Automated multidimensional single molecule fluorescence microscopy feature detection and tracking.

Characterisation of multi-protein interactions in cellular networks can be achieved by optical microscopy using multidimensional single molecule fluorescence imaging. Proteins of different species, individually labelled with a single fluorophore, can be imaged as isolated spots (features) of different colour light in different channels, and their diffusive behaviour in cells directly measured through time. Challenges in data analysis have, however, thus far hindered its application in biology. A set of methods for the automated analysis of multidimensional single molecule microscopy data from cells is presented, incorporating Bayesian segmentation-based feature detection, image registration and particle tracking. Single molecules of different colours can be simultaneously detected in noisy, high background data with an arbitrary number of channels, acquired simultaneously or time-multiplexed, and then tracked through time. The resulting traces can be further analysed, for example to detect intensity steps, count discrete intensity levels, measure fluorescence resonance energy transfer (FRET) or changes in polarisation. Examples are shown illustrating the use of the algorithms in investigations of the epidermal growth factor receptor (EGFR) signalling network, a key target for cancer therapeutics, and with simulated data.