103 results about "Cell type" patented technology

It can be useful to regulate the growth of microbial cells. Some embodiments herein provide genetically engineered microbial cells that can produce bacteriocins to control the growth of microbial cells. In some embodiments, microbial cells are contained within a desired environment. In some embodiments, contaminating microbial cells are neutralized. In some embodiments, a first microbial cell type regulates the growth of a second microbial cell type so as to maintain a desired ratio of the two cell types.

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 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.

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 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.

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.

In some aspects, a method, a system, or a non-transitory computer-readable storage medium are described for training one or more models to predict ulcerative colitis (UC) severity based on human-interpretable image features extracted from a whole-slide image, including acts of accessing a plurality of annotated whole-slide images associated with a plurality of UC patients, wherein each of the plurality of annotated whole-slide images includes at least one annotation describing a cell-type label or a tissue-type segmentation for a portion of the whole-slide image, extracting a plurality of human-interpretable image features based on cell-type labels and tissue-type segmentations associated with the plurality of annotated whole-slide images, training a statistical model based on the plurality of human-interpretable image features to predict the UC severity for a whole-slide image, and storing the trained model on at least one storage device.

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.

This application discloses a method, system, device, and medium for cell type detection. The method acquires whole-genome expression data, marker gene information, and images of a batch of cells to be detected; based on the whole-genome expression data and marker gene information, it determines the first expression level of the marker gene corresponding to each cell to be detected; based on the first expression level, it filters out a set of cells with significant expression, which includes several first cells; based on the whole-genome expression data and marker gene information of the first cells, it determines the cell type of the first cells; based on the cell types of each first cell and the image, it determines the cell type of a second cell using a K-nearest neighbor algorithm; the second cell is any other cell to be detected besides the first cells. This method can quickly and accurately determine the type information of a large number of cells and can be used in single-cell type labeling. This application can be widely applied in the field of bioinformatics.

The application provides a non-coding mutation-induced risk gene expression up-regulation and down-regulation prediction method and system, comprising the following steps: step 1, obtaining a data set and performing pretreatment; step 2, information input standardization between non-coding mutations and gene transcription start points TSS; step 3, non-coding mutation information input standardization; step 4, sequence length alignment and representation learning framework; and step 5, embedded feature merging and prediction output. The application is matched with chromatin accessibility sequencing data to migrate to any tissue or cell type, make accurate and reliable prediction, and utilize an attention mechanism to endow the model with interpretability.

A point-of-care device for biopsy assessment includes a receptacle for receiving a biological sample; a light source for emitting light at a single or plurality of wavelengths and illuminate at least a portion of the biological sample; an image sensor for receiving at least a portion of the emitted light and convert at least the portion of the emitted light to a signal; a memory to store processor-executable instructions; and a processor coupled to the memory and the image sensor. The instructions cause the processor to: receive the signal from the image sensor; convert the signal into a first image representing the biological sample; identify a first cell type represented in the first image; determine whether a quality of the biological sample is above a predefined; and when the quality is above the predefined threshold, generate an indication of an adequacy and / or diagnostics of the biological sample.

The present disclosure provides techniques for accessing FADS1 activity in a patient. Also provided are techniques for determining the appropriateness of treatment of a patient with a FADS1 modulating (e.g., inhibiting) compound. This determination may be made by analyzing one or more biological indicators of FADS1-mediated disease or disorder in the subject. The one or more biological indicators may include one or more of a ratio of polyunsaturated fatty acids (“PUFAs”) in the subject, a relative abundance of one or more cell types, a relative abundance of one or more differentially expressed genes (“DEGs”) (or gene signatures, such as RNA, for such DEGs), and / or a relative abundance of one or more metabolites. Also disclosed are methods of using FADS1 inhibitors in methods of treating metabolic disorders and obesity.

Disclosed herein are modified RNAs comprising one or more one or more modified nucleotides at position +1 to position +6 with reference to a 5' terminus of the RNA, and methods of making the same. Also provided are compositions comprising one or more of the modified RNAs provided herein, and methods of using said compositions for therapeutic applications.

