52 results about "Omics technologies" patented technology

The invention discloses a transcriptomics spatial domain identification method, and belongs to the technical field of transcriptomics. The objective of the invention is to solve the problems of low data noise reduction precision and poor recognition effect of an existing spatial transcriptional spatial domain recognition method. The method comprises the following steps: firstly, obtaining an undirected neighborhood graph according to a gene expression matrix, obtaining embedded representation of the gene expression matrix by utilizing an encoder, obtaining a corresponding reconstruction matrix by utilizing a decoder, and further determining reconstruction loss; meanwhile, a ZINB model is used for fitting a reconstruction matrix, and a ZINB loss function is obtained; then, an augmented graph is constructed based on the undirected neighborhood graph, respective embedded matrixes are obtained through an encoder, the comparison loss of the undirected neighborhood graph and the comparison loss of the augmented graph are obtained through a comparison representation learning mechanism, and then the neighbor comparison loss is obtained; total target loss is obtained based on all losses, a joint optimization strategy is adopted for training, and after training of the whole model is completed, dimensionality reduction and spatial domain recognition are carried out on a generated reconstruction matrix.

The invention discloses the technical field of pathology and space transcriptomics, and particularly relates to a gene expression prediction method and system based on multi-modal comparative learning and a guidance mechanism. Cutting the histological slice image into image blocks according to space coordinates; according to the method, a local convolution branch and a global Transform branch are combined to extract image features, the image features are mapped to a shared potential space through projection, soft contrast, hard contrast and global consistency constraints are introduced into the space, and cross-modal alignment of an image modal and a gene expression modal is realized; an expression prediction head is introduced in the training stage, representation learning is directly guided by a regression signal, and the relation between feature learning and gene expression prediction is broken through; in the inference stage, k-nearest neighbor retrieval and a multi-distance weighted aggregation strategy are combined to infer a gene expression profile of an unknown position. According to the method, the accuracy and robustness of space gene expression prediction can be effectively improved, the tissue space heterogeneity structure is kept, and the method has high clinical application and scientific research and popularization value.

The invention belongs to the technical field of single-cell multi-omics, and discloses a single-cell multi-omics data analysis system, method and device and a storage medium, a data import module is used for reading sequencing data from different sequencing platforms and storing the sequencing data as S4 objects; the cross-language analysis module is used for calling a single-cell multi-omics analysis tool based on an R language and a Python language to analyze an S4 object according to an analysis process in a unified framework; the deep learning optimization module is used for training a deep learning model based on the cross-language interaction interface and integrating an analysis result of the deep learning model to an object S4; and the interaction and extension module is used for obtaining an analysis process and visualizing an analysis result, constructing a user-defined single-cell multi-omics analysis tool in a plug-in form and loading the tool to the S4 object for analysis. Cross-sequencing platform data compatibility, multi-language tool seamless integration and deep learning framework efficient access are achieved, and the problems of data isomerism, language ecological splitting and algorithm expansibility in the prior art are solved.

The invention relates to the technical field of forensic genomics, in particular to a primer group and a kit for detecting genetic markers of 365 Y chromosomes and application of the primer group and the kit. According to the invention, an amplification primer group for specific 57 Y-STRs and 308 Y-SNPs of Chinese population is optimally designed, and a joint detection method with high sensitivity and high resolution is constructed. Y-STR haplotype analysis and Y-SNP haplogroup accurate typing can be synchronously realized, and the problems of high family checking false positive rate, insufficient geographic ancestor inference resolution and the like caused by single site type detection in the traditional technology are solved. The method is suitable for diversified biological samples such as blood, seminal stains and saliva, and provides a novel technical scheme of high-throughput, low-cost and multi-dimensional paternal genetic information analysis for forensic practice. Comprising construction of a Y-STR and Y-SNP composite amplification system based on next-generation sequencing and application of the Y-STR and Y-SNP composite amplification system in forensic medicine individual recognition, family investigation and biogeography ancestor inference.

