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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Vydané v:	Microscopy research and technique Ročník 74; číslo 7; s. 605 - 613
Hlavní autori:	Zhang, Kai, Osakada, Yasuko, Xie, Wenjun, Cui, Bianxiao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.07.2011
Predmet:	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
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Abstract	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.
AbstractList	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. 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 paper 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 2-D 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. 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.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. 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. 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.
Author	Cui, Bianxiao Osakada, Yasuko Xie, Wenjun Zhang, Kai
Author_xml	– sequence: 1 givenname: Kai surname: Zhang fullname: Zhang, Kai organization: Department of Chemistry, Stanford University, Stanford, California 94305 – sequence: 2 givenname: Yasuko surname: Osakada fullname: Osakada, Yasuko organization: Department of Chemistry, Stanford University, Stanford, California 94305 – sequence: 3 givenname: Wenjun surname: Xie fullname: Xie, Wenjun organization: Department of Chemistry, Stanford University, Stanford, California 94305 – sequence: 4 givenname: Bianxiao surname: Cui fullname: Cui, Bianxiao email: bcui@stanford.edu organization: Department of Chemistry, Stanford University, Stanford, California 94305
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Snippet	The dynamics of cargo movement in axons encodes crucial information about the underlying regulatory mechanisms of the axonal transport process in neurons, a...
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SubjectTerms	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
Title	Automated image analysis for tracking cargo transport in axons
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