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Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images
When studying physical cellular response observed by light microscopy, variations in cell behavior are difficult to quantitatively measure and are often only discussed on a subjective level. Hence, cell properties are described qualitatively based on a researcher’s impressions. In this study, we aim...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546915/ https://www.ncbi.nlm.nih.gov/pubmed/36207347 http://dx.doi.org/10.1038/s41598-022-20598-6 |
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author | Baar, Stefan Kuragano, Masahiro Tokuraku, Kiyotaka Watanabe, Shinya |
author_facet | Baar, Stefan Kuragano, Masahiro Tokuraku, Kiyotaka Watanabe, Shinya |
author_sort | Baar, Stefan |
collection | PubMed |
description | When studying physical cellular response observed by light microscopy, variations in cell behavior are difficult to quantitatively measure and are often only discussed on a subjective level. Hence, cell properties are described qualitatively based on a researcher’s impressions. In this study, we aim to define a comprehensive approach to estimate the physical cell activity based on migration and morphology based on statistical analysis of a cell population within a predefined field of view and timespan. We present quantitative measurements of the influence of drugs such as cytochalasin D and taxol on human neuroblastoma, SH-SY5Y cell populations. Both chemicals are well known to interact with the cytoskeleton and affect the cell morphology and motility. Being able to compute the physical properties of each cell for a given observation time, requires precise localization of each cell even when in an adhesive state, where cells are not visually differentiable. Also, the risk of confusion through contaminants is desired to be minimized. In relation to the cell detection process, we have developed a customized encoder-decoder based deep learning cell detection and tracking procedure. Further, we discuss the accuracy of our approach to quantify cell activity and its viability in regard to the cell detection accuracy. |
format | Online Article Text |
id | pubmed-9546915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95469152022-10-09 Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images Baar, Stefan Kuragano, Masahiro Tokuraku, Kiyotaka Watanabe, Shinya Sci Rep Article When studying physical cellular response observed by light microscopy, variations in cell behavior are difficult to quantitatively measure and are often only discussed on a subjective level. Hence, cell properties are described qualitatively based on a researcher’s impressions. In this study, we aim to define a comprehensive approach to estimate the physical cell activity based on migration and morphology based on statistical analysis of a cell population within a predefined field of view and timespan. We present quantitative measurements of the influence of drugs such as cytochalasin D and taxol on human neuroblastoma, SH-SY5Y cell populations. Both chemicals are well known to interact with the cytoskeleton and affect the cell morphology and motility. Being able to compute the physical properties of each cell for a given observation time, requires precise localization of each cell even when in an adhesive state, where cells are not visually differentiable. Also, the risk of confusion through contaminants is desired to be minimized. In relation to the cell detection process, we have developed a customized encoder-decoder based deep learning cell detection and tracking procedure. Further, we discuss the accuracy of our approach to quantify cell activity and its viability in regard to the cell detection accuracy. Nature Publishing Group UK 2022-10-07 /pmc/articles/PMC9546915/ /pubmed/36207347 http://dx.doi.org/10.1038/s41598-022-20598-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Baar, Stefan Kuragano, Masahiro Tokuraku, Kiyotaka Watanabe, Shinya Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images |
title | Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images |
title_full | Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images |
title_fullStr | Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images |
title_full_unstemmed | Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images |
title_short | Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images |
title_sort | towards a comprehensive approach for characterizing cell activity in bright-field microscopic images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546915/ https://www.ncbi.nlm.nih.gov/pubmed/36207347 http://dx.doi.org/10.1038/s41598-022-20598-6 |
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