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Diadem X: automated 4 dimensional analysis of morphological data.

He HY, Cline HT - Neuroinformatics (2011)

View Article: PubMed Central - PubMed

Affiliation: The Scripps Research Institute, La Jolla, CA 92037, USA.

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The development of multi-photon imaging technique has greatly facilitated in vivo time-lapse imaging and enables comparison of the fine morphological structures of individual neurons over time... Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking... Although the mechanisms underlying this widely observed effect of TTX treatment are still unclear, this was the first demonstration that neuronal activity affected the growth and structure of individual neurons... Subsequently Antonini and Stryker conducted heroic experiments (Fig.  1) which demonstrated that the morphology of individual geniculocortical axons changes over periods of days in response to decreased visual experience. 7 Specifically, by comparing populations of neurons from animals treated with monocular deprivation, they found that geniculocortical axons carrying information in the open eye pathway elaborated more complex axon arbors than axons in the deprived-eye pathway. 8 These experiments were important because they demonstrated that sensory input activity governed the elaboration of neuronal axons and that the gross re-organization of ocular dominance columns in monocularly-deprived animals seen using radioactive tracers reported a population-level change in neuronal structure, rather than, for instance, a change in the distribution of axons within layer 4 of visual cortex... It is important to point out that these conclusions were generated by comparing populations of neurons from animals at different stages and treated with different visual stimulation or deprivation paradigms, so that specific information about cellular mechanisms governing elaboration or regression of axon arbor development could not be determined... Many studies have documented the invasion and development of axon arbors by comparing samples across different developmental timepoints. 9 In parallel, other studies demonstrated an increase followed by a gradual decrease in synapse density. 10 Together these studies suggested a model in which axon arbors go through a period of exuberant elaboration and excess synaptogenesis followed by an elimination phase, in which both synapses and axon branches were pruned... As described by Hua and Smith,11 this classical model of sequential axon arbor elaboration and pruning is not borne out by time-lapse in vivo imaging of developing retinotectal axons in Xenopus frog tadpoles and Zebrafish. 1213 Rather, branch addition and synaptogenesis are concurrent with branch retraction and synapse elimination for both axons and dendrites, as suggested by light microscopy time-lapse data14 and demonstrated more conclusively by combining in vivo time-lapse imaging with subsequent serial section electron microscopy. 15 Importantly, the final structure of the axon is indistinguishable (Fig.  2), and these fundamental differences in the cellular mechanisms, and therefore the molecular/genetic/signaling events underlying arbor development, would only be recognized by time-lapse in vivo imaging data... This serves as but one example of the essential need for in vivo time-lapse imaging data for accurate identification of mechanisms governing brain development, circuit plasticity and neurological diseases... The development of multi-photon imaging techniques has greatly facilitated in vivo time-lapse imaging, which enables comparison of the fine morphological structures of individual neurons over time... Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking... A second technical issue is the identification of persistent and new structures (branch tips, boutons, spines) in sequential images... Despite the fact that comparison of 3 dimensional data sets remains a significant challenge, recent progress in a number of labs suggests that semi-automated analysis of time-lapse images of neuronal structures is a tractable problem within the near future... Such automation will have a significant impact on the ability to assess developmental, plasticity-induced, regressive or therapeutic changes in nervous system structure and will follow smoothly from the advances seen as a result of the Diadem Challenge.

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Analysis and presentation of data from time-lapse images of dendritic arbor plasticity. a Dendritic arbor plasticity observed by in vivo time-lapse images collected at 2 h intervals over 6 h. This imaging protocol allows every branch in the arbor to be identified and their dynamic behaviors to be presented in a chronogram (right). Adapted from Wu and Cline, 1998. b Rearrangements of dendritic arbor structures observed with images collected at 2-hour intervals over 6 h permit a variety of representations of branch dynamics. Here, branches were color coded according to their dynamics: the grey arbor is stable throughout the imaging period, red branches were present at the first image and disappeared by the last image, green branches were transient, and yellow branches were added during the imaging period and maintained to the final image. Adapted from Haas et al., 2006. c Branch dynamics can be quantified as branch additions, extensions, retractions, eliminations, and presented as the relative proportion of branches with different behaviors under separate experimental conditions
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Fig4: Analysis and presentation of data from time-lapse images of dendritic arbor plasticity. a Dendritic arbor plasticity observed by in vivo time-lapse images collected at 2 h intervals over 6 h. This imaging protocol allows every branch in the arbor to be identified and their dynamic behaviors to be presented in a chronogram (right). Adapted from Wu and Cline, 1998. b Rearrangements of dendritic arbor structures observed with images collected at 2-hour intervals over 6 h permit a variety of representations of branch dynamics. Here, branches were color coded according to their dynamics: the grey arbor is stable throughout the imaging period, red branches were present at the first image and disappeared by the last image, green branches were transient, and yellow branches were added during the imaging period and maintained to the final image. Adapted from Haas et al., 2006. c Branch dynamics can be quantified as branch additions, extensions, retractions, eliminations, and presented as the relative proportion of branches with different behaviors under separate experimental conditions

