Advertisement

Neuroinformatics

, Volume 12, Issue 3, pp 487–507 | Cite as

SPIN: A Method of Skeleton-Based Polarity Identification for Neurons

  • Yi-Hsuan Lee
  • Yen-Nan Lin
  • Chao-Chun Chuang
  • Chung-Chuan Lo
Original Article
First Online:

Abstract

Directional signal transmission is essential for neural circuit function and thus for connectomic analysis. The directions of signal flow can be obtained by experimentally identifying neuronal polarity (axons or dendrites). However, the experimental techniques are not applicable to existing neuronal databases in which polarity information is not available. To address the issue, we proposed SPIN: a method of Skeleton-based Polarity Identification for Neurons. SPIN was designed to work with large-scale neuronal databases in which tracing-line data are available. In SPIN, a classifier is first trained by neurons with known polarity in two steps: 1) identifying morphological features that most correlate with the polarity and 2) constructing a linear classifier by determining a discriminant axis (a specific combination of the features) and decision boundaries. Each polarity-undefined neuron is then divided into several morphological substructures (domains) and the corresponding polarities are determined using the classifier. Finally, the result is evaluated and warnings for potential errors are returned. We tested this method on fruitfly (Drosophila melanogaster) and blowfly (Calliphora vicina and Calliphora erythrocephala) unipolar neurons using data obtained from the Flycircuit and Neuromorpho databases, respectively. On average, the polarity of 84–92 % of the terminal points in each neuron could be correctly identified. An ideal performance with an accuracy between 93 and 98 % can be achieved if we fed SPIN with relatively “clean” data without artificial branches. Our result demonstrates that SPIN, as a computer-based semi-automatic method, provides quick and accurate polarity identification and is particularly suitable for analyzing large-scale data. We implemented SPIN in Matlab and released the codes under the GPLv3 license.

Keywords

Neuronal polarity Dendrite Axon Drosophila Automated neural reconstruction Connectome 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This work is supported by the National Science Council grants #NSC 101-2311-B-007-008-MY3 and Free Excellent Projects, and by the Aim for the Top University Project of the Ministry of Education, Taiwan. We thank the National Center for High-performance Computing for providing the Flycircuit data; Drs. Ann-Shyn Chiang and Hsiu-Ming Chang for helpful discussion. We also thank Dr. Chih-Yung Lin for providing PB data.

Conflict of Interest

The authors declare that they have no conflict of interests.

Supplementary material

12021_2014_9225_MOESM1_ESM.docx (1.1 mb)
ESM 1 (DOCX 1.08 MB)

