51 results about "Cellular heterogeneity" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The invention relates to the technical field of spectral imaging detection, in particular to a spontaneous-stimulated Raman co-localization single-cell substructure atlas analysis system and a spontaneous-stimulated Raman co-localization single-cell substructure atlas analysis method. The system comprises a femtosecond pulse excitation module used for outputting two beams of broadband femtosecond pulse laser with different frequencies and continuously adjustable optical power; the Stokes light modulation module is used for modulating the Stokes light and carrying out space beam combination on the Stokes light and the pump light; the pump light modulation module is used for modulating pump light; the spontaneous Raman laser continuous adjusting module is used for outputting spontaneous Raman laser; the stimulated Raman exciting light co-positioning module is used for combining the combined laser and the spontaneous Raman laser again, focusing the combined laser and the spontaneous Raman laser on a sample, exciting a stimulated Raman scattering signal, converting the stimulated Raman scattering signal into a digital signal and carrying out data acquisition; and the spectrum detection module is used for collecting the spontaneous Raman scattering light which is reflected and returned along the original light path, and collecting a spectrum signal to form a Raman spectrum. The method has the advantage of improving the accuracy of single-cell heterogeneity structure and cancer cell type discrimination.

The application discloses a cell heterogeneity analysis method, device and computer readable storage medium for large-scale single cell sequencing data. The application combines kernel non-negative matrix factorization with deep neural network to construct a clustering method called KNMF-DNN. The method selects some representative subsets from the overall data, and uses the kernel non-negative matrix factorization method to cluster the subsets; then, the representative subsets are used as a training set, the remaining data is used as a test set, the labels obtained by clustering are used to classify the remaining data through the deep neural network, so that the clustering of the whole data is realized. In addition, the application also uses the idea of stratified sampling to select the most representative samples, and the best division of the clustering samples and the classification samples is determined by the KL divergence. Experimental results show that, compared with other latest methods, the KNMF-DNN has more excellent results on three real scRNA sequencing data sets.

The invention discloses a spatial domain identification method and system based on spatial multi-omics data. The method comprises the following steps: obtaining an expression profile and a spatial position coordinate through spatial multi-omics sequencing, and preprocessing the expression profile and the spatial position coordinate; decomposing and extracting a shared low-dimensional incidence matrix of the multi-omics data by using an anchor concept; constructing an intercellular similarity matrix based on the spatial coordinates, and generating a spatial location map by combining the k-nearest neighbor map and the high-order adjacency matrix; integrating multi-base clustering results by adopting an element-by-element weighting strategy to form an integrated graph; solving an optimal low-dimensional feature through a double-graph regularization objective function (fusing Laplacian matrix constraints of a spatial position graph and an integrated graph); and finally realizing high-precision spatial domain clustering. The method breaks through the limitation that a traditional method ignores spatial dependency and integrated interference, effectively fuses the high-order relation and spatial information of multi-omics data through anchor concept decomposition and a double-graph regularization framework, and remarkably improves cross-scene robustness; the method is suitable for biomedical scenes such as tumor microenvironment analysis and cell heterogeneity analysis.

The present disclosure relates to methods and kits for generating single cell barcodes and imparting them to the constituent molecules within a single cell. Additionally, methods to overlay sample barcode and spatial barcode information onto the single cell barcodes are also described. Generation of single cell barcodes is achieved by labeling the genomic DNA of a cell / nucleus with a small handful, preferably just a one or two cellular barcode probes (CBP) that can be amplified and propagated to label the constituent molecules within the cell. The disclosure finds utility in applications such as characterization of cellular heterogeneity, comprehensive profiling of tissue composition, characterization of adherent cells, discovery of new cell subtypes and functions of individual cells in the context of its microenvironment, and others.

The invention relates to the technical field of immunohistochemical staining, and discloses an automatic scoring method and system for immunohistochemical staining results. The method comprises the following steps: performing spectral signal intelligent deconvolution processing on a multi-immunohistochemical staining image to obtain target image data; performing multi-scale context modeling on the target image data to obtain a sub-region segmentation mask; performing depth map feature extraction on the target image data based on the subregion segmentation mask to obtain a multi-dimensional image feature vector; inputting the multi-dimensional image feature vector into a mixed deep learning recognition model to carry out marker intelligent recognition to obtain a multi-marker expression state recognition result; collaborative scoring is carried out based on the multiple marker expression state recognition result, a personalized unified scoring value is obtained, cell heterogeneity analysis is carried out on the personalized unified scoring value, and an intelligent scoring report is generated. According to the invention, the problem of spectrum crosstalk in multiple staining is effectively solved, and high-precision intelligent identification of multiple immunohistochemical markers is realized.