The invention discloses an identification method, device and system for a high gene module scoring area, and relates to the technical field of space transcriptomics. The identification method comprises the following steps: acquiring space transcriptome data, dividing cells into a plurality of unit grids according to space coordinate information of the cells, and determining corresponding grid space coordinates for each unit grid; calculating a module score value of each cell and determining a high-score cell set; calculating an integrated gene set scoring index; and carrying out region division on the unit grid and determining a high gene module scoring region. According to the method, a plurality of genes are analyzed as a functional whole, the limitation that traditional single-gene analysis cannot reveal collaborative regulation is overcome, spatial quantization of a gene set synergistic effect is realized, and the problem that a gene set enrichment region cannot be positioned in the prior art is solved.

This application relates to the field of biomedical technology, and in particular to a method for evaluating the effectiveness of autism intervention and its application. The method includes the following steps: collecting a test sample and a control sample; the test sample is a urine sample from an autism patient, and the control sample is a urine sample from a healthy volunteer; processing the test sample and the control sample separately to obtain NMR samples; testing the NMR samples using an NMR spectrometer to obtain NMR spectra; performing multivariate statistical analysis on the NMR spectra to construct a urinary metabolite model; and using the urinary metabolite model to screen and evaluate autism patients. This application provides a method for evaluating the effectiveness of autism intervention and its application, constructing a urinary metabolite model through NMR metabolomics technology, providing practical evidence for establishing methods for early screening and treatment process evaluation of autism.

This invention discloses a method for delaying leaf senescence in Paspalum notatum under salt stress. The method includes the following steps: plant material culture and salt stress treatment; H2O2 content determination; DAB staining; chlorophyll content and Fv / Fm value determination; lipidomics data determination and analysis; endogenous hormone level analysis; real-time quantitative PCR analysis of gene expression levels; and transcriptomics data analysis. This invention discovers that root-derived H2O2 in Paspalum notatum under salt-driven conditions delays leaf senescence by participating in the regulation of jasmonic acid. The role of root-derived H2O2 was studied using diphenylthioiodine (DPI), a specific inhibitor of NADPH oxidase. Furthermore, the influence of the presence or absence of root-derived H2O2 on the expression of genes related to jasmonic acid synthesis precursors was investigated at the gene expression level. Mass spectrometry-based omics technology and data analysis are the first key technologies for implementing this project. Lipidomics, as one of the most important branches of metabolomics, can perform qualitative and quantitative analysis at the level of individual lipid species.

The invention discloses an animal lactation period determination method based on milk component content, and belongs to the technical field of milk quality detection and livestock breeding. Comprising the following steps: screening emulsion components which can be used as biomarkers in different lactation periods by utilizing an omics technology, carrying out quantitative analysis, and constructing a judgment model, so as to accurately judge the lactation periods of animals. The method comprises the specific steps of sample collection and grouping, biomarker screening and quantitative detection, model construction, to-be-detected sample detection and lactation period judgment. The animal lactation period determination method based on the animal milk component content is not interfered by factors such as a breeding mode and a feed type, is simple in process and high in universality, can provide technical support for raw milk grading of a dairy product enterprise, quality control in a dairy product processing process and accurate feeding of a farmer, and has a wide application prospect. The dairy product quality stability and culture benefits are effectively improved.

The invention relates to the technical field of biomedicine, in particular to an ELS-induced depression-like behavior rat whole-brain tissue lipid space metabolism analysis method which comprises the following steps: firstly, constructing an ELS-induced depression-like behavior rat model, and determining a depression model group by adopting a depression-like behavior test; then, taking a conventionally fed rat as a control group, respectively carrying out 2D scanning on the control group and the depression model group based on a mass spectrum space omics technology, constructing a 2D brain lipid map, and carrying out whole-brain 3D reconstruction to obtain brain tissue lipid mass spectrum imaging data; and finally, carrying out metabolic heterogeneity analysis on brain tissue lipid mass spectrum imaging data. The method provided by the invention aims to provide technical and data atlas support for research on a lipid metabolism disorder molecular mechanism of occurrence and development of early-stage negative stress induced depression from an animal model level, and meanwhile, the application range of a mass spectrum space omics technology is expanded.