References

  1. Alivisatos, A. P., Chun, M., Church, G. M., Greenspan, R. J., Roukes, M. L., & Yuste, R. (2012). The brain activity map project and the challenge of functional connectomics. Neuron, 74(6), 970–974. doi: 10.1016/j.neuron.2012.06.006.PubMedCentralPubMedCrossRefGoogle Scholar
  2. Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies. The Journal of Neuroscience, 27(35), 9247–9251. doi: 10.1523/JNEUROSCI.2055-07.2007.PubMedCrossRefGoogle Scholar
  3. Bas, E., & Erdogmus, D. (2011). Principal curves as skeletons of tubular objects: locally characterizing the structures of axons. Neuroinformatics, 9(2–3), 181–191. doi: 10.1007/s12021-011-9105-2.PubMedCrossRefGoogle Scholar
  4. Billeci, L., Magliaro, C., & Ahluwalia, A. (2013). NEuronMOrphological analysis tool: open-source software for quantitative morphometrics. Frontiers in Neuroinformatics, 7, 2. doi: 10.3389/fninf.2013.00002.PubMedCentralPubMedCrossRefGoogle Scholar
  5. Borst, A., & Haag, J. (1996). The intrinsic electrophysiological characteristics of fly lobula plate tangential cells: I. Passive membrane properties. Journal of Computational Neuroscience, 3(4), 313–336. doi: 10.1007/BF00161091.PubMedCrossRefGoogle Scholar
  6. Brown, K. M., Barrionuevo, G., Canty, A. J., Paola, V., Hirsch, J. A., Jefferis, G. S. X. E., et al. (2011). The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics, 9(2–3), 143–157. doi: 10.1007/s12021-010-9095-5.PubMedCrossRefGoogle Scholar
  7. Campagne, M. V. L., Oestreicher, A. B., Henegouwen, P. M. P. V. B. E., & Gispen, W. H. (1990). Ultrastructural double localization of B-50/GAP43 and synaptophysin (p38) in the neonatal and adult rat hippocampus. Journal of Neurocytology, 19(6), 948–961. doi: 10.1007/BF01186822.CrossRefGoogle Scholar
  8. Cannon, R., Turner, D., Pyapali, G., & Wheal, H. (1998). An on-line archive of reconstructed hippocampal neurons. Journal of Neuroscience Methods, 84(1–2), 49–54. doi: 10.1016/S0165-0270(98)00091-0.PubMedCrossRefGoogle Scholar
  9. Chiang, A.-S., Lin, C.-Y., 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. doi: 10.1016/j.cub.2010.11.056.PubMedCrossRefGoogle Scholar
  10. Chothani, P., Mehta, V., & Stepanyants, A. (2011). Automated tracing of neurites from light microscopy stacks of images. Neuroinformatics, 9(2–3), 263–278. doi: 10.1007/s12021-011-9121-2.PubMedCentralPubMedCrossRefGoogle Scholar
  11. Chou, Y.-H., Spletter, M. L., Yaksi, E., Leong, J. C. S., Wilson, R. I., & Luo, L. (2010). Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe. Nature Neuroscience, 13(4), 439–449. doi: 10.1038/nn.2489.PubMedCentralPubMedCrossRefGoogle Scholar
  12. Craig, A. M., & Banker, G. (1994). Neuronal polarity. Annual Review of Neuroscience, 17(1), 267–310. doi: 10.1146/annurev.ne.17.030194.001411.PubMedCrossRefGoogle Scholar
  13. Cuntz, H., Forstner, F., Haag, J., & Borst, A. (2008). The morphological identity of insect dendrites. PLoS Computational Biology, 4(12), e1000251. doi: 10.1371/journal.pcbi.1000251.PubMedCentralPubMedCrossRefGoogle Scholar
  14. Cuntz, H., Forstner, F., Borst, A., & Häusser, M. (2010). One rule to grow them all: a general theory of neuronal branching and its practical application. PLoS Computational Biology, 6(8), e1000877. doi: 10.1371/journal.pcbi.1000877.PubMedCentralPubMedCrossRefGoogle Scholar
  15. Donohue, D. E., & Ascoli, G. A. (2011). Automated reconstruction of neuronal morphology: an overview. Brain Research Reviews, 67(1–2), 94–102. doi: 10.1016/j.brainresrev.2010.11.003.PubMedCentralPubMedCrossRefGoogle Scholar
  16. Duchene, J., & Leclercq, S. (1988). An optimal transformation for discriminant and principal component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6), 978–983. doi: 10.1109/34.9121.CrossRefGoogle Scholar
  17. Feinberg, E. H., VanHoven, M. K., Bendesky, A., Wang, G., Fetter, R. D., Shen, K., et al. (2008). GFP reconstitution across synaptic partners (GRASP) defines cell contacts and synapses in living nervous systems. Neuron, 57(3), 353–363. doi: 10.1016/j.neuron.2007.11.030.PubMedCrossRefGoogle Scholar
  18. Fischbach, P. K.-F., & Dittrich, A. P. M. (1989). The optic lobe of Drosophila melanogaster. I. A Golgi analysis of wild-type structure. Cell and Tissue Research, 258(3), 441–475. doi: 10.1007/BF00218858.CrossRefGoogle Scholar
