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Challenges facing quantitative large-scale optical super-resolution, and some simple solutions

Optical super-resolution microscopy (SRM) has enabled biologists to visualize cellular structures with near-molecular resolution, giving unprecedented access to details about the amounts, sizes, and spatial distributions of macromolecules in the cell. Precisely quantifying these molecular details re...

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Detalles Bibliográficos
Autores principales: Dankovich, Tal M., Rizzoli, Silvio O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898072/
https://www.ncbi.nlm.nih.gov/pubmed/33665555
http://dx.doi.org/10.1016/j.isci.2021.102134
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author Dankovich, Tal M.
Rizzoli, Silvio O.
author_facet Dankovich, Tal M.
Rizzoli, Silvio O.
author_sort Dankovich, Tal M.
collection PubMed
description Optical super-resolution microscopy (SRM) has enabled biologists to visualize cellular structures with near-molecular resolution, giving unprecedented access to details about the amounts, sizes, and spatial distributions of macromolecules in the cell. Precisely quantifying these molecular details requires large datasets of high-quality, reproducible SRM images. In this review, we discuss the unique set of challenges facing quantitative SRM, giving particular attention to the shortcomings of conventional specimen preparation techniques and the necessity for optimal labeling of molecular targets. We further discuss the obstacles to scaling SRM methods, such as lengthy image acquisition and complex SRM data analysis. For each of these challenges, we review the recent advances in the field that circumvent these pitfalls and provide practical advice to biologists for optimizing SRM experiments.
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spelling pubmed-78980722021-03-03 Challenges facing quantitative large-scale optical super-resolution, and some simple solutions Dankovich, Tal M. Rizzoli, Silvio O. iScience Review Optical super-resolution microscopy (SRM) has enabled biologists to visualize cellular structures with near-molecular resolution, giving unprecedented access to details about the amounts, sizes, and spatial distributions of macromolecules in the cell. Precisely quantifying these molecular details requires large datasets of high-quality, reproducible SRM images. In this review, we discuss the unique set of challenges facing quantitative SRM, giving particular attention to the shortcomings of conventional specimen preparation techniques and the necessity for optimal labeling of molecular targets. We further discuss the obstacles to scaling SRM methods, such as lengthy image acquisition and complex SRM data analysis. For each of these challenges, we review the recent advances in the field that circumvent these pitfalls and provide practical advice to biologists for optimizing SRM experiments. Elsevier 2021-02-03 /pmc/articles/PMC7898072/ /pubmed/33665555 http://dx.doi.org/10.1016/j.isci.2021.102134 Text en © 2021 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review
Dankovich, Tal M.
Rizzoli, Silvio O.
Challenges facing quantitative large-scale optical super-resolution, and some simple solutions
title Challenges facing quantitative large-scale optical super-resolution, and some simple solutions
title_full Challenges facing quantitative large-scale optical super-resolution, and some simple solutions
title_fullStr Challenges facing quantitative large-scale optical super-resolution, and some simple solutions
title_full_unstemmed Challenges facing quantitative large-scale optical super-resolution, and some simple solutions
title_short Challenges facing quantitative large-scale optical super-resolution, and some simple solutions
title_sort challenges facing quantitative large-scale optical super-resolution, and some simple solutions
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898072/
https://www.ncbi.nlm.nih.gov/pubmed/33665555
http://dx.doi.org/10.1016/j.isci.2021.102134
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