Quantitative Fluorescence Co-localization to Study Protein–Receptor Complexes

Abstract

Fluorescence microscopy can be used to assess quantitatively the interaction between a ligand and its receptor, between two macromolecules, or between a macromolecule and a particular intracellular compartment by co-localization analysis. In general, this analysis involves tagging potential interacting partners with distinct fluorophores-by direct labeling of a small ligand, by expression of fluorescent cDNA constructs, by immunofluorescence labeling, or by some combination of these methods. Pairwise comparison of the fluorescence intensity of the two fluorophores at each pixel in a two channel digital image of the sample reveals regions where both are present. With appropriate protocols, the image data can be interpreted to indicate where the potential interacting partners are co-localized. Keeping in mind the limited resolution of the light microscope, co-localization is often used to support the claim that two molecules are interacting.All quantitative methods for evaluating co-localization begin with identifying the pixels where the intensities of both color channels are above background. Typically this involves two sequential image segmentation steps: the first to exclude pixels where neither channel is above background, and the second to set overlap thresholds that exclude pixels where only one color channel is present. Following segmentation, various quantitative measures can be computed to describe the remaining subset of pixels where the two color channels overlap. These metrics range from simple calculation of the fraction of pixels where overlap occurs to more sophisticated image correlation metrics. Additional constraints may be employed to distinguish true co-localization from random overlap. Finally, an image map showing only the co-localized pixels may be displayed as an additional image channel in order to visualize the spatial distribution of co-localized pixels. Several commercial and open source software solutions provide this type of co-localization analysis, making image segmentation and calculation of metrics relatively straightforward. As an example, we provide a protocol for the time-dependent co-localization of fluorescently tagged lipoproteins with LDL receptor (LDLR) and with the early endosome marker EEA1.

Coordinated recruitment of Spir actin nucleators and myosin V motors to Rab11 vesicle membranes

“…Therefore, the pixels in one image were scrambled randomly and the correlation with the other (unscrambled) image was measured. Significance regarding correlation was observed when at least 95% of randomized images show a PCC less than that of the original image, meaning that the probability for the measured correlation of two colors is significantly greater than the correlation of random overlap ( Costes et al, 2004 ; Pompey et al, 2013 ). Statistical data analysis was performed using SPSS 22 (IBM, Armonk, NY, USA).…”

Rab27a co-ordinates actin-dependent transport by controlling organelle-associated motors and track assembly proteins

“…Quantitative GST pull-down assays. GST pull-down assays were performed as described above from image, meaning that the probability for the measured correlation of two colours is significantly greater than the correlation of random overlap 66,67 .…”

Enrichment of GABAA Receptor α-Subunits on the Axonal Initial Segment Shows Regional Differences

“…Non-AIS regions, with contours identical to AIS regions were also defined as ROIs. Both Pearson’s and Mander’s coefficients were computed using the Coloc 2 plugin in Fiji to quantify co-localization ( Dunn et al, 2011 ; Schindelin et al, 2012 ; Adler and Parmryd, 2013 ; Pompey et al, 2013 ). A total of 37–44 AIS and non-AIS ROIs were sampled from 2 to 3 images taken in each brain area of three mice.…”