Automated quantification of neurite outgrowth orientation distributions on patterned surfaces

Authors

Matthew Payne, University of Western Sydney, CSIRO
Dadong Wang, CSIRO
Catriona M. Sinclair, St Vincent's Hospital Melbourne
Robert M. I Kapsa, University of WollongongFollow
Anita Quigley, University of WollongongFollow
Gordon G. Wallace, University of WollongongFollow
Joselito M. Razal, University of WollongongFollow
Ray Baughman, University of TexasFollow
Gerald Münch, University of Western SydneyFollow
Pascal Vallotton, CSIRO

RIS ID

91782

Publication Details

Payne, M., Wang, D., Sinclair, C. M., Kapsa, R. M. I., Quigley, A. F., Wallace, G. G., Razal, J. M., Baughman, R. H., Munch, G. & Vallotton, P. (2014). Automated quantification of neurite outgrowth orientation distributions on patterned surfaces. Journal of Neural Engineering, 11 (4), 1-13.

Abstract

Objective. We have developed an image analysis methodology for quantifying the anisotropy of neuronal projections on patterned substrates. Approach. Our method is based on the fitting of smoothing splines to the digital traces produced using a non-maximum suppression technique. This enables precise estimates of the local tangents uniformly along the neurite length, and leads to unbiased orientation distributions suitable for objectively assessing the anisotropy induced by tailored surfaces. Main results. In our application, we demonstrate that carbon nanotubes arrayed in parallel bundles over gold surfaces induce a considerable neurite anisotropy; a result which is relevant for regenerative medicine. Significance. Our pipeline is generally applicable to the study of fibrous materials on 2D surfaces and should also find applications in the study of DNA, microtubules, and other polymeric materials.