|ZFIN ID: ZDB-PUB-150331-17|
Bayesian Model Selection Applied to the Analysis of FCS Data of Fluorescent Proteins in vitro and in vivo
Sun, G., Guo, S.M., Teh, C., Korzh, V., Bathe, M., Wohland, T.
|Source:||Analytical chemistry 87(8): 4326-33 (Journal)|
|Registered Authors:||Korzh, Vladimir, Teh, Cathleen|
|PubMed:||25815704 Full text @ Anal. Chem.|
Sun, G., Guo, S.M., Teh, C., Korzh, V., Bathe, M., Wohland, T. (2015) Bayesian Model Selection Applied to the Analysis of FCS Data of Fluorescent Proteins in vitro and in vivo. Analytical chemistry. 87(8):4326-33.
ABSTRACTFluorescence 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 labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to the often low signal-to-noise ratio of FCS data and high correlations present in autocorrelation data. As a result, naive fitting procedures typically provide similarly good fits for multiple competing models without clear distinction of which model is most consistent with the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for correlated noise in FCS datasets and additionally penalizes model complexity appropriately to prevent over-fitting and erroneous interpretation of FCS data. Here, we apply this procedure to evaluate FCS data of fluorescent proteins in vitro and in vivo. We demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, i.e. excitation intensity and measurement time, and is additionally highly sensitive to saturation artifacts. Under fixed, low intensity excitation conditions, models can unambiguously be identified, but at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in non-physical models to be preferred. We then 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 thus provides a robust procedure to identify correct transport and related models for fluorescent proteins to eliminate experimental artifacts in solution, cells and in living organisms.