dc.creatorUriu, Koichiro
dc.creatorMorelli, Luis Guillermo
dc.date.accessioned2018-06-19T19:20:03Z
dc.date.accessioned2018-11-06T14:35:49Z
dc.date.available2018-06-19T19:20:03Z
dc.date.available2018-11-06T14:35:49Z
dc.date.created2018-06-19T19:20:03Z
dc.date.issued2017-06
dc.identifierUriu, Koichiro; Morelli, Luis Guillermo; Determining the impact of cell mixing on signaling during development; Wiley Blackwell Publishing, Inc; Development Growth & Differentiation; 59; 5; 6-2017; 351-368
dc.identifier0012-1592
dc.identifierhttp://hdl.handle.net/11336/49417
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1888085
dc.description.abstractCell 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.
dc.languageeng
dc.publisherWiley Blackwell Publishing, Inc
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1111/dgd.12366
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectCELL MOVEMENT
dc.subjectCOUPLED OSCILLATORS
dc.subjectDELTA-NOTCH SIGNAL
dc.subjectMEAN SQUARED DISPLACEMENT
dc.subjectSYNCHRONIZATION
dc.titleDetermining the impact of cell mixing on signaling during development
dc.typeArtículos de revistas
dc.typeArtículos de revistas
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución