BAYESIAN MODEL SELECTION APPLIED TO THE ANALYSIS OF FCS DATA OF FLUORESCENT PROTEINS IN VITRO AND IN VIVO
by Guangyu Sun,†$ Syuan-Ming Guo,‡ Cathleen Teh,§ Vladimir Korzh,§# Mark Bathe,‡ and Thorsten Wohland*,†$#
†Department of Chemistry, National University of Singapore, 117543 Singapore, $Centre for Bioimaging Sciences, National University of Singapore, 117557 Singapore, ‡Laboratory for Computational Biology and Biophysics, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States, §Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, 138673 Singapore, #Department of Biological Sciences, National University of Singapore, 117543 Singapore
Sun et al. – Anal. Chem. 87 (2015) 4326−4333
Fluorescence Correlation Spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated datasets. As a result, naive fitting procedures that ignore these important issues typically provide similarly good fits for multiple competing models without clear distinction of which model is preferred given the signal-to-noise ratio present in the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for the highly correlated noise that is present in FCS datasets and additionally penalizes model complexity to prevent over interpretation of FCS data. Here, we apply this procedure to evaluate FCS data from fluorescent proteins assayed in vitro and in vivo. Consistent with previous work, we demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, namely excitation intensity and measurement time, and is sensitive to saturation artifacts. Under fixed, low intensity excitation conditions physical transport models can unambiguously be identified. However, at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in non-physical models to be preferred. We also determined the appropriate fitting models of a GFP tagged secreted signaling protein, Wnt3, in live zebrafish embryos, which is necessary for the investigation of Wnt3 expression and secretion in development. Bayes model selection therefore provides a robust procedure to determine appropriate transport and photophysical models for fluorescent proteins when appropriate models are provided, to help detect and eliminate experimental artifacts in solution, cells and in living organisms.