ZFIN ID: ZDB-PUB-160322-2
Quantitative Analysis of Axonal Branch Dynamics in the Developing Nervous System
Chalmers, K., Kita, E.M., Scott, E.K., Goodhill, G.J.
Date: 2016
Source: PLoS Computational Biology   12: e1004813 (Journal)
Registered Authors: Scott, Ethan
Keywords: Axons, Death rates, Birth rates, Retinal ganglion cells, Superior colliculus, Zebrafish, Larvae, Markov processes
MeSH Terms:
  • Animals
  • Computer Simulation
  • Connectome/methods
  • Image Interpretation, Computer-Assisted
  • Models, Anatomic
  • Models, Neurological*
  • Neurogenesis/physiology*
  • Superior Colliculi/cytology*
  • Superior Colliculi/growth & development*
  • Time-Lapse Imaging/methods
  • Zebrafish/anatomy & histology*
  • Zebrafish/physiology*
PubMed: 26998842 Full text @ PLoS Comput. Biol.
Branching is an important mechanism by which axons navigate to their targets during neural development. For instance, in the developing zebrafish retinotectal system, selective branching plays a critical role during both initial pathfinding and subsequent arborisation once the target zone has been reached. Here we show how quantitative methods can help extract new information from time-lapse imaging about the nature of the underlying branch dynamics. First, we introduce Dynamic Time Warping to this domain as a method for automatically matching branches between frames, replacing the effort required for manual matching. Second, we model branch dynamics as a birth-death process, i.e. a special case of a continuous-time Markov process. This reveals that the birth rate for branches from zebrafish retinotectal axons, as they navigate across the tectum, increased over time. We observed no significant change in the death rate for branches over this time period. However, blocking neuronal activity with TTX slightly increased the death rate, without a detectable change in the birth rate. Third, we show how the extraction of these rates allows computational simulations of branch dynamics whose statistics closely match the data. Together these results reveal new aspects of the biology of retinotectal pathfinding, and introduce computational techniques which are applicable to the study of axon branching more generally.