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Stochastic Micro-Pattern for Automated Correlative Fluorescence - Scanning Electron Microscopy
Studies of cellular surface features gain from correlative approaches, where live cell information acquired by fluorescence light microscopy is complemented by ultrastructural information from scanning electron micrographs. Current approaches to spatially align fluorescence images with scanning elec...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4673610/ https://www.ncbi.nlm.nih.gov/pubmed/26647824 http://dx.doi.org/10.1038/srep17973 |
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author | Begemann, Isabell Viplav, Abhiyan Rasch, Christiane Galic, Milos |
author_facet | Begemann, Isabell Viplav, Abhiyan Rasch, Christiane Galic, Milos |
author_sort | Begemann, Isabell |
collection | PubMed |
description | Studies of cellular surface features gain from correlative approaches, where live cell information acquired by fluorescence light microscopy is complemented by ultrastructural information from scanning electron micrographs. Current approaches to spatially align fluorescence images with scanning electron micrographs are technically challenging and often cost or time-intensive. Relying exclusively on open-source software and equipment available in a standard lab, we have developed a method for rapid, software-assisted alignment of fluorescence images with the corresponding scanning electron micrographs via a stochastic gold micro-pattern. Here, we provide detailed instructions for micro-pattern production and image processing, troubleshooting for critical intermediate steps, and examples of membrane ultra-structures aligned with the fluorescence signal of proteins enriched at such sites. Together, the presented method for correlative fluorescence – scanning electron microscopy is versatile, robust and easily integrated into existing workflows, permitting image alignment with accuracy comparable to existing approaches with negligible investment of time or capital. |
format | Online Article Text |
id | pubmed-4673610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-46736102015-12-14 Stochastic Micro-Pattern for Automated Correlative Fluorescence - Scanning Electron Microscopy Begemann, Isabell Viplav, Abhiyan Rasch, Christiane Galic, Milos Sci Rep Article Studies of cellular surface features gain from correlative approaches, where live cell information acquired by fluorescence light microscopy is complemented by ultrastructural information from scanning electron micrographs. Current approaches to spatially align fluorescence images with scanning electron micrographs are technically challenging and often cost or time-intensive. Relying exclusively on open-source software and equipment available in a standard lab, we have developed a method for rapid, software-assisted alignment of fluorescence images with the corresponding scanning electron micrographs via a stochastic gold micro-pattern. Here, we provide detailed instructions for micro-pattern production and image processing, troubleshooting for critical intermediate steps, and examples of membrane ultra-structures aligned with the fluorescence signal of proteins enriched at such sites. Together, the presented method for correlative fluorescence – scanning electron microscopy is versatile, robust and easily integrated into existing workflows, permitting image alignment with accuracy comparable to existing approaches with negligible investment of time or capital. Nature Publishing Group 2015-12-09 /pmc/articles/PMC4673610/ /pubmed/26647824 http://dx.doi.org/10.1038/srep17973 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Begemann, Isabell Viplav, Abhiyan Rasch, Christiane Galic, Milos Stochastic Micro-Pattern for Automated Correlative Fluorescence - Scanning Electron Microscopy |
title | Stochastic Micro-Pattern for Automated Correlative Fluorescence - Scanning Electron Microscopy |
title_full | Stochastic Micro-Pattern for Automated Correlative Fluorescence - Scanning Electron Microscopy |
title_fullStr | Stochastic Micro-Pattern for Automated Correlative Fluorescence - Scanning Electron Microscopy |
title_full_unstemmed | Stochastic Micro-Pattern for Automated Correlative Fluorescence - Scanning Electron Microscopy |
title_short | Stochastic Micro-Pattern for Automated Correlative Fluorescence - Scanning Electron Microscopy |
title_sort | stochastic micro-pattern for automated correlative fluorescence - scanning electron microscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4673610/ https://www.ncbi.nlm.nih.gov/pubmed/26647824 http://dx.doi.org/10.1038/srep17973 |
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