Interpreting the Entire Connectivity of Individual Neurons in Micropatterned Neural Culture With an Integrated Connectome Analyzer of a Neuronal Network (iCANN)

June Hoan Kim, Jae Ryun Ryu, Boram Lee, Uikyu Chae, Jong Wan Son, Bae Ho Park, Il Joo Cho, Woong Sun

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The function of a neural circuit can be determined by the following: (1) characteristics of individual neurons composing the circuit, (2) their distinct connection structure, and (3) their neural circuit activity. However, prior research on correlations between these three factors revealed many limitations. In particular, profiling and modeling of the connectivity of complex neural circuits at the cellular level are highly challenging. To reduce the burden of the analysis, we suggest a new approach with simplification of the neural connection in an array of honeycomb patterns on 2D, using a microcontact printing technique. Through a series of guided neuronal growths in defined honeycomb patterns, a simplified neuronal circuit was achieved. Our approach allowed us to obtain the whole network connectivity at cellular resolution using a combination of stochastic multicolor labeling via viral transfection. Therefore, we were able to identify several types of hub neurons with distinct connectivity features. We also compared the structural differences between different circuits using three-node motif analysis. This new model system, iCANN, is the first experimental model of neural computation at the cellular level, providing neuronal circuit structures for the study of the relationship between anatomical structure and function of the neuronal network.

Original languageEnglish
Article number746057
JournalFrontiers in Neuroanatomy
Volume15
DOIs
Publication statusPublished - 2021 Oct 20

Keywords

  • axon guidance
  • connectivity
  • connectome
  • micropattern
  • network

ASJC Scopus subject areas

  • Anatomy
  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

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