Automated multidimensional single molecule fluorescence microscopy feature detection and tracking

Abstract

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.

Affimer proteins are versatile and renewable affinity reagents

“…To detect single particles, the binding affinity of the Affimer must be in the low nM range to avoid saturating the sample and to reduce non-specific binding. HER4 particles were tracked with a Bayesian tracking algorithm (Rolfe et al, 2011) and the diffusion coefficient and Minimum Square Displacement (MSD) were calculated from the resulting trajectories. The data show that there is an immobile, or scarcely mobile, population of HER4 receptors on MCF7 cells, associated with a tail of highly mobile molecules (Figure 7D, left panel).…”

“…Data were acquired at 20 Hz for 30 s. Images were saved in HDF5 format for subsequent processing using custom-designed software. All Single-Molecule time series data were analysed using the multidimensional analysis software described previously (Rolfe et al, 2011). …”

Resolving the kinetics of lipid, protein and peptide diffusion in membranes

“…Historically, monitoring the movement of a single molecule bearing a fluorescent marker was challenging, principally because the period in which the fluorophore could be tracked before photobleaching occurred was short and detectors lacked the sensitivity required. More recently however, the advent of new chromophores, allied to improvements in detector sensitivity, has made SFMT a F o r P e e r R e v i e w O n l y feasible process (Kusumi et al, 2011;Rolfe et al, 2011;Kusumi et al, 2010). A key requirement for SFMT is to track the movement of single molecules with a high enough frame rate to capture sufficient data for analysis (Skaug et al, 2011).…”

Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy

“…Finally, tracks shorter than L min are discarded as false positives. Nearest-neighbor linking techniques are used in one form or another by many particle tracking methods (Apgar et al, 2000;Celler et al, 2013;Ghosh and Webb, 1994;Goulian and Simon, 2000;Ku et al, 2007;Marston et al, 1996;Rolfe et al, 2011). In the particle tracking challenge, several methods (Chenouard et al, 2014, methods 9, 10, 13, 14) used either exactly the same technique described here, or additionally used image information about object appearance to solve the correspondence problem.…”