The disclosure relates to methods of identifying cellular heterogeneity, comprising of acquiring a plurality of images of one or more cells, applying a trained artificial neural network to classify one or more cells of the plurality of images, and identifying one or more features associated with cellular heterogeneity in one or more classified cells of the plurality of images. Also described are methods for providing the test set to train an artificial neural network. The disclosure also relates to a method that accurately classify images and identify subcellular / cellular features that indicate cellular heterogeneity.

The present invention relates to the field of bioinformatics technology, and in particular to a data classification method and system for single-cell sequencing. The method comprises the following steps: obtaining an original gene expression matrix; performing gene data preprocessing on the original gene expression matrix to obtain a preprocessed gene expression matrix; constructing a cell topology map based on the preprocessed gene expression matrix to obtain a cell topology map; calculating cell division rates based on the cell topology map and the preprocessed gene expression matrix, and performing RNA rate calculation to obtain RNA rate data; adding node rates to the cell topology map based on the cell division rate data and the RNA rate data to obtain an enhanced cell topology map. The present invention better captures the dynamic changes of cell states, reduces sensitivity to parameters, processes complex cell population structures, and provides quantitative cell heterogeneity assessment indicators by constructing a dynamic map and analyzing its changing patterns.

Exemplary systems, methods, and computer-accessible medium are provided for Chimeric Antigen Receptor (CAR)-T therapy. Thus, the exemplary systems, methods, and computer-accessible medium are provided that select a first set of targets highly expressed in cancer and lowly in normal tissues, select a second set of targets that are highly co-expressed in the cancer and lowly in normal tissues, generate an input set by combining the first and second set of targets with dependency data, single-cell heterogeneity, and antibody availability, determine, by a learning model, at least one target pair for use in CAR-T therapy, and apply a cell therapy based on the determined target pair(s).

The invention relates to the field of medicines, in particular to application of tRF-5030c in preparation of a product for treating non-small cell lung cancer. The tRF-5030c provided by the invention is not a simple sequence design product, but is a direct experimental evidence based on the interaction of the tRF-5030c and the double-target protein. Cooperative regulation and control of two key mechanisms are achieved through the single molecular entity, and the problems of low synergistic efficiency and toxicity superposition caused by pharmacokinetic differences, structural isomerism and action target cell heterogeneity of multiple drugs are fundamentally solved. It is ensured that dual inhibition of PARP1 mediated DNA repair and EGFR driven survival signals is highly consistent in time and space.

The invention relates to the field of bioinformatics and spatial multi-omics analysis, and discloses a spatial multi-omics data clustering method combining a mask mechanism and cross-modal fusion. The method comprises the following steps: firstly, constructing and optimizing a spatial adjacency matrix and a feature similarity matrix, and executing spot-level mask operation after preprocessing multiple omics data; then, respectively extracting potential expressions of spatial neighborhood information and high-dimensional features of each group of mechanical modals by adopting an encoder with shared architecture and specific parameter modals, and generating a unified cross-modal fusion expression through weighted fusion; and finally, combining the comparison loss, the reconstruction loss, the clustering hierarchy comparison loss and the KL divergence to construct a total loss function, and performing joint optimization to realize cross-modal information fusion and consistent clustering between modals. Experiments show that the clustering accuracy, the noise robustness and the cross-modal integration quality of the method are remarkably superior to those of an existing method in simulation and real data sets, and powerful technical support is provided for analyzing the tissue microenvironment and the cell heterogeneity.

The application discloses a kind of single-cell transcriptome analysis methods and application of Crohn's disease creeping fat microenvironment, belong to the field of biological medicine technology.The method is by obtaining the single-cell and batch transcriptome data of mesenteric adipose tissue of Crohn's disease patient and healthy control, constructs relevant gene set and carries out data quality control, clustering and cell annotation;Mesenchymal stem cells are subpopulation identified, abundance analysis and function typing, combined with signal path analysis and cell communication analysis, and the key factor of regulating creeping fat microenvironment is screened out.The application first systematically analyzes the cell heterogeneity and MSC subpopulation characteristics of Crohn's disease creeping fat microenvironment, and determines the key signal path and regulating molecule, which can provide efficient and reliable technical means for the pathogenesis research, treatment target screening and creeping fat related molecule identification of Crohn's disease, and has high scientific research and clinical application value.