  19. Gillette, T. A., Brown, K. M., & Ascoli, G. A. (2011). The DIADEM metric: comparing multiple reconstructions of the same neuron. Neuroinformatics, 9(2–3), 233–245. doi: 10.1007/s12021-011-9117-y.PubMedCrossRefGoogle Scholar
  20. Glaser, J. R., & Glaser, E. M. (1990). Neuron imaging with Neurolucida–a PC-based system for image combining microscopy. Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society, 14(5), 307–317.CrossRefGoogle Scholar
  21. Gordon, M. D., & Scott, K. (2009). Motor control in a Drosophila taste circuit. Neuron, 61(3), 373–384. doi: 10.1016/j.neuron.2008.12.033.PubMedCentralPubMedCrossRefGoogle Scholar
  22. Hanesch, U., Fischbach, K.-F., & Heisenberg, M. (1989). Neuronal architecture of the central complex in Drosophila melanogaster. Cell and Tissue Research, 257(2), 343–366. doi: 10.1007/BF00261838.CrossRefGoogle Scholar
  23. Heinze, S., & Homberg, U. (2008). Neuroarchitecture of the central complex of the desert locust: intrinsic and columnar neurons. The Journal of Comparative Neurology, 511(4), 454–478. doi: 10.1002/cne.21842.PubMedCrossRefGoogle Scholar
  24. Ikeno, H., Kanzaki, R., Aonuma, H., Takahata, M., Mizunami, M., Yasuyama, K., et al. (2008). Development of invertebrate brain platform: Management of research resources for invertebrate neuroscience and neuroethology. In M. Ishikawa, K. Doya, H. Miyamoto, & T. Yamakawa (Eds.), Neural information processing (pp. 905–914). Springer: Berlin. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-69162-4_94.CrossRefGoogle Scholar
  25. Ito, M., Masuda, N., Shinomiya, K., Endo, K., & Ito, K. (2013). Systematic analysis of neural projections reveals clonal composition of the Drosophila brain. Current Biology, 23(8), 644–655. doi: 10.1016/j.cub.2013.03.015.PubMedCrossRefGoogle Scholar
  26. Jang, J.-S. R. (2012). Machine Learning Toolbox. http://mirlab.org/jang/matlab/toolbox/machineLearning. Accessed 12 June 2012.
  27. Lee, P.-C., Chuang, C.-C., Chiang, A.-S., & Ching, Y.-T. (2012). High-throughput computer method for 3D neuronal structure reconstruction from the image stack of the Drosophila brain and its applications. PLoS Computational Biology, 8(9), e1002658. doi: 10.1371/journal.pcbi.1002658.PubMedCentralPubMedCrossRefGoogle Scholar
  28. Lin, C.-Y., Chuang, C.-C., Hua, T.-E., Chen, C.-C., Dickson, B. J., Greenspan, R. J., et al. (2013a). A comprehensive wiring diagram of the protocerebral bridge for visual information processing in the Drosophila brain. Cell Reports, 3(5), 1739–1753. doi: 10.1016/j.celrep.2013.04.022.PubMedCrossRefGoogle Scholar
  29. Lin, H.-H., Chu, L.-A., Fu, T.-F., Dickson, B. J., & Chiang, A.-S. (2013b). Parallel neural pathways mediate CO2 avoidance responses in Drosophila. Science, 340(6138), 1338–1341. doi: 10.1126/science.1236693.PubMedCrossRefGoogle Scholar
  30. Luczak, A. (2010). Measuring neuronal branching patterns using model-based approach. Frontiers in Computational Neuroscience, 4, 10. doi: 10.3389/fncom.2010.00135.Google Scholar
  31. Matus, A., Bernhardt, R., & Hugh-Jones, T. (1981). High molecular weight microtubule-associated proteins are preferentially associated with dendritic microtubules in brain. Proceedings of the National Academy of Sciences of the United States of America, 78(5), 3010–3014.PubMedCentralPubMedCrossRefGoogle Scholar
  32. Müller, M., Homberg, U., & Kühn, A. (1997). Neuroarchitecture of the lower division of the central body in the brain of the locust (Schistocerca gregaria). Cell and Tissue Research, 288(1), 159–176.PubMedCrossRefGoogle Scholar
  33. Parekh, R., & Ascoli, G. A. (2013). Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron, 77(6), 1017–1038. doi: 10.1016/j.neuron.2013.03.008.PubMedCentralPubMedCrossRefGoogle Scholar
  34. Pastrana, E. (2013). Focus on mapping the brain. Nature Methods, 10(6), 481. doi: 10.1038/nmeth.2509.PubMedCrossRefGoogle Scholar
  35. Peng, H., Ruan, Z., Long, F., Simpson, J. H., & Myers, E. W. (2010). V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology, 28(4), 348–353. doi: 10.1038/nbt.1612.PubMedCentralPubMedCrossRefGoogle Scholar
  36. Robinson, I. M., Ranjan, R., & Schwarz, T. L. (2002). Synaptotagmins I and IV promote transmitter release independently of Ca2+ binding in the C2A domain. Nature, 418(6895), 336–340. doi: 10.1038/nature00915.PubMedCrossRefGoogle Scholar
