Quantitative analysis of axonal branch dynamics in the developing nervous system

Chalmers, Kelsey, Kita, Elizabeth M., Scott, Ethan K. and Goodhill, Geoffrey J. (2016) Quantitative analysis of axonal branch dynamics in the developing nervous system. PLoS Computational Biology, 12 3: . doi:10.1371/journal.pcbi.1004813


Author Chalmers, Kelsey
Kita, Elizabeth M.
Scott, Ethan K.
Goodhill, Geoffrey J.
Title Quantitative analysis of axonal branch dynamics in the developing nervous system
Journal name PLoS Computational Biology   Check publisher's open access policy
ISSN 1553-7358
Publication date 2016-03-21
Year available 2016
Sub-type Article (original research)
DOI 10.1371/journal.pcbi.1004813
Open Access Status DOI
Volume 12
Issue 3
Total pages 25
Place of publication San Francisco, United States
Publisher Public Library of Science
Collection year 2017
Language eng
Formatted abstract
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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
HERDC Pre-Audit
Queensland Brain Institute Publications
School of Biomedical Sciences Publications
 
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