Colocalization

In fluorescence microscopy, colocalization refers to observation of the spatial overlap between two (or more) different fluorescent labels, each having a separate emission wavelength, to see if the different "targets" are located in the same area of the cell or very near to one another. The definition can be split into two different phenomena, co-occurrence, which refers to the presence of two (possibly unrelated) fluorophores in the same pixel, and correlation, a much more significant statistical relationship between the fluorophores indicative of a biological interaction.[1] This technique is important to many cell biological and physiological studies during the demonstration of a relationship between pairs of bio-molecules.

History

The ability to demonstrate a correlation between a pair of bio-molecules was greatly enhanced by Erik Manders of the University of Amsterdam who introduced Pearson's correlation coefficient to microscopists,[2] along with other coefficients of which the "overlap coefficients" M1 and M2 have proved to be the most popular and useful.[3][4] The purpose of using coefficients is to characterize the degree of overlap between images, usually two channels in a multidimensional microscopy image recorded at different emission wavelengths. A popular approach was introduced by Sylvain Costes, who utilized Pearson's correlation coefficient as a tool for setting the thresholds required by M1 and M2 in an objective fashion.[5] Costes approach makes the assumption that only positive correlations are of interest, and does not provide a useful measurement of PCC.

Although the use of coefficients can significantly improve the reliability of colocalization detection, it depends on the number of factors, including the conditions of how samples with fluorescence were prepared and how images with colocalization were acquired and processed. Studies should be conducted with great caution, and after careful background reading. Currently the field is dogged by confusion and a standardized approach is yet to be firmly established.[6] Attempts to rectify this include re-examination and revision of some of the coefficients,[7][8] application of a factor to correct for noise,[1] "Replicate based noise corrected correlations for accurate measurements of colocalization".[9] and the proposal of further protocols,[10] which were thoroughly reviewed by Bolte and Cordelieres (2006).[6] In addition, due to the tendency of fluorescence images to contain a certain amount of out-of-focus signal, and poisson shot and other noise, they usually require pre-processing prior to quantification.[11][12] Careful image restoration by deconvolution removes noise and increases contrast in images, improving the quality of colocalization analysis results. Up to now, most frequently used methods to quantify colocalization calculate the statistical correlation of pixel intensities in two distinct microscopy channels. More recent studies have shown that this can lead to high correlation coefficients even for targets that are known to reside in different cellular compartments.[13] A more robust quantification of colocalization can be achieved by combining digital object recognition, the calculation of the area overlap and combination with a pixel-intensity correlation value. This led to the concept of an object-corrected Pearson's correlation coefficient.[13]

Examples of use

Some impermeable fluorescent zinc dyes can detectably label the cytosol and nuclei of apoptizing and necrotizing cells among each of four different tissue types examined. Namely: the cerebral cortex, the hippocampus, the cerebellum, and it was also demonstrated that colocalized detection of zinc increase and the well accepted cell death indicator propidium iodide also occurred in kidney cells. Using the principles of fluorescent colocalization. coincident detection of zinc accumulation and propidium iodide (a traditional cell death indicator) uptake in multiple cell types was demonstrated.[14] Various examples of quantification of colocalization in the field of neuroscience can be found in a review.[15] Detailed protocols on the quantification of colocalization can be found in a book chapter.[16]

Single-molecule resolution

Colocalization is used in real-time single-molecule fluorescence microscopy to detect interactions between fluorescently labeled molecular species. In this case, one species (e.g. a DNA molecule) is typically immobilized on the imaging surface, and the other species (e.g. a DNA-binding protein) is supplied to the solution. The two species are labeled with dyes of spectrally resolved (>50 nm) colors, e.g. cyanine-3 and cyanine-5. Fluorescence excitation is typically carried out in total internal reflection mode which increases the signal-to-noise ratio for the molecules at the surface with respect to the molecules in bulk solution. The molecules are detected as spots appearing on the surface in real-time, and their locations are found to within 10-20 nm by fitting of point-spread functions. Since typical sizes of biomolecules are on the order of 10 nm, this precision is usually sufficient for calling of molecular interactions [17]

Interpretation of results

For the purpose of better interpretation of the results of qualitative and quantitative colocalization studies, it was suggested to use a set of five linguistic variables tied to the values of colocalization coefficients, such as very weak, weak, moderate, strong, and very strong, for describing them. The approach is based on the use of the fuzzy system model and computer simulation. When new coefficients are introduced, their values can be fitted into the set.[18]

Benchmark images

The degree of colocalization in fluorescence microscopy images can be validated using the Colocalization Benchmark Source, a free collection of downloadable image sets with pre-defined values of colocalization.

