Quantitative Colocalization Analysis of Confocal Fluorescence Microscopy Images

Abstract

Colocalization is an important finding in many cell biological studies. This unit describes a protocol for quantitative evaluation of images with colocalization based on the calculation of a number of specialized coefficients. First, images of double‐stained sections are subjected to background correction. Then, various coefficients are calculated. Meanings of the coefficients and a guide to interpretation of their results indicating either presence or absence of colocalization are given. Success in colocalization studies depends on the quality of analyzed images, proper preparation of them for coefficients calculations, and correct interpretation of obtained results. This protocol helps to ensure reliability of colocalization coefficients calculations. Curr. Protoc. Cell Biol. 39:4.19.1‐4.19.16. © 2008 by John Wiley & Sons, Inc.

Evaluating the Role of Retinal Membrane Guanylyl Cyclase 1 (RetGC1) Domains in Binding Guanylyl Cyclase-activating Proteins (GCAPs)

“…The untagged M26R GCAP1 displaced GCAP1-GFP in the same manner (Fig. 3D), causing PCC to fall to 0.32 Ϯ 0.16 (n ϭ 36), which is below the co-localization threshold (51). Hence, the M26R GCAP1 eliminated RetGC1 activation in Fig.…”

Reduction of Synaptojanin 1 Accelerates Aβ Clearance and Attenuates Cognitive Deterioration in an Alzheimer Mouse Model

“…Similar experiments were performed in N2a cells after control or synj1 siRNA transfection, followed by incubation with Alexa-A␤ 555 or Alexa-A␤ 488 . Fluorescent colocalization analysis was quantified using the Zen program to calculate colocalized pixels (subtracted by background) and colocalization coefficiency as previously described (30,31). The data are presented as percentages of control Ϯ S.E.…”

Quantifying molecular colocalization in live cell fluorescence microscopy

“…It turns out that the most commonly used parameter, the so-called Pearson's Correlation Coefficient (PCC) is superior to the Manders Overlap Coefficient [13,14] (MOC) which is currently implemented in most commercial software packages for image processing [15]. A closer look at the PCC, however, reveals that it has some frequently cited major drawbacks which significantly limit its utility for colocalization analysis in certain cases [16][17][18][19][20]. For example, it was found that the PCC, which can adopt values between þ1 and À1 as a measure of the similarity of channels (+1: perfect colocalization; values between 0 and À1: no colocalization), is very sensitive to strong intensity fluctuations or threshold variations.…”