The present invention belongs to the field of bioproteomics technology, specifically relating to a hair identification method based on 4D Label-Free technology and the protein markers used to identify hair. This identification method utilizes 4D Label-Free analysis technology to obtain differentially expressed protein markers capable of distinguishing between human axillary and pubic hair samples. The protein GPRC5D described in the present invention is a protein marker specific for axillary hair, and CTNNBIP1 is a protein marker specific for pubic hair. The hair protein markers obtained in this invention can be used to identify case-related hair samples as pubic hair, which is of great significance for the determination of criminal cases.

The application provides a 5mCpG high-throughput detection method TnM-seq based on TnM transposase, which adopts transposase TnM including an MBD domain to detect 5mCpG in situ, and comprises the following steps: after pretreatment of cells to be detected, the cells are incubated with transposase TnM; DNA in the target sample after transposition is recovered and purified; the purified DNA is subjected to PCR library construction; and the library construction product is subjected to high-throughput sequencing. The new transposase TnM newly designed and synthesized is used, combined with high-throughput sequencing, to realize convenient, fast, low-cost and high-sensitivity detection of 5mCpG in a sample. Based on the transposase TnM, a multi-omics technology HAM-seq for simultaneously detecting histone modification H3K9me3, chromatin accessibility and DNA methylation modification 5mCpG in the same sample is further developed, and after being combined with a commercial single-cell microfluidic platform, an epigenome and gene expression four-omics technology EpiX-seq for single-cell analysis in a complex tissue is obtained.

This invention relates to a novel method for screening bitter peptides based on peptidomics and machine learning. In this invention, we established a more comprehensive benchmark dataset of bitter and non-bitter peptides, created a new set of bitter peptide characteristic factors, and constructed a classification and prediction model based on a novel data processing method and the Light Gradient Boosting Machine (LightGBM) algorithm for screening bitter peptides. Simultaneously, by combining machine learning and peptidomics technologies, high-throughput identification and prediction of potential bitter peptides are achieved. This method has potential applications in the food and pharmaceutical fields.

The invention discloses a preparation for visual and enrichment extraction of lysosome and a method thereof. According to the invention, a part of transmembrane regions in TMEM192 are designed as lysosome characteristic targeting elements for the first time, so that cell proteome disturbance caused by overexpression of complete functional proteins is avoided. Secondly, the invention also designs a fluorescent protein and an enrichment label based on the nano antibody, so that accurate extraction and cytological imaging of the lysosome are realized. The technology can be used for extracting and imaging the lysosome of a target body, can be combined with other omics technologies to realize systematic research on the lysosome, and has an important application prospect.

The invention provides a method for predicting the influence of graphene quantum dots on biological organisms based on transcriptome sequencing, and belongs to the technical field of gene transcriptomics. The invention adopts a high-throughput transcriptome sequencing technology to analyze the influence of 10 [mu] g / ml GOQD on the mouse bone marrow MSC transcriptome level, and reveals the potential results of the biological process caused by the stimulation of 10 [mu] g / ml GOQD on mouse bone marrow MSC, molecular function and molecular biology signal pathway. The invention further discloses the influence of 10mu g / ml GOQD on the immune function, biological development and cell division process and cardiovascular generation and growth process of mice and an action target gene of the GOQD. The hub protein coded by the acting target gene is screened from the perspective of proteomics, and a detection index is provided for monitoring the influence of GOQD on organisms.

