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Fluorescence Correlation Spectroscopy (FCS) is a powerful technique to measure molecular dynamics ranging from binding to transport in living and non-living samples (1, 2). Like all measurement techniques, interpretation of FCS data requires fitting a model that is assumed to govern the physical process under study. While the choice of the model may be unambiguous for in vitro systems with known composition, the correct choice of model becomes considerably less unambiguous in the application of FCS to complex living systems where the underlying physical process is unknown (2, 3). Moreover, by using the fast camera as the detector, the recently developed imaging FCS technique allows for simultaneous hundreds to thousands of FCS measurements at contiguous spatial locations with millisecond time resolution, making it an ideal tool to investigate spatially heterogeneous system (4-6). However, the large number of Temporal Autocorrelation Functions (TACFs) with varying noise and unknown underlying physical processes generated by imaging FCS measurements requires an automated, objective analysis procedure for its proper interpretation. For these reasons, we recently developed an approach based on the Bayesian inference (7-9) for objective and unbiased evaluation of competing models in the analysis of FCS data (10-12). As an important component of this approach, we introduce two means of estimating the noise and noise covariance in TACF curves: from multiple independent TACF curves, or a single raw underlying intensity trace using the blocking procedure (13). This Bayesian approach selects the simplest hypothesis that best describes the FCS data given sampling and signal limitations, naturally avoiding over-fitting. Further, model probabilities computed using the Bayesian approach provide a reliability test for the downstream interpretation of model parameter values estimated from FCS data. This approach follows closely on recent work applying Bayesian inference to mean square displacement (MSD) data.


Please cite references 11-13 if you use FCS-Bayes in your research.

 

1. Elson, E. L., and D. Magde. 1974. Fluorescence correlation spectroscopy. 1. conceptual basis and theory. Biopolymers 13:1-27.

2. Schwille, P. 2001. Fluorescence correlation spectroscopy and its potential for intracellular applications. Cell Biochem. Biophys. 34:383-408.

3. Wachsmuth, M., W. Waldeck, and J. Langowski. 2000. Anomalous diffusion of fluorescent probes inside living cell nuclei investigated by spatially-resolved fluorescence correlation spectroscopy. J. Mol. Biol. 298:677-689.

4. Kannan, B., L. Guo, T. Sudhaharan, S. Ahmed, I. Maruyama, and T. Wohland. 2007. Spatially resolved total internal reflection fluorescence correlation microscopy using an electron multiplying charge-coupled device camera. Anal. Chem. 79:4463.

5. Wohland, T., X. K. Shi, J. Sankaran, and E. H. K. Stelzer. 2010. Single plane illumination fluorescence correlation spectroscopy (SPIM-FCS) probes inhomogeneous three-dimensional environments. Opt. Express 18:10627-10641.

6. Capoulade, J., M. Wachsmuth, L. Hufnagel, and M. Knop. 2011. Quantitative fluorescence imaging of protein diffusion and interaction in living cells. Nature Biotechnology 29:835-842.

7. Sivia, D. S., and J. Skilling. 2006. Data analysis : a Bayesian tutorial. Oxford University Press, Oxford.

8. Kass, R. E., and A. E. Raftery. 1995. Bayes factors. J. Am. Stat. Assoc. 90:773-795.

9. Raftery, A. E. 1995. Bayesian model selection in social research. In Sociological Methodology 1995, Vol 25. 111-163.

10. He, J., S.-M. Guo, and M. Bathe. 2012. Bayesian approach to the analysis of fluorescence correlation spectroscopy data I: theory. Anal. Chem. 84:3871-3879.

11. Guo, S.-M., J. He, N. Monnier, G. Sun, T. Wohland, and M. Bathe. 2012. Bayesian approach to the analysis of fluorescence correlation spectroscopy data II: application to simulated and in vitro data. Anal. Chem. 84:3880-3888.

12. Guo, S.-M., N. Bag, A. Mishra, T. Wohland, and M. Bathe. 2013. Bayesian total internal reflection fluorescence correlation spectroscopy reveals hIAPP-induced plasma membrane domain organization in live cells. Submitted.

13. Flyvbjerg, H., and H. G. Petersen. 1989. Error estimates on averages of correlated data. Journal of Chemical Physics 91:461-466.