A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila.

Autor: Guangda Liu, Tanmay Nath, Gerit A Linneweber, Annelies Claeys, Zhengyu Guo, Jin Li, Mercedes Bengochea, Steve De Backer, Barbara Weyn, Manu Sneyders, Hans Nicasy, Peng Yu, Paul Scheunders, Bassem A Hassan
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: PLoS Computational Biology, Vol 14, Iss 8, p e1006410 (2018)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1006410
Popis: Isolation profoundly influences social behavior in all animals. In humans, isolation has serious effects on health. Drosophila melanogaster is a powerful model to study small-scale, temporally-transient social behavior. However, longer-term analysis of large groups of flies is hampered by the lack of effective and reliable tools. We built a new imaging arena and improved the existing tracking algorithm to reliably follow a large number of flies simultaneously. Next, based on the automatic classification of touch and graph-based social network analysis, we designed an algorithm to quantify changes in the social network in response to prior social isolation. We observed that isolation significantly and swiftly enhanced individual and local social network parameters depicting near-neighbor relationships. We explored the genome-wide molecular correlates of these behavioral changes and found that whereas behavior changed throughout the six days of isolation, gene expression alterations occurred largely on day one. These changes occurred mostly in metabolic genes, and we verified the metabolic changes by showing an increase of lipid content in isolated flies. In summary, we describe a highly reliable tracking and analysis pipeline for large groups of flies that we use to unravel the behavioral, molecular and physiological impact of isolation on social network dynamics in Drosophila.
Databáze: Directory of Open Access Journals
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