According to the invention, a human renal cortex proximal tubule epithelial cell HK2-CaOx model and a rat kidney stone animal model are adopted, differential modified proteins are analyzed and verified through a protein modification omics technology, and key factors and key sites of lactic acid modification participating in regulation and control of kidney stone generation are identified; single cell sequencing is combined with space transcriptome analysis, identification and subdivision of kidney cell subgroups, and differential expression ligand and receptor pairs and key signal channels of macrophages and renal tubular epithelial cells are analyzed through cell communication. On the basis, the key function of the LDHB protein lactic acid modification site K156 in the kidney stone progress process is identified, and a new molecular marker and a treatment target are provided for early diagnosis of kidney stone and intervention of kidney stone.

The invention discloses a method for analyzing an action mechanism of a breast nodule removing tablet. The method comprises the following steps: identifying chemical components of the breast nodule removing tablets; predicting effective target spots of the chemical components based on network pharmacology, and predicting target spots of hyperplasia of mammary glands; and performing clustering analysis and other operations on the relative content of differential proteins in the mammary gland hyperplasia model before and after the breast nodule removing tablets are taken based on proteomics. According to the application, a method for integrating multi-dimensional data through a'chemical component-target network-omics technology 'is used for discussing the action mechanism of the breast nodule removing tablets, important proteins, metabolites or pathways are screened out, research targets are more easily locked, and the action mechanism of the traditional Chinese medicine compound breast nodule removing tablets is disclosed.

Provided is a method for inducing differentiation of human pluripotent stem cells into pancreatic progenitor cells. Compared with conventional induced differentiation processes, the present method not only shortens a process that takes more than 30 days and requires 6-7 stages to only 19 days and 5 stages, but also significantly increases the proportion of β-like cells to 60%–70%, effectively improving hyperglycemic symptoms in diabetic mouse models. Furthermore, single-cell transcriptome detection of the grafts revealed that 60%-70% β cells could be detected, which were more mature than the cell state before transplantation. A method for assessing cell quality is also provided, which is used to evaluate the quality of in vitro induced cells. The method no longer relies on conventional detection based on the expression of a few specific genes and proteins, but uses single-cell omics technology to qualitatively and quantitatively evaluate the characteristics and differentiation efficiency of the induced cells based on a "three-module gene co-expression network," providing a new evaluation standard for the field.

Compositions and methods for analysis of protein expression at the single cell level are described. The single cell proteomic technology described herein implements peptide barcode molecules to uniquely label all of the proteins and / or polypeptides present in a single cell, whereby all of the proteins / polypeptides (proteome) present in a single intact cell are uniquely labeled with a peptide barcode molecule in a partition. Polypeptides from a single cell can be uniquely barcoded and via protein sequencing, such as nanopore sequencing. Proteomic analysis of the protein sequencing data elucidates the entire proteome that was expressed in the single cell.

This invention provides the application of SPP1 or SPP1-positive macrophages as biomarkers in the preparation of diagnostic reagents for chronic gouty arthritis. It also provides the application of SPP1 or SPP1-CD44 signaling axis inhibitors in the preparation of drugs for the prevention and / or treatment of chronic gouty arthritis. This invention is the first to utilize SPP1... + Macrophages are key biomarkers and therapeutic targets for the chronicity of gout. Through single-cell and spatial transcriptomics technologies, we systematically analyzed their functional network in the microenvironment of tophi, providing a new direction for clinical prediction and intervention.

The application belongs to the technical field of antibacterial peptides, and discloses a novel antibacterial peptide for marine pathogen infection and application thereof. The application takes Aequorea victoria as a research object, analyzes the differential expression of genes of Aequorea victoria after bacterial infection by means of multi-omics technology, predicts candidate antibacterial peptides by using an AI model, verifies the activity by determining the MIC of the polypeptide on bacteria, and thus obtains a high-activity Aequorea victoria antibacterial peptide. The in-vitro activity of the antibacterial peptide is further explored. Then, the hemolytic activity, cytotoxicity and stability of the antibacterial peptide are determined, and the antibacterial mechanism thereof is preliminarily studied. The safety and antibacterial activity of the antibacterial peptide in vivo are further explored through an animal model of Vibrio vulnificus peritoneal infection. The obtained antibacterial peptide 21546, after modification and optimization, is likely to become a powerful lead peptide for developing a novel antibacterial drug for marine pathogen infection, and provides a novel candidate drug for treating secondary infection of marine biological injury, sea combat injury and clinical pathogenic bacteria infection.

