Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

About samples, giving examples: Optimized Single Molecule Localization Microscopy

Angélique Jimenez, Karoline Friedl, View ORCID ProfileChristophe Leterrier
doi: https://doi.org/10.1101/568295
Angélique Jimenez
1Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Karoline Friedl
1Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France
2Abbelight, Paris, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christophe Leterrier
1Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Christophe Leterrier
  • For correspondence: christophe.leterrier@univ-amu.fr
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Super-resolution microscopy has profoundly transformed how we study the architecture of cells, revealing unknown structures and refining our view of cellular assemblies. Among the various techniques, the resolution of Single Molecule Localization Microscopy can reach the size of macromolecular complexes and offer key insights on their nanoscale arrangement in situ. SMLM is thus a demanding technique and taking advantage of its full potential requires specifically optimized procedures. Here we describe how we perform the successive steps of an SMLM workflow, focusing on single-color Stochastic Optical Reconstruction Microscopy (STORM) as well as multicolor DNA Points Accumulation for imaging in Nanoscale Topography (DNA-PAINT) of fixed samples. We provide detailed procedures for careful sample fixation and immunostaining of typical cellular structures: cytoskeleton, clathrin-coated pits, and organelles. We then offer guidelines for optimal imaging and processing of SMLM data in order to optimize reconstruction quality and avoid the generation of artifacts. We hope that the tips and tricks we discovered over the years and detail here will be useful for researchers looking to make the best possible SMLM images, a pre-requisite for meaningful biological discovery.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted March 05, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
About samples, giving examples: Optimized Single Molecule Localization Microscopy
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
About samples, giving examples: Optimized Single Molecule Localization Microscopy
Angélique Jimenez, Karoline Friedl, Christophe Leterrier
bioRxiv 568295; doi: https://doi.org/10.1101/568295
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
About samples, giving examples: Optimized Single Molecule Localization Microscopy
Angélique Jimenez, Karoline Friedl, Christophe Leterrier
bioRxiv 568295; doi: https://doi.org/10.1101/568295

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Cell Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4222)
  • Biochemistry (9095)
  • Bioengineering (6733)
  • Bioinformatics (23916)
  • Biophysics (12066)
  • Cancer Biology (9484)
  • Cell Biology (13720)
  • Clinical Trials (138)
  • Developmental Biology (7614)
  • Ecology (11644)
  • Epidemiology (2066)
  • Evolutionary Biology (15459)
  • Genetics (10610)
  • Genomics (14281)
  • Immunology (9447)
  • Microbiology (22749)
  • Molecular Biology (9056)
  • Neuroscience (48811)
  • Paleontology (354)
  • Pathology (1478)
  • Pharmacology and Toxicology (2558)
  • Physiology (3817)
  • Plant Biology (8300)
  • Scientific Communication and Education (1466)
  • Synthetic Biology (2285)
  • Systems Biology (6163)
  • Zoology (1295)