30 results about "Cellular resolution" patented technology

A spatial in situ sequencing method, belonging to the field of biology, is proposed. It utilizes an electric field-assisted directed migration of mRNA and in-situ capture with primers on a microarray surface to enrich tissue and release mRNA. In-situ reverse transcription generates covalently fixed cDNA, ensuring high positional stability during multiple rounds of hybridization and imaging. After reverse transcription, tissue is digested to remove tissue, reducing spatial hindrance and background interference, while the cDNA remains at its original coordinates due to covalent anchoring. Combining coding probe hybridization and RCA, single-molecule-level signal amplification and recognition are achieved. Through decoding and single-cell segmentation, transcripts are mapped to their respective cells, constructing a single-cell resolution spatial gene expression map. This method does not rely on multi-round DAPI mapping or other endogenous morphological marker-based multi-cycle image registration methods, making it suitable for high-throughput spatial in situ sequencing and significantly improving robustness and versatility in complex imaging scenarios such as thick tissue sections and low signal-to-noise ratios. It is applicable to high spatial resolution, high-throughput spatial transcriptome research.

The present invention relates to a system (1) for in vivo full-field transmission interference cell resolution imaging of an eye (6) of an individual and to a method (100) for in vivo full-field transmission interference imaging of an eye (6) of an individual.

The present application belongs to the field of genetic engineering, and provides a method for detecting RNA and ribosome interaction in situ, and a probe combination of a padlock probe and an 18S rRNA primer probe, a decoding probe and a fluorescent probe used in the detection process. The detection method of the present application has the advantages of high signal-to-noise ratio, high throughput coding capacity, subcellular resolution, low cost, simple operation and the like, and is significantly superior to traditional translational omics research technology.

The invention relates to a method to indicate the clinical outcome of a cancer patient by labelling a cancer sample with labelled molecular probes, assaying the expression of a plurality of biomolecules at the resolution of a single cell and assigning a cellular identity (CI) to each single cell in the sample based on their expression pattern; then assigning a single cell pathology (SCP) patient group according to the proportion of each CI the sample contains.The invention in other aspects relates to methods of treatment of a patient with anticancer drugs according to the patient's assignment to particular SCPs. Alternatively, this aspect may be formulated as the provision of certain drugs for treatment of cancer in patients characterized by tumours assigned to certain SCPs.

The present disclosure provides a fusion protein comprising a base-editing enzyme and a protein that binds to a fragment crystallizable region (Fc region) of an immunoglobulin (e.g., nanobody, protein A, protein G, or pAG). Methods, kits, or systems of using such fusion proteins for profiling the binding events of a transcription factor or a chromatin modeling factor to a DNA in a cell or a population of cells are also disclosed.

The invention discloses a rapid evaluation method of host caries risk based on Raman spectrum, and relates to the technical field of microbiological detection. Aiming at the problem that in the prior art, it is difficult to rapidly and quantitatively reflect the early metabolic activity of the oral flora, the method introduces a stable isotope signal through heavy water labeling to obtain single-cell Raman spectrum information of the oral flora, and evaluates the metabolic activity of the flora on the basis of a characteristic parameter C-D ratio reflecting the metabolic state of microorganisms in a Raman spectrum, so that the early metabolic activity of the oral flora can be rapidly and quantitatively reflected. Therefore, the judgment of the caries risk of the host is realized. The method comprises the steps of sample collection, flora growth and metabolism law research under different concentration heavy water labeling, and metabolic activity detection under different environmental conditions. By adopting the heavy water concentration of 40%, on the premise that flora growth is not obviously influenced, rapid characterization of metabolic activity can be realized in a short time, the resolution ratio of single cells is high, and disturbance is small. The method provides a new tool for early warning of caries and oral flora metabolism research, and has a wide application prospect.

The embodiments disclosed herein provide spatially tagged nuclei that are compatible with any genomic or multi-omic single cell / nucleus assay, enabling the generation of spatially resolved single cell sequencing libraries with single cell resolution.

