期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2021
卷号：118
期号：32
DOI：10.1073/pnas.2026123118
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
Cells are under constant pressure to integrate information from both their environment and internal cellular processes. However, these effects often use the same signaling pathways, making autonomous and coupled signaling difficult to decouple from one another. Here, we present a statistical modeling framework, the cellular point process (CPP), that decouples these two modes of signaling using videos of living, actively signaling cells as input. Our model reveals modulation of autonomous and coupled signaling parameters in a number of contexts ranging from pharmacological treatment to wound healing that were previously unavailable. The CPP enhances our understanding of cellular information processing and can be extended to a wide range of systems.
Multicellular organisms rely on spatial signaling among cells to drive their organization, development, and response to stimuli. Several models have been proposed to capture the behavior of spatial signaling in multicellular systems, but existing approaches fail to capture both the autonomous behavior of single cells and the interactions of a cell with its neighbors simultaneously. We propose a spatiotemporal model of dynamic cell signaling based on Hawkes processes—self-exciting point processes—that model the signaling processes within a cell and spatial couplings between cells. With this cellular point process (CPP), we capture both the single-cell pathway activation rate and the magnitude and duration of signaling between cells relative to their spatial location. Furthermore, our model captures tissues composed of heterogeneous cell types with different bursting rates and signaling behaviors across multiple signaling proteins. We apply our model to epithelial cell systems that exhibit a range of autonomous and spatial signaling behaviors basally and under pharmacological exposure. Our model identifies known drug-induced signaling deficits, characterizes signaling changes across a wound front, and generalizes to multichannel observations.
