Super-resolution microscopy depends on steps that can contribute to the formation of image artifacts, leading to misinterpretation of biological information. We present NanoJ-SQUIRREL, an ImageJ-based analytical approach that provides quantitative assessment of super-resolution image quality. By comparing diffraction-limited images and super-resolution equivalents of the same acquisition volume, this approach generates a quantitative map of super-resolution defects and can guide researchers in optimizing imaging parameters.
We thank A. Knight (Holistx Ltd.) and S. Holden (Newcastle University) for critical reading of the manuscript; J. Ries (European Laboratory for Molecular Biology, Heidelberg) for provision of customized MATLAB software and critical reading of the manuscript; K. Tosheva (University College London) for critical reading of the manuscript and beta testing of the software; and B. Baum (University College London) for reagents. Many of the look-up tables used here are based on the open-source repository of D. Williamson at King′s College London. This work was funded by grants from the UK Biotechnology and Biological Sciences Research Council (BB/M022374/1; BB/P027431/1; BB/R000697/1) (R.H. and P.M.P.), MRC Programme Grant (MC_UU12018/7) (J.M.), the European Research Council (649101–UbiProPox) (J.M.), the UK Medical Research Council (MR/K015826/1) (R.H. and J.M.), the Wellcome Trust (203276/Z/16/Z) (S.C. and R.H.) and the Centre National de la Recherche Scientifique (CNRS ATIP-AVENIR program AO2016) (C.L.). D.A. is presently a Marie Curie fellow (Marie Sklodowska-Curie grant agreement No 750673). C.J. funded by a Commonwealth scholarship, funded by the UK government.
Binaries, source code and user manual for NanoJ-SQUIRREL