A Stochastic Framework for Neuronal Morphological Comparison: Application to the Study of imp Knockdown Effects in Drosophila Gamma Neurons

Agustina Razetti 1 Xavier Descombes 1, 2 Caroline Medioni 1, 3 Florence Besse 1, 3
1 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : 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.
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https://hal.inria.fr/hal-01502735
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Agustina Razetti, Xavier Descombes, Caroline Medioni, Florence Besse. A Stochastic Framework for Neuronal Morphological Comparison: Application to the Study of imp Knockdown Effects in Drosophila Gamma Neurons. Ana Fred; Hugo Gamboa. Biomedical Engineering Systems and Technologies, 690, Springer, pp.145-166, 2017, CCIS, ⟨10.1007/978-3-319-54717-6_9⟩. ⟨hal-01502735⟩

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