Automated image analysis for tracking cargo transport in axons

The dynamics of cargo movement in axons encodes crucial information about the underlying regulatory mechanisms of the axonal transport process in neurons, a central problem in understanding many neurodegenerative diseases. Quantitative analysis of cargo dynamics in axons usually includes three steps...

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Bibliographic Details
Published in:Microscopy research and technique Vol. 74; no. 7; pp. 605 - 613
Main Authors: Zhang, Kai, Osakada, Yasuko, Xie, Wenjun, Cui, Bianxiao
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.07.2011
Subjects:
Algorithms
Animals
axonal transport
Axonal Transport - physiology
Axons
Axons - physiology
Cells, Cultured
Construction
curve tracing
Dynamic tests
Dynamics
image processing
Image Processing, Computer-Assisted - methods
kymograph
Kymography
Microscopy, Fluorescence
Neurons - physiology
particle tracking
Rats
Rats, Sprague-Dawley
time series analysis
Time-Lapse Imaging - methods
Tracking
Trajectories
Two dimensional
ISSN:1059-910X, 1097-0029, 1097-0029
Online Access:Get full text
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Summary:The dynamics of cargo movement in axons encodes crucial information about the underlying regulatory mechanisms of the axonal transport process in neurons, a central problem in understanding many neurodegenerative diseases. Quantitative analysis of cargo dynamics in axons usually includes three steps: (1) acquiring time‐lapse image series, (2) localizing individual cargos at each time step, and (3) constructing dynamic trajectories for kinetic analysis. Currently, the later two steps are usually carried out with substantial human intervention. This article presents a method of automatic image analysis aiming for constructing cargo trajectories with higher data processing throughput, better spatial resolution, and minimal human intervention. The method is based on novel applications of several algorithms including 2D kymograph construction, seed points detection, trajectory curve tracing, back‐projection to extract spatial information, and position refining using a 2D Gaussian fitting. This method is sufficiently robust for usage on images with low signal‐to‐noise ratio, such as those from single molecule experiments. The method was experimentally validated by tracking the axonal transport of quantum dot and DiI fluorophore‐labeled vesicles in dorsal root ganglia neurons. Microsc. Res. Tech., 2011. © 2010 Wiley‐Liss, Inc.
Bibliography:istex:B8C2FE39CE334C38EC78C92C2E7CC2D32006FCDB
Dreyfus New Faculty Award, Searle Scholar Award, Packard Fellowship
ArticleID:JEMT20934
National Institute of Health (NIH) - No. NS057906
ark:/67375/WNG-8PZ0WFL5-0
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SourceType-Scholarly Journals-1
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ISSN:1059-910X
1097-0029
1097-0029
DOI:10.1002/jemt.20934