Colocalization Quantification
Colocalization Quantification - Related Videos
03:13
Immunostaining to Visualize Viral Antigen and Host Protein Colocalization in the Insect Gut
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2026
Source: Zhang, L., et al. Immunofluorescent Labeling of Plant Virus and Insect Vector Proteins in Hemipteran Guts. J. Vis. Exp. (2021)The video demonstrates the preparation of gut tissue from a plant virus–infected insect vector for fluorescence imaging. Fixed guts were washed, permeabilized, and incubated with two fluorescent antibodies in a blocking solution—one targeting a viral antigen in vesicles, the other a vesicle-associated membrane protein. The tissue was counterstained for actin...
08:44
Invertebrate Lifespan Quantification
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2023
Many animals naturally stop growing upon reaching adulthood, after which they undergo aging or "senescence" until dying. The amount of time between an organism\'s birth and death is called its lifespan, which can be influenced by various biological and environmental factors. By exposing organisms to different growth conditions, scientists can better understand the factors affecting lifespan. Flies and worms are ideal organisms to perform such experiments, given their short generation time and...
12:58
Visualisation and Quantification of Intracellular Interactions of Neisseria meningitidis and Human α-actinin by Confocal Imaging
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2010
Neisseria meningitidis (Nm), a gram negative human-specific respiratory pathogen, can bind to human α-actinin. Here we present a protocol for visualisation of colocalisation of the bacterium with intracellular α-actinin after bacterial entry into human brain microvascular endothelial cells (HBMECs).
08:38
Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS
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Cited by 28 •
2015
Here we describe a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) assay to quantify the immunosuppressant tacrolimus in dried blood spots using a simple manual protein precipitation step and online column extraction.
09:31
Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
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Cited by 2 •
2015
A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.
FAQs
Pearson’s correlation coefficient evaluates how closely the fluorescence intensity distributions of two labeled signals vary within the same image. A stronger correlation indicates that intensity changes tend to occur in corresponding locations, whereas weaker correlation indicates less coordinated spatial distribution. This measure helps quantify signal relationships across developmental stages or experimental conditions, provided background and imaging conditions remain comparable.
Manders’ overlap coefficients assess the degree to which the signals occupy overlapping image regions, complementing Pearson correlation’s focus on intensity distributions. This distinction matters when two markers share locations without showing identical intensity patterns. Using the appropriate measure allows researchers to describe spatial overlap more specifically when examining regulatory proteins, signaling components, or developmental markers.
Signals may appear spatially coincident because the imaging system cannot fully distinguish nearby structures, rather than because the labeled molecules or organelles share a true location. Noise and background can also alter measured intensity distributions or overlap. Consequently, apparent colocalization should be interpreted with controls and standardized imaging conditions that help separate biological association from measurement-related signal overlap.
Reliable measurements depend on background treatment, consistent imaging conditions, and comparable acquisition across samples. Changes in these factors can modify fluorescence intensity distributions and therefore affect Pearson or Manders values without reflecting a biological change. Standardization is especially important when comparing developmental stages or experimental conditions, because the conclusions depend on differences being attributable to biology rather than image variability.
A practical workflow begins by acquiring images of the labeled structures under standardized conditions, then accounting for background before calculating a suitable overlap measure. Researchers can compare the resulting values across developmental stages or experimental conditions and evaluate them with appropriate controls. This sequence supports more defensible interpretation of shared signal locations during differentiation, migration, or tissue formation.
The method is useful when researchers need quantitative evidence that regulatory proteins, signaling components, or cellular markers occupy shared locations during development. Measurements can be compared across differentiation, migration, or tissue-formation contexts, as well as between experimental conditions. These comparisons may reveal stage-dependent changes in spatial relationships, while controls remain necessary to avoid mistaking optical overlap for biological association.
A high Pearson or Manders value indicates substantial similarity or overlap between measured signals, but it does not by itself establish a direct molecular interaction. Limited resolution, background, noise, or other imaging conditions can create apparent coincidence. Researchers should therefore interpret the value alongside controls and standardized acquisition, especially when assigning biological meaning to patterns observed during tissue formation or cell differentiation.