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Colocalization

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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.

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 wave lengths. 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 standarized approach is yet to be firmly established [6]. Attempts to rectify this include re-examination and revision of some of the coefficients [7] and application of a factor to correct for noise [8]. Some authors have both proposed propocols [9] and sell accompanying software. In addition, due to the tendency of fluorescence images to contain a certain amount of out-of-focus noise, they usually require pre-processing prior to quantification.[10]. This pre-processing can be done using background thresholding or deconvolution.

Examples of use

It was shown that 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 could be demonstrated. (& Li, The Journal of Neuroscience Methods, 2006). Various examples of quantification of colocalization in the field of neuroscience can be found in a review. [11]

Scientific applications

  • AxioVision Colocalization Module [9]
  • CoLocalizer Pro [10]
  • CoLocalizer Express [11]
  • Quantitative Colocalization Service [12]
  • Huygens Colocalization Analyzer [13]
  • Volocity [14]

Free colocalization software

  • ImageJ [15]
  • JACoP -ImageJ plugin [16]
  • ColocalizationColormap -ImageJ plugin [17]
  • Volocity Demo [18]

References

  1. ^ Adler et al (2008). "Replicate based noise corrected correlations for accurate measurements of colocalization". [1]
  2. ^ Manders et al (1992). "Dynamics of three-dimensional replication patterns during the S-phase, analysed by double labelling of DNA and confocal microscopy." [2]
  3. ^ Manders et al (1993). "Measurement of co-localisation of objects in dual-colour confocal images. Journal of Microscopy 169:375-382
  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." [3]
  6. ^ BOLTE and CORDELIÈRES (2006) "A guided tour into subcellular colocalization analysis in light microscopy." [4]
  7. ^ Adler and Parmryd (2010)"Quantifying colocalization by correlation: The Pearson correlation coefficient is superior to the Mander's overlap coefficient." [5]
  8. ^ Adler et al (2008). "Replicate based noise corrected correlations for accurate measurements of colocalization". [6]
  9. ^ Curr Protoc Cell Biol "Quantitative colocalization analysis of confocal fluorescence microscopy images."
  10. ^ Pawley JB (2006). Handbook of Biological Confocal Microscopy. [7]
  11. ^ Zinchuk V & Grossenbacher-Zinchuk O (2009). "Recent advances in quantitative colocalization analysis: Focus on neuroscience". [8]


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