A machine learning approach for single cell interphase cell cycle staging

Abstract The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of...

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Autores principales:	Hemaxi Narotamo, Maria Sofia Fernandes, Ana Margarida Moreira, Soraia Melo, Raquel Seruca, Margarida Silveira, João Miguel Sanches
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ad30e0a2942d469a9a290f003248ca72
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spelling	oai:doaj.org-article:ad30e0a2942d469a9a290f003248ca722021-12-02T17:37:12ZA machine learning approach for single cell interphase cell cycle staging10.1038/s41598-021-98489-52045-2322https://doaj.org/article/ad30e0a2942d469a9a290f003248ca722021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98489-5https://doaj.org/toc/2045-2322Abstract The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited. It is therefore urgent to establish accurate strategies combining bioimaging with high-content image analysis for a bona fide classification. In this study we developed a supervised machine learning method for interphase cell cycle staging of individual adherent cells using in situ fluorescence images of nuclei stained with DAPI. A Support Vector Machine (SVM) classifier operated over normalized nuclear features using more than 3500 DAPI stained nuclei. Molecular ground truth labels were obtained by automatic image processing using fluorescent ubiquitination-based cell cycle indicator (Fucci) technology. An average F1-Score of 87.7% was achieved with this framework. Furthermore, the method was validated on distinct cell types reaching recall values higher than 89%. Our method is a robust approach to identify cells in G1 or S/G2 at the individual level, with implications in research and clinical applications.Hemaxi NarotamoMaria Sofia FernandesAna Margarida MoreiraSoraia MeloRaquel SerucaMargarida SilveiraJoão Miguel SanchesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hemaxi Narotamo Maria Sofia Fernandes Ana Margarida Moreira Soraia Melo Raquel Seruca Margarida Silveira João Miguel Sanches A machine learning approach for single cell interphase cell cycle staging
description	Abstract The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited. It is therefore urgent to establish accurate strategies combining bioimaging with high-content image analysis for a bona fide classification. In this study we developed a supervised machine learning method for interphase cell cycle staging of individual adherent cells using in situ fluorescence images of nuclei stained with DAPI. A Support Vector Machine (SVM) classifier operated over normalized nuclear features using more than 3500 DAPI stained nuclei. Molecular ground truth labels were obtained by automatic image processing using fluorescent ubiquitination-based cell cycle indicator (Fucci) technology. An average F1-Score of 87.7% was achieved with this framework. Furthermore, the method was validated on distinct cell types reaching recall values higher than 89%. Our method is a robust approach to identify cells in G1 or S/G2 at the individual level, with implications in research and clinical applications.
format	article
author	Hemaxi Narotamo Maria Sofia Fernandes Ana Margarida Moreira Soraia Melo Raquel Seruca Margarida Silveira João Miguel Sanches
author_facet	Hemaxi Narotamo Maria Sofia Fernandes Ana Margarida Moreira Soraia Melo Raquel Seruca Margarida Silveira João Miguel Sanches
author_sort	Hemaxi Narotamo
title	A machine learning approach for single cell interphase cell cycle staging
title_short	A machine learning approach for single cell interphase cell cycle staging
title_full	A machine learning approach for single cell interphase cell cycle staging
title_fullStr	A machine learning approach for single cell interphase cell cycle staging
title_full_unstemmed	A machine learning approach for single cell interphase cell cycle staging
title_sort	machine learning approach for single cell interphase cell cycle staging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ad30e0a2942d469a9a290f003248ca72
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A machine learning approach for single cell interphase cell cycle staging

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