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Determining the impact of cell mixing on signaling during development
Author(s) -
Uriu Koichiro,
Morelli Luis G.
Publication year - 2017
Publication title -
development, growth and differentiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.864
H-Index - 66
eISSN - 1440-169X
pISSN - 0012-1592
DOI - 10.1111/dgd.12366
Subject(s) - mixing (physics) , biology , cell signaling , embryonic stem cell , biological system , cell , intracellular , microbiology and biotechnology , signal transduction , neuroscience , physics , genetics , gene , quantum mechanics
Cell movement and intercellular signaling occur simultaneously to organize morphogenesis during embryonic development. Cell movement can cause relative positional changes between neighboring cells. When intercellular signals are local such cell mixing may affect signaling, changing the flow of information in developing tissues. Little is known about the effect of cell mixing on intercellular signaling in collective cellular behaviors and methods to quantify its impact are lacking. Here we discuss how to determine the impact of cell mixing on cell signaling drawing an example from vertebrate embryogenesis: the segmentation clock, a collective rhythm of interacting genetic oscillators. We argue that comparing cell mixing and signaling timescales is key to determining the influence of mixing. A signaling timescale can be estimated by combining theoretical models with cell signaling perturbation experiments. A mixing timescale can be obtained by analysis of cell trajectories from live imaging. After comparing cell movement analyses in different experimental settings, we highlight challenges in quantifying cell mixing from embryonic timelapse experiments, especially a reference frame problem due to embryonic motions and shape changes. We propose statistical observables characterizing cell mixing that do not depend on the choice of reference frames. Finally, we consider situations in which both cell mixing and signaling involve multiple timescales, precluding a direct comparison between single characteristic timescales. In such situations, physical models based on observables of cell mixing and signaling can simulate the flow of information in tissues and reveal the impact of observed cell mixing on signaling.

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