Colocalization: Difference between revisions
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* Immuno precipitation (IP) dropdowns / pulldowns |
* Immuno precipitation (IP) dropdowns / pulldowns |
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* Yeast 2 hybrid - protein interaction mapping |
* Yeast 2 hybrid - protein interaction mapping |
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==Benchmark images== |
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Colocalization in fluorescence microscopy images can be validated using the [[Colocalization Benchmark Source]], a free collection of images with pre-defined values of colocalization. |
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==Software implementations== |
==Software implementations== |
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Revision as of 02:31, 14 December 2012
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 of Colocalization
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 standardized approach is yet to be firmly established.[6] Attempts to rectify this include re-examination and revision of some of the coefficients,[7] application of a factor to correct for noise,[1] and the proposal of further protocols,[8] which were thoroughly reviewed by Bolte and Cordelieres (2006).[9] 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.[10] Careful image restoration by deconvolution removes noise and increases contrast in images, improving the quality of colocalization analysis results.
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. (Stork & 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]
Related techniques
- Förster resonance energy transfer (FRET): 10 nm proximity
- (Light microscopy: only 250 nm resolution; no certainty of effective interaction)
- Immuno precipitation (IP) dropdowns / pulldowns
- Yeast 2 hybrid - protein interaction mapping
Benchmark images
Colocalization in fluorescence microscopy images can be validated using the Colocalization Benchmark Source, a free collection of images with pre-defined values of colocalization.
Software implementations
open source
closed source
- AxioVision Colocalization Module [11]
- Colocalization Research Software [12]
- CoLocalizer Pro CoLocalizer Pro
- Elements Colocalization Module [13]
- Huygens Colocalization Analyzer [14]
- Volocity [15]
- Volocity Demo [16]
- Image Pro [17]
- Bitplane Imaris [18]
References
- ^ a b Adler et al. (2008). "Replicate based noise corrected correlations for accurate measurements of colocalization". [1]
- ^ Manders et al (1992). "Dynamics of three-dimensional replication patterns during the S-phase, analysed by double labelling of DNA and confocal microscopy." [2]
- ^ Manders et al (1993). "Measurement of co-localisation of objects in dual-colour confocal images. Journal of Microscopy 169:375-382
- ^ 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.
- ^ Costes et al (2004) "Automatic and Quantitative Measurement of Protein-Protein Colocalization in Live Cells." [3]
- ^ BOLTE and CORDELIÈRES (2006) "A guided tour into subcellular colocalization analysis in light microscopy." [4]
- ^ Adler and Parmryd (2010)"Quantifying colocalization by correlation: The Pearson correlation coefficient is superior to the Mander's overlap coefficient." [5]
- ^ Curr Protoc Cell Biol "Quantitative colocalization analysis of confocal fluorescence microscopy images."
- ^ BOLTE and CORDELIÈRES (2006) "A guided tour into subcellular colocalization analysis in light microscopy." [6]
- ^ Pawley JB (2006). Handbook of Biological Confocal Microscopy. [7]
- ^ Zinchuk V & Grossenbacher-Zinchuk O (2009). "Recent advances in quantitative colocalization analysis: Focus on neuroscience". [8]