Mentions: Subsequently, reconstructions were done to maintain the 3 dimensional information content, so more accurate identification of neuronal branches and spines could be made and more accurate measurements of changes in dendritic, axonal or spine structures could be accomplished using macros written in NIH Image, Image J19 or other custom analysis software.20 Quantitative analysis of structural dynamics includes changes in dynamic behaviors of individual branches, branch lifetimes, and rates of branch additions or retractions.21 Further automated quantitative analysis of changes in length of dynamic structures required the experimenter to manually assign an identification number manually to each branch tip or spine and that the same id number be maintained for the branch for all time points.22 Currently, quantitative analysis of time-lapse imaging data is done predominantly by labor-intensive comparisons of sequential pairs of 3 dimensional reconstructions of structures from individual time-points until reconstructions through the entire time-lapse sequence have been compared. Each branch within the axonal or dendritic arbor is assigned an identifier, each maintained branch is then identified in the subsequent reconstruction and length changes in each maintained branch are determined by comparing measurements in the 3-dimensional datasets. In addition, branches which either disappear or are added during the imaging interval are noted, with the assignment of new identifiers, as necessary (Fig. 4). Quantitative analysis of time-lapse data requires particular care be taken in the initial 3D reconstructions of the neuronal structures, so the errors in the identification of branch dynamic events do not arise from misidentification of branches throughout the imaging sequence. In our experience, computer-assisted manual reconstruction of the entire dendritic or axonal arbor for a single time-point of one neuron using commercially available software takes from ~1–4 h depending on the complexity of the neuronal morphology. As mentioned above, alignment of reconstructions from multiple time-points for dynamic analysis and quantitative analysis requires that branchpoints, branch tips/spines/boutons have unique identifiers that are propagated through the time-series. Assignment and propagation of the identifiers through two aligned reconstructions can take an additional 1–5 h, again depending on the complexity and plasticity of the structures. In addition to increasing the pace of quantitative analysis of 4 D data sets, broadly applicable and broadly available automated alignment and automated quantitative analysis methods would significantly improve the reproducibility of the analysis.Fig. 4


Diadem X: automated 4 dimensional analysis of morphological data.

He HY, Cline HT - Neuroinformatics (2011)

Analysis and presentation of data from time-lapse images of dendritic arbor plasticity. a Dendritic arbor plasticity observed by in vivo time-lapse images collected at 2 h intervals over 6 h. This imaging protocol allows every branch in the arbor to be identified and their dynamic behaviors to be presented in a chronogram (right). Adapted from Wu and Cline, 1998. b Rearrangements of dendritic arbor structures observed with images collected at 2-hour intervals over 6 h permit a variety of representations of branch dynamics. Here, branches were color coded according to their dynamics: the grey arbor is stable throughout the imaging period, red branches were present at the first image and disappeared by the last image, green branches were transient, and yellow branches were added during the imaging period and maintained to the final image. Adapted from Haas et al., 2006. c Branch dynamics can be quantified as branch additions, extensions, retractions, eliminations, and presented as the relative proportion of branches with different behaviors under separate experimental conditions
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Fig4: Analysis and presentation of data from time-lapse images of dendritic arbor plasticity. a Dendritic arbor plasticity observed by in vivo time-lapse images collected at 2 h intervals over 6 h. This imaging protocol allows every branch in the arbor to be identified and their dynamic behaviors to be presented in a chronogram (right). Adapted from Wu and Cline, 1998. b Rearrangements of dendritic arbor structures observed with images collected at 2-hour intervals over 6 h permit a variety of representations of branch dynamics. Here, branches were color coded according to their dynamics: the grey arbor is stable throughout the imaging period, red branches were present at the first image and disappeared by the last image, green branches were transient, and yellow branches were added during the imaging period and maintained to the final image. Adapted from Haas et al., 2006. c Branch dynamics can be quantified as branch additions, extensions, retractions, eliminations, and presented as the relative proportion of branches with different behaviors under separate experimental conditions
Mentions: Subsequently, reconstructions were done to maintain the 3 dimensional information content, so more accurate identification of neuronal branches and spines could be made and more accurate measurements of changes in dendritic, axonal or spine structures could be accomplished using macros written in NIH Image, Image J19 or other custom analysis software.20 Quantitative analysis of structural dynamics includes changes in dynamic behaviors of individual branches, branch lifetimes, and rates of branch additions or retractions.21 Further automated quantitative analysis of changes in length of dynamic structures required the experimenter to manually assign an identification number manually to each branch tip or spine and that the same id number be maintained for the branch for all time points.22 Currently, quantitative analysis of time-lapse imaging data is done predominantly by labor-intensive comparisons of sequential pairs of 3 dimensional reconstructions of structures from individual time-points until reconstructions through the entire time-lapse sequence have been compared. Each branch within the axonal or dendritic arbor is assigned an identifier, each maintained branch is then identified in the subsequent reconstruction and length changes in each maintained branch are determined by comparing measurements in the 3-dimensional datasets. In addition, branches which either disappear or are added during the imaging interval are noted, with the assignment of new identifiers, as necessary (Fig. 4). Quantitative analysis of time-lapse data requires particular care be taken in the initial 3D reconstructions of the neuronal structures, so the errors in the identification of branch dynamic events do not arise from misidentification of branches throughout the imaging sequence. In our experience, computer-assisted manual reconstruction of the entire dendritic or axonal arbor for a single time-point of one neuron using commercially available software takes from ~1–4 h depending on the complexity of the neuronal morphology. As mentioned above, alignment of reconstructions from multiple time-points for dynamic analysis and quantitative analysis requires that branchpoints, branch tips/spines/boutons have unique identifiers that are propagated through the time-series. Assignment and propagation of the identifiers through two aligned reconstructions can take an additional 1–5 h, again depending on the complexity and plasticity of the structures. In addition to increasing the pace of quantitative analysis of 4 D data sets, broadly applicable and broadly available automated alignment and automated quantitative analysis methods would significantly improve the reproducibility of the analysis.Fig. 4

