cytoNet: Spatiotemporal network analysis of cell communities.

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.

Control of synthetic microbial consortia in time, space, and composition.

While synthetic microbial systems are becoming increasingly complicated, single-strain systems cannot match the complexity of their multicellular counterparts. Such complexity, however, is much more difficult to control. Recent advances have increased our ability to control temporal, spatial, and community compositional organization, including modular adhesive systems, strain growth relationships, and asymmetric cell division. While these systems generally work independently, combining them into unified systems has proven difficult. Once such unification is proven successful we will unlock a new frontier of synthetic biology and open the door to the creation of synthetic biological systems with true multicellularity.