Cell Detection
Cell Detection - Related Videos
09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
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Cited by 8 •
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.
12:44
Use of LysoTracker to Detect Programmed Cell Death in Embryos and Differentiating Embryonic Stem Cells
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Cited by 53 •
2012
We present a simple protocol to visualize regions of programmed cell death (PCD) in mouse embryos and differentiating embryonic stem (ES) cell cultures using a highly soluble dye called LysoTracker.
06:19
Comet Assay for DNA Damage Detection in Cells
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2025
The video demonstrates the comet assay for detecting DNA damage following exposure to harmful agents. Under alkaline conditions, the DNA unwinds and denatures, and upon electrophoresis, the undamaged and damaged DNA migrate differently in the agarose gel, forming a comet-shaped structure. The slowly migrating undamaged DNA forms the circular comet head, while the rapidly migrating damaged DNA fragments form the elongated comet tail.
06:31
Effective Detection of Hoechst Side Population Cells by Flow Cytometry
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Cited by 1 •
2024
Here, we show that high-power 375 nm and 405 nm lasers can effectively excite Hoechst 33342 and serve as a viable alternative to the 355 nm laser for side population (SP) cell detection, thereby expanding the range of available lasers in flow cytometry applications.
04:16
Cell Aggregation Assay to Detect Trans Interactions between Cell Adhesion Molecules
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2025
In this video, we demonstrate the technique to measure transcellular interactions between synaptic cell adhesion molecules through the HEK cell aggregation assay. In this assay, an equal number of HEK cells expressing a desired presynaptic cell adhesion molecule and a fluorescent protein are mixed with HEK cells expressing a post-synaptic cell adhesion molecule and another fluorescent protein. The interaction between the cell adhesion molecules forces the cells to aggregate, which can be...
FAQs
Fluorescent antibodies and genetically encoded reporters provide signal patterns that can be associated with particular cells or cellular states. Microscopy captures these signals, while image-processing algorithms evaluate boundaries, nuclei, or localized fluorescence against background. Combining molecular specificity with spatial image information helps separate relevant cellular signals from surrounding tissue.
Algorithms may rely on visible cellular boundaries, nuclear signals, or characteristic fluorescence patterns. These features offer different ways to identify cells when complete boundaries are not equally apparent throughout a sample. Comparing each feature with surrounding background allows the analysis to separate cellular structures or labeled signals from noncellular image regions.
Background separation determines whether a detected signal is attributed to a cell or treated as unrelated image information. This distinction affects measurements such as cell counts, population distributions, and activation-related patterns. In neuroscience samples, separating signal from background is especially relevant when molecular labels produce localized patterns within complex tissue.
A typical workflow begins by preparing or identifying a molecular label, such as a fluorescent antibody or genetically encoded reporter, and imaging the sample. Researchers then apply image-processing algorithms to locate boundaries, nuclei, or signal patterns and distinguish them from background. The resulting detections can support counting, spatial analysis, or further interpretation.
Detected cells can be quantified by number and distribution, while their positions can be examined within the surrounding tissue. Labeled signal patterns may also support assessment of activation states or biomarker presence. These measurements provide structured information for analyzing tissue organization, comparing biological conditions, and interpreting single-cell or spatial datasets.
In neuroscience, detected neuronal and glial populations can be examined to study tissue organization, development, disease-associated changes, and activity-related states. The same measurements can contribute to circuit mapping and connectivity-related analysis. By linking cellular locations with molecular signals, researchers can connect microscopic observations to broader patterns in neural tissue.