A Stochastic Framework for Neuronal Morphological Comparison: Application to the Study of imp Knockdown Effects in Drosophila Gamma Neurons
In order to reach their final adult morphology, Gamma neurons in Drosophila brain undergo a process of pruning followed by regrowth of their main axons and branches called remodelling. The mRNA binding protein Imp was identified to play a fundamental role in this process. One of Imp targets, profilin mRNA, encodes for an actin regulator that has been shown to be involved in axon remodelling. In this paper we intend to further understand the role of Imp and the importance of profilin mRNA expression regulation during remodelling. To do so, we propose a stochastic framework to exhaustively compare the adult morphology between wild type (WT), imp knockdown (Imp) and imp knockdown rescued by Profilin (Prof Rescue) neurons. Our framework consists in (i) the selection of the main neuron morphological features, (ii) their stochastic modelling and parameter estimation from data and (iii) a maximum likelihood analysis for each individual neuron to quantitatively assess the similarity or difference between groups. Thanks to this framework we show that imp mutant neurons can be divided in two phenotypical groups with a different aberrancy degree, and that profilin overexpression partially rescues the main axon and branch development thereby it reduces the proportion of neurons with the strongest remodelling phenotype.
KeywordsGamma neurons Remodelling Stochastic models Maximum likelihood analysis
This work was supported by the French Government (National Research Agency, ANR) through the « Investments for the Future » LABEX SIGNALIFE: program reference # ANR-11-LABX-0028-01.
All the authors are within Morpheme (a joint team between Inria CRI-SAM, I3S and IBV).
- 7.Verheyen, E.M., Cooley, L.: Profilin mutations disrupt multiple actin-dependent processes during Drosophila development. Development 120(4), 717–728 (1994)Google Scholar
- 14.Myatt, D.R., Hadlington, T., Ascoli, G.A., Nasuto, S.J.: Neuromantic–from semi-manual to semi-automatic reconstruction of neuron morphology. Front. Neuroinform. 6, 4 (2012)Google Scholar
- 15.Mottini, A., Descombes, X., Besse, F., Pechersky, E.: Discrete stochastic model for the generation of axonal trees. In: EMBS, pp. 6814–6817 (2014)Google Scholar
- 17.Keller, M.T., Trotter, W.T.: Applied Combinatorics. Georgia, Atlanta (2015)Google Scholar
- 18.Szebenyi, G., Callaway, J.L., Dent, E.W., Kalil, K.: Interstitial branches develop from active regions of the axon demarcated by the primary growth cone during pausing behaviours. J. Neurosci. 18(19), 7930–7940 (1998)Google Scholar
- 21.Mottini, A., Descombes, X., Besse, F.: Tree-like shapes distance using the elastic shape analysis framework. In: British Machine Vision Conference (2013)Google Scholar