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

Authors :
Linneweber, Gerit A.
Claeys, Annelies
Liu, Guangda
Hassan, Bassem A.
Sneyders, Manu
Nicasy, Hans
Scheunders, Paul
Nath, Tanmay
De Backer, Steve
Weyn, Barbara
Guo, Zhengyu
Li, Jin
Yu, Peng
Bengochea, Mercedes
Source :
PLoS Computational Biology; 8/30/2018, Vol. 14 Issue 8, p1-23, 23p, 2 Diagrams, 1 Chart, 3 Graphs
Publication Year :
2018

Abstract

Isolation profoundly influences social behavior in all animals. In humans, isolation has serious effects on health and disease. 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
14
Issue :
8
Database :
Complementary Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
131500898
Full Text :
https://doi.org/10.1371/journal.pcbi.1006410