Software implementations

open source
  • FIJI is just ImageJ - batteries included
  • BioImage XD

closed source
  • AxioVision Colocalization Module
  • Colocalization Research Software
  • CoLocalizer Pro CoLocalizer Pro
  • Nikon's NIS-Elements Colocalization Module
  • Scientific Volume Imaging's Huygens Colocalization Analyzer
  • Quorum Technology's Volocity
  • Media Cybernetics's Image-Pro
  • Bitplane's Imaris
  • arivis Vision4D

References
  1. Adler et al. (2008)
  2. Manders et al (1992). "Dynamics of three-dimensional replication patterns during the S-phase, analysed by double labelling of DNA and confocal microscopy."
  3. Manders; et al. (1993). "Measurement of co-localisation of objects in dual-colour confocal images". Journal of Microscopy. 169 (3): 375–382. doi:10.1111/j.1365-2818.1993.tb03313.x.
  4. Zinchuk V et al (2007). "Quantitative colocalization analysis of multicolor confocal immunofluorescence microscopy images: pushing pixels to explore biological phenomena". Acta Histochem Cytochem 40:101-111.
  5. Costes et al (2004) "Automatic and Quantitative Measurement of Protein-Protein Colocalization in Live Cells."
  6. BOLTE and CORDELIÈRES (2006) "A guided tour into subcellular colocalization analysis in light microscopy."
  7. Adler and Parmryd (2010)"Quantifying colocalization by correlation: The Pearson correlation coefficient is superior to the Mander's overlap coefficient."
  8. Krauß et al (2015). "Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study." Analyst, volume 140, issue 7, pages 2360-2368.
  9. Adler, J.; Pagakis, S. N.; Parmryd, I. (1 April 2008). "Replicate-based noise corrected correlation for accurate measurements of colocalization". Journal of Microscopy. 230 (1): 121–133. doi:10.1111/j.1365-2818.2008.01967.x. PMID 18387047.
  10. Curr Protoc Cell Biol "Quantitative colocalization analysis of confocal fluorescence microscopy images." Archived 2009-11-28 at the Wayback Machine
  11. Pawley JB (2006). Handbook of Biological Confocal Microscopy
  12. Zinchuk V et al (2011). "Quantifying spatial correlations of fluorescent markers using enhanced background reduction with protein proximity index and correlation coefficient estimations". Nat Protoc 6:1554-1567.
  13. Moser, Bernhard; Hochreiter, Bernhard; Herbst, Ruth; Schmid, Johannes A. (2016-07-01). "Fluorescence colocalization microscopy analysis can be improved by combining object-recognition with pixel-intensity-correlation". Biotechnology Journal. 12 (1): 1600332. doi:10.1002/biot.201600332. ISSN 1860-7314. PMC 5244660. PMID 27420480.
  14. Stork, Christian J.; Li, Yang V. (15 September 2006). "Measuring cell viability with membrane impermeable zinc fluorescent indicator". Journal of Neuroscience Methods. 155 (2): 180–186. doi:10.1016/j.jneumeth.2005.12.029. PMID 16466804.
  15. Zinchuk V & Grossenbacher-Zinchuk O (2009). "Recent advances in quantitative colocalization analysis: Focus on neuroscience". Prog Histochem Cytochem 44:125-172
  16. "Adler J & Parmryd I (2013) Methods Mol Biol 931, 97-109". Colocalization analysis in fluorescence microscopy. Retrieved 2016-04-19.
  17. Gelles, Friedman L. (17 February 2012). "Mechanism of Transcription Initiation at an Activator-Dependent Promoter Defined by Single-Molecule Observation". Cell. 148 (4): 635–637. doi:10.1016/j.cell.2012.01.018. PMC 3479156. PMID 22341441.
  18. Zinchuk, V; et al. (2013). "Bridging the gap between qualitative and quantitative colocalization results in fluorescence microscopy studies". Sci Rep. 3: 1365. doi:10.1038/srep01365. PMC 3586700. PMID 23455567.
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