|ZFIN ID: ZDB-PUB-170214-212|
The dynamics of growth cone morphology
Goodhill, G.J., Faville, R.A., Sutherland, D.J., Bicknell, B.A., Thompson, A.W., Pujic, Z., Sun, B., Kita, E.M., Scott, E.K.
|Source:||BMC Biology 13: 10 (Journal)|
|Registered Authors:||Pujic, Zac, Scott, Ethan|
|Keywords:||Axon guidance, Neurite growth, Neural development, Eigenshape analysis, Shape analysis, Brain morphometry, Oscillations, Microtubules|
|PubMed:||25729914 Full text @ BMC Biol.|
Goodhill, G.J., Faville, R.A., Sutherland, D.J., Bicknell, B.A., Thompson, A.W., Pujic, Z., Sun, B., Kita, E.M., Scott, E.K. (2015) The dynamics of growth cone morphology. BMC Biology. 13:10.
Background Normal brain function depends on the development of appropriate patterns of neural connections. A critical role in guiding axons to their targets during neural development is played by neuronal growth cones. These have a complex and rapidly changing morphology; however, a quantitative understanding of this morphology, its dynamics and how these are related to growth cone movement, is lacking.
Results Here we use eigenshape analysis (principal components analysis in shape space) to uncover the set of five to six basic shape modes that capture the most variance in growth cone form. By analysing how the projections of growth cones onto these principal modes evolve in time, we found that growth cone shape oscillates with a mean period of 30 min. The variability of oscillation periods and strengths between different growth cones was correlated with their forward movement, such that growth cones with strong, fast shape oscillations tended to extend faster. A simple computational model of growth cone shape dynamics based on dynamic microtubule instability was able to reproduce quantitatively both the mean and variance of oscillation periods seen experimentally, suggesting that the principal driver of growth cone shape oscillations may be intrinsic periodicity in cytoskeletal rearrangements.
Conclusions Intrinsically driven shape oscillations are an important component of growth cone shape dynamics. More generally, eigenshape analysis has the potential to provide new quantitative information about differences in growth cone behaviour in different conditions.