The invention relates to the technical field of single-cell sequencing, and discloses a high-quality single-cell Hi-C library building method, which comprises the following steps: by utilizing a cumulative effect, carrying out cross-linking immobilization, enzyme digestion, biotinylated nucleotide filling-in and connection on population cells, sorting single cells, immediately marking different bar code combinations on each cell, mixing extracts of each cell, and carrying out high-quality single-cell Hi-C library building. After mixing, the cell population is equivalent to a small amount of cell population, enriching biotinylated DNA by using streptavidin magnetic beads, and finally establishing a library. According to the method, the library quality can be remarkably improved while the single cell resolution is maintained.

This invention discloses a single-cell spatial transcriptome sequencing method based on spatial coding, belonging to the field of spatial transcriptome sequencing technology. Two microfluidic chips with mutually perpendicular channels are used to add two sets of spatial coding sequences to tissue slices. Unique spatial coding combinations are formed at the intersection of the two channels, marking the spatial location information of cells. After coding, cell nuclei are extracted from the tissue slices, and high-throughput single-cell nuclear sequencing is performed to add cellular coding to single cells. Through sequencing analysis, transcripts are mapped to spatial locations according to spatial coding, and to single cells according to cellular coding, reconstructing a single-cell spatial transcriptome atlas. This method achieves single-cell spatial coding in a simple and low-cost manner, belonging to spatial transcriptome sequencing technology with single-cell resolution.

The invention discloses a method and a system for synchronously detecting intracellular microbial colonization and host response at a single cell level, and belongs to the technical field of single cell epimics and microbiomics. Microorganism colonization information and host chromatin openness information of each single cell are synchronously extracted from single cell chromatin accessibility sequencing data, and correlation analysis is performed on the microorganism colonization information and the host chromatin openness information at the single cell level, so that a cell specificity mechanism of host-microorganism interaction is analyzed. At present, a systematic method capable of synchronously realizing microbiological detection and host chromatin accessibility analysis under single cell resolution is not reported yet, and the method fills the technical blank. The method can solve the problem that the existing sequencing technology based on 16S rRNA, 18S rRNA or scRNA-seq is difficult to analyze microorganism colonization and host chromatin accessibility synchronously and highly sensitively at the single cell level, and provides an innovative tool for revealing a host-microorganism interaction mechanism of cell specificity in complex tissues.

The invention belongs to the technical field of biology, and discloses a single-cell communication tracing system based on a cell penetrating nucleic acid tag and application thereof. The tracing system comprises a carrier containing an inducible promoter, wherein the carrier expresses CellTag-RNA (Ribonucleic Acid), fat-soluble secretory fluorescent protein and fluorescent protein fusion protein which cannot be secreted. After the system is expressed in cells, fat-soluble secretory fluorescent protein and CellTag-RNA form a compound and are transferred into adjacent receiving cells, CellTag bar codes of the receiving cells and sending cells can be detected at the same time by performing single-cell sequencing on the whole tissue, and whether the receiving cells are amplified or not can be judged according to fluorescence expression and CellTag occurrence frequency. By analyzing the sharing mode of the bar code, the development pedigree, the spatial proximity relation and the interaction network of the cells can be synchronously reconstructed with the single cell resolution, so that the dynamic structure and the functional module of the tissue are disclosed on the spatial-temporal scale.

The application discloses a kind of based on deep learning's pathological image proteomics analysis method and system, it is related to bioinformatics, the method includes: using laser microdissection and mass spectrometry constructs paired image-protein dataset;Adopt pre-training VGG16 convolutional neural network to extract image deep feature, fusion cell morphological feature constructs whole proteome regression mapping model;Through U-Net single cell recognition and iterative deconvolution algorithm output cell level protein expression matrix;Combining mixed semi-supervised learning strategy handles label scarcity problem;Based on protein expression matrix, dimension reduction clustering, spatial heterogeneity quantification (CV+SHI) and visual analysis are carried out.The application realizes the regression mapping of pathological image to whole proteome (>2800) for the first time, breaks through the flux bottleneck of traditional virtual staining, realizes true single cell resolution proteomics, and provides new means for precision medicine.

