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Preprint (Author's original)CC BY-NC-SA V4.0, Open
Preprint
Automatic detection of spatio-temporal signaling patterns in cell collectives
bioArkiv.org, 2022.07.12.499734
12 Jul 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Increasing experimental evidence points to the physiological importance of space-time correlations in signaling of cell collectives. From wound healing to epithelial homeostasis to morphogenesis, coordinated activation of biomolecules between cells allows the collectives to perform more complex tasks and to better tackle environmental challenges. To capture this information exchange and to advance new theories of emergent phenomena, we created ARCOS, a computational method to detect and quantify collective signaling. We demonstrate ARCOS on cell and organism collectives with space-time correlations on different scales in 2D and 3D. We made a new observation that oncogenic mutations in the MAPK/ERK and PIK3CA/Akt pathways of MCF10A epithelial cells hyperstimulate intercellular ERK activity waves that are largely dependent on matrix metalloproteinase intercellular signaling. ARCOS is open-source and available as R and Python packages. It also includes a plugin for the napari image viewer to interactively quantify collective phenomena without prior programming experience.
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Details
- Title
- Automatic detection of spatio-temporal signaling patterns in cell collectives
- Creators
- Paolo Armando Gagliardi - University of BernBenjamin Grädel - University of BernMarc-Antoine Jacques - University of BernLucien Hinderling - University of BernPascal Ender - University of BernAndrew R Cohen - Drexel University, Electrical and Computer EngineeringGerald Kastberger - University of GrazOlivier Pertz - University of BernMaciej Dobrzyński - University of Bern
- Publication Details
- bioArkiv.org, 2022.07.12.499734
- Publisher
- American Physical Society (APS)
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001069543600001
- Other Identifier
- 991021893410604721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Cell Biology