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cytoNet: Spatiotemporal Network Analysis of Cell Communities

View ORCID ProfileArun S. Mahadevan, Byron L. Long, Chenyue W. Hu, David T. Ryan, Nicolas E. Grandel, Zacharie Maloney, George L. Britton, Maria A. Gonzalez Porras, Katerina Stojkova, Andrew Ligeralde, Hyeonwi Son, John Shannonhouse, Jacob T. Robinson, Aryeh Warmflash, Eric Brey, Yu Shin Kim, Amina A. Qutub
doi: https://doi.org/10.1101/180273
Arun S. Mahadevan
1Department of Bioengineering, University of Pennsylvania
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Byron L. Long
2Department of Biomedical Engineering, University of Texas, San Antonio
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Chenyue W. Hu
4Department of Bioengineering, Rice University
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David T. Ryan
4Department of Bioengineering, Rice University
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Nicolas E. Grandel
5Systems, Synthetic and Physical Biology Program, Rice University
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Zacharie Maloney
2Department of Biomedical Engineering, University of Texas, San Antonio
3UTSA – UT Health Joint Graduate Group in Biomedical Engineering
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George L. Britton
5Systems, Synthetic and Physical Biology Program, Rice University
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Maria A. Gonzalez Porras
2Department of Biomedical Engineering, University of Texas, San Antonio
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Katerina Stojkova
2Department of Biomedical Engineering, University of Texas, San Antonio
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Andrew Ligeralde
8Biophysics Graduate Program, University of California, Berkeley
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Hyeonwi Son
9Department of Oral & Maxillofacial Surgery, University of Texas Health at San Antonio
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John Shannonhouse
9Department of Oral & Maxillofacial Surgery, University of Texas Health at San Antonio
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Jacob T. Robinson
4Department of Bioengineering, Rice University
6Department of Electrical and Computer Engineering, Rice University
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Aryeh Warmflash
4Department of Bioengineering, Rice University
5Systems, Synthetic and Physical Biology Program, Rice University
7Department of Biosciences, Rice University
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Eric Brey
2Department of Biomedical Engineering, University of Texas, San Antonio
3UTSA – UT Health Joint Graduate Group in Biomedical Engineering
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Yu Shin Kim
3UTSA – UT Health Joint Graduate Group in Biomedical Engineering
9Department of Oral & Maxillofacial Surgery, University of Texas Health at San Antonio
10Programs in Integrated Biomedical Sciences, Translational Sciences, Radiological Sciences, University of Texas Health at San Antonio
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Amina A. Qutub
2Department of Biomedical Engineering, University of Texas, San Antonio
3UTSA – UT Health Joint Graduate Group in Biomedical Engineering
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  • For correspondence: amina.qutub@utsa.edu
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Abstract

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet’s capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

Availability and Implementation QutubLab.org/how | cytoNet contact: cytoNetProject{at}gmail.com Brain Initiative Alliance Toolmaker cytoNet site: https://www.braininitiative.org/toolmakers/resources/cytonet/

Author / Lay Summary cytoNet provides an online tool to rapidly characterize relationships between objects within images and video frames. To study complex tissue, cell and subcellular topologies, cytoNet integrates vision science with the mathematical technique of graph theory. This allows the method to simultaneously identify environmental effects on single cells and on network topology. cytoNet has versatile use across neuroscience, stem cell biology and regenerative medicine. cytoNet applications described in this study include: (1) characterizing how sensing pain alters neural circuit activity, (2) quantifying how vascular cells respond to neurotrophic stimuli overexpressed in the brain after injury or exercise, (3) delineating features of fat tissue that may confer resistance to obesity and (4) uncovering structure-function relationships of human stem cells as they transform into neurons.

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 February 11, 2021.
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cytoNet: Spatiotemporal Network Analysis of Cell Communities
Arun S. Mahadevan, Byron L. Long, Chenyue W. Hu, David T. Ryan, Nicolas E. Grandel, Zacharie Maloney, George L. Britton, Maria A. Gonzalez Porras, Katerina Stojkova, Andrew Ligeralde, Hyeonwi Son, John Shannonhouse, Jacob T. Robinson, Aryeh Warmflash, Eric Brey, Yu Shin Kim, Amina A. Qutub
bioRxiv 180273; doi: https://doi.org/10.1101/180273
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cytoNet: Spatiotemporal Network Analysis of Cell Communities
Arun S. Mahadevan, Byron L. Long, Chenyue W. Hu, David T. Ryan, Nicolas E. Grandel, Zacharie Maloney, George L. Britton, Maria A. Gonzalez Porras, Katerina Stojkova, Andrew Ligeralde, Hyeonwi Son, John Shannonhouse, Jacob T. Robinson, Aryeh Warmflash, Eric Brey, Yu Shin Kim, Amina A. Qutub
bioRxiv 180273; doi: https://doi.org/10.1101/180273

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