Abstract

Localisation microscopy is a powerful tool for imaging structures at a lengthscale of tens of nm, but its utility for live cell imaging is limited by the time it takes to acquire the data needed for a super-resolution image. The acquisition time can be cut by more than two orders of magnitude by using advanced algorithms which can analyse dense data, trading off acquisition and processing time. We have developed two methods which allow different tradeoffs to be made. Modelling the entire localisation microscopy dataset using a Hidden Markov Model allows localisation information to be extracted from extremely dense datasets. This Bayesian analysis of blinking and bleaching (3B) is able to image dynamic processes in live cells at a timescale of a few seconds, though it is very computationally intensive, requiring at least several hours of analysis.

Analysis speed can be improved over 3B by a factor of ten by instead modelling the data using an alternative statistical approach, which automatically determines the number, position, and brightness of fluorescing molecules within a particular image region. This method treats the background noise as a parameter to be found, avoiding the background removal step in 3B or the need to hand set thresholds in more conventional analysis techniques.

Our methods are demonstrated on various live cell systems, including cardiac myocytes and podosomes, showing a resolution of tens of nm with acquisition times down to a second. We also compare our methods to other high density algorithms and discuss the artefacts which can occur during reconstruction of the super-resolution image.