• Top page
  • Timetable
  • Per session
  • Per presentation
  • How to
  • Meeting Planner



Spinal cord Motor Neurous and Muscle

開催日 2014/9/11
時間 16:00 - 17:00
会場 Poster / Exhibition(Event Hall B)

Statistical analysis of global spontaneous activity of central neurons in Drosophila larvae

  • P1-126
  • 尹 永択 / Youngtaek Yoon:1 中江 健 / Ken Nakae:3 高坂 洋史 / Hiroshi Kohsaka:2 石井 信 / Shin Ishii:3 能瀬 聡直 / Akinao Nose:1,2 
  • 1:東京大学 / University of Tokyo 2:東京大学大学院 新領域創成科学研究科 複雑理工学専攻 / Dept Complexity Sci, Univ of Tokyo, Tokyo, Japan 3:京都大学大学院 情報学研究科 システム科学専攻 / Dept Systems Sci, Kyoto Univ, Kyoto, Japan 

To fully understand how the neurons interact with each other to generate network function, it is necessary to obtain the information about the activities of the entire population of neurons in the system. Toward this goal, we combine 4-D calcium imaging (with spinning-disc confocal microscopy and a high-speed Piezo Z stage) which enables monitoring of the activity of neurons in a 3-dimension space in real time, with automated cell sorting which enables extracting the activity of hundreds of neurons from the recording, to study the dynamics of neural populations in the central nervous system (CNS) of Drosophila larvae. In this presentation, we introduce the experimental system, the method of cell sorting, and statistical analysis of the network dynamics.

We used the genetically encoded calcium indicator GCaMP6 to detect the calcium signals of the cell. We crossed the UAS-GCaMP6 transgene to several Gal4 lines, isolated the ventral nerve cord (VNC) from the larvae, and recorded the activities of populations of genetically defined cells, which occur spontaneously and reflect fictive locomotion and other behavioral states. We then used custom-made cell sorting methods to automatically extract the activity pattern of tens to hundreds of neurons. We used K-means clustering and Isomap methods to systematically analyze the activity data, and successfully classified the activity of the cell ensembles into several states, including those corresponding to forward and backward locomotion. We also determined the activity peaks of individual recorded neurons to assign their phases during locomotion, and together with the information on the position of the cells, categorized these neurons. We are now trying to combine these experimental and data-analyses methods with optogenetics to study the effect of activity perturbation and to extract further information on the dynamics of the neural network.

Copyright © Neuroscience2014. All Right Reserved.