This invention discloses a spatial multi-omics sequencing method based on cross-modal generation, comprising the following steps: acquiring H&E staining images of tissue sample slices; automatically identifying and locating regions of interest (ROIs); manually cutting the tissue sample slices based on the ROIs and transferring them to a detection chip; acquiring a multi-omics expression matrix at single-cell resolution; extracting spatial feature anchor points that match the H&E staining images and the multi-omics expression matrix; performing coordinate transformation based on the coordinate mapping relationship of the spatial feature anchor points to achieve physical spatial dimension registration; and utilizing a cross-modal prediction model to perform spatial inference and full-sample information completion on discrete detection signals through spatial expression reconstruction methods. This invention effectively integrates high-precision local physical detection with global algorithm inference, ultimately outputting high-fidelity multi-omics expression information containing the entire tissue sample, completing cross-scale completion from local targeted detection to full-sample multi-omics.

To provide a solution for quantitative measurement and monitoring of cellular mechanical forces that overcomes the shortcomings of existing technologies, requires no microscope, offers real-time, high-throughput, and low-cost capabilities, the inventors provide a cellular mechanical force detection device, comprising: a base, and a micropillar array consisting of multiple micropillars disposed on the base, capable of deformation under cellular mechanical forces. Each micropillar has a light-reflecting layer at its top or upper surface. The inventors also provide a cellular mechanical force detection system including the above-mentioned device, a detection method for detecting cellular mechanical forces using the above-mentioned device, and a method for preparing the above-mentioned device. Unlike existing technologies, the above-mentioned solution offers advantages such as high throughput and low cost, single-cell resolution, real-time monitoring, high sensitivity, and the ability to simulate the cellular microenvironment; it can also simulate the composition and morphology of the extracellular matrix, thus meeting a wider range of technical needs.

Microarrays and methods of using the microarrays in spatially resolving gene expression are provided. The microarrays and methods provide high cellular resolution and enable the acquisition and sequencing of a wide array of RNA types, including small RNAs (sRNA) and other non-mRNA entities.

This invention discloses a method for detecting nucleic acid mutations with spatial single-cell resolution and its application. The method includes the following steps: (1) pre-staining, fixing, and digesting the tissue sample or cells to be tested; (2) adding isBDA reaction solution for in situ PCR amplification, discarding the reaction solution after the reaction, fixing again, and performing confocal imaging. This invention's detection method is an innovative in situ cell imaging detection method that can achieve localization and visualization at the single-cell level. This invention's detection method selectively inhibits the amplification of wild-type templates, thereby efficiently enriching the target mutation sequence. It can identify cell types while detecting mutation-positive cells, exhibiting high sensitivity and low cost. Furthermore, through specific reaction conditions, it maintains high inhibition efficiency in complex in situ environments, demonstrating good spatial single-cell resolution.

The invention discloses a third harmonic microscopic imaging device with a three-dimensional rapid intelligent noise reduction function, which omits a complex and tedious processing process of an imaging sample by means of the imaging advantage of third harmonic, avoids photobleaching and light damage to the greatest extent, can rapidly image for a long time, and has the advantages of simple structure and high efficiency. Meanwhile, the optical slicing capability is crucial to acquisition of a three-dimensional image, and millimeter-level depth subcellular resolution microscopic imaging can be realized; according to the zero-sample unsupervised intelligent noise reduction algorithm, the obtained high-noise third harmonic microscopic image can be quickly subjected to noise reduction, the signal-to-noise ratio of the image is improved, and subsequent cell segmentation and counting tasks are facilitated.

The application discloses a nucleic acid aptamer combination targeting pancreatic cancer cell membrane proteins and application thereof in single-cell high-throughput sequencing analysis, and belongs to the technical field of biological detection.The nucleic acid aptamer combination of the application is shown as SEQ ID NO.1-SEQ ID NO.101, the nucleic acid aptamer combination can specifically recognize a plurality of cell membrane surface molecular targets, high-throughput sequencing analysis of nucleic acid aptamer probe sequences of the combination is carried out by using single-cell high-throughput sequencing technology, the expression amount of single-nucleus cell resolution transcriptome and cell membrane proteome for a single nucleus cell can be obtained, so that double-omics single-cell high-throughput analysis of single-nucleus cells from peripheral blood of a pancreatic cancer patient is realized, and a solution is provided for improving the accuracy of early diagnosis of pancreatic cancer through liquid biopsy.

