cytoNet: Spatiotemporal network analysis of cell communities.

Autor: Mahadevan AS; Department of Bioengineering, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America.; Department of Bioengineering, Rice University, Houston, Texas, United States of America., Long BL; Department of Bioengineering, Rice University, Houston, Texas, United States of America.; Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America.; Department of Computer Science, University of Texas at San Antonio, San Antonio, Texas, United States of America., Hu CW; Department of Bioengineering, Rice University, Houston, Texas, United States of America., Ryan DT; Department of Bioengineering, Rice University, Houston, Texas, United States of America., Grandel NE; Systems, Synthetic and Physical Biology Program, Rice University, Houston, Texas, United States of America., Britton GL; Systems, Synthetic and Physical Biology Program, Rice University, Houston, Texas, United States of America., Bustos M; Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America., Gonzalez Porras MA; Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America., Stojkova K; Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America., Ligeralde A; Biophysics Graduate Program, University of California, Berkeley, California, United States of America., Son H; Department of Oral & Maxillofacial Surgery, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America., Shannonhouse J; Department of Oral & Maxillofacial Surgery, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America., Robinson JT; Department of Electrical and Computer Engineering, Rice University, Houston, Texas, United States of America., Warmflash A; Systems, Synthetic and Physical Biology Program, Rice University, Houston, Texas, United States of America.; Department of Biosciences, Rice University, Houston, Texas, United States of America., Brey EM; Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America.; UTSA-UT Health Joint Graduate Group in Biomedical Engineering, San Antonio, Texas, United States of America., Kim YS; Department of Oral & Maxillofacial Surgery, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America.; UTSA-UT Health Joint Graduate Group in Biomedical Engineering, San Antonio, Texas, United States of America.; Programs in Integrated Biomedical Sciences, Translational Sciences, Radiological Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America., Qutub AA; Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America.; UTSA-UT Health Joint Graduate Group in Biomedical Engineering, San Antonio, Texas, United States of America.; UTSA AI MATRIX Consortium, San Antonio, Texas, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2022 Jun 13; Vol. 18 (6), pp. e1009846. Date of Electronic Publication: 2022 Jun 13 (Print Publication: 2022).
DOI: 10.1371/journal.pcbi.1009846
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist. Author David T Ryan was unable to confirm their authorship contributions. On their behalf, the corresponding author has reported their contributions to the best of their knowledge.
Databáze: MEDLINE
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