SRRF and SQUIRREL: Open-source analytics for super-resolution microscopy

SRRF and SQUIRREL: Open-source analytics for super-resolution microscopy

Speaker: Dr Siân Culley, MRC Laboratory for Molecular Cell Biology, UCL

Host: Susan Cox

Abstract: Super-resolution microscopy techniques hold great promise for achieving near-electron microscopy resolutions in living biological samples. However, although these techniques have been established for nearly two decades, they are still not yet routine methods for imaging live cells. There are two major contributing factors in this limited uptake. Firstly, the majority of super-resolution techniques use high laser intensities that cause photodamage to live samples. Secondly, due to the complex experimental and computational methods necessary to achieve sub-diffraction limit imaging, there is an element of mistrust surrounding super-resolution data as images are prone to artefacts. Our research focuses on analytical methods for addressing both of these issues, two of which are presented here. Super-resolution radial fluctuations (SRRF) is a method for extracting sub-diffraction limit information from fluorescence images obtained using conventional fluorescent probes and widely-available confocal and widefield microscopes. We demonstrate the applicability of SRRF to light-sensitive biological processes. Super-resolution quantitative image rating and reporting of error locations (SQUIRREL) is an analytical approach for assessing the quality of super-resolution imaging data and highlighting artefacts. We demonstrate how this can be used to optimise super-resolution imaging pipelines. Both SRRF and SQUIRREL are implemented as easy-to-use plugins for the freely available ImageJ/Fiji image processing software.