  37. Rolls, M. M. (2011). Neuronal polarity in Drosophila: sorting out axons and dendrites. Developmental Neurobiology, 71(6), 419–429. doi: 10.1002/dneu.20836.PubMedCentralPubMedCrossRefGoogle Scholar
  38. Rolls, M. M., Satoh, D., Clyne, P. J., Henner, A. L., Uemura, T., & Doe, C. Q. (2007). Polarity and intracellular compartmentalization of Drosophila neurons. Neural Development, 2(1), 7. doi: 10.1186/1749-8104-2-7.PubMedCentralPubMedCrossRefGoogle Scholar
  39. 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. doi: 10.1038/nprot.2008.51.PubMedCrossRefGoogle Scholar
  40. Squire, L. R., Berg, D., Bloom, F., Lac, S. du, & Ghosh, A. (2008). Subcellular organization of the nervous system: organelles and their functions. In Fundamental Neuroscience (3rd ed., pp. 59–86). Amsterdam; Boston: Elsevier/Academic Press.Google Scholar
  41. Strausfeld, N. J., & Hausen, K. (1977). The resolution of neuronal assemblies after cobalt injection into neuropil. Proceedings of the Royal Society of London. Series B: Biological Sciences, 199(1136), 463–476. doi: 10.1098/rspb.1977.0154.CrossRefGoogle Scholar
  42. Takemura, S., Bharioke, A., Lu, Z., Nern, A., Vitaladevuni, S., Rivlin, P. K., et al. (2013). A visual motion detection circuit suggested by Drosophila connectomics. Nature, 500(7461), 175–181. doi: 10.1038/nature12450.PubMedCentralPubMedCrossRefGoogle Scholar
  43. Türetken, E., González, G., Blum, C., & Fua, P. (2011). Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics, 9(2–3), 279–302. doi: 10.1007/s12021-011-9122-1.PubMedCrossRefGoogle Scholar
  44. Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H., & Chklovskii, D. B. (2011). Structural properties of the Caenorhabditis elegans neuronal network. PLoS Computational Biology, 7(2), e1001066. doi: 10.1371/journal.pcbi.1001066.PubMedCentralPubMedCrossRefGoogle Scholar
  45. Wang, T., & Liao, D. (2011). Neuronal morphology classification based on SVM. In Computer Science and Service System (CSSS), 2011 International Conference on (pp. 3344–3347). doi: 10.1109/CSSS.2011.5972187.
  46. Wang, Y., Narayanaswamy, A., Tsai, C.-L., & Roysam, B. (2011b). A broadly applicable 3-D neuron tracing method based on open-curve snake. Neuroinformatics, 9(2–3), 193–217. doi: 10.1007/s12021-011-9110-5.PubMedCrossRefGoogle Scholar
  47. Wang, J., Ma, X., Yang, J. S., Zheng, X., Zugates, C. T., Lee, C.-H. J., et al. (2004). Transmembrane/juxtamembrane domain-dependent dscam distribution and function during mushroom body neuronal morphogenesis. Neuron, 43(5), 663–672. doi: 10.1016/j.neuron.2004.06.033.PubMedCrossRefGoogle Scholar
  48. White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986). The structure of the nervous system of the nematode Caenorhabditis elegans. Philosophical Transactions of the Royal Society of London B. Biological Sciences, 314(1165), 1–340. doi: 10.1098/rstb.1986.0056.CrossRefGoogle Scholar
  49. Whitney, A. W. (1971). A direct method of nonparametric measurement selection. IEEE Transactions on Computers, C-20(9), 1100–1103. doi: 10.1109/T-C.1971.223410.CrossRefGoogle Scholar
  50. Wichterle, H., Gifford, D., & Mazzoni, E. (2013). Mapping neuronal diversity one cell at a time. Science, 341(6147), 726–727. doi: 10.1126/science.1235884.PubMedCrossRefGoogle Scholar
  51. Xiao, H., & Peng, H. (2013). APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree. Bioinformatics (Oxford, England), 29(11), 1448–1454. doi: 10.1093/bioinformatics/btt170.CrossRefGoogle Scholar
  52. Yu, H.-H., Awasaki, T., Schroeder, M. D., Long, F., Yang, J. S., He, Y., et al. (2013). Clonal development and organization of the adult Drosophila central brain. Current Biology, 23(8), 633–643. doi: 10.1016/j.cub.2013.02.057.PubMedCentralPubMedCrossRefGoogle Scholar
  53. Zhao, T., Xie, J., Amat, F., Clack, N., Ahammad, P., Peng, H., et al. (2011). Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics, 9(2–3), 247–261. doi: 10.1007/s12021-011-9120-3.PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Yi-Hsuan Lee
    • 1
  • Yen-Nan Lin
    • 1
  • Chao-Chun Chuang
    • 3
  • Chung-Chuan Lo
    • 1
    • 2
  1. 1.Institute of Systems NeuroscienceNational Tsing Hua UniversityHsinchuTaiwan
  2. 2.Brain Research CenterNational Tsing Hua UniversityHsinchuTaiwan
  3. 3.National Center for High-Performance ComputingHsinchuTaiwan

Personalised recommendations

Cite article

Buy options