ShuTu: Open-Source Software for Efficient and Accurate Reconstruction of Dendritic Morphology

Dezhe Z. Jin, Ting Zhao, David L. Hunt, Rachel P. Tillage, Ching Lung Hsu, Nelson Spruston

Research output: Contribution to journalArticle

Abstract

Neurons perform computations by integrating inputs from thousands of synapses—mostly in the dendritic tree—to drive action potential firing in the axon. One fruitful approach to studying this process is to record from neurons using patch-clamp electrodes, fill the recorded neurons with a substance that allows subsequent staining, reconstruct the three-dimensional architectures of the dendrites, and use the resulting functional and structural data to develop computer models of dendritic integration. Accurately producing quantitative reconstructions of dendrites is typically a tedious process taking many hours of manual inspection and measurement. Here we present ShuTu, a new software package that facilitates accurate and efficient reconstruction of dendrites imaged using bright-field microscopy. The program operates in two steps: (1) automated identification of dendritic processes, and (2) manual correction of errors in the automated reconstruction. This approach allows neurons with complex dendritic morphologies to be reconstructed rapidly and efficiently, thus facilitating the use of computer models to study dendritic structure-function relationships and the computations performed by single neurons.

Original languageEnglish (US)
Article number68
JournalFrontiers in Neuroinformatics
Volume13
DOIs
StatePublished - Oct 31 2019

All Science Journal Classification (ASJC) codes

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

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