Author
Listed:
- Daisuke Imoto
- Nen Saito
- Akihiko Nakajima
- Gen Honda
- Motohiko Ishida
- Toyoko Sugita
- Sayaka Ishihara
- Koko Katagiri
- Chika Okimura
- Yoshiaki Iwadate
- Satoshi Sawai
Abstract
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.Author summary: Migratory cells that move by crawling do so by extending and retracting their plasma membrane. When and where these events take place determine the cell shape, and this is directly linked to the movement patterns. Understanding how the highly plastic and interconvertible morphologies appear from their underlying dynamics remains a challenge partly because their inherent complexity makes quantitatively comparison against the outputs of mathematical models difficult. To this end, we employed machine-learning based classification to extract features that characterize the basic migrating morphologies. The obtained features were then used to compare real cell data with outputs of a conceptual model that we introduced which describes coupling via feedback between local protrusive dynamics and polarity. The feature mapping showed that the model successfully recapitulates the shape dynamics that were not covered by previous related models and also hints at the critical parameters underlying state transitions. The ability of the present approach to compare model outputs with real cell data systematically and objectively is important as it allows outputs of future mathematical models to be quantitatively tested in an accessible and common reference frame.
Suggested Citation
Daisuke Imoto & Nen Saito & Akihiko Nakajima & Gen Honda & Motohiko Ishida & Toyoko Sugita & Sayaka Ishihara & Koko Katagiri & Chika Okimura & Yoshiaki Iwadate & Satoshi Sawai, 2021.
"Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space,"
PLOS Computational Biology, Public Library of Science, vol. 17(8), pages 1-30, August.
Handle:
RePEc:plo:pcbi00:1009237
DOI: 10.1371/journal.pcbi.1009237
Download full text from publisher
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.
- Rajib Chakravorty & David Rawlinson & Alan Zhang & John Markham & Mark R Dowling & Cameron Wellard & Jie H S Zhou & Philip D Hodgkin, 2014.
"Labour-Efficient In Vitro Lymphocyte Population Tracking and Fate Prediction Using Automation and Manual Review,"
PLOS ONE, Public Library of Science, vol. 9(1), pages 1-19, January.
- Edward J Banigan & Tajie H Harris & David A Christian & Christopher A Hunter & Andrea J Liu, 2015.
"Heterogeneous CD8+ T Cell Migration in the Lymph Node in the Absence of Inflammation Revealed by Quantitative Migration Analysis,"
PLOS Computational Biology, Public Library of Science, vol. 11(2), pages 1-20, February.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1009237. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.