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Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own exp...

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Detalles Bibliográficos
Autores principales: Poon, Candice C., Ebacher, Vincent, Liu, Katherine, Yong, Voon Wee, Kelly, John James Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MyJove Corporation 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6101103/
https://www.ncbi.nlm.nih.gov/pubmed/29781988
http://dx.doi.org/10.3791/57440
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author Poon, Candice C.
Ebacher, Vincent
Liu, Katherine
Yong, Voon Wee
Kelly, John James Patrick
author_facet Poon, Candice C.
Ebacher, Vincent
Liu, Katherine
Yong, Voon Wee
Kelly, John James Patrick
author_sort Poon, Candice C.
collection PubMed
description Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis.
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spelling pubmed-61011032018-09-06 Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System Poon, Candice C. Ebacher, Vincent Liu, Katherine Yong, Voon Wee Kelly, John James Patrick J Vis Exp Bioengineering Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis. MyJove Corporation 2018-05-03 /pmc/articles/PMC6101103/ /pubmed/29781988 http://dx.doi.org/10.3791/57440 Text en Copyright © 2018, Journal of Visualized Experiments http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visithttp://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Bioengineering
Poon, Candice C.
Ebacher, Vincent
Liu, Katherine
Yong, Voon Wee
Kelly, John James Patrick
Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
title Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
title_full Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
title_fullStr Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
title_full_unstemmed Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
title_short Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
title_sort automated slide scanning and segmentation in fluorescently-labeled tissues using a widefield high-content analysis system
topic Bioengineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6101103/
https://www.ncbi.nlm.nih.gov/pubmed/29781988
http://dx.doi.org/10.3791/57440
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