Subpixel Colocalization
Subpixel Colocalization - 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...
08:41
Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Cited by 11 •
2012
Lensfree optical tomography is a three-dimensional microscopy technique that offers a spatial resolution of <1 μm × <1 μm × <3 μm in x, y and z dimensions, respectively, over a large imaging-volume of 15-100 mm3, which can be particularly useful for integration with lab-on-a-chip platforms.
07:48
Tracking Drug-induced Changes in Receptor Post-internalization Trafficking by Colocalizational Analysis
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Cited by 1 •
2015
Receptor trafficking modulates signaling and cell responsiveness to ligands and is, itself, responsive to cell conditions, including ligand-induced signaling. Here, we describe a powerful and flexible technique for quantitatively assessing drug-induced receptor trafficking using immunolabeling and colocalizational analysis.
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).
13:28
Three-dimensional Confocal Analysis of Microglia/macrophage Markers of Polarization in Experimental Brain Injury
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Cited by 51 •
2013
A way to gain new insights into the complexity of the brain inflammatory response is presented. We describe immunofluorescence-based protocols followed by three-dimensional confocal analysis to investigate the pattern of co-expression of microglia/macrophage phenotype markers in a mouse model of focal ischemia.
FAQs
The analysis models the intensity distribution of each fluorescence signal rather than treating all signal within a pixel as identical. It then estimates a subpixel coordinate, such as the fluorophore centroid, from that distribution. Comparing these coordinates reveals whether labeled structures share a location or lie close together despite appearing within one camera pixel.
Image registration aligns the channels so that a coordinate in one signal corresponds to the same physical location in the other. Background correction removes nontarget fluorescence that could distort each intensity distribution. Without these steps, apparent coordinate differences or similarities may reflect channel misalignment or background signal rather than the spatial relationship of the labeled structures.
Pixel-based overlap asks whether signals occupy common pixels, whereas subpixel analysis compares estimated positions within those pixels. This distinction matters when multiple structures fall inside one diffraction-limited region: shared pixel occupancy cannot by itself indicate whether labels coincide or are merely nearby. Finer coordinate comparison therefore provides a more informative description of their spatial relationship.
A practical workflow begins with fluorescence images of labeled structures, followed by background correction and registration of the signal channels. The analysis then models each signal’s intensity distribution, estimates coordinates such as centroids, and compares the resulting positions. This sequence produces a coordinate-based spatial comparison rather than relying only on pixel occupancy.
They can indicate whether the estimated locations coincide closely or show a measurable separation within that shared region. The result is a spatial relationship between the labels, not simply a statement that both generated signal in one pixel or diffraction-limited area. In neuroscience, this distinction is useful when nearby molecular structures may look visually merged.
Neuroscience studies can use the method to map the organization of proteins, receptors, and cellular compartments in neurons. Its spatial comparisons support investigations of synaptic architecture, molecular trafficking, and neural signaling, especially when relevant structures occupy the same diffraction-limited region. The resulting measurements help describe nanoscale relationships that conventional pixel-based overlap may not distinguish.