The present application relates to a single-cell protein subcellular localization model based on weakly supervised multi-instance learning, and relates to the technical field of biological information. The model comprises a cell feature extractor, an image branch, a cell branch and a class-aware adaptive pruning module. The cell feature extractor is used to encode single-cell images into single-cell feature vectors. An image-level classifier in the image branch is used to output an image-level protein subcellular localization prediction result according to the image-level feature representation. A cell-level classifier in the cell branch is used to output a single-cell-level protein subcellular localization prediction result according to the single-cell feature vector. The class-aware adaptive pruning module generates single-cell pseudo labels through several label sources. The model of the present application can solve the problems of cell label noise and class long-tail distribution under weak supervision, thereby realizing more accurate and stable single-cell localization prediction and providing a powerful tool for single-cell heterogeneity analysis.

The application relates to the field of biomedical technology and discloses a peripheral blood leukocyte DNA methylation gene network model for lung nodule benignity and malignancy and infiltration risk assessment and a construction method thereof. The method is based on an epigenome network medical algorithm, specific methylation characteristics are screened from whole blood leukocytes of sub-centimeter lung nodule patients, and a diagnostic model is constructed. Research proves that, by using peripheral blood leukocyte DNA methylation combined with network topology analysis, the low cfDNA content, the PBMC separation complexity and the cell heterogeneity interference can be effectively overcome, the systemic immune remodeling signal of early lung cancer can be accurately captured, benignity and malignancy classification and infiltration risk stratification can be performed, the model has good sensitivity and specificity, can be used as an important supplement of LDCT screening, can improve early risk assessment accuracy, can guide high-risk patient treatment and reduce over-treatment of low-risk patients, and realizes precise management of lung small nodules.

The invention is applicable to the technical field of cell culture, and provides a method for amplifying a liver cancer primary cell line by using a liver cancer organoid, which comprises the following steps: constructing the liver cancer organoid; preparing matrigel with a concentration of 2-5%, adding the matrigel into a culture plate, and carrying out coating treatment; digesting the liver cancer organoid into small cell clusters; adding the digested cell mass into a pre-cooled 2% buffer to terminate digestion, and performing centrifugal operation at low temperature; re-suspending the centrifuged cells by using a DMEM (Dulbecco Modified Eagle Medium) high-glucose culture medium containing 10% of serum until the cells are uniform, and paving the cells into a coated culture plate; on the second day, a mixed culture medium containing an HCC culture medium and a DMEM culture medium containing 10% of serum is adopted. The invention establishes a novel primary cell amplification method capable of realizing stable passage and retaining tumor cell heterogeneity, and by synergistically integrating the technical advantages of two culture systems, the efficiency of multiple experiments is improved, and the biological and clinical application scenarios are widened.

The invention relates to the technical field of biological medicine, provides novel application of NME2, and particularly relates to application of an NME2 inhibitor in preparation of a medicine for treating sepsis-related encephalopathy. The invention further provides an NME2 inhibitor recombinant vector and a sepsis-related encephalopathy treatment pharmaceutical composition taking the NME2 inhibitor or a recombinant expression vector thereof as an active component. According to the present invention, CLP-induced scRNA-seq of a mouse brain after sepsis is used to analyze microglial cell heterogeneity and identify inflammasome activated subgroups, and a new pathway is found, i.e., NME2 is combined with an Nlrp3 promoter, and chromatin-related protein EPC2 is recruited to promote acetylation of histone H2AK5 so as to drive Nlrp3 transcription and NLRP3 inflammasome activation; according to the discovery, epigenetic control of microglial cell subset heterogeneity, transcriptional regulation and inflammasome activation is linked, and a potential therapeutic target is provided.

The invention discloses a culture medium and a method for obtaining an induced cortical-like neuron precursor cell cluster from iPSCs (induced pluripotent stem cells), and belongs to the technical field of biology. The culture medium comprises a neural stem cell induction liquid I and a neural stem cell induction liquid II, a single-layer adherent induction method is adopted, and iPSCs are efficiently differentiated into an induced cortical neuron precursor cell cluster through optimized inoculation density, matrix treatment and a staged induction process. The neural stem cells comprise neural stem cells, transition-state nerve cells and differentiated mature nerve cells (the cell type composition is highly similar to that of tissue-derived neural stem cells). The method overcomes the problems of complex induction process and high cell heterogeneity in a traditional embryoid body method, the induction efficiency can reach 100%, single cell sequencing analysis shows that all cells are nerve cells, the purity is 100%, and non-target cells are not detected. A uniform, stable and efficient cell tool is provided for brain development research, nervous system disease modeling and cell therapy.

