論文

査読有り
2018年1月23日

Design and implementation of multi-signal and time-varying neural reconstructions

Scientific Data
  • Sumit Nanda
  • ,
  • Hanbo Chen
  • ,
  • Ravi Das
  • ,
  • Shatabdi Bhattacharjee
  • ,
  • Hermann Cuntz
  • ,
  • Benjamin Torben-Nielsen
  • ,
  • Hanchuan Peng
  • ,
  • Daniel N. Cox
  • ,
  • Erik De Schutter
  • ,
  • Giorgio A. Ascoli

5
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1038/sdata.2017.207
出版者・発行元
Nature Publishing Groups

Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.

リンク情報
DOI
https://doi.org/10.1038/sdata.2017.207
URL
http://orcid.org/0000-0001-8618-5138
ID情報
  • DOI : 10.1038/sdata.2017.207
  • ISSN : 2052-4463
  • ORCIDのPut Code : 40871979
  • SCOPUS ID : 85040984305

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