Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.

Navigation of fast migrating cells such as amoeba Dictyostelium and immune cells are tightly associated with their morphologies that range from steady polarized forms that support high directionality to those more complex and variable when making frequent turns. Model simulations are essential for q...

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Autores principales:	Daisuke Imoto, Nen Saito, Akihiko Nakajima, Gen Honda, Motohiko Ishida, Toyoko Sugita, Sayaka Ishihara, Koko Katagiri, Chika Okimura, Yoshiaki Iwadate, Satoshi Sawai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/209d54d962e84374aebd4258c6fc93b1
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id	oai:doaj.org-article:209d54d962e84374aebd4258c6fc93b1
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spelling	oai:doaj.org-article:209d54d962e84374aebd4258c6fc93b12021-12-02T19:58:05ZComparative mapping of crawling-cell morphodynamics in deep learning-based feature space.1553-734X1553-735810.1371/journal.pcbi.1009237https://doaj.org/article/209d54d962e84374aebd4258c6fc93b12021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009237https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Navigation of fast migrating cells such as amoeba Dictyostelium and immune cells are tightly associated with their morphologies that range from steady polarized forms that support high directionality to those more complex and variable when making frequent turns. Model simulations are essential for quantitative understanding of these features and their origins, however systematic comparisons with real data are underdeveloped. Here, by employing deep-learning-based feature extraction combined with phase-field modeling framework, we show that a low dimensional feature space for 2D migrating cell morphologies obtained from the shape stereotype of keratocytes, Dictyostelium and neutrophils can be fully mapped by an interlinked signaling network of cell-polarization and protrusion dynamics. Our analysis links the data-driven shape analysis to the underlying causalities by identifying key parameters critical for migratory morphologies both normal and aberrant under genetic and pharmacological perturbations. The results underscore the importance of deciphering self-organizing states and their interplay when characterizing morphological phenotypes.Daisuke ImotoNen SaitoAkihiko NakajimaGen HondaMotohiko IshidaToyoko SugitaSayaka IshiharaKoko KatagiriChika OkimuraYoshiaki IwadateSatoshi SawaiPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009237 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Daisuke Imoto Nen Saito Akihiko Nakajima Gen Honda Motohiko Ishida Toyoko Sugita Sayaka Ishihara Koko Katagiri Chika Okimura Yoshiaki Iwadate Satoshi Sawai Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
description	Navigation of fast migrating cells such as amoeba Dictyostelium and immune cells are tightly associated with their morphologies that range from steady polarized forms that support high directionality to those more complex and variable when making frequent turns. Model simulations are essential for quantitative understanding of these features and their origins, however systematic comparisons with real data are underdeveloped. Here, by employing deep-learning-based feature extraction combined with phase-field modeling framework, we show that a low dimensional feature space for 2D migrating cell morphologies obtained from the shape stereotype of keratocytes, Dictyostelium and neutrophils can be fully mapped by an interlinked signaling network of cell-polarization and protrusion dynamics. Our analysis links the data-driven shape analysis to the underlying causalities by identifying key parameters critical for migratory morphologies both normal and aberrant under genetic and pharmacological perturbations. The results underscore the importance of deciphering self-organizing states and their interplay when characterizing morphological phenotypes.
format	article
author	Daisuke Imoto Nen Saito Akihiko Nakajima Gen Honda Motohiko Ishida Toyoko Sugita Sayaka Ishihara Koko Katagiri Chika Okimura Yoshiaki Iwadate Satoshi Sawai
author_facet	Daisuke Imoto Nen Saito Akihiko Nakajima Gen Honda Motohiko Ishida Toyoko Sugita Sayaka Ishihara Koko Katagiri Chika Okimura Yoshiaki Iwadate Satoshi Sawai
author_sort	Daisuke Imoto
title	Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
title_short	Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
title_full	Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
title_fullStr	Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
title_full_unstemmed	Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
title_sort	comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/209d54d962e84374aebd4258c6fc93b1
work_keys_str_mv	AT daisukeimoto comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT nensaito comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT akihikonakajima comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT genhonda comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT motohikoishida comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT toyokosugita comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT sayakaishihara comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT kokokatagiri comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT chikaokimura comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT yoshiakiiwadate comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace AT satoshisawai comparativemappingofcrawlingcellmorphodynamicsindeeplearningbasedfeaturespace
_version_	1718375767092494336

Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.

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