Journal
NATURE METHODS
Volume 15, Issue 4, Pages 263-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.4605
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Funding
- BBSRC [BB/R000697/1, BB/M022374/1, BB/P027431/1] Funding Source: UKRI
- MRC [MC_UU_12018/7, MR/K015826/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/P027431/1, BB/M022374/1] Funding Source: researchfish
- Medical Research Council [MC_UU_12018/7, MC_UU_00012/7, MR/K015826/1] Funding Source: researchfish
- MRC Laboratory for Molecular Cell Biology (LMCB) [Mercer ERC Research Grant] Funding Source: researchfish
- Biotechnology and Biological Sciences Research Council [BB/M022374/1] Funding Source: Medline
- Medical Research Council [MC_UU_12018/7, MR/K015826/1] Funding Source: Medline
- Wellcome Trust Funding Source: Medline
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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.
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