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NanoJ-SQUIRREL: quantitative mapping and minimisation of super-resolution optical imaging artefacts

Most super-resolution microscopy techniques depend on steps that can contribute to the formation of image artefacts, leading to misinterpretation of biological information. We present NanoJ-SQUIRREL, an ImageJ-based analytical approach that provides quantitative assessment of super-resolution image...

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Detalles Bibliográficos
Autores principales: Culley, Siân, Albrecht, David, Jacobs, Caron, Pereira, Pedro Matos, Leterrier, Christophe, Mercer, Jason, Henriques, Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884429/
https://www.ncbi.nlm.nih.gov/pubmed/29457791
http://dx.doi.org/10.1038/nmeth.4605
Descripción
Sumario:Most super-resolution microscopy techniques depend on steps that can contribute to the formation of image artefacts, leading to misinterpretation of biological information. We present NanoJ-SQUIRREL, an ImageJ-based analytical approach that provides quantitative assessment of super-resolution image quality, capable of guiding researchers in optimising imaging parameters. By comparing diffraction-limited images and super-resolution equivalents of the same acquisition volume, this approach generates a quality score and quantitative map of super-resolution defects.