The invention discloses a single cell type automatic labeling method and system based on an attention network, and the method comprises the steps: obtaining original single cell transcriptome data, and carrying out the preprocessing, and obtaining high-variation gene data; performing low-dimensional feature mapping and gene layer comparative learning on the obtained high-variation gene data, and screening key gene features based on an attention mechanism; carrying out biological pathway mapping on the obtained key gene features, carrying out feature integration to obtain pathway features, carrying out pathway layer comparative learning, and screening key pathway features based on an attention mechanism; constructing a gene layer global feature and a pathway layer global feature, and performing feature fusion to obtain a cell global feature; and carrying out cell layer contrast learning based on the obtained global cell features, and carrying out single cell type classification prediction. According to the method, high-precision, explainable and high-robustness automatic labeling of the single cell type can be realized, and a more reliable technical support is provided for researches such as cell heterogeneity analysis and rare subtype recognition in single cell omics data analysis.

The invention relates to the technical field of biology, in particular to a racoon ussuriensis dog skin hair follicle single cell transcriptome map and a construction method thereof. The construction method comprises the following steps: collecting a skin tissue sample in the middle of the back of a male racoon ussuriensis dog in a winter hair period, dissociating and preparing a single-cell suspension, constructing a cDNA library and sequencing; comparing original sequencing data to a reference genome, and quantifying high-throughput single cell transcriptome data; further quality control is carried out, low-quality cells and double cells are removed, and standardization processing is carried out on data; screening hypervariable genes, and performing dimensionality reduction and visualization on a gene expression profile to obtain marker genes of a cell population; and calculating the correlation between the to-be-identified cell expression profile and the reference data set, and identifying the cell type. Based on a single cell transcriptome sequencing technology, the cell heterogeneity problem which cannot be solved by tissue sample sequencing is solved, a new way is provided for exploring a hair follicle development heterogeneity mechanism, and a new direction is also provided for biological research of racoon dog hair follicles.

This application discloses a CD4 + T cell-derived tsRNA biomarkers and their applications, wherein the biomarkers are derived from CD4 + A phenylalanine-tRNA fragment from T cells, wherein the phenylalanine-tRNA fragment is 5'tiRNA-36-PheGAA-8, and the nucleotide sequence of the 5'tiRNA-36-PheGAA-8 is shown in SEQ ID NO:5. This application relates to purified pathogenic CD4+ from MG patients. + T cell tsRNA sequencing significantly reduced background noise caused by cellular heterogeneity, providing more reliable data support for elucidating the regulatory mechanisms of tsRNA at the level of specific immune cells, and screening for CD4+ in MG patients. + The abnormally elevated expression of biomarkers in T cells facilitates dynamic monitoring of patients.

The inventors establish a role for primary cilia in human TNBC chemotherapeutic resistance. They developed patient-derived organoids, and showed that these recapitulated the cellular heterogeneity of TNBC biopsies. They treated their TNBC organoids with chemotherapeutics and observed partial killing. The surviving cells with organoid-reconstituting capacity showed selective enrichment for the quasi-mesenchymal ciliated cell subpopulation. They developed a family of small-molecule inhibitors of ciliogenesis and show that these, or genetic ablation of primary cilia, suppress chemoresistance. In particular, the present invention relates to a method of preventing therapeutic resistance in a patient suffering from a cancer comprising administering to the patient a therapeutically effective amount of an inhibitor of ciliogenesis. The present invention also relates to a method of treating a therapy-resistant cancer in a patient suffering from a cancer comprising administering to the patient a therapeutically effective amount of an inhibitor of ciliogenesis.

The invention discloses a culture system and a culture method for intestinal organs of euryhaline fish, and belongs to the technical field of bioengineering. The culture system is composed of a basic culture medium and a complete culture medium, wherein the basic culture medium is Advanced DMEM / F12, and is supplemented with a GlutaMax, Hepes and mycillin double-antibody solution; and on the basis of the complete culture medium, N-2, B-27, N-acetylcysteine, EGF (Epidermal Growth Factor), Noggin, R-Spondin 1 and other key cell factor combinations are further added. By optimizing the tissue pretreatment and digestion process, the efficient fish intestinal tract organoid culture and passage method is established, the organoid model with the crypt-villus-like structure, the cell heterogeneity and the intestinal tract barrier function is successfully constructed, and the model provides a highly bionic in-vitro research tool for fish physiology and toxicology research.

