No 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.

We present novel Bayesian methods for the analysis of exponential decay data that exploit the evidence carried by every detected decay event and enables robust extension to advanced processing. Our algorithms are presented in the context of fluorescence lifetime imaging microscopy (FLIM) and particular attention has been paid to model the time-domain system (based on time-correlated single photon counting) with unprecedented accuracy. We present estimates of decay parameters for mono- and bi-exponential systems, offering up to a factor of two improvement in accuracy compared to previous popular techniques. Results of the analysis of synthetic and experimental data are presented, and areas where the superior precision of our techniques can be exploited in Förster Resonance Energy Transfer (FRET) experiments are described. Furthermore, we demonstrate two advanced processing methods: decay model selection to choose between differing models such as mono- and bi-exponential, and the simultaneous estimation of instrument and decay parameters.

Overexpression of HER2 is an important prognostic marker, and the only predictive biomarker of response to HER2-targeted therapies in invasive breast cancer. HER2-HER3 dimer has been shown to drive proliferation and tumor progression, and targeting of this dimer with pertuzumab alongside chemotherapy and trastuzumab, has shown significant clinical utility. The purpose of this study was to accurately quantify HER2-HER3 dimerisation in formalin fixed paraffin embedded (FFPE) breast cancer tissue as a novel prognostic biomarker.FFPE tissues were obtained from patients included in the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. HER2-HER3 dimerisation was quantified using an improved fluorescence lifetime imaging microscopy (FLIM) histology-based analysis. Analysis of 131 tissue microarray cores demonstrated that the extent of HER2-HER3 dimer formation as measured by Förster Resonance Energy Transfer (FRET) determined through FLIM predicts the likelihood of metastatic relapse up to 10 years after surgery (hazard ratio 3.91 (1.61–9.5), p = 0.003) independently of HER2 expression, in a multivariate model. Interestingly there was no correlation between the level of HER2 protein expressed and HER2-HER3 heterodimer formation. We used a mathematical model that takes into account the complex interactions in a network of all four HER proteins to explain this counterintuitive finding.Future utility of this technique may highlight a group of patients who do not overexpress HER2 protein but are nevertheless dependent on the HER2-HER3 heterodimer as driver of proliferation. This assay could, if validated in a group of patients treated with, for instance pertuzumab, be used as a predictive biomarker to predict for response to such targeted therapies.