Cell Surface Area Measurement
Cell Surface Area Measurement - Related Videos
12:49
Measurement of Aggregate Cohesion by Tissue Surface Tensiometry
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Cited by 10 •
2011
We describe a method of measuring binding energy, expressible as tissue surface tension, between cells within 3D tissue-like aggregates. Differences in tissue surface tension have been demonstrated to correlate with invasiveness of lung, muscle, and brain tumors, and are fundamental determinants of establishing spatial relationships between different cell types.
08:49
Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens
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Cited by 8 •
2020
This manuscript describes a genome-scale cell-based screening approach to identify extracellular receptor-ligand interactions.
09:13
Cell-surface Biotinylation Assay
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2023
A cell can regulate the amount of particular proteins on its cell membrane through endocytosis, following which cell surface proteins are effectively sequestered in the cytoplasm. Once within a cell, these surface proteins can be either destroyed or “recycled” back to the membrane. The cell surface biotinylation assay provides researchers with a way to study these phenomena. The technique makes use of a derivative of the small molecule biotin, which can label surface proteins and then be...
01:21
Cell-surface Signaling
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2019
Hormones—or any molecule that binds to a receptor, known as a ligand—that are lipid-insoluble (water-soluble) are not able to diffuse across the cell membrane. In order to be able to affect a cell without entering it, these hormones bind to receptors on the cell membrane. When a first messenger, a hormone, binds to a receptor, a signal cascade is set off, causing second messengers, proteins inside the cell, to become activated, resulting in downstream effects. Variety of Receptor Families
08:55
In vivo Measurement of the Mouse Pulmonary Endothelial Surface Layer
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Cited by 17 •
2013
The endothelial glycocalyx/endothelial surface layer is ideally studied using intravital microscopy. Intravital microscopy is technically challenging in a moving organ such as the lung. We demonstrate how simultaneous brightfield and fluorescent microscopy may be used to estimate endothelial surface layer thickness in a freely-moving in vivo mouse lung.
FAQs
Calibration establishes the relationship between image pixels and physical distance, allowing the segmented cell boundary to be converted into an area value rather than a raw pixel count. Without this conversion, measurements from images acquired at different magnifications or imaging settings cannot be compared reliably. Consistent calibration therefore supports meaningful comparisons among cancer cell populations and experimental conditions.
Segmentation determines which pixels belong to the cell and which belong to the surrounding field, creating the outline used for area calculation. Manual segmentation can identify boundaries through researcher judgment, whereas automated segmentation provides a repeatable computational approach when image quality permits. The resulting area is therefore influenced by how accurately the exposed cell boundary is identified.
Two-dimensional analysis estimates the area represented in a microscopy image, while three-dimensional imaging can capture more of the cell’s complete surface when that information is available. This distinction matters for cells whose morphology is not fully represented in a single plane. In cancer models, a three-dimensional estimate may provide a more complete view of changes in physical state or cell-microenvironment interactions.
Changes in spreading, shape, adhesion, and growth can produce different measured surface areas even when the cells come from the same tumor model. Treatment conditions may also shift these morphological features, making area a useful quantitative component of phenotypic comparison. Interpreting the value alongside the experimental condition helps distinguish a morphological response from a simple difference between cell populations.
A typical workflow begins by acquiring a microscopy image, applying the relevant spatial calibration, and identifying each cell boundary through manual or automated segmentation. The selected boundary pixels are then converted into an area value using the calibration. Researchers can compare these measurements across cell populations, tumor models, or treatment conditions to evaluate differences in morphology and physical state.
Researchers can compare surface-area values before and after treatment or between treated and untreated cancer cell populations. A shift in area may indicate treatment-associated changes in spreading, shape, adhesion, or growth. Because the measurement is quantitative, it can contribute to phenotypic profiling and help characterize how cancer cells respond under defined experimental conditions.