Skip to main content

Advertisement

Log in

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Altschul, S. F., et al. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215, 403–410.

    Article  CAS  PubMed  Google Scholar 

  • Ascoli, G. A., et al. (2001). Generation, description and storage of dendritic morphology data. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 356, 1131–1145.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies, The. Journal of Neuroscience, 27, 9247–9251.

    Article  CAS  PubMed  Google Scholar 

  • Basu, S., Condron, B., & Acton, S. T. (2011). Path2Path: Hierarchical path-based analysis for neuron matching. In Biomedical imaging: From nano to macro, 2011 I.E. international symposium on (pp. 996–999). IEEE.

  • Belongie, S., & Malik, J. (2000). Matching with shape contexts. IEEE Workshop on Content-Based Access of Image and Video Libraries. Proceedings, 20–26.

  • Bille, P. (2005). A survey on tree edit distance and related problems. Theoretical Computer Science, 337, 217–239.

    Article  Google Scholar 

  • Bustos, B., et al. (2005). Feature-based similarity search in 3D object databases. ACM Computing Surveys, 37, 345–387.

    Article  Google Scholar 

  • Cannon, R. C., et al. (1998). An on-line archive of reconstructed hippocampal neurons. Journal of Neuroscience Methods, 84, 49–54.

    Article  CAS  PubMed  Google Scholar 

  • Cardona, A., et al. (2010). Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 30, 7538–7553.

    Article  CAS  Google Scholar 

  • Chiang, A. S., Lin, C. Y., Chuang, C. C., Chang, H. M., Hsieh, C. H., Yeh, C. W., & Hwang, J. K. (2011). Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Current Biology, 21(1), 1–11.

    Article  CAS  PubMed  Google Scholar 

  • Chklovskii, D. B., Vitaladevuni, S., & Scheffer, L. K. (2010). Semi-automated reconstruction of neural circuits using electron microscopy. Current Opinion in Neurobiology, 20, 667–675.

    Article  CAS  PubMed  Google Scholar 

  • Costa, M., et al. (2014). NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases. bioRxiv, 006346.

  • Denk, W., & Horstmann, H. (2004). Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biology, 2, e329.

    Article  PubMed  PubMed Central  Google Scholar 

  • Dumitriu, D., Cossart, R., Huang, J., & Yuste, R. (2007). Correlation between axonal morphologies and synaptic input kinetics of interneurons from mouse visual cortex. Cerebral Cortex, 17(1), 81–91.

    Article  PubMed  Google Scholar 

  • Ganglberger, F., et al. (2014). Structure-based neuron retrieval across Drosophila brains. Neuroinformatics, 12, 423–434.

    Article  PubMed  Google Scholar 

  • Gillette, T.A., & Ascoli, G.A. (2015). Topological characterization of neuronal arbor morphology via sequence representation. I. Motif analysis (in press).

  • Gillette, T.A., Hosseini, P., & Ascoli, G.A. (2015). Topological characterization of neuronal arbor morphology via sequence representation. II. Global alignment (in press).

  • Heumann, H., & Wittum, G. (2009). The tree-edit-distance, a measure for quantifying neuronal morphology. Neuroinformatics, 7(3), 179–190.

    Article  PubMed  Google Scholar 

  • Hu, M. (1962). Visual-pattern recognition by moment invariants. IRE Transactions on Information Theory 8, 179-&.

  • Jacobs, G. A., & Theunissen, F. E. (2000). Extraction of sensory parameters from a neural map by primary sensory interneurons. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 20, 2934–2943.

    CAS  Google Scholar 

  • Jefferis, G. S., et al. (2007). Comprehensive maps of Drosophila higher olfactory centers: spatially segregated fruit and pheromone representation. Cell, 128, 1187–1203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Koene, R. A., et al. (2009). NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies. Neuroinformatics, 7, 195–210.

    Article  PubMed  Google Scholar 

  • Lo, C. H., & Don, H. S. (1989). 3-D moment forms - their construction and application to object identification and positioning. IEEE Transactions on Pattern Analysis, 11, 1053–1064.

    Article  Google Scholar 

  • Mayerich, D., et al. (2012). NetMets: software for quantifying and visualizing errors in biological network segmentation. BMC Bioinformatics, 13(Suppl 8), S7.

    PubMed  PubMed Central  Google Scholar 

  • Ming, X., et al. (2013). Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling. PLoS One, 8, e84557.

    Article  PubMed  PubMed Central  Google Scholar 

  • Peng, H., et al. (2010). V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology, 28, 348–353.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peng, H., Chung, P., Long, F., Qu, L., Jenett, A., Seeds, A. M., & Simpson, J. H. (2011). BrainAligner: 3D registration atlases of Drosophila brains. Nature Methods, 8(6), 493–498.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peng, H., Roysam, B., & Ascoli, G. A. (2013). Automated image computing reshapes computational neuroscience. BMC Bioinformatics, 14(1), 293.

    Article  PubMed  PubMed Central  Google Scholar 

  • Peng, H., Bria, A., Zhou, Z., Iannello, G., & Long, F. (2014). Extensible visualization and analysis for multidimensional images using Vaa3D. Nature Protocols, 9(1), 193–208.

    Article  CAS  PubMed  Google Scholar 

  • Peng, H., Meijering, E., & Ascoli, G. (2015) From DIADEM to BigNeuron. NeuroInformatics. doi:10.1007/s12021-015-9270-9.

  • Schnabel, R., Wahl, R., & Klein, R. (2007). Efficient RANSAC for point-cloud shape detection. In Computer graphics forum (26(2), 214–226). Blackwell Publishing Ltd.

  • Scorcioni, R., Polavaram, S., & Ascoli, G. A. (2008). L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nature Protocols, 3(5), 866–876.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sebastian, T. B., Klein, P. N., & Kimia, B. B. (2003). On aligning curves. IEEE Transactions on Pattern Analysis, 25, 116–125.

    Article  Google Scholar 

  • Tschirren, J., et al. (2005). Matching and anatomical labeling of human airway tree. IEEE Transactions on Medical Imaging, 24, 1540–1547.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, Y., et al. (2011). A broadly applicable 3-D neuron tracing method based on open-curve snake. Neuroinformatics, 9, 193–217.

    Article  PubMed  Google Scholar 

  • Xiao, H., & Peng, H. (2013). APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree. Bioinformatics, 29(11), 1448–1454.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang, K., & Shasha, D. (1989). Simple fast algorithms for the editing distance between trees and related problems. SIAM Journal on Computing, 18(6), 1245–1262.

    Article  Google Scholar 

  • Zhao, T., Xie, J., Amat, F., Clack, N., Ahammad, P., Peng, H., & Myers, E. (2011). Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics, 9(2–3), 247–261.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zheng, Q., et al. (2010). Consensus skeleton for non-rigid space-time registration. Computer Graphics Forum, 29, 635–644.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported primarily by the Janelia Research Campus of HHMI and the Allen Institute for Brain Science. Lei Qu was also partially supported by Chinese Natural Science Foundation Project (61201396, 61301296, 61377006, U1201255); Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry; Technology Foundation for Selected Overseas Chinese Scholar, Ministry of Personnel of China. We thank Zhi Zhou for providing some neuron reconstructions for testing in Fig. 8.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hanchuan Peng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wan, Y., Long, F., Qu, L. et al. BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies. Neuroinform 13, 487–499 (2015). https://doi.org/10.1007/s12021-015-9272-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-015-9272-7

Keywords

Navigation