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AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner

Protein quality control is maintained by a number of integrated cellular pathways that monitor the folding and functionality of the cellular proteome. Defects in these pathways lead to the accumulation of misfolded or faulty proteins that may become insoluble and aggregate over time. Protein aggrega...

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Autores principales: Klickstein, Jacob Aaron, Mukkavalli, Sirisha, Raman, Malavika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Biochemistry and Molecular Biology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762942/
https://www.ncbi.nlm.nih.gov/pubmed/33454006
http://dx.doi.org/10.1074/jbc.RA120.015398
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author Klickstein, Jacob Aaron
Mukkavalli, Sirisha
Raman, Malavika
author_facet Klickstein, Jacob Aaron
Mukkavalli, Sirisha
Raman, Malavika
author_sort Klickstein, Jacob Aaron
collection PubMed
description Protein quality control is maintained by a number of integrated cellular pathways that monitor the folding and functionality of the cellular proteome. Defects in these pathways lead to the accumulation of misfolded or faulty proteins that may become insoluble and aggregate over time. Protein aggregates significantly contribute to the development of a number of human diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Alzheimer's disease. In vitro, imaging-based, cellular studies have defined key biomolecular components that recognize and clear aggregates; however, no unifying method is available to quantify cellular aggregates, limiting our ability to reproducibly and accurately quantify these structures. Here we describe an ImageJ macro called AggreCount to identify and measure protein aggregates in cells. AggreCount is designed to be intuitive, easy to use, and customizable for different types of aggregates observed in cells. Minimal experience in coding is required to utilize the script. Based on a user-defined image, AggreCount will report a number of metrics: (i) total number of cellular aggregates, (ii) percentage of cells with aggregates, (iii) aggregates per cell, (iv) area of aggregates, and (v) localization of aggregates (cytosol, perinuclear, or nuclear). A data table of aggregate information on a per cell basis, as well as a summary table, is provided for further data analysis. We demonstrate the versatility of AggreCount by analyzing a number of different cellular aggregates including aggresomes, stress granules, and inclusion bodies caused by huntingtin polyglutamine expansion.
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spelling pubmed-77629422021-01-07 AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner Klickstein, Jacob Aaron Mukkavalli, Sirisha Raman, Malavika J Biol Chem Cell Biology Protein quality control is maintained by a number of integrated cellular pathways that monitor the folding and functionality of the cellular proteome. Defects in these pathways lead to the accumulation of misfolded or faulty proteins that may become insoluble and aggregate over time. Protein aggregates significantly contribute to the development of a number of human diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Alzheimer's disease. In vitro, imaging-based, cellular studies have defined key biomolecular components that recognize and clear aggregates; however, no unifying method is available to quantify cellular aggregates, limiting our ability to reproducibly and accurately quantify these structures. Here we describe an ImageJ macro called AggreCount to identify and measure protein aggregates in cells. AggreCount is designed to be intuitive, easy to use, and customizable for different types of aggregates observed in cells. Minimal experience in coding is required to utilize the script. Based on a user-defined image, AggreCount will report a number of metrics: (i) total number of cellular aggregates, (ii) percentage of cells with aggregates, (iii) aggregates per cell, (iv) area of aggregates, and (v) localization of aggregates (cytosol, perinuclear, or nuclear). A data table of aggregate information on a per cell basis, as well as a summary table, is provided for further data analysis. We demonstrate the versatility of AggreCount by analyzing a number of different cellular aggregates including aggresomes, stress granules, and inclusion bodies caused by huntingtin polyglutamine expansion. American Society for Biochemistry and Molecular Biology 2020-12-18 2020-10-20 /pmc/articles/PMC7762942/ /pubmed/33454006 http://dx.doi.org/10.1074/jbc.RA120.015398 Text en © 2020 Klickstein et al. Author's Choice—Final version open access under the terms of the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0) .
spellingShingle Cell Biology
Klickstein, Jacob Aaron
Mukkavalli, Sirisha
Raman, Malavika
AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner
title AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner
title_full AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner
title_fullStr AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner
title_full_unstemmed AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner
title_short AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner
title_sort aggrecount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner
topic Cell Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762942/
https://www.ncbi.nlm.nih.gov/pubmed/33454006
http://dx.doi.org/10.1074/jbc.RA120.015398
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