- Poster presentation
- Open Access
Deciphering the axonal transport kinetics of neurofilaments using the fluorescence photo-activation pulse-escape method
© Li et al; licensee BioMed Central Ltd. 2014
- Published: 21 July 2014
- Dorsal Root Ganglion
- Axonal Transport
- Fluorescence Decay
- Molecular Motor
- Superior Cervical Ganglion
Neurofilaments, one of cytoskeletal filaments determining the axonal calibers, are transported by molecular motors along microtubule tracks in a stochastic manner named ‘stop and go’[1, 2, 4, 7], which is called the slow axonal transport. Experiments using photo-bleached live-cell imaging observed that the slow axonal transport of NFs is characterized by a rapidly intermittent, bidirectional movement [1, 3]. The overall process of brief moving interspersed by short or long term pausing can be modeled by a well-tested mathematical model [5, 6], where NFs could stay on-track running, on-track pausing and off-track pausing for both anterograde and retrograde directions. The shortage of photo-bleached live-cell imaging is that it under-estimates the population of NFs which stay in the long-term pausing state. Fluorescence photo-activation pulse-escape method is designed to observe the long-term decay of the total population of NFs, which can provide accurate information about the time that NFs stay off-track. We developed a systematical method based on the model  and analyzed the pulse-escape experimental data in Superior Cervical Ganglion (SCG) and Dorsal Root Ganglion (DRG) neurons. The fluorescence decay of the photo-activated neurofilaments can be fit by a double exponential function, where we can extract the short-time initial decaying rate and long-term decaying rate; simultaneously, those two rates can be obtained by solving the partial differential equation which describes the stochastic movement of NFs. By using those two conditions we constrain our parameter space such that we can find a unique set of transition rates that describe the kinetics of NFs. Therefore, all the other dynamic characterization such as the average velocity of NFs, average on-track running and pausing, off-track pausing time, percentage of population of NFs at each state can all be predicted.
An important advantage of our new analytical approach is that it can permit the characterization of neruofilament transport in mature axons for which single-neurofilament tracking is optically challenging due to the thickness or abundance of neurofilaments. This combined experimental and computational approach is a powerful tool for the analysis of the moving and pausing behavior of neurofilaments in axon.
This project was funded by collaborative NSF grants to Anthony Brown and Peter Jung and by a grant of the China Scholarship Council to Yinyun Li.
- Brown A: Slow axonal transport: stop and go traffic in the axon. Nat. Rev. Mol. Cell Biol. 2000, 1: 153-156. 10.1038/35040102.View ArticlePubMedGoogle Scholar
- Brown A: Axonal transport of membraneous and nonmembranous cargoes: a unified perspective. J Cell Biol. 2003, 160: 817-821. 10.1083/jcb.200212017.PubMed CentralView ArticlePubMedGoogle Scholar
- Brown A: Live-cell imaging of slow axonal transport in cultured neurons. Methods Cell Biol. 2003, 71: 305-323.PubMed CentralView ArticlePubMedGoogle Scholar
- Brown A, Wang L, Jung P: Stochastic simulation of neurofilament transport in axons: the “stop-and-go” hypothesis. Mol. Biol. Cell. 2005, 16: 4243-4255. 10.1091/mbc.E05-02-0141.PubMed CentralView ArticlePubMedGoogle Scholar
- Trivedi N, Jung P, Brown A: Neurofilaments switch between distinct mobile and stationary states during their transport along axons. J Neurosci. 2007, 27: 507-516. 10.1523/JNEUROSCI.4227-06.2007.PubMed CentralView ArticlePubMedGoogle Scholar
- Jung P, Brown A: Modeling the slowing of neurofilament transport along the mouse sciatic nerve. Phys Biol. 2009, 6: 046002-10.1088/1478-3975/6/4/046002.View ArticlePubMedGoogle Scholar
- Brown A, Jung P: A critical reevaluation of the stationary axonal cytoskeleton hypothesis. Cytoskeleton (Hoboken). 2013, 70: 1-11. 10.1002/cm.21083.View ArticleGoogle Scholar
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