Bayesian analysis of fluorescence lifetime imaging data

Student thesis: Doctoral ThesisDoctor of Philosophy


The development of a novel photon-by-photon Bayesian analysis for time-domain Fluorescence Lifetime Imaging Microscopy (FLIM) data, and its application to both real experimental biological and synthetic data, is presented in this thesis. FLIM is an intensity-independent and sensitive optical technique for studying the cellular envi- ronment and can robustly exploit Fo¨rster Resonance Energy Transfer (FRET) to enable protein-protein interactions to be located within living or fixed cells. Careful analysis of fluorescence lifetime data, often comprising multi-exponential kinetics, is crucial to elucidating FRET via FLIM.
The developed Bayesian analysis is demonstrated to offer more accurate fitting of data with lower photon counts, allowing greater acquisition speeds. As well as revealing infor- mation previously unobtainable, such as direct error estimates, fitting model probabilities, and instrument response extraction, the developed approach allows for future extensions which can exploit the full probability distribution. In a section of this work already pub- lished [1], Bayesian mono-exponential analysis was shown to offer robust estimation with greater precision at low total photon counts, estimating fluorescent lifetimes to a level of accuracy not obtained using other techniques. Bayesian mono-exponential parameter es- timates obtained with the developed Bayesian analysis are improved compared to those obtained using maximum likelihood, least squares, and the phasor data fitting approaches. In this work, Bayesian bi-exponential analysis based on an improved fully-analytic time- domain FLIM system model is shown to also offer improved decay parameter estimates.
The developed analysis offers fluorescence decay model selection by exploiting the hierarchical nature of Bayesian analysis. This innovation enables the quantitative determi- nation of the likelihood of the data being due to mono- or bi-exponential decay processes, for example. Model selection applied to FLIM promises to simplify processing where the exact kinetics are not known. Finally, the determination of an approximated instrument response function from observed fluorescence decay data alone is also possible.
Date of Award1 Oct 2013
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorBoris Vojnovic (Supervisor) & Ton Coolen (Supervisor)

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