The Microscopy Innovation Centre (MIC) at King’s College London, aims to provide advanced and home-built imaging solutions to its users. Such microscopy systems are often not provided by core facilities due to issues including the need for specific expertise, specific sample requirements, and continued maintenance. The MIC provides a home to such imaging platforms and has dedicated staff members to maintain as well as provide them to a wide user base. We present a general overview of the MIC as well as showing some of the work done using our home-built microscopes.
KEYWORDS: Microscopy, Point spread functions, Super resolution, Data modeling, Signal to noise ratio, Luminescence, Image resolution, Molecules, Image processing, Digital filtering
Localization microscopy methods allow the acquisition of fluorescence microscopy images with a resolution of tens of nanometres. However, these techniques are generally limited in speed because of the requirement that in each frame fluorophores must be well separated. This condition can be lifted by developing improved analysis techniques which allow data to be extracted from images in which the point spread functions of the fluorophores overlap (at the expense of an increased computational load). These improved analysis techniques build more information into the model for the system, either about the probable appearance of overlapping fluorophores in the spatial domain, or about blinking behaviour in the temporal domain. Here we discuss a method which incorporates both types of information, where we create a Hidden Markov Model including the photophysical processes occuring when fluorophores blink and bleach. This Bayesian analysis of blinking and bleaching (3B) technique allows super-resolution information to be obtained from live cells labelled with standard fluorescent proteins, with a temporal resolution of a few seconds and a spatial resolution of 50 nm.
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