Single-cell Segmentation
Single-cell Segmentation - Related Videos
10:41
Generation of Dispersed Presomitic Mesoderm Cell Cultures for Imaging of the Zebrafish Segmentation Clock in Single Cells
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Cited by 8 •
2014
Somitogenesis is a rhythmic developmental process that spatially patterns the body axis of vertebrate embryos. Previously, we developed transgenic zebrafish lines that use fluorescent reporters to observe the cyclic genes that drive this process. Here, we culture dispersed cells from these lines and image their oscillations over time in vitro.
07:44
Microdissection and Dissociation of the Murine Oviduct: Individual Segment Identification and Single Cell Isolation
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Cited by 1 •
2021
A method for microdissection of the mouse oviduct that allows collection of the individual segments while maintaining RNA integrity is presented. In addition, non-enzymatic oviductal cell dissociation procedure is described. The methods are appropriate for subsequent gene and protein analysis of the functionally different oviductal segments and dissociated oviductal cells.
08:46
Single Cell Transfection in Chick Embryos
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Cited by 3 •
2010
Using fine tip micropipettes we inject plasmid DNA into subdomains of chicken somites or neural tubes. The concentration of the plasmid is adjusted to generate single transfected cells. We then allow the cells to develop into clonal populations.
09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
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Cited by 9 •
2017
Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.
02:10
Single Cell Sampling Using Micromanipulation: A Technique to Isolate Single Cells from Cancer Cell Suspension
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2023
This video describes the technique for isolating single cells from a pool of target cells to carry out downstream analysis. In this method, a micromanipulator fitted to a microscope is used for high-precision isolation of a single target cell.
FAQs
The analysis can rely on several image cues rather than one signal alone. Nuclear detection identifies central cellular structures, membrane markers indicate boundaries, and intensity patterns or local pixel changes help separate cells from surrounding tissue. Combining these cues allows image analysis to outline cellular regions even when tissue contains varied visual features.
Adjacent cells may share borders or appear as one continuous region, making it difficult to assign pixels to individual cells. Machine-learning models can help separate these touching cells by recognizing image patterns associated with distinct cellular objects. This separation is important because merged cells would distort measurements of cell number, morphology, density, and marker expression.
Boundary quality depends on whether the image provides distinguishable nuclei, membrane markers, intensity patterns, or changes in pixel structure. When these signals clearly differ between a cell and its surroundings, outlining becomes more informative. In cancer tissue, variation across the sample can affect how consistently the same image-analysis approach identifies cellular boundaries.
Once cells are separated, researchers can quantify features at the cellular level instead of treating the tissue as one population. These measurements include tumor morphology, cell density, spatial organization, and marker expression. The resulting data can reveal differences among cells within the same sample, supporting analysis of tumor heterogeneity.
A typical workflow begins with a microscopy image and identifies cellular cues such as nuclei, membrane markers, intensity patterns, or pixel-structure changes. Image-analysis methods then assign boundaries around individual cells, with machine-learning support when cells touch. Researchers can subsequently measure cellular features and relate them to tissue organization or marker patterns.
Cell-level measurements allow researchers to compare morphology, density, spatial position, and marker expression across cells in the same tumor. These differences provide a quantitative view of tumor heterogeneity rather than a single tissue-wide average. The approach therefore helps characterize how cellular features vary within tumor regions and across the broader sample.
By outlining cells and retaining their spatial organization, image analysis can examine where malignant and immune cells occur relative to one another. Combining spatial information with cellular morphology or marker expression helps characterize patterns of interaction within tumor tissue. This provides context that would be lost if the sample were analyzed only as an undifferentiated population.