Image Correlation Spectroscopy for Measurements of Particle Densities and Colocalization

Benjamin Rappaz1, Paul W. Wiseman2

1 Department of Physics, McGill University, Montreal, Quebec, Canada, 2 Department of Chemistry, McGill University, Montreal, Quebec, Canada
Publication Name:  Current Protocols in Cell Biology
Unit Number:  Unit 4.27
DOI:  10.1002/0471143030.cb0427s59
Online Posting Date:  June, 2013
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Cells interact with their environment through receptor proteins expressed at their plasma membrane, and protein‐protein interactions govern the transduction of signals across the membrane into the cell. Therefore, the ability to measure receptor densities and protein colocalization within the membrane of intact cells is of paramount importance. This unit describes a technique to extract these parameters from fluorescence microscopy images obtained using a commercial confocal laser scanning microscope (CLSM) and other similar types of microscopes. It is based on the analysis of spatial fluorescence intensity fluctuations in the images, which can then be related to particle density and aggregation state via calculation of a spatial autocorrelation function, or used to measure particle colocalization via calculation of a spatial cross‐correlation function from dual‐color images of proteins tagged with two different fluorophores and imaged in two detection channels. These parameters offer key insights on the interaction of the cell with its environment. Curr. Protoc. Cell Biol. 59:4.27.1‐4.27.15. © 2013 by John Wiley & Sons, Inc.

Keywords: fluorescence fluctuations; image correlation spectroscopy; particle density; colocalization; aggregation state; cross‐correlation

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Table of Contents

  • Introduction
  • Basic Protocol 1: Image Correlation Spectroscopy (ICS) and Image Cross‐Correlation Spectroscopy (ICCS)
  • Basic Protocol 2: Determination of Aggregation State
  • Alternate Protocol 1: Large Macroscopic Structures (Clusters, Focal Adhesions)
  • Commentary
  • Literature Cited
  • Figures
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Basic Protocol 1: Image Correlation Spectroscopy (ICS) and Image Cross‐Correlation Spectroscopy (ICCS)

  • Fluorophore‐conjugated antibodies for cell labeling
  • Microscope able to selectively image the cell membrane, or produce a homogenous field of view: suitable types include confocal laser scanning, TIRF, spinning‐disk, or 2‐photon microscopes; the microscope should be equipped with a high‐NA objective to obtain a field of view at high resolution enabling reliable images for ICS and ICCS analysis; it is important to collect images with a pixel size that is smaller than the point spread function resolution, to enable spatial oversampling and create spatial correlation between adjacent image pixels
  • Appropriate filters and dichroic mirror
  • Computer to perform the analysis of the collected images
  • Analysis software (see Internet Resources)
  • Additional reagents and equipment for cell culture (unit 1.1) and transfection of cells (Chapter 20)
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Literature Cited

   Balghi, H., Robert, R., Rappaz, B., Zhang, X., Wohlhuter‐Haddad, A., Evagelidis, A., Luo, Y., Goepp, J., Ferraro, P., Romeo, P., Trebak, M., Wiseman, P.W., Thomas, D.Y., and Hanrahan, J.W. 2011. Enhanced Ca2+ entry due to Orai1 plasma membrane insertion increases IL‐8 secretion by cystic fibrosis airways. FASEB J. 25:4274‐4291.
   Brzostowski, J.A., Meckel, T., Hong, J., Chen, A., and Jin, T. 2009. Imaging protein‐protein interactions by Förster resonance energy transfer (FRET) microscopy in live cells. Curr. Protoc. Protein Sci. 56:19.5.1‐19.5.12.
   Comeau, J. 2008. Measuring Interactions in Cells with Spatial Image Cross‐Correlation Spectroscopy: Characterization, Application and Advances. Ph.D. thesis, McGill University, Montreal, Quebec, Canada.
   Comeau, J.W., Kolin, D.L., and Wiseman, P.W. 2008. Accurate measurements of protein interactions in cells via improved spatial image cross‐correlation spectroscopy. Mol. Biosyst. 4:672‐685.
   Costantino, S., Comeau, J.W., Kolin, D.L., and Wiseman, P.W. 2005. Accuracy and dynamic range of spatial image correlation and cross‐correlation spectroscopy. Biophys. J. 89:1251‐1260.
   Kolin, D.L. and Wiseman, P.W. 2007. Advances in image correlation spectroscopy: Measuring number densities, aggregation states, and dynamics of fluorescently labeled macromolecules in cells. Cell. Biochem. Biophys. 49:141‐164.
   Manders, E.M., Stap, J., Brakenhoff, G.J., van Driel, R., and Aten, J.A. 1992. Dynamics of three‐dimensional replication patterns during the S‐phase, analysed by double labelling of DNA and confocal microscopy. J. Cell Sci. 103:857‐862.
   Manders, E.M.M., Verbeek, F.J., and Aten, J.A. 1993. Measurement of co‐localization of objects in dual‐colour confocal images. J. Microsc. 169:375‐382.
   Schwille, P. 2001. Cross‐correlation analysis in FCS. In Fluorescence Correlation Spectroscopy: Theory and Applications (R. Rigler and E.S. Elson, eds.) pp. 361‐378. Springer‐Verlag, Berlin, Heidelberg.
   Schwille, P., Meyer‐Almes, F.J., and Rigler, R. 1997. Dual‐color fluorescence cross‐correlation spectroscopy for multicomponent diffusional analysis in solution. Biophys. J. 72:1878‐1886.
   Wiseman, P.W. 2012. Image correlation spectroscopy. In Comprehensive Biophysics (E. Egelman, ed.) Academic Press, Waltham, Mass.
   Wiseman, P.W. and Petersen, N.O. 1999. Image correlation spectroscopy. II. Optimization for ultrasensitive detection of preexisting platelet‐derived growth factor‐beta receptor oligomers on intact cells. Biophys. J. 76:963‐977.
   Wiseman, P.W., Hoddelius, P., Petersen, N.O., and Magnusson, K.E. 1997. Aggregation of PDGF‐beta receptors in human skin fibroblasts: Characterization by image correlation spectroscopy (ICS). FEBS Lett. 401:43‐48.
   Ulbrich, M.H. and Isacoff, E.Y. 2007. Subunit counting in membrane‐bound proteins. Nat. Methods 4:319‐321.
Key References
  Comeau et al., . See above.
  These references contain extensive details on the image correlation techniques in terms of theory and present many applications.
  Costantino et al., . See above.
  Wiseman, . See above.
Internet Resources
  We have developed a group of stand‐alone graphical user interface (GUI) ICS and ICCS programs based on MATLAB for PC that are available via the author's research group at McGill University. The programs allow the user to import microscopy image and selection ROIs for ICS/ICCS analysis. The program can be downloaded from the Wiseman research group's Web site at the above URL (choose the software link), and they are continually being updated.
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