View Article: PubMed Central - PubMed

Affiliation: The Scripps Research Institute, La Jolla, CA 92037, USA.

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

The development of multi-photon imaging technique has greatly facilitated in vivo time-lapse imaging and enables comparison of the fine morphological structures of individual neurons over time... Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking... Although the mechanisms underlying this widely observed effect of TTX treatment are still unclear, this was the first demonstration that neuronal activity affected the growth and structure of individual neurons... Subsequently Antonini and Stryker conducted heroic experiments (Fig.  1) which demonstrated that the morphology of individual geniculocortical axons changes over periods of days in response to decreased visual experience. 7 Specifically, by comparing populations of neurons from animals treated with monocular deprivation, they found that geniculocortical axons carrying information in the open eye pathway elaborated more complex axon arbors than axons in the deprived-eye pathway. 8 These experiments were important because they demonstrated that sensory input activity governed the elaboration of neuronal axons and that the gross re-organization of ocular dominance columns in monocularly-deprived animals seen using radioactive tracers reported a population-level change in neuronal structure, rather than, for instance, a change in the distribution of axons within layer 4 of visual cortex... It is important to point out that these conclusions were generated by comparing populations of neurons from animals at different stages and treated with different visual stimulation or deprivation paradigms, so that specific information about cellular mechanisms governing elaboration or regression of axon arbor development could not be determined... Many studies have documented the invasion and development of axon arbors by comparing samples across different developmental timepoints. 9 In parallel, other studies demonstrated an increase followed by a gradual decrease in synapse density. 10 Together these studies suggested a model in which axon arbors go through a period of exuberant elaboration and excess synaptogenesis followed by an elimination phase, in which both synapses and axon branches were pruned... As described by Hua and Smith,11 this classical model of sequential axon arbor elaboration and pruning is not borne out by time-lapse in vivo imaging of developing retinotectal axons in Xenopus frog tadpoles and Zebrafish. 1213 Rather, branch addition and synaptogenesis are concurrent with branch retraction and synapse elimination for both axons and dendrites, as suggested by light microscopy time-lapse data14 and demonstrated more conclusively by combining in vivo time-lapse imaging with subsequent serial section electron microscopy. 15 Importantly, the final structure of the axon is indistinguishable (Fig.  2), and these fundamental differences in the cellular mechanisms, and therefore the molecular/genetic/signaling events underlying arbor development, would only be recognized by time-lapse in vivo imaging data... This serves as but one example of the essential need for in vivo time-lapse imaging data for accurate identification of mechanisms governing brain development, circuit plasticity and neurological diseases... The development of multi-photon imaging techniques has greatly facilitated in vivo time-lapse imaging, which enables comparison of the fine morphological structures of individual neurons over time... Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking... A second technical issue is the identification of persistent and new structures (branch tips, boutons, spines) in sequential images... Despite the fact that comparison of 3 dimensional data sets remains a significant challenge, recent progress in a number of labs suggests that semi-automated analysis of time-lapse images of neuronal structures is a tractable problem within the near future... Such automation will have a significant impact on the ability to assess developmental, plasticity-induced, regressive or therapeutic changes in nervous system structure and will follow smoothly from the advances seen as a result of the Diadem Challenge.

Show MeSH
Related in: MedlinePlus