A novel method for studying intercellular networks: quantifying cellular cooperativity and inferring the extracellular signals driving it

Abstract

Abstract Objective: Develop an integrated experimental-computational approach to determine the functional role played by different cell subsets and extracellular communication signals in shaping the behavior of intercellular networks. Methods: We present a microwell-based experimental system, that allows isolating and culturing small groups (up to a few dozens) of cells over extended periods of time, while monitoring their interactions and states using fluorescence microscopy. In parallel, we present a computational framework that uses the data generated by this system for estimating the degree of intercellular cooperativity associated with observed behaviors of the interacting cells, and for inferring the contribution of measured extracellular signals in shaping these behaviors. Results: We used our approach to study the differentiation of mouse Th17 cells. We show that this process requires a relatively large number (> 10) of intermediate intercellular communication steps, in which information processing is carried out by a relatively small percentage (10–20%) of the interacting cells. We correctly predict a correlation between the time in which communication signals are expressed during differentiation, and the degree of intercellular cooperativity associated with their production. Furthermore, we show that we can accurately predict cell-level behaviors. In addition, we correctly identify previously-known effects of cytokines on cell survival, and further predict novel relations, which we validate experimentally. Conclusions: We present a new and general approach for the study of collective cellular behaviors, identifying the key cell subsets and extracellular communication signals driving them. This work was supported in part by National Human Genome Research Institute Centers of Excellence in Genomics Science P50 HG006193 (N.H.), by National Institutes of Health Grants P01 AI045757 and DP3 DK09768101 (J.C.L.) and by the U. S. Army Research Office through the Institute for Soldier Nanotechnologies, under contract number W911NF-13-D-0001 (J.C.L.). This work was also supported in part by the Koch Institute Support (core) Grant P30-CA14051 from the National Cancer Institute. J.C.L. is a Camille Dreyfus Teacher-Scholar.