A genetic and computational approach to structurally classify neuronal types

Nature Communications

Volumn 5, Issue , 2014, Pages

  Sümbül, Uygar ^a,b   Song, Sen ^a,c   McCulloch, Kyle ^b,d   Becker, Michael ^b   Lin, Bin ^b,e   Sanes, Joshua R ^f   Masland, Richard H ^b   Seung, H Sebastian ^a,g  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c Tsinghua University   (China)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e UNIVERSITY OF HONG KONG   (Hong Kong)

^f HARVARD UNIVERSITY   (United States)

^g PRINCETON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BRAIN; CELLS AND CELL COMPONENTS; GENETIC ANALYSIS; RODENT;

CLASSIFICATION; DENDRITE; NEURITE; QUANTITATIVE STUDY; RETINA GANGLION CELL; ALGORITHM; ANIMAL; BIOLOGY; CYTOLOGY; IMAGE PROCESSING; MOUSE; NERVE CELL; PROCEDURES; RETINA; ULTRASTRUCTURE;

ALGORITHMS; ANIMALS; COMPUTATIONAL BIOLOGY; DENDRITES; IMAGE PROCESSING, COMPUTER-ASSISTED; MICE; NEURONS; RETINA;

EID: 84903177431     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms4512     Document Type: Article

References (36)

1. Masland, R. H. The neuronal organization of the retina. Neuron 76, 266-280 (2012).
2. Gollisch, T. & Meister, M. Eye smarter than scientists believed: neural computations in circuits of the retina. Neuron 65, 150-164 (2010).
3. Sher, A. & De Vries, S. H. A non-canonical pathway for mammalian blue-green color vision. Nat. Neurosci. 15, 952-953 (2012).
4. Badea, T. C. & Nathans, J. Quantitative analysis of neuronal morphologies in the mouse retina visualized by using a genetically directed reporter. J. Comp. Neurol. 480, 331-351 (2004).
5. Kong, J. H., Fish, D. R., Rockhill, R. L. & Masland, R. H. Diversity of ganglion cells in the mouse retina: unsupervised morphological classification and its limits. J. Comp. Neurol. 489, 293-310 (2005).
6. Coombs, J., van der List, D., Wang, G.-Y. & Chalupa, L. M. Morphological properties of mouse retinal ganglion cells. Neuroscience 140, 123-136 (2006).
7. Völgyi, B., Chheda, S. & Bloomfield, S. A. Tracer coupling patterns of the ganglion cell subtypes in the mouse retina. J. Comp. Neurol. 512, 664-687 (2009).
8. Kim, I. J., Zhang, Y., Yamagata, M., Meister, M. & Sanes, J. R. Molecular identification of a retinal cell type that responds to upward motion. Nature 452, 478-482 (2008).
9. Huberman, A. D. et al. Architecture and activity-mediated refinement of axonal projections from a mosaic of genetically identified retinal ganglion cells. Neuron 59, 425-438 (2008).
10. Huberman, A. D. et al. Genetic identification of an On-Off direction-selective retinal ganglion cell subtype reveals a layer-specific subcortical map of posterior motion. Neuron 62, 327-334 (2009).
11. Yonehara, K. et al. Identification of retinal ganglion cells and their projections involved in central transmission of information about upward and downward image motion. PLoS One 4, (2009).
12. Kim, I. J., Zhang, Y., Meister, M. & Sanes, J. R. Laminar restriction of retinal ganglion cell dendrites and axons: subtype-specific developmental patterns revealed with transgenic markers. J. Neurosci. 30, 1452-1462 (2010).
13. Helmstaedter, M. et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168-174 (2013).
14. Kalisman, N., Silberberg, G. & Markram, H. Deriving physical connectivity from neuronal morphology. Biol. Cybern. 88, 210-218 (2003).
15. Stepanyants, A. & Chklovskii, D. B. Neurogeometry and potential synaptic connectivity. Trends Neurosci. 28, 387-394 (2005).
16. Osterhout, J. A. et al. Cadherin-6 mediates axon-target matching in a non-image-forming visual circuit. Neuron 71, 632-639 (2011).
17. Feng, G. et al. Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron 28, 41-51 (2000).
18. Haverkamp, S. &Wässle, H. Immunocytochemical analysis of the mouse retina. J. Comp. Neurol. 424, 1-23 (2000).
19. Manookin, M. B., Beaudoin, D. L., Ernst, Z. R., Flagel, L. J. & Demb, J. B. Disinhibition combines with excitation to extend the operating range of the OFF visual pathway in daylight. J. Neurosci. 28, 4136-4150 (2008).
20. Siegert, S. et al. Genetic address book for retinal cell types. Nat. Neurosci. 12, 1197-1204 (2009).
21. Jefferis, G.S.X.E. et al. Comprehensive maps of Drosophila higher olfactory centers: spatially segregated fruit and pheromone representation. Cell 128, 1187-1203 (2007).
22. Rand, W. M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66, 846-850 (1971).
23. Houchmandzadeh, B., Wieschaus, E. & Leibler, S. Establishment of developmental precision and proportions in the early Drosophila embryo. Nature 798-802 (2002).
24. Berson, D. M., Dunn, F. A. & Takao, M. Phototransduction by retinal ganglion cells that set the circadian clock. Science 295, 1070-1073 (2002).
25. Hattar, S., Liao, H. W., Takao, M., Berson, D. M. & Yau, K. W. Melanopsin-containing retinal ganglion cells: architecture, projections, and intrinsic photosensitivity. Science 295, 1065-1070 (2002).
26. Buffelli, M. et al. Genetic evidence that relative synaptic efficacy biases the outcome of synaptic competition. Nature 424, 430-434 (2003).
27. Gong, S. C. et al. A gene expression atlas of the central nervous system based on bacterial artificial chromosomes. Nature 425, 917-925 (2003).
28. Le Cun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278-2324 (1998).
29. Helmstaedter, M., Briggman, K. L. & Denk, W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat. Neurosci. 14, 1081-1088 (2011).
30. Bertrand, G. & Malandain, G. A new characterization of 3-dimensional simple points. Pattern Recogn. Lett. 15, 169-175 (1994).
31. Longair, M. H., Baker, D. A. & Armstrong, J. D. Simple Neurite Tracer: open source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics 27, 2453-2454 (2011).
32. Rousseeuw, P. J. Silhouettes-A graphical aid to the interpretation and validation of cluster-analysis. J. Comput. Appl. Math. 20, 53-65 (1987).
33. Szekely, G. J. & Rizzo, M. L. Hierarchical clustering via joint between-within distances: extending Ward's minimum variance method. J. Classif. 22, 151-183 (2005).
34. Levy, B., Petitjean, S., Ray, N. & Maillot, J. Least squares conformal maps for automatic texture atlas generation. ACM Trans. Graph. 21, 362-371 (2002).
35. Dijkstra, E. W. A note on two problems in connexion with graphs. Num. Math. 1, 269-271 (1959).
36. Jackson, J. I., Meyer, C. H., Nishimura, D. G. & Macovski, A. Selection of a convolution function for Fourier inversion using gridding. IEEE Trans. Med. Imaging 10, 473-478 (1991).