Techniques for identifying a target condition of a subject based on non-target cells of the subject are described. In an example method, sequence read data associated with a first cell type in a sample obtained from the subject is identified. The sequence read data is indicative of endpoint positions of nucleic acid molecules associated with the first cell type in the sample. The example method further comprises determining endpoint positions of the nucleic acid molecules, generating input features based on the endpoint positions of the nucleic acid molecules, and classifying, using a classifier, a condition associated with a second cell type of the subject based on the input features.

This invention discloses a method and system for single-cell transcriptome cell type annotation based on deep learning, belonging to the field of bioinformatics data processing technology. The method includes five steps: data quality control preprocessing, Transformer cell encoder pre-training, graph attention network cell relationship modeling, hierarchical classification annotation, and zero-shot transfer annotation. This invention deeply couples Transformer representation learning with graph attention networks, obtains general representations through masked gene prediction pre-training, enhances rare cell type features using KNN graphs and graph attention message passing, improves recognition accuracy by adopting a hierarchical classification architecture from main lineage to subtype, and supports zero-shot cross-modal annotation based on text description.

The invention relates to an activating composition comprising a cell, which may be any cell type used for cell therapy, wherein the cell is activated by a chemotherapy agent. Further, there is provided an activating composition comprising a supernatant of a composition comprising a cell, which may be any cell type used for cell therapy, wherein the cell is activated by a chemotherapy agent and wherein the supernatant is used as a therapy. The invention further provides methods for treating or preventing a disease or a condition comprising the use of the activated composition.

The present disclosure relates generally to the field of delivery systems using contractile injection systems (CIS). Specifically disclosed are engineered extracellular CISs (eCISs) that can deliver non-natural protein payloads to non-natural target cells such as human cells. In addition, methods of using the engineered eCISs are also disclosed.

This invention discloses a high-throughput screening method based on cytokinesis for bidirectional recognition of TCR and pMHC. The method involves displaying both pMHC and TCR on the cell membrane, with the TCR simultaneously fused with the MS2 phage capsid protein for capturing barcode and MS2 RNA. The two cell types are co-cultured, and cytokinesis-mediated membrane transfer occurs between cells exhibiting bidirectional TCR and pMHC recognition. Cells displaying pMHC are isolated and collected for single-cell transcriptome sequencing analysis. The sequencing results are grouped according to different pMHCs, and the corresponding TCR is determined using the barcode sequence, thus identifying the TCR that specifically recognizes pMHC bidirectionally. This high-throughput screening method based on cytokinesis for bidirectional specific recognition of TCR and pMHC overcomes, to some extent, the limitations of existing technologies in terms of screening throughput, physiological relevance, and bidirectional matching ability.

The application discloses a kind of lncRNA subcellular localization prediction method based on lightGBM, comprising, first, the nucleotide of the front section multiple of known lncRNA sequence is intercepted as sequence sample one;Then by based on single strand multiclass position-specific three nucleotide bias and reverse complementary kmer, sequence sample one is respectively characterized coding, the combination of two kinds of feature coding is vector;Use lightGMB as learning algorithm;Using 5-fold cross validation optimization reverse complementary kmer and LightGBM's hyperparameter;Most intercept unknown lncRNA sequence front end multiple bases as sequence sample two, and its single strand multiclass position-specific three nucleotide bias and the combination of optimized reverse complementary kmer feature coding is input in trained lightGBM, will obtain its localization subcellular type, the patent of the application can be according to long-chain non-coding RNA sequence prediction in cytoplasm, nucleus, ribosome, cytosol, exosome five subcellular positions, the application realizes simply, and prediction precision is high.

The object of this invention is to provide a method of producing a nerve bundle including efficiently extending axons of neural cells. As a solution to accomplish this end, neural cells are cultivated in the presence of feeder cells including at least one type of cells selected from vascular component cells and perivascular cells.

The present disclosure relates to microbial stem cell technology that enables a growing microbial culture to stably maintain two or more distinct cell types in a ratio that can be genetically programmed and / or dynamically controlled during cultivation. It is contemplated that embodiments described herein can be utilized to increase product yield in microbial fermentations and advanced engineering of biomaterials using genetically engineered microbial cells, among others.

A viral vector having a lipid bilayer envelope is provided, comprising a cell-type specific antibody-binding domain presented outside the envelope, a viral envelope protein presented outside the envelope capable of promoting infection of the same cell type, and a nucleic acid molecule containing a promoter expressible in the same cell type. Furthermore, a method for producing the viral vector and a method for modifying cells and treating diseases / conditions using this viral vector are also provided.

