Quantitative Colocalisation Imaging: Concepts, Measurements, and Pitfalls

DOI: 10.1007/978-3-540-71331-9_5

ABSTRACT Many questions in cell biology and biophysics involve the quantitation of the colocalisation of proteins tagged with different
fluorophores and their interaction. However, the incomplete separation of the different colour channels due to the presence
of autofluorescence, along with cross-excitation and emission ‘bleed-through’ of one colour channel into the other, all combine
to render the interpretation of multiband images ambiguous. Traditionally often used in a qualitative manner by simply overlaying
fluorescence images (‘red plus green equals yellow’), multicolour fluorescence is increasingly moving away from static dual-colour
images towards more quantitative studies involving the investigation of dynamical three-dimensional interaction of proteins
tagged with different fluorophores in live cells. Quantifying fluorescence resonance energy transfer efficiency, fluorescence
complementation and colour merging following photoactivation or photoswitching provide related examples in which quantitative
image analysis of multicolour fluorescence is required. Despite its widespread use, reliable standards for evaluating the
degree of spectral overlap in multicolour fluorescence and calculating quantitative colocalisation estimates are missing.
In this chapter, using a number of intuitive yet practical examples, we discuss the factors that affect image quality and
study their impact on the measured degree of colocalisation. We equally compare different pixel-based and object-based descriptors
for analysing colocalisation of spectrally separate fluorescence. Finally, we discuss the use of spectral imaging and linear
unmixing to study the presence in a ‘mixed pixel’ of spectrally overlapping fluorophores and discuss how this technique can
be used to provide quantitative colocalisation information in more complex experimental scenarios in which classic dual- or
triple-colour fluorescence would produce erroneous results.

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    • "All too often, the interpretation becomes subjective and the researcher must consider the likelihood that the low frequency of colocalization is caused by a biological mechanism rather than chance. To answer this question wither greater accuracy, new approaches are being developed for quantitative colocalization, including image crosscorrelation methods and the Manders coefficients (Oheim, 2007; Comeau et al., 2006). Effectively, these methods quantify how often and in how many pixels two different probes are present. "
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