This thesis investigates methods of increasing the imaging speed of Stochastic Optical Reconstruction Microscopy (STORM); a superresolution imaging technique which breaks the diffraction limit by imaging single molecules. Initially the imaging conditions were optimised to maximize both the Signal-to-Noise Ratio (SNR) and the number of molecules localised in order to push the system to image at the fastest rate possible. It was found that the lowest readout laser power possible should be used at a frame rate between 100 - 150 fps. The optimum concentration of MEA - a component of the STORM imaging buffer - was found to be 100 mM. Whilst the optimized conditions afford some speed increase, there is a more fundamental question to be investigated: how many localisations are required for an accurate reconstruction of the sample? The answer to this question will allow a reduction in the image acquisition time by only imaging until the minimum number of molecules have been localised. The density of localisations was studied over time and a simple histogram analysis suggested that using a trade off between density and localisation limited regimes is a valid method to increase the imaging speed by determining a "finishing point". The localisation density increased linearly over time for all samples tested, however some areas reached the cut off density more quickly than others. Using several analysis methods and simulated data it was shown that the blinking behaviour of molecules is a random process and that the variability in resolution across an image is mostly due to a non uniform labelling distribution. Finally, dual colour samples were imaged, as labelling the target structure with two coloured dyes was hypothesised to double the imaging speed. This was found to be true, however there was no overall reduction in acquisition time as dual labelled samples have a slower increase in localisation density over time.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:727273 |
| Date | January 2017 |
| Creators | Snape, Mary Louise |
| Contributors | Cadby, Ashley ; Furley, Andrew |
| Publisher | University of Sheffield |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.whiterose.ac.uk/18415/ |
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