ArticleThe Journal of cell biology2019
Automated profiling of growth cone heterogeneity defines relations between morphology and motility.
Article in The Journal of cell biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Tunnelling nanotube formation is driven by Eps8/IRSp53-dependent linear actin polymerization.The EMBO journal · 2023Article
- A new view of axon growth and guidance grounded in the stochastic dynamics of actin networks.Open biology · 2023Article
- Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.Physical biology · 2021Review
- Data science in cell imaging.Journal of cell science · 2021Review
- Abl signaling directs growth of a pioneer axon inMolecular biology of the cell · 2020Article
- Molecular basis of the functions of the mammalian neuronal growth cone revealed using new methods.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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