404results about "Omics" patented technology

Provided herein are methods for preparing biological samples for spatial proteomic analysis, methods of determining a location of a protein analyte in a biological sample, and methods of determining a location of a protein analyte and a nucleic acid analyte in a biological sample.

Described herein are systems and methods for analyzing biological samples. Including a method for processing an analyte, comprising providing a fluidic device comprising the analyte and one or more polymer precursors; selecting a discrete area within said fluidic device; providing an energy source in optical communication with fluidic device; and selectively supplying a unit of energy generated from the energy source to the fluidic device to generate a polymer matrix within the fluidic device, wherein the polymer matrix is within the discrete area or adjacent to the discrete area.

Described herein are systems and methods for analyzing biological samples. Including a method for processing an analyte, comprising providing a fluidic device comprising the analyte and one or more polymer precursors; selecting a discrete area within said fluidic device; providing an energy source in optical communication with fluidic device; and selectively supplying a unit of energy generated from the energy source to the fluidic device to generate a polymer matrix within the fluidic device, wherein the polymer matrix is within the discrete area or adjacent to the discrete area.

The present disclosure is directed to methods for measuring proteomes of single cells. In one aspect, methods comprise disposing cells on a surface, synthesizing hydrogel chambers around each of the cells, lysing the cells to release intracellular proteins for adsorption onto the surface bounded by the hydrogel chambers, depolymerizing the hydrogel chambers, and quantifying the adsorbed proteins with detection antibodies to determine the single cell proteomes.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

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.

The present disclosure provides systems and methods for analyzing a mixed population of cells. In particular, the present disclosure provides systems and methods for digital cytometry of a biological sample, digital analysis of a biological sample, digital purification of a biological sample, evaluation of a disease in an individual, and prediction of a clinical outcome of a disease therapy.

Described herein are systems and methods for analyzing cells, including a method comprising: (a) forming a compartment within a fluid, wherein the fluid comprises a plurality of cells, wherein the compartment encloses a cell of the plurality of cells, and wherein the compartment physically separates the cell from an additional cell of the plurality of cells; (b) incubating the cell for about 1 minute to about 10 hours, wherein the cell is inside of the compartment during the incubating; and (c) performing an assay within the compartment, thereby analyzing the cell.

The invention discloses application of TACSTD2 protein in preparation of a product for detecting and identifying human amniotic epithelial cells, and belongs to the technical field of biological medicine. The invention provides a protein marker for detecting and identifying human amniotic epithelial stem cells. The protein marker comprises a TACSTD2 protein, or a combination of the TACSTD2 protein and one or more selected from the group consisting of CD29, CD166, CD73, CD90, CD105, CD324, CD326, SSEA4, Netin and Vimentin. The invention further provides application of the reagent for detecting TACSTD2 protein expression in preparation of products for detecting and identifying human amniotic epithelial cells, and the reagent has high specific expression in human amniotic epithelial stem cells and can be used for detecting and identifying the human amniotic epithelial cells independently or in combination with other cells.

Described herein are methods for screening for a disease state. The method may include obtaining multiple data sets and identifying the disease state based on a combination of the data sets. The data sets may include biomolecule measurements obtained by multiple methods, such as through the use of particles and reference biomolecules.

There is provided a method for diagnosing a subject with early onset muscle atrophy, said method comprising: obtaining a biosample from the subject; and assaying the biosample for one or more biomarkers, said one more biomarkers are taurine, proline, citrulline, trigonelline, thymidine, ornithine, glutamate, L-pyroglutamic acid, creatinine, adenine, nicotinamide, 2-methylhippuric acid, maltol, L-arginine, hypotaurine, L-glutamine, homogentisic acid, methylhistidine, oxoglutaric acid, xanthine, L-carnitine, succinate, or a combination thereof; and identifying the subject with early onset muscle atrophy on the basis of a deviation in the one or more of said one more biomarkers.

