2016
DOI: 10.1083/jcb.201506018
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Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling

Abstract: NeuriteTracker is a computer vision approach used to analyze neuronal morphodynamics and to examine spatiotemporal Rho GTPase signaling networks regulating neurite outgrowth.

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Cited by 21 publications
(33 citation statements)
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“…However, this simplified view is not consistent with the finding that multiple GEFs, GAPs, and effectors outnumber their cognate Rho GTPases [58][59][60] , suggesting a more complex scenario in which multiple spatiotemporal Rho GTPase signaling networks (including GAP, GEFs, GTPases, and effectors) modulate different morphological processes. A recent observation that different RhoA-specific GAPs regulate two distinct RhoA signaling complexes lends support to this hypothesis: while ARHGAP5 is involved in the RhoA-mediated growth cone collapse, RhoA/ DLC1 might trigger mDia1 formin to polymerize F-actin and to enable filopodia extension 61 We have recently reported that a distinct serotonin receptor type, 5-HT 7 R, prompts morphological effects opposite to those reported here for the 5-HT 4 R: activation of the 5-HT 7 R facilitates Hippocampal neurons (DIV12) isolated from WT (a) or 5-HT 4 R-deficient mice (KO) (b) were treated with the 5-HT 4 R agonist BIMU8 and/or antagonist GR with or without pre-treatment with the specific ROCK inhibitor Y-27632 (50 µM), followed by the western blot with antibodies against phosphorylated (upper row, p-Cof), total cofilin (middle, Cof) and GAPDH as a loading control (bottom). (Left) Representative western blot showing cofilin phosphorylation in WT neurons (a) and in 5-HT 4 R KO neurons (b).…”
Section: Discussionmentioning
confidence: 93%
“…However, this simplified view is not consistent with the finding that multiple GEFs, GAPs, and effectors outnumber their cognate Rho GTPases [58][59][60] , suggesting a more complex scenario in which multiple spatiotemporal Rho GTPase signaling networks (including GAP, GEFs, GTPases, and effectors) modulate different morphological processes. A recent observation that different RhoA-specific GAPs regulate two distinct RhoA signaling complexes lends support to this hypothesis: while ARHGAP5 is involved in the RhoA-mediated growth cone collapse, RhoA/ DLC1 might trigger mDia1 formin to polymerize F-actin and to enable filopodia extension 61 We have recently reported that a distinct serotonin receptor type, 5-HT 7 R, prompts morphological effects opposite to those reported here for the 5-HT 4 R: activation of the 5-HT 7 R facilitates Hippocampal neurons (DIV12) isolated from WT (a) or 5-HT 4 R-deficient mice (KO) (b) were treated with the 5-HT 4 R agonist BIMU8 and/or antagonist GR with or without pre-treatment with the specific ROCK inhibitor Y-27632 (50 µM), followed by the western blot with antibodies against phosphorylated (upper row, p-Cof), total cofilin (middle, Cof) and GAPDH as a loading control (bottom). (Left) Representative western blot showing cofilin phosphorylation in WT neurons (a) and in 5-HT 4 R KO neurons (b).…”
Section: Discussionmentioning
confidence: 93%
“…Another example is the regulation of the exquisitely focused RhoA activity pattern at the tip of F-actin bundles that form neuronal growth cone filopodia ( Figure 1D ). A recent study identified the RhoA-specific GAP DLC1 (deleted in liver cancer 1) to spatially regulate the filopodial RhoA activity pattern 37 . RNA interference (RNAi)-mediated DLC1 knockdown leads to widening of the RhoA activity domain, suggesting that DLC1 acts by shaping the focused RhoA activity zone at filopodial tips.…”
Section: Gef/gap-mediated Rho Gtpase Fluxes Underlie Spatio-temporal mentioning
confidence: 99%
“…Almost all GEFs bear a lipid-interaction domain 37 , 39 , 40 : a pleckstrin homology (PH), a DOCK homology region 1 (DHR-1), or a Bin-Amphiphysin-Rvs (BAR) domain 41 43 . Many GAPs also contain a variety of lipid-binding domains 44 .…”
Section: Spatio-temporal Regulation Of Gefs and Gapsmentioning
confidence: 99%
“…Fluorescent biosensors are ideal tools to examine the activity of molecules at the subcellular level, in the context of real-time cellular behaviors. When combined with computational image analysis and statistical modeling, biosensors can provide quantitative insights into how molecules interact in space and time to generate specific biological behaviors ( Machacek et al, 2009 ; Fusco et al, 2016 ).…”
Section: Introductionmentioning
confidence: 99%