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

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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
27 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number170207
JournalScientific Data
Volume5
DOIs
Publication statusPublished - 23 Jan 2018

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