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.

Massively parallel high throughput single-cell optoperation and their uses thereof are disclosed. The disclosed method teaches the use of parallel single-cell printing technique using SU-8 membrane and nanosecond pulsed laser to espouse on single-cell with micro-dish pattern structure. The platform is able to effectively deliver different (small to large) cargo in a different cell type with high transfection efficiency and high cell viability at parallel single-cell resolution. The proposed Bio-MEMS device has potential applications in personalized and regenerative medicine.

The invention discloses an intelligent nanometer classifier based on glutathione gating and a preparation method and application thereof, and the preparation method comprises the following steps: 1, annealing four DNA chains T1, T2, T3 and T4 in an annealing buffer solution to form programmable atomic nanoparticles (PAN); annealing the four DNA chains H1, H2, H3 and H4 in an annealing buffer solution to form four hairpins; and step 2, incubating programmable atomic nano-particles (PAN) and four hairpins, and assembling the programmable atomic nano-particles and the four hairpins into the intelligent nano-classifier. The intelligent nano classifier can be used for realizing spatial selective visualization and cooperative photodynamic therapy of various circRNAs. The intelligent nano classifier can realize sensitive quantification of circRNA under single cell resolution, distinguish circRNA and mismatched variants thereof on a single base resolution level, and accurately identify breast cancer and lung cancer in different stages.

The invention discloses a method for detecting chromatin modification and whole genome information based on single molecule sequencing and application of the method. The invention provides a single molecule long read sequencing platform and a sequencing technology capable of researching protein-DNA interaction under single cell resolution, and the method can accurately analyze epigenetic information such as chromatin modification and the like in a single cell. Comprising histone covalent modification and a binding distribution mode of chromatin binding protein in a genome. According to the construction method of the long-read sequencing library and the single-molecule long-read sequencing data obtained by single-molecule sequencing based on the library, the data can be applied to analysis of repetitive sequences of each copy in a genome and chromatin modification of a'blacklist 'region by analyzing the data.

Processes to spatially align single cells to yield a specimen map with single cell resolution are provided. Methods can perform spatial omics on a specimen to yield spatial omics data. The spatial omics data can be used in combination with single cell omics data to assign single cells to spatial coordinates to yield a resolved specimen map.

Methods for single-cell DNA methylation profiling are disclosed herein, with embodiments featuring use of droplet-based cell barcoding and conversion reagents to generate high-quality methylomes and / or methylome-plus-transcriptomes, for single cell genomic analysis and correlation of methylation state with transcript expression levels at single-cell resolution. The present disclosure is based, at least in part, upon discovery of methods and compositions capable of leveraging cell barcoding and conversion reagents to generate high-quality single-cell methylomes capable of being assessed rapidly in parallel across populations of cells, as well as coordinated single-cell methylome-plus-transcriptomes formatted for parallel, high-throughput assessment of cellular populations with precise single-cell resolution.

The present disclosure provides a fusion protein comprising a base-editing enzyme and a protein that binds to a fragment crystallizable region (Fc region) of an immunoglobulin (e.g., nanobody, protein A, protein G, or pAG). Methods, kits, or systems of using such fusion proteins for profiling the binding events of a transcription factor or a chromatin modeling factor to a DNA in a cell or a population of cells are also disclosed.

The invention discloses a method and equipment for predicting a non-coding mutation function based on single-cell multi-omics, and a medium, and belongs to the technical field of single-cell multi-omics. The invention provides a unified deep learning framework, and the chromatin accessibility and the gene expression level can be jointly predicted under the single cell resolution directly from the DNA sequence. The invention provides two novel quantitative scoring systems of regulation effect score and expression effect score, which can significantly improve the accuracy and interpretability of non-coding variation function annotation based on the chromatin accessibility and gene expression prediction effect. The application can be applied to research of diseases such as Alzheimer's disease and the like, and analysis of a pathogenic cascade process of transcription factor binding change, cis-regulation reconstruction and key gene specific transcriptional disorder related to candidate mutation.