The application discloses a kind of based on deep residual generation algorithm automatic identification single cell type method, this method is based on the convolutional neural network in residual module, since convolution is connected with partial neural network, compared with the full connection mode in prior art, the problem of low efficiency is solved.Because using the convolution structure in residual module, local features of single cell transcriptome data can be captured, therefore the model improves the feature extraction capability, makes the inference of cell type more accurate.The residual structure used in the application can improve the degradation problem of neural network, and using this point to make the network layer become deeper, which is also conducive to further feature extraction of data.In addition, since semi-supervised learning can utilize original label and additional data to improve the bias of sample, the scRSSL proposed in the application can improve the imbalance problem of sample in single cell data set by using the characteristics of semi-supervised learning, and higher accuracy is obtained.

The application provides a single-cell RNA sequencing annotation method and device based on a dynamic hypergraph, relates to the technical field of bioinformatics, and comprises the following steps: obtaining a data set, extracting a low-dimensional embedding vector of each cell from a gene expression vector, and constructing a dynamic hypergraph with cells as nodes and gene pathways as hyperedges; extracting a pathway feature of each hyperedge from the dynamic hypergraph; calculating the importance weight of each cell in each hyperedge based on the low-dimensional embedding vector and the pathway feature of the hyperedge; inputting the dynamic hypergraph, the pathway feature and the importance weight into a preset hypergraph neural network for message aggregation and feature learning to generate a prediction label of a cell type; and training the hypergraph neural network through an optimization algorithm to obtain a trained cell annotation model. The application realizes more accurate and more biologically interpretable cell type and function annotation by introducing a hypergraph structure and a metabolic pathway activity dynamic modeling mechanism.

This application relates to a spatial transcriptome gene expression prediction method. The method includes: acquiring stained pathological image data of a target tissue; dividing the stained pathological image data into image blocks to obtain at least two image blocks to be identified; for each image block, determining a first cross-modal image feature of the image block and a second cross-modal image feature of the adjacent image blocks; predicting the image block based on the aggregation result of the first and second cross-modal image features to determine the gene expression prediction result and cell type prediction result corresponding to the image block; and generating a spatial transcriptome gene expression prediction map of the target tissue based on the gene expression prediction result and cell type prediction result of each image block. This method can improve the prediction accuracy of gene expression profiles.

The present application relates to a tunable annular diaphragm cell imaging system and a dye-free deep learning identification method, relating to the technical field of optical imaging and image processing, comprising an annular diaphragm illumination module; the present application establishes a quantitative mapping relationship between the outer diameter of the annular diaphragm parameter and the image quality index, and adaptively finds the best bright field and dark field illumination conditions for different cell types, densities and overall profiles and internal structures, so as to obtain an original image with sharp edges and distinct internal structures without using any dye, the improved deep learning segmentation model has a two-way attention gate module, which can capture spatial details and texture semantic information important for cell recognition in parallel and differentially, realize high-accuracy dye-free cell dead or alive automatic identification, make the segmentation result more consistent with the biophysical characteristics of cells, improve the segmentation accuracy and recognition accuracy, and automatically distinguish the state of cells with complex background without fluorescent labeling.

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 classifying a sample of a body liquid, bone marrow or cytology smear, in particular a blood smear sample, as indicative or non-indicative of 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 the sample.

The present disclosure provides surfactant-based compositions, methods, kits, and systems for use in cell permeabilization, analytical and diagnostic assays, polypeptide stabilization, viral inactivation, and bioprocess workflows. In certain aspects, permeabilization buffers comprising defined concentrations of nonionic surfactants in polar protic solvents permeabilize diverse cell types while preserving intracellular targets and compatibility with downstream detection chemistry and nucleic acid amplification. Related compositions include loading, blocking, running, and mountant buffers, as well as polypeptide stabilization and viral inactivation solutions formulated with the surfactants. In additional aspects, the surfactants are used in lateral flow devices, bead-based assays, and lipid nanoparticle workflows, including determination of encapsulation efficiency. The disclosure further provides methods and systems in which data generated from surfactant-containing workflows are analyzed by machine-learning processes to determine attributes of buffer performance and recommend protocol parameters.