The invention discloses a rare cell capturing device and a cell heterogeneity analysis method, the device comprises: a sample liquid inlet for receiving a sample liquid; the first conveying channel is connected with the sample liquid inlet; the filtering area is connected with the first conveying channel and is used for filtering the sample liquid to obtain target liquid; the cell zoning and separating area is connected with the filtering area and is used for capturing rare cells from the target liquid; the second conveying channel is connected with the cell partition separation region; and the filtrate outlet is connected with the second conveying channel and is used for outputting the filtrate. The cell separation is realized, and the cell purity, the recovery rate and the sample capacity are improved. The method can be widely applied to the technical field of cell separation.

The invention discloses a single-cell multi-omics dimension reduction method based on a Gaussian process hidden variable model, and aims to solve the problems that single-omics and multi-omics data analysis requirements are difficult to consider and high sparsity and technical noise of single-cell data cannot be effectively handled in the prior art. According to the method, a shared potential space probability generation framework is constructed, variational inference and a sparse Gaussian regression strategy are combined, data of different modalities are non-linearly mapped to a unified low-dimensional space, and meanwhile, an adjustable modal weight is introduced, so that the model can perform efficient dimensionality reduction on single-cell transcriptome and multi-omics data. When the method is used for carrying out dimension reduction on single cell sequencing data, low-dimensional representation learned by the model can be used for completing downstream tasks such as cell clustering, visualization and differential expression analysis, cell heterogeneity and potential biological regulation signals are accurately revealed, and the method has a good application prospect.

The invention discloses a human renal tubular epithelial cell immortalized cell strain as well as a construction method and application thereof, and belongs to the technical field of cell biology. In the process of constructing the renal tubular epithelial cell immortalized cell strain, a high-quality renal cortex sample is firstly obtained, high-purity primary cells are separated out, the cell activity and proliferation are guaranteed in the culture process, the problem of cell heterogeneity caused by traditional manual operation is solved, and a foundation is laid for subsequent research. The infection efficiency is improved by optimizing virus infection conditions, and stable transfection is ensured in combination with optimal BSD concentration screening. According to the construction method disclosed by the invention, the virus infection efficiency and the gene integration stability are remarkably improved, the immortalized cell strain capable of infinitely proliferating is successfully obtained, the problem that primary cell amplification is limited is solved, and a cell source with stable functions is provided for the bioartificial renal tubule.

The invention discloses a multi-mode vision-assisted single cell control and packaging system and a multi-mode vision-assisted single cell control and packaging method. The system comprises a charge-coupled device (CCD) vision module, a digital micromirror (DMD) light control module, an ultraviolet light source assembly and a digital micro-fluidic chip unit. The system is characterized in that after image enhancement and single cell identification positioning operation, the system can obtain single cell coordinates with extremely high precision; then coordinate mapping and mask generation operation are combined to realize high-precision conversion from a CCD coordinate system (visual system) to a DMD coordinate system (printing system); meanwhile, an operation based on a space-time discrete printing strategy is carried out, and the exposure energy of each micromirror unit is dynamically regulated and controlled successfully by regulating and controlling the switching time of each micromirror unit of the DMD, so that the mechanical hardness gradient construction of the subcellular scale microgel and the precise packaging of the single cell are realized. According to the invention, on-demand identification, positioning and heterogeneous microenvironment packaging of single cells can be realized, and efficient, controllable and biocompatible technical support is provided for cell heterogeneity research.

The invention provides a single cell level dynamic gene regulation network inference method based on a graph neural network and comparative learning, and aims to solve the problem that dynamic change of a gene regulation relation under single cell resolution is difficult to reveal due to the fact that cell heterogeneity cannot be described by an existing monomics method. The method comprises the following steps: firstly, pre-training to generate a cell token and a gene token through comparative learning and graph representation learning; then, carrying out multi-modal fusion on the two genes and the interpolated gene expression data, and constructing context specific representation of each gene in each cell; further, a graph neural network with a cross attention mechanism is adopted to model cell specific regulation interaction between a transcription factor and a target gene, and dynamic gene regulation network inference of a single cell level is realized; and finally, carrying out joint optimization on model parameters through comparative learning and expression reconstruction. According to the method, the dynamic regulation and control relation of cell state dependence can be accurately deduced, and an important calculation tool is provided for development analysis and research.

The present invention discloses a single-base mutation imaging method based on in situ ARMS-PCR and Cas labeling. This method belongs to the field of single-cell analysis. By adding base mismatch sites to PCR primers, effective amplification and specific identification of single nucleotide variation information within bacteria are achieved. At the same time, the amplified products are labeled by modified gRNA / Cas12a. This method can also accurately quantify Salmonella resistant bacteria in complex environments and obtain cellular heterogeneity and spatial location information.