The invention belongs to a mass spectrometry technology, and particularly provides a single cell metabonomics analysis method based on the mass spectrometry technology, which comprises the following steps: (A1) collecting population cell data including a plurality of average mass spectrograms of a plurality of population cells in a low-resolution mode and a high-resolution mode; (A2) constructing an SCSR-Unet super-division model by using the population cell data, and training, verifying and testing the model; a loss function CL is used in network construction; (A3) collecting single cell data, wherein the single cell data comprises an average mass spectrum of a plurality of single cells in a low-resolution mode and a high-resolution mode; and (A4) enabling the average mass spectrum data in the low-resolution mode to pass through an SCSR-Unet model to obtain high-resolution mass spectrum data of predicted absolute intensity, and synchronously preprocessing the high-resolution mass spectrum data and the mass spectrum data in the high-resolution mode for single-cell metabonomics analysis.

A system and method for monitoring the health of dialysis patients with Raman spectroscopy measurements of one or more target analytes is described. The methods include irradiating one or more fluids of interest with light to produce one or more spectrum and detecting the spectrum with a detector. The fluids of interest are preferably those related to dialysis, including hemodialysis and peritoneal dialysis. In a preferred embodiment, the fluids are irradiated with monochromatic light, and one or more Raman spectra are detected as a result of the irradiation. The fluids may be irradiated within the dialysis tubing itself, or removed from the dialysis tubing and irradiated in a separate chamber. The Raman spectra of one or more target analytes of a dialysis patient may be followed over time or compared to one or more reference spectra, thereby providing information on the health of dialysis patients.

The invention relates to the technical field of biology, in particular to a urine marker for ischemic acute kidney injury as well as a screening method and application of the urine marker. The urine marker comprises a metabolism marker and a protein marker, the metabolism marker comprises Pyroglumic acid, L-Leucine, Mannitol, Homocysteine, [gamma]-Aminobubic acid, 4-Hydroxy-L-glumic acid, Dulcitol, Ophthemic acid, Biocytin, 2-oxo-4-methylidylthio-butymic acid, the protein marker comprises a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker and a protein marker, and the protein marker comprises the following components: a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker, a protein marker and a protein marker. The protein marker comprises the following components: GGCT, GGT6, TPI1, GRB2, HSPA2, BHMT2, MPST, SHMT1, PKM, PDGFRB, FOLR1, GGH, AOC1, GLB1 and HEBP2. The occurrence of the ischemic acute kidney injury can be early judged by noninvasive detection of the expression levels of the metabolic marker and the protein marker in urine of a patient suffering from a major cardiac surgery. The urine marker of the ischemic acute kidney injury is screened by adopting a mass spectrum multi-omics technology and a mathematical statistics method, theoretical support is provided for diagnosis and treatment of patients with the ischemic acute kidney injury, early discovery and early treatment of the patients with the ischemic acute kidney injury are realized, and the urine marker has great economic value and social benefit.

The invention discloses a gastric cancer biomarker composition as well as a screening method and application thereof, and belongs to the technical field of bioinformatics. The screening method of the gastric cancer biomarker composition comprises the following steps: firstly, collecting blood specimens of a gastric cancer patient and a healthy control, carrying out high-throughput plasma proteomics detection, and screening out differential expression proteins; and verifying the candidate protein markers by using a prospective queue in the British biological sample library, and finally determining 17 core proteins to obtain the gastric cancer biomarker composition. A gastric cancer risk prediction model is constructed by combining clinical risk factors and protein expression levels of gastric cancer biomarkers. The gastric cancer risk prediction model constructed by the invention can realize accurate layering of gastric cancer risks, decision curve analysis shows that the net benefit of the model is obviously superior to that of a traditional screening method, and an efficient tool is provided for early warning and personalized intervention of gastric cancer.

The present disclosure provides methods for molecular neighborhood detection of molecules, such as by iterative proximity ligation or split-and-pool methods for obtaining positional information.

