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.

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. We demonstrate the performance of 3B on various live cell systems, including cardiac myocytes and podosomes, showing a resolution of tens of nm with acquisition times down to a second.

While analysing higher density images can improve the speed at which the data required for a super-resolution reconstruction is acquired, there are still limits to the speed which can be achieved. Unlike in conventional fluorescence microscopy, these limits are not just set by the properties of the microscope, but are determined by the structure of the sample itself. This is because the local sample structure affects how quickly the information necessary to create an image of a certain resolution can be transmitted through the optical system. The theoretical limits will be discussed, and the effect on live cell experiments demonstrated.

Figures:

« Back to The 16th European Microscopy Congress 2016

EMC Abstracts - https://emc-proceedings.com/abstract/information-in-localisation-microscopy/