity but an implicit nonlinear similarity, so that the accuracy of the gene data processing process is greatly improved.

Devices and methods are presented for delivery of various macrostructures into cells, such as depleted cells as well as methods of generating engineered cells using said devices and methods. Cells are placed on a porous membrane. A force is applied by a configurable actuator to a deformable fluid reservoir that generates an applied pressure to macrostructures in a solution, causing the macrostructures to pass through the porous membrane and triggering uptake of at least some of the macrostructures into the cells to form transfected cells. Said devices and methods may be used in a process to replace defective endogenous mtDNA with corrected mtDNA to generate cellular-based therapeutics for administration to a patient. The customizable actuator may be configured with parameters to optimize efficiency for a given cell type and material to be transfected. Machine learning techniques also may be utilized to optimize transfection parameters.

The invention belongs to the technical field of cell data processing, and relates to a single cell gene characterization system and method based on deep learning. The system comprises a gene information input module, a data processing module, a context sensing interpolation module, a gene feature fusion module, a deep representation learning and joint optimization module and a cell typing and differentiation track inference module. The input module integrates a data source; the processing module preprocesses and enhances the original expression matrix; the interpolation module performs weighted interpolation by using a conditional Gaussian graph model; the gene feature fusion module generates a gene module activity matrix through 1 * 1 convolution and the like; the fusion module fuses the sample meta-information with the active matrix, the sequencing depth and the gene feature vector; the depth representation module captures high-dimensional dependence through a multi-layer Transform network, and generates cell high-order potential representation; the typing module outputs a cell population division and differentiation trajectory based on the deduced pseudo-time trajectory. The method has the beneficial effects that cell expression characteristics are recovered under high sparseness and noise, and a new way is provided for cell type identification and the like.

The invention provides a plasma proteome data difference evaluation method and device based on two-group comparison and a medium, and relates to the technical field of biological information analysis. Comprising the following steps: S101, sorting omics data of two single cell transcriptomes, and annotating the cell type of each cell to obtain a first single cell gene expression profile; s102, splitting the first single-cell gene expression profile according to cell types to obtain a plurality of second single-cell gene expression profiles, wherein each second single-cell gene expression profile corresponds to one cell type; s103, calculating a state difference score of each cell type based on logistic regression and ten-fold cross validation; and S104, sorting according to the state difference scores from high to low to obtain a difference evaluation result of the corresponding cell type. The method is not limited to the difference of a single feature (such as a gene) and is not limited by specific omics, the cell type difference among multiple groups can be evaluated from the overall level, and the comparability among omics data is improved.

The application discloses a kind of based on spatial transcriptome technology automatic construction pathology image dataset and training cell nucleus detection and classification method, belong to image processing and artificial intelligence auxiliary pathological diagnosis field.The method is by obtaining spatial transcriptome public data set, after pretreatment, deconvolution cell type annotation and cell nucleus instance segmentation, automatic construction includes image block, weak supervision / semi-supervised label and cell nucleus boundary information dataset, reduce dependence on artificial annotation.Further, design detection and classification model, adopt multiscale deformable attention encoder, decoupled detection and classification decoder, and introduce limited deformable cross attention mechanism in classification decoder, combined with KL divergence classification loss, realize from regional level proportion label learning instance level cell nucleus class.The application realizes end-to-end automation, improves cell nucleus detection and classification precision and efficiency, provides high-quality pre-training model basis for downstream pathological analysis.