Journal article

Publication year: 2015

Pages: 5617-5624

Journal: Journal of Experimental Botany

Volume number: 66

Issue number: 18

ISSN: 0022-0957

Open access status: Green

DOI Link: https://doi.org/10.1093/jxb/erv235

Publisher: Oxford University Press

Abstract: 
The Sholl method, an old, established neurobiology technique for neural network analysis, is ideal for characterization and differentiation of interactive plant root responses to stress.Roots play an immediate role as the interface for water acquisition. To improve sustainability in low-water environments, breeders of major crops must therefore pay closer attention to advantageous root phenotypes; however, the complexity of root architecture in response to stress can be difficult to quantify. Here, the Sholl method, an established technique from neurobiology used for the characterization of neural network anatomy, was adapted to more adequately describe root responses to osmotic stress. This method was used to investigate the influence of in vitro osmotic stress on early root architecture and distribution in drought-resistant and -susceptible genotypes of winter oilseed rape. Interactive changes in root architecture can be easily captured by individual intersection profiles generated by Sholl analysis. Validation using manual measurements confirmed that the number of lateral roots decreased, while mean lateral root length was enhanced, under osmotic stress conditions. Both genotypes reacted to osmotic stress with a shift in their intersection patterns measured with Sholl analysis. Changes in interactive root architecture and distribution under stress were more pronounced in the drought-resistant genotype, indicating that these changes may contribute to drought resistance under mild osmotic stress conditions. The Sholl methodology is presented as a promising tool for selection of cultivars with advantageous root phenotypes under osmotic stress conditions.

Harvard Citation style: Hatzig, S., Schiessl, S., Stahl, A. and Snowdon, R. (2015) Characterizing root response phenotypes by neural network analysis, Journal of Experimental Botany, 66(18), pp. 5617-5624. https://doi.org/10.1093/jxb/erv235

APA Citation style: Hatzig, S., Schiessl, S., Stahl, A., & Snowdon, R. (2015). Characterizing root response phenotypes by neural network analysis. Journal of Experimental Botany. 66(18), 5617-5624. https://doi.org/10.1093/jxb/erv235