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

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.

A balance between actin and Eps8/IRSp53 utilization in branched versus linear actin networks determines tunneling nanotube formation

“…For fixed conditions (Fig. 1g), filopodia were quantified using the automated analysis pipeline developed by Bagonis et al ( 85 ). Maximum intensity projection images of filopodia (both surface- and non-attached) were created and the cell bodies were manually segmented for filopodia detection using phalloidin staining.…”

Abl signaling directs growth of a pioneer axon inDrosophilaby shaping the intrinsic fluctuations of actin

“…In recent years, it has come to be appreciated that cytoskeletal dynamics in general, and motility in particular, are in their essence stochastic processes with a high level of variability, most notably for single-cell processes, such as the advance of single growth cones (Kulkarni et al, 2013;Padmanabhan and Goodhill, 2018;Bagonis et al, 2019). It has been shown that fluctuation, variation, noise, and the averaging of information in time and space are of the essence of single-cell morphogenetic mechanisms, as they allow efficient and reliable extraction of information even in the context of the extremely low signal-to-noise that is often found, for example, in gradients of biological signals in the complex milieu of a developing animal (Cohen et al, 2010;He et al, 2010).…”

Emerging machine learning approaches to phenotyping cellular motility and morphodynamics

“…The neuronal growth cone is an essential structure for guiding axons to their targets during neural development. It exhibits complex and rapidly changing morphology, and the morphology of neuronal growth cones is highly correlated with neurite outgrowth [ 71 ]. The significance of morphodynamic phenotypes is not limited to cell motility.…”

“…Filopodia are finger-like protrusive structures containing thin actin bundles that play a role as sensory organelles for cell migration. Several computational platforms for the analysis of filopodial dynamics have been developed, including cellGeo [ 86 ], filopodyan [ 87 ], filoQuant [ 88 ], and GCA [ 71 ]. In neuronal growth cones, there are multiple filopodial phenotypes whereby Ena and VASP play differential roles in associated filopodial dynamics [ 87 ].…”