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  4. Computer vision profiling of neurite outgrowth dynamics reveals spatio-temporal modularity of Rho GTPase signaling
 
research article

Computer vision profiling of neurite outgrowth dynamics reveals spatio-temporal modularity of Rho GTPase signaling

Pertz, O.
•
Fusco, L.
•
Lefort, Riwal
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2016
Journal of Cell Biology (JCB)

Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neurite initiation, elongation, retraction, and branching cycles that are likely to be regulated by specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present Neu - riteTracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifies dynamic neurite outgrowth phenotypes. We identify a set of stereotypic neurite outgrowth morphodynamic behaviors in a cultured neuronal cell system. Systematic RNA interference perturba - tion of a Rho GTPase interactome consisting of 219 proteins reveals a limited set of morphodynamic phenotypes. As proof of concept, we show that loss of function of two distinct RhoA-specific GTPase-activating proteins (GAPs) leads to opposite neurite outgrowth phenotypes. Imaging of RhoA activation dynamics indicates that both GAPs regulate differ - ent spatiotemporal Rho GTPase pools, with distinct functions. Our results provide a starting point to dissect spatiotempo - ral Rho GTPase signaling networks that regulate neurite outgrowth.

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Type
research article
DOI
10.1083/jcb.201506018
Web of Science ID

WOS:000370486100010

Author(s)
Pertz, O.
Fusco, L.
Lefort, Riwal
Smith, Kevin C.
Benmansour, F.
Gonzalez, German  
Barilari, Caterina
Rinn, Bernd
Fleuret, Francois  
Fua, Pascal  
Date Issued

2016

Publisher

Rockefeller University Press

Published in
Journal of Cell Biology (JCB)
Volume

212

Issue

1

Start page

91

End page

111

Editorial or Peer reviewed

NON-REVIEWED

Written at

EPFL

EPFL units
CVLAB  
Available on Infoscience
December 19, 2015
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/121844
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