Vega-Lugo et al., 2022 - Google Patents
Analysis of conditional colocalization relationships and hierarchies from three-color microscopy imagesVega-Lugo et al., 2022
View PDF- Document ID
- 14382025632675650548
- Author
- Vega-Lugo J
- da Rocha-Azevedo B
- Dasgupta A
- Touret N
- Jaqaman K
- Publication year
- Publication venue
- Biophysical Journal
External Links
Snippet
Colocalization is a cornerstone approach in cellular biophysics for the analysis of multicolor microscopy images. It provides information on the localization of molecules within various subcellular compartments and allows the interrogation of molecular interactions in their …
- 238000004458 analytical method 0 title abstract description 131
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
- G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
- G01N33/502—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing non-proliferative effects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
- G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
- G01N33/5082—Supracellular entities, e.g. tissue, organisms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lukonin et al. | Organoids in image-based phenotypic chemical screens | |
Danuser | Computer vision in cell biology | |
Xu et al. | SOAX: a software for quantification of 3D biopolymer networks | |
Balbo et al. | The shape of protein crowders is a major determinant of protein diffusion | |
Récamier et al. | Single cell correlation fractal dimension of chromatin: a framework to interpret 3D single molecule super-resolution | |
Xia et al. | Microrheology, advances in methods and insights | |
Patel et al. | Rapid, topology-based particle tracking for high-resolution measurements of large complex 3D motion fields | |
Ma et al. | Phase correlation imaging of unlabeled cell dynamics | |
Musser et al. | Deciphering the structure and function of nuclear pores using single-molecule fluorescence approaches | |
He et al. | Cell cycle stage classification using phase imaging with computational specificity | |
Vega-Lugo et al. | Analysis of conditional colocalization relationships and hierarchies from three-color microscopy images | |
Ceballos et al. | Active intracellular transport in metastatic cells studied by spatial light interference microscopy | |
Vicar et al. | Cancer cell viscoelasticity measurement by quantitative phase and flow stress induction | |
Gutierrez et al. | Force sensors for measuring microenvironmental forces during mesenchymal condensation | |
Cislo et al. | Active cell divisions generate fourfold orientationally ordered phase in living tissue | |
Ohno et al. | Recent advancement in the challenges to connectomics | |
Zepeda O et al. | Untying the gordian KNOT: Unbiased single particle tracking using point clouds and adaptive motion analysis | |
Francis et al. | Mechanisms of frustrated phagocytic spreading of human neutrophils on antibody-coated surfaces | |
Stamile et al. | A graph based classification method for multiple sclerosis clinical forms using support vector machine | |
Hardiman et al. | Living cells as a biological analog of optical tweezers–a non-invasive microrheology approach | |
Xu et al. | Automating approach to study inter-and intracellular signaling events using machine learning algorithm | |
Saed et al. | Using statistical discrimination to distinguish between fluorescence images of T cell receptors immobilized on different surface conditions | |
Saxton | Immobilization in single-particle tracking (SPT): escape from the point spread function (PSF) of the microscope | |
US11081212B2 (en) | System and method for computing drug controlled release performance using images | |
Park et al. | T cell migration in microchannels densely packed with T cells |