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Topological properties accurately predict cell division events and organization of Arabidopsis thaliana’s shoot apical meristem

View ORCID ProfileTimon W. Matz, Yang Wang, Ritika Kulshreshtha, Arun Sampathkumar, View ORCID ProfileZoran Nikoloski
doi: https://doi.org/10.1101/2021.10.05.463218
Timon W. Matz
1Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam,14476 Potsdam, Germany
2Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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  • ORCID record for Timon W. Matz
Yang Wang
3Plant Cell Biology and Microscopy, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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Ritika Kulshreshtha
3Plant Cell Biology and Microscopy, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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Arun Sampathkumar
3Plant Cell Biology and Microscopy, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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Zoran Nikoloski
1Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam,14476 Potsdam, Germany
2Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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  • For correspondence: nikoloski@mpimp-golm.mpg.de
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Abstract

Cell division and the resulting changes to the cell organization affect the shape and functionality of all tissues. Thus, understanding the determinants of the tissue-wide changes imposed by cell division is a key question in developmental biology. Here, we use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at a tissue level. We show that a classifier based on the SAM network properties is predictive of cell division events, with validation accuracy of 82%, on par with that based on cell size alone. Further, we demonstrate that the combination of topological and biological properties, including: cell size, perimeter, distance, and shared cell wall between cells, can further boost the prediction accuracy of resulting changes in topology triggered by cell division. Using our classifiers, we demonstrate the importance of microtubule mediated cell-to-cell growth coordination in influencing tissue-level topology. Altogether, the results from our network-based analysis demonstrates a feedback mechanism between tissue topology and cell division in A. thaliana’s SAMs.

Summary statement we use a network representation of live cell imaging data from SAMs in Arabidopsis thaliana to predict cell division events and their consequences at a tissue level.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 05, 2021.
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Topological properties accurately predict cell division events and organization of Arabidopsis thaliana’s shoot apical meristem
Timon W. Matz, Yang Wang, Ritika Kulshreshtha, Arun Sampathkumar, Zoran Nikoloski
bioRxiv 2021.10.05.463218; doi: https://doi.org/10.1101/2021.10.05.463218
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Topological properties accurately predict cell division events and organization of Arabidopsis thaliana’s shoot apical meristem
Timon W. Matz, Yang Wang, Ritika Kulshreshtha, Arun Sampathkumar, Zoran Nikoloski
bioRxiv 2021.10.05.463218; doi: https://doi.org/10.1101/2021.10.05.463218

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