The invention belongs to the technical field of biological medicine, discloses a double phenotype hepatocellular carcinoma diagnostic kit and application thereof, and integrates and analyzes DPHCC stem cell heterogeneity through a single-cell multi-omics technology. The characteristics of tumor stem cells (CSCs) of DPHCC are analyzed from a proteome and transcriptome two-dimensional system on the single cell level. And the influence of Non-DPHCC tumor cells in a DPHCC sample is avoided. The correlation between the SPINT2 and the CK19 and the DPHCC is determined, and the SPINT2 is poorly related to the prognosis of the DPHCC. The discovery provides a new target spot for targeting CSCs.

The invention provides an application research of nano layered double hydroxides (LDH) for immunoregulation of interleukin-6 (IL-6) to promote liver regeneration, and based on the biomedical potential of the layered double hydroxide nano material, the action mechanism of the layered double hydroxides nano material in liver regeneration is systematically clarified. According to the application, a potential molecular mechanism of promoting liver regeneration by LDH is disclosed for the first time from the perspectives of immunoregulation and signal transduction, and a new theoretical basis and an experimental basis are provided for application of a nano material in tissue regeneration and liver function reconstruction. In the future, physicochemical properties and targeting of LDH can be further optimized, interaction with immune cells, signal transduction network remodeling and long-term safety of LDH can be deeply discussed in combination with a multi-omics technology and an in-vivo imaging means, and a direction is provided for developing novel immunoregulation type nano-drugs.

The invention relates to the technical field of cell detection, in particular to a method for accurately and quantitatively detecting apoptotic cells in a cell product. The apoptotic cells in the cell product are detected by adopting a single cell omics technology, and the apoptotic cells are identified and quantified more truly and objectively from the single cell level. By utilizing the method disclosed by the invention, all signal channels related to each stage of the apoptosis process can be analyzed, the apoptotic cells can be determined according to the signal channels, and a cell preparation process can be better guided and adjusted in time so as to reduce the proportion of the apoptotic cells.

The invention discloses a tumor metastasis risk assessment method and system based on circulating tumor DNA (ctDNA) spatial heterogeneity. The core of the method is as follows: blood samples of at least two different anatomical sites (such as peripheral veins and tumor drainage veins) of the same subject are obtained at the same time, and ctDNA in the samples is subjected to sequencing analysis; calculating characteristic data (such as a relative abundance ratio) representing ctDNA spatial distribution based on the mutation abundance difference between different parts; and finally, inputting the feature data into a pre-trained metastasis risk assessment model, and outputting a quantitative tumor metastasis risk score. According to the method, the limitation of traditional single-point liquid biopsy is broken through, the detection sensitivity of tiny metastases can be remarkably improved by utilizing the spatial distribution information of the ctDNA, potential metastasis positioning information is provided, and powerful technical support is provided for accurate diagnosis and treatment decision of tumor patients.

The invention relates to the technical field of medical imaging omics, in particular to a pancreatic cancer early recurrence prediction method and system based on an interpretable machine model, and the method comprises the following steps: extracting tumor internal and external imaging omics features from a CT image; performing single-factor analysis and multivariable logistic regression analysis in the body composition parameters and the clinical pathology data to obtain clinical features; six groups of classifier models are constructed based on tumor internal and external radiomics characteristics through six machine learning algorithms, and a radiomics model is obtained according to model performance comparison; and constructing a clinical-radiomics combined model by using the clinical features and the radiomics model, and carrying out interpretable SHAP analysis on the clinical-radiomics combined model. According to the method, intratumoral-peritumoral CT image omics characteristics and body composition parameters are combined, a machine learning model for predicting the early recurrence risk after PDAC excision is constructed, interpretable SHAP analysis is incorporated, and the transparency of the machine learning model decision making process is enhanced.

