Bayesian analysis of fluorescence lifetime imaging data

Abstract

Fluorescence Lifetime Imaging (FLIM) is an intensity independent and sensitive optical technique for studying the cellular environment but its accuracy is often compromised when low photon counts are available for analysis. We have developed a photon-by-photon Bayesian analysis method targeted at the accurate analysis of low photon count time-domain FLIM data collected using Time Correlated Single Photon Counting (TCSPC). Parameter estimates obtained with our monoexponential Bayesian analysis compare favorably with those using maximum likelihood, least squares, and phasor analysis, offering robust estimation with greater precision at very low total photon counts, particularly in the presence of significant background levels. Details of the Bayesian implementation are presented alongside results of mono-exponential analysis of both real and synthetic data. We demonstrate that for low photon count data, obtained by imaging human epithelial carcinoma cells expressing cdc42-GFP, Bayesian analysis estimates the green fluorescent protein (GFP) lifetime to a level of accuracy not obtained using maximum likelihood estimation or other techniques. These results are echoed by the analysis of synthetic decay data incorporating a 10% uniform background, with our Bayesian analysis routines yielding lifetime estimates within an accuracy of 20% with about 50 counts. This level of precision is not achieved with maximum likelihood nor phasor analysis techniques with fewer than 100 counts.