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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.


Dendrite clusters. a. Clusters of (non-apical) dendrites are shown in two perspectives of alignment space. Each perspective highlights dendrites in three of the six clusters, with dendrites in the other three shown in gray. b. The association matrix of the clusters and seven groups defined by metadata combination of species, region, cell type, and age range. The number of dendrites in each group and cluster are shown in parentheses. Values reflect the natural log of the contingency matrix p-values, with absolute values above 3 being significant (p ≤ 0.05). Dark gray cells represent cases in which significantly fewer dendrites satisfy the cluster/group association than expected given the marginals. Positive values in colored cells represent cases in which more dendrites fall into the cell than expected. Darker colors signify that an example morphology and dendrogram is provided in c (NMO_05022, 09439, 00298, 00625, 05409, 06531, 07043, 06659) [54-61].
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Fig3: Dendrite clusters. a. Clusters of (non-apical) dendrites are shown in two perspectives of alignment space. Each perspective highlights dendrites in three of the six clusters, with dendrites in the other three shown in gray. b. The association matrix of the clusters and seven groups defined by metadata combination of species, region, cell type, and age range. The number of dendrites in each group and cluster are shown in parentheses. Values reflect the natural log of the contingency matrix p-values, with absolute values above 3 being significant (p ≤ 0.05). Dark gray cells represent cases in which significantly fewer dendrites satisfy the cluster/group association than expected given the marginals. Positive values in colored cells represent cases in which more dendrites fall into the cell than expected. Darker colors signify that an example morphology and dendrogram is provided in c (NMO_05022, 09439, 00298, 00625, 05409, 06531, 07043, 06659) [54-61].

Mentions: We next looked at how dendrites sub-clustered by topological alignment. Dendrites, which make up the largest arbor type subset of the data (N = 5,411), fell into 6 clusters with Gaussians of varying cluster size, shape, and orientation (Figure 3a). Four alignment dimensions were required to distinguish those clusters. Upon considering dendrite classes with sufficient specificity and size, seven classes showed highly significant association with one or more clusters (Figure 3b). The 6 dendrite clusters clearly separated cortical neurons from sensory neurons of several different species. The only class that reciprocally associated with only one cluster (and with a very high level of significance) consisted of primate neocortical (layer 2/3) pyramidal basal dendrites (cluster 1). Rodent neocortical pyramidal basal dendrites alone associated with cluster 2 and jointly with rodent neocortical interneuron dendrites in cluster 3. Clusters 4–6 associated with sensory and motor neurons. Motoneurons, primarily from cat, rat, and mouse, as well as rodent retinal ganglion cells, associated with cluster 4. The rodent retinal ganglion cells associated most significantly with cluster 5 along with adult fly tangential cells (visual system) and larval fly sensory cells. The latter two groups also associated with cluster 6. Thus, the three types of sensory neurons co-clustered, suggesting that topological features are shared by neurons which respond directly to the external environment. Tree size clearly distinguishes some of the clusters exemplified in some sample dendrites from each group, but differences in the number and distribution of major branches are also evident (Figure 3c).Figure 3


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

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

Dendrite clusters. a. Clusters of (non-apical) dendrites are shown in two perspectives of alignment space. Each perspective highlights dendrites in three of the six clusters, with dendrites in the other three shown in gray. b. The association matrix of the clusters and seven groups defined by metadata combination of species, region, cell type, and age range. The number of dendrites in each group and cluster are shown in parentheses. Values reflect the natural log of the contingency matrix p-values, with absolute values above 3 being significant (p ≤ 0.05). Dark gray cells represent cases in which significantly fewer dendrites satisfy the cluster/group association than expected given the marginals. Positive values in colored cells represent cases in which more dendrites fall into the cell than expected. Darker colors signify that an example morphology and dendrogram is provided in c (NMO_05022, 09439, 00298, 00625, 05409, 06531, 07043, 06659) [54-61].
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Dendrite clusters. a. Clusters of (non-apical) dendrites are shown in two perspectives of alignment space. Each perspective highlights dendrites in three of the six clusters, with dendrites in the other three shown in gray. b. The association matrix of the clusters and seven groups defined by metadata combination of species, region, cell type, and age range. The number of dendrites in each group and cluster are shown in parentheses. Values reflect the natural log of the contingency matrix p-values, with absolute values above 3 being significant (p ≤ 0.05). Dark gray cells represent cases in which significantly fewer dendrites satisfy the cluster/group association than expected given the marginals. Positive values in colored cells represent cases in which more dendrites fall into the cell than expected. Darker colors signify that an example morphology and dendrogram is provided in c (NMO_05022, 09439, 00298, 00625, 05409, 06531, 07043, 06659) [54-61].
Mentions: We next looked at how dendrites sub-clustered by topological alignment. Dendrites, which make up the largest arbor type subset of the data (N = 5,411), fell into 6 clusters with Gaussians of varying cluster size, shape, and orientation (Figure 3a). Four alignment dimensions were required to distinguish those clusters. Upon considering dendrite classes with sufficient specificity and size, seven classes showed highly significant association with one or more clusters (Figure 3b). The 6 dendrite clusters clearly separated cortical neurons from sensory neurons of several different species. The only class that reciprocally associated with only one cluster (and with a very high level of significance) consisted of primate neocortical (layer 2/3) pyramidal basal dendrites (cluster 1). Rodent neocortical pyramidal basal dendrites alone associated with cluster 2 and jointly with rodent neocortical interneuron dendrites in cluster 3. Clusters 4–6 associated with sensory and motor neurons. Motoneurons, primarily from cat, rat, and mouse, as well as rodent retinal ganglion cells, associated with cluster 4. The rodent retinal ganglion cells associated most significantly with cluster 5 along with adult fly tangential cells (visual system) and larval fly sensory cells. The latter two groups also associated with cluster 6. Thus, the three types of sensory neurons co-clustered, suggesting that topological features are shared by neurons which respond directly to the external environment. Tree size clearly distinguishes some of the clusters exemplified in some sample dendrites from each group, but differences in the number and distribution of major branches are also evident (Figure 3c).Figure 3

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.