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Assessment of protein inclusions in cultured cells using automated image analysis

Proteinaceous inclusions are associated with neurodegenerative diseases and cell models are often used to determine genetic and chemical modifiers of their formation. This protocol involves the usage of automated microscopy and machine learning-based image analysis to accurately quantify the levels...

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Detalles Bibliográficos
Autores principales: McAlary, Luke, Shephard, Victoria K., Sher, Mine, Rice, Lauren J., Yerbury, Justin J., Cashman, Neil R., Plotkin, Steven S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535320/
https://www.ncbi.nlm.nih.gov/pubmed/36201320
http://dx.doi.org/10.1016/j.xpro.2022.101748
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author McAlary, Luke
Shephard, Victoria K.
Sher, Mine
Rice, Lauren J.
Yerbury, Justin J.
Cashman, Neil R.
Plotkin, Steven S.
author_facet McAlary, Luke
Shephard, Victoria K.
Sher, Mine
Rice, Lauren J.
Yerbury, Justin J.
Cashman, Neil R.
Plotkin, Steven S.
author_sort McAlary, Luke
collection PubMed
description Proteinaceous inclusions are associated with neurodegenerative diseases and cell models are often used to determine genetic and chemical modifiers of their formation. This protocol involves the usage of automated microscopy and machine learning-based image analysis to accurately quantify the levels of protein inclusion formation in cultured cells from fluorescence microscopy images. This protocol is highly scalable and can be applied to a few images or large datasets. For complete details on the use and execution of this protocol, please refer to McAlary et al. (2022).
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spelling pubmed-95353202022-10-07 Assessment of protein inclusions in cultured cells using automated image analysis McAlary, Luke Shephard, Victoria K. Sher, Mine Rice, Lauren J. Yerbury, Justin J. Cashman, Neil R. Plotkin, Steven S. STAR Protoc Protocol Proteinaceous inclusions are associated with neurodegenerative diseases and cell models are often used to determine genetic and chemical modifiers of their formation. This protocol involves the usage of automated microscopy and machine learning-based image analysis to accurately quantify the levels of protein inclusion formation in cultured cells from fluorescence microscopy images. This protocol is highly scalable and can be applied to a few images or large datasets. For complete details on the use and execution of this protocol, please refer to McAlary et al. (2022). Elsevier 2022-10-04 /pmc/articles/PMC9535320/ /pubmed/36201320 http://dx.doi.org/10.1016/j.xpro.2022.101748 Text en © 2022 The Authors. https://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 Protocol
McAlary, Luke
Shephard, Victoria K.
Sher, Mine
Rice, Lauren J.
Yerbury, Justin J.
Cashman, Neil R.
Plotkin, Steven S.
Assessment of protein inclusions in cultured cells using automated image analysis
title Assessment of protein inclusions in cultured cells using automated image analysis
title_full Assessment of protein inclusions in cultured cells using automated image analysis
title_fullStr Assessment of protein inclusions in cultured cells using automated image analysis
title_full_unstemmed Assessment of protein inclusions in cultured cells using automated image analysis
title_short Assessment of protein inclusions in cultured cells using automated image analysis
title_sort assessment of protein inclusions in cultured cells using automated image analysis
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535320/
https://www.ncbi.nlm.nih.gov/pubmed/36201320
http://dx.doi.org/10.1016/j.xpro.2022.101748
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