Quantitative neuronal morphometry by supervised and unsupervised learning

STAR Protoc. 2021 Sep 30;2(4):100867. doi: 10.1016/j.xpro.2021.100867. eCollection 2021 Dec 17.

Abstract

We present a protocol to characterize the morphological properties of individual neurons reconstructed from microscopic imaging. We first describe a simple procedure to extract relevant morphological features from digital tracings of neural arbors. Then, we provide detailed steps on classification, clustering, and statistical analysis of the traced cells based on morphological features. We illustrate the pipeline design using specific examples from zebrafish anatomy. Our approach can be readily applied and generalized to the characterization of axonal, dendritic, or glial geometry. For complete context and scientific motivation for the studies and datasets used here, refer to Valera et al. (2021).

Keywords: Bioinformatics; Cell Biology; Computer sciences; Microscopy; Neuroscience.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Image Processing, Computer-Assisted / methods*
  • Machine Learning*
  • Microscopy
  • Neurons / cytology*
  • Software
  • Zebrafish