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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Minhua Qiu Hao-Chih Lee Ge Yang |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Biomedical Engineering, Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA (Minhua Qiu; Hao-Chih Lee; Ge Yang) |
| Abstract | Survival and function of neurons depend critically on bidirectional transport of essential cargoes within their axons. To study related molecular mechanisms, we developed an automated single particle tracking method that combines nanometer resolution detection of cargo positions with kymograph-based multiple hypothesis tracking of cargo motion for precise and complete recovery of cargo trajectories. Experiments confirmed that the method can precisely track vesicles in dense axonal traffic of Drosophila motor neurons at 10∼20 nm resolution and reliably resolve their trajectories in events of crossing, merging, and splitting. Recovered cargo trajectories were statistically analyzed to identify motion patterns and computationally represented using hidden Markov models to characterize behavior changes. Together, these tools allowed us to reliably detect and measure subtle changes in dynamics of axonal transport and to discover previously unknown axonal transport properties. |
| Starting Page | 992 |
| Ending Page | 995 |
| File Size | 797235 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457718571 |
| ISSN | 19457928 |
| e-ISBN | 9781457718588 |
| DOI | 10.1109/ISBI.2012.6235724 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-02 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Trajectory Hidden Markov models Nerve fibers Image resolution Tracking Computational modeling image analysis axonal transport single particle tracking kymograph fluorescence imaging |
| Content Type | Text |
| Resource Type | Article |
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