The invention discloses a preparation method of an embryoid body for overexpressing PRDM1, which accelerates the formation and maturation process of the embryoid body and shortens the amplification period of the embryoid body through a lentivirus-mediated PRDM1 overexpression system, reveals the regulation mechanism of the embryoid body in combination with a genomics technology, and provides a new technical strategy and theoretical basis for optimizing the differentiation efficiency of stem cells.

Ballistic microscopy—a completely new approach to “image” a cell utilizing particle bombardment, is described. These are ballistic micro and nano particles that travel through a cell at ballistic speed and capture a pico or femto-liter of cellular content and bring it out for analysis without harming the cell. This enables a new approach to omics-based imaging where millions of these particles are bombarded on cells with resolved space and time and captured to process using well known omics techniques including proteomics (mass spec) or sequencing—while keeping the spatial and temporal resolution. This work provides—for the first time—a way to resolve atomic details of live cells without any labels.

This invention provides a method, system, device, medium, and program product for identifying the authenticity of traditional Chinese medicine (TCM) based on artificial intelligence and multi-omics technology, relating to the field of medicinal material identification. The method includes: acquiring metallomics and metabolomics data of the medicinal material to be tested; calculating the CPS response values ​​of key characteristic elements in the metallomics data; processing the metabolomics data to obtain mass spectrometry data containing peak information of key chemical components; inputting the CPS response values ​​of the key characteristic elements and the peak information of key chemical components into an authentic producing area identification model to obtain a classification result indicating whether it belongs to an authentic producing area. This application also discloses key characteristic elements containing Ca in metallomics data and key peak information containing peak 17307 in metabolomics data. The method of this invention, by combining machine learning, achieves rapid capture of features of complex systems, establishes a high-precision classification model, and realizes the traceability of the authenticity of TCM.

The invention discloses a serum metabolism marker glycine of heat-resistant mutton sheep as well as a screening method and application of the serum metabolism marker glycine, and belongs to the technical field of livestock heat stress marker screening. The serum metabolism marker of the heatproof mutton sheep is glycine; the screening method comprises the following steps: (1) performing serum sample collection on heat-resistant mutton sheep and heat-sensitive mutton sheep under a heat stress condition; (2) pre-treating the serum sample through protein precipitation, dilution, internal standard method sample correction and filtration; (3) measuring the concentration of metabolites in each blood sample by adopting an ultrahigh liquid chromatography-mass spectrometry combined mode; and (4) analyzing the sample by an orthogonal-partial least square discriminant analysis method, and screening the serum metabolism marker of the heat-resistant mutton sheep. The Hu sheep serum metabolism marker is screened through a biomics technology system, the serum metabolism marker glycine of the heat-resistant mutton sheep is obtained through screening, the method is suitable for being popularized and used in actual production, and a basis is provided for further improving the Hu sheep variety.

The present invention relates to the technical field of spatial transcriptomics, and in particular to a method for identifying a cell boundary applied in spatial transcriptomic analysis. In general, during spatial transcriptomic analysis, the procedure substantially comprises sample preparation, optical structure image generation, spatial transcriptomic information collection, alignment of optical structure images with spatial transcriptomic information, and single-cell annotation. The characteristic of the present invention lies in selecting a specific fluorescent protein transgenic animal model and employing an optical and transcriptional image alignment method compatible with the animal model, thereby enabling clear visualization of the structure and contour of a single cell in a sample under a fluorescence imaging system without requiring cell membrane or nuclear staining to obtain a fluorescence image. Consequently, drawbacks of dye‑induced effects on mRNA capture efficiency and sample displacement are avoided. In addition to significantly improving the efficiency of cell boundary identification, the method further enhances the accuracy of subsequent algorithm‑assisted cell image segmentation and large‑language‑model‑assisted cell annotation.