To provide a 100% natural product comprising a natural matrix exhibiting emergence properties that enable re-establishment of correct metabolism, cancer treatment and support of tissue homeostasis in tissues infiltrated by cancer cells.SOLUTION: The product of the present invention, which serves to solve the above problems, is a natural matrix which, as demonstrated by the inventors, represents a native natural intelligence in itself, said natural intelligence being the only one capable of allowing physiological endogenous interconnections with other entities self-assembled in nature, such as human species. By modulating the tumor microenvironment and thus selectively inducing cancer cell mortality, said product achieves a reconstitution of conditions favorable to tissue homeostasis in tissues infiltrated by cancer cells. Indeed, the present invention allows a paradigm shift based on the transition from deterministic validation of properties to probabilistic validation based on functional redundancy logic, which will provide an industrially viable source of innovation for manufacturers in the field of 100% natural therapeutic or beneficial products.SELECTED DRAWING: None

The invention belongs to the technical field of biology, and relates to a proteomics-based adenoid hypertrophy grading diagnosis biomarker and application thereof. Comprising BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A and MRPS28. The invention further discloses a preparation method of the high-efficiency and high-efficiency biomedical kit. The screened biomarker can accurately diagnose the severity of adenoid hypertrophy in a non-large-area invasive manner, and especially can accurately distinguish severe adenoid hypertrophy from moderate adenoid hypertrophy.

The present application provides methods and systems for single-run or minimally sequential detection and quantification of proteins, metabolites, and lipids using a unified instrumentation setup spanning a broad dynamic range (˜1 ng / L to 100 mg / L). In some embodiments, a two-phase database architecture transitions newly observed analytes from a discovery repository into a validated repository, enabling reproducible detection (CV<10%) with precise parameters (e.g., retention time, transitions). A machine-learning pipeline enhances automated peak selection by integrating large-scale manual curation with advanced feature extraction. The disclosed platform supports high-throughput multi-omics profiling of plasma or dried blood spot (DBS) samples and enables correlation with clinical factors such as age, BMI, or genetics. These systems maintain high sensitivity, scalability, and reproducibility, addressing long-standing limitations in clinical proteomics. As a result, the disclosed approach facilitates translational research, remote patient monitoring, and global healthcare implementation.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

A method for analyzing macromolecules, including peptides, polypeptides, and proteins, employing nucleic acid encoding is disclosed.

The invention belongs to the technical field of biological materials and biological information, and provides a space phosphorylation modification omics detection method. According to the detection method disclosed by the invention, proteomics analysis can be carried out on trace sample phosphorylation, 4259 phosphorylation sites can be identified by 5 micrograms of peptide fragments, and the credibility of 3506 sites is greater than 0.75.

Methods of identifying a sequence of a polypeptide within a heterogenous mixture of polypeptides, the polypeptide being immobilized to a support and having at least one labeled amino acid residue. Methods involve detecting at least one signal or signal change from the immobilized polypeptide and subjecting the polypeptide to conditions sufficient to remove at least one amino acid residue from the polypeptide.

The invention discloses a biomarker for predicting recurrence and metastasis risks of breast cancer and application of the biomarker. The invention discloses application of a substance for detecting a biomarker in preparation of a product for predicting the recurrence or metastasis risk of breast cancer. The biomarker comprises one or more of FBLN5, MUC16, ORM1, ADH1B, CD36, KRT19, DCD, DEFA3, RNASE1 and KLHL22. The invention further constructs a breast cancer recurrence or metastasis risk prediction model. According to the biomarker and the breast cancer recurrence or metastasis risk prediction model, the breast cancer recurrence or metastasis risk can be accurately, noninvasively and efficiently predicted, and clinical requirements can be met.

Provided herein are compositions and methods for the identification of an expression profile in a single cell or population of cells. Kits for use with the disclosed methods are also provided, including antibodies, with a unique molecular identifier and antibody identifier, and primers for amplification of the antibody identifier sequence.

The invention provides a biomarker for predicting the recurrence risk of papillary thyroid carcinoma and application of the biomarker, and a proteomics method is utilized to analyze proteins with significant abundance level difference in blood of two groups of people with papillary thyroid carcinoma recurrence and non-recurrence after papillary thyroid carcinoma patients are subjected to operative treatment, so that the papillary thyroid carcinoma recurrence risk can be predicted, and the papillary thyroid carcinoma recurrence risk can be predicted. According to the method, a biomarker capable of being used for predicting the papillary thyroid carcinoma recurrence risk is screened out, and a multi-marker joint detection model is further constructed, so that the papillary thyroid carcinoma recurrence risk can be accurately, noninvasively and efficiently predicted, and clinical requirements are met.