An approach for forming organized 3D single-cell networks where their real-time signaling responses to a range of stimuli can be accurately captured using simple cell seeding and easy-to-handle microfluidic chips. Crosslinked collagen within multi-chambered microfluidic chips is formed, followed by femtosecond laser ablation of 3D microchannel networks and cell seeding. Cells migrate within ablated networks within hours, self-organize and form viable, interconnected, 3D networks in custom architectures such as square grid, concentric circle, parallel lines, and spiral patterns. The functionality of cell networks can be studied by monitoring the real-time calcium signaling response of individual cells and signal propagation when subjected to flow stimulus alone or a sequential combination of flow and biochemical stimuli. Furthermore, user-defined disrupted networks can be generated by lethally injuring target cells within the 3D network and analyzing the changes in their signaling dynamics.

Surgical assistance system comprising a group of devices (1, 101), a pathology unit and a bidirectional data link (69, 105) between a transmitting and receiving unit (72) of the pathology unit (70, 170) and a transmitting and receiving unit (60) of the group of devices (1, 101), wherein a) the group of devices (1, 101) includes at least: - an endomicroscope (4, 104) for acquiring cell-resolved image data from a surgical field (3, 103), - an operating microscope (2, 102) with at least one observation beam path for displaying an observation image for recording overview image data of the operating field (3, 103), wherein the endomicroscope (4, 104) and the operating microscope (2, 102) each contain at least one functional unit or are assigned at least one functional unit, wherein a functional control unit (65) for controlling the functional unit is assigned to the respective functional unit, and the transmitting and receiving unit (60) comprises a transmitting unit (63) for transmitting the acquired cellularly resolved image data and / or the acquired overview image data to the pathology unit (70, 170) and a data receiving unit (64) configured for receiving functional control data from the pathology unit (70, 170) and for forwarding the received functional control data to the respective functional control unit (65), b) which has at least one pathology unit (70, 170): - an input unit (77) for entering function control data for the at least one function control unit (65) of the group of devices (1, 101), - a display unit (76) for displaying received cellularly resolved image data and / or the received overview image data, wherein the transmitting and receiving unit (72) is equipped with a receiving unit (74) connected to the transmitting unit (63) of the group of devices (1, 101) for receiving the cellularly resolved image data and / or the overview image data and with a transmitting unit (73) for sending the function control data to the data receiving unit (64) of the group of devices (1, 101).

A method for compensating for quality variation in an imager capturing microscope images or sequencing images associated with a tissue is disclosed. A plurality of images of an optical target, captured by an imager, the optical target including a distortion control region formed of a plurality of patterned fiducial regions each defining a different area of the distortion control region, the plurality of images including an image of each different area of the distortion control region, are provided. One or more of the images of the different areas of the distortion control region are fed to a distortion correction process configured to perform a distortion correction based on locations of one or more of the patterned fiducial regions, and profile data is generated for the imager, the profile data comprising the distortion correction data from the distortion correction process. The quality variation profile data is then stored for use in correcting subsequent microscope images or subsequent sequencing images received from the imager. An optical target is also disclosed. The optical target includes a metal patterned substrate having a blank region, a focusing zone region comprising a plurality of focusing features, and a distortion control region formed of a plurality of patterned fiducial regions each defining a different area of the distortion control region.

The invention discloses a pathological image proteomics analysis method and system based on deep learning, and relates to bioinformatics, and the method comprises the steps: constructing a paired image-protein data set through laser microdissection and mass spectrometry; a pre-trained VGG16 convolutional neural network is adopted to extract image deep features, and cell morphological features are fused to construct a whole proteome regression mapping model; outputting a cellular level protein expression matrix through a U-Net single cell recognition and iterative deconvolution algorithm; processing a label scarcity problem in combination with a hybrid semi-supervised learning strategy; and carrying out dimensionality reduction clustering, spatial heterogeneity quantification (CV + SHI) and visual analysis based on the protein expression matrix. According to the method, the regression mapping from the pathological image to the whole proteome (gt; 2800) is realized for the first time, the flux bottleneck of traditional virtual staining is broken through, real single cell resolution proteomics is realized, and a new means is provided for precise medical treatment.