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J Cell Biol. 2018 Dec 6. pii: jcb.201711023. doi: 10.1083/jcb.201711023. [Epub ahead of print]

Automated profiling of growth cone heterogeneity defines relations between morphology and motility.

Author information

1
Departments of Bioinformatics and Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX.
2
Department of Cell Biology, Harvard Medical School, Boston, MA.
3
Department of Biomedicine, University of Basel, Basel, Switzerland.
4
Institute of Cell Biology, University of Bern, Bern, Switzerland.
5
Departments of Bioinformatics and Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX gaudenz.danuser@utsouthwestern.edu.

Abstract

Growth cones are complex, motile structures at the tip of an outgrowing neurite. They often exhibit a high density of filopodia (thin actin bundles), which complicates the unbiased quantification of their morphologies by software. Contemporary image processing methods require extensive tuning of segmentation parameters, require significant manual curation, and are often not sufficiently adaptable to capture morphology changes associated with switches in regulatory signals. To overcome these limitations, we developed Growth Cone Analyzer (GCA). GCA is designed to quantify growth cone morphodynamics from time-lapse sequences imaged both in vitro and in vivo, but is sufficiently generic that it may be applied to nonneuronal cellular structures. We demonstrate the adaptability of GCA through the analysis of growth cone morphological variation and its relation to motility in both an unperturbed system and in the context of modified Rho GTPase signaling. We find that perturbations inducing similar changes in neurite length exhibit underappreciated phenotypic nuance at the scale of the growth cone.

PMID:
30523041
DOI:
10.1083/jcb.201711023

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