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Topological characterization of neuronal arbor morphology via sequence representation: II--global alignment.

Gillette TA, Hosseini P, Ascoli GA - BMC Bioinformatics (2015)

Bottom Line: The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively.Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites.Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. todd.gillette@gmail.com.

ABSTRACT

Background: The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees.

Results: The space generated from pairwise similarities is capable of classifying neuronal arbor types as well as, or better than, traditional topological metrics. Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites. Furthermore, the distinguishing consensus topology generated by multiple sequence alignment of a group of neurons reveals their shared branching blueprint. Interestingly, the axons of dendritic-targeting interneurons in the rodent cortex associates with pyramidal axons but apart from the (more topologically symmetric) axons of perisomatic-targeting interneurons.

Conclusions: Global pairwise and multiple sequence alignment of neurite topologies enables detailed comparison of neurites and identification of conserved topological features in alignment-defined clusters. The methods presented also provide a framework for incorporation of additional branch-level morphological features. Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.

No MeSH data available.


Axon clusters. a. Axons fall into four spherical clusters of varying sizes. Colored dots correspond to axons classified with the cluster associated with the matching metadata group. Diamonds represent rodent cortical dendritic-targeting interneurons. Circles are defined by the covariance matrix of their cluster model with a radius of one standard deviation. b. Association matrix for axon metadata groups and clusters. Colored and dark gray cells show significantly positive and negative associations, respectively, with the number of axons in each group and cluster in parentheses. c. Representative morphologies and dendrograms are shown for each significantly positive association (NMO_02574, 00315, 01209, 00424, 00424) [62-64].
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Fig4: Axon clusters. a. Axons fall into four spherical clusters of varying sizes. Colored dots correspond to axons classified with the cluster associated with the matching metadata group. Diamonds represent rodent cortical dendritic-targeting interneurons. Circles are defined by the covariance matrix of their cluster model with a radius of one standard deviation. b. Association matrix for axon metadata groups and clusters. Colored and dark gray cells show significantly positive and negative associations, respectively, with the number of axons in each group and cluster in parentheses. c. Representative morphologies and dendrograms are shown for each significantly positive association (NMO_02574, 00315, 01209, 00424, 00424) [62-64].

Mentions: Axons, though with far fewer total reconstructions (N = 1,230) and representing fewer metadata groups, tend to be larger and so have the potential to separate more clearly. Perhaps due to their relatively limited diversity, axons exhibited structure in just two dimensions and fell into 4 clusters (Figure 4a). Nevertheless, the clusters (spherical but varied in size) had strong associations with metadata groups, specifically separating insects from mammals, pyramidal cells from different rodent species, and distinct interneuron types (Figure 4b). The fly olfactory neuron axons most clearly associated with cluster 1 and are substantially dissociated from clusters 3 and 4. Rat neocortical pyramidal axons associated with cluster 2, while mouse neocortical pyramidal axons associated with cluster 3. This result should be interpreted with caution since the larger axons of rat neurons may be more prone to slicing artifacts. Since the specific histological and imaging procedures can also affect the reconstructions, it is important to note that the mouse pyramidal axons primarily come from a single archive while the rat data have substantial contributions (>10% each) from four different laboratories. Rodent (mouse and rat) neocortical dendritic targeting interneurons (primarily Martinotti and somatostatin-positive cells) also associated with cluster 3, though not as strongly. Rodent neocortical perisomatic targeting interneurons (primarily Basket and parvalbumin-positive cells) associated with cluster 4. Representative morphologies and dendrograms (Figure 4c) suggest features which are shared within or even define each cluster. Derivation of consensus representations and the qualitative and quantitative description of those features follow.Figure 4


Topological characterization of neuronal arbor morphology via sequence representation: II--global alignment.

Gillette TA, Hosseini P, Ascoli GA - BMC Bioinformatics (2015)

Axon clusters. a. Axons fall into four spherical clusters of varying sizes. Colored dots correspond to axons classified with the cluster associated with the matching metadata group. Diamonds represent rodent cortical dendritic-targeting interneurons. Circles are defined by the covariance matrix of their cluster model with a radius of one standard deviation. b. Association matrix for axon metadata groups and clusters. Colored and dark gray cells show significantly positive and negative associations, respectively, with the number of axons in each group and cluster in parentheses. c. Representative morphologies and dendrograms are shown for each significantly positive association (NMO_02574, 00315, 01209, 00424, 00424) [62-64].
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4491275&req=5

Fig4: Axon clusters. a. Axons fall into four spherical clusters of varying sizes. Colored dots correspond to axons classified with the cluster associated with the matching metadata group. Diamonds represent rodent cortical dendritic-targeting interneurons. Circles are defined by the covariance matrix of their cluster model with a radius of one standard deviation. b. Association matrix for axon metadata groups and clusters. Colored and dark gray cells show significantly positive and negative associations, respectively, with the number of axons in each group and cluster in parentheses. c. Representative morphologies and dendrograms are shown for each significantly positive association (NMO_02574, 00315, 01209, 00424, 00424) [62-64].
Mentions: Axons, though with far fewer total reconstructions (N = 1,230) and representing fewer metadata groups, tend to be larger and so have the potential to separate more clearly. Perhaps due to their relatively limited diversity, axons exhibited structure in just two dimensions and fell into 4 clusters (Figure 4a). Nevertheless, the clusters (spherical but varied in size) had strong associations with metadata groups, specifically separating insects from mammals, pyramidal cells from different rodent species, and distinct interneuron types (Figure 4b). The fly olfactory neuron axons most clearly associated with cluster 1 and are substantially dissociated from clusters 3 and 4. Rat neocortical pyramidal axons associated with cluster 2, while mouse neocortical pyramidal axons associated with cluster 3. This result should be interpreted with caution since the larger axons of rat neurons may be more prone to slicing artifacts. Since the specific histological and imaging procedures can also affect the reconstructions, it is important to note that the mouse pyramidal axons primarily come from a single archive while the rat data have substantial contributions (>10% each) from four different laboratories. Rodent (mouse and rat) neocortical dendritic targeting interneurons (primarily Martinotti and somatostatin-positive cells) also associated with cluster 3, though not as strongly. Rodent neocortical perisomatic targeting interneurons (primarily Basket and parvalbumin-positive cells) associated with cluster 4. Representative morphologies and dendrograms (Figure 4c) suggest features which are shared within or even define each cluster. Derivation of consensus representations and the qualitative and quantitative description of those features follow.Figure 4

Bottom Line: The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively.Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites.Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. todd.gillette@gmail.com.

ABSTRACT

Background: The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees.

Results: The space generated from pairwise similarities is capable of classifying neuronal arbor types as well as, or better than, traditional topological metrics. Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites. Furthermore, the distinguishing consensus topology generated by multiple sequence alignment of a group of neurons reveals their shared branching blueprint. Interestingly, the axons of dendritic-targeting interneurons in the rodent cortex associates with pyramidal axons but apart from the (more topologically symmetric) axons of perisomatic-targeting interneurons.

Conclusions: Global pairwise and multiple sequence alignment of neurite topologies enables detailed comparison of neurites and identification of conserved topological features in alignment-defined clusters. The methods presented also provide a framework for incorporation of additional branch-level morphological features. Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.

No MeSH data available.