Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Connectomic reconstruction of the inner plexiform layer in the mouse retina

Subjects

A Corrigendum to this article was published on 15 October 2014

Abstract

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer—the main computational neuropil region in the mammalian retina—the dense reconstruction of 950 neurons and their mutual contacts. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. We characterize a new type of retinal bipolar interneuron and show that we can subdivide a known type based on connectivity. Circuit motifs that emerge from our data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Raw data, skeletons and bipolar cell analysis.
Figure 2: Ganglion and amacrine cells.
Figure 3: Automatic segmentation and contact detection.
Figure 4: Contact matrices.
Figure 5: CBC5 subtypes.
Figure 6: Circuits originating from the XBC and ON/OFF cells.

Similar content being viewed by others

References

  1. White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. Lond. B 314, 1–340 (1986)

    ADS  CAS  Google Scholar 

  2. Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H. & Chklovskii, D. B. Structural properties of the Caenorhabditis elegans neuronal network. PLOS Comput. Biol. 7, e1001066 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Binzegger, T., Douglas, R. J. & Martin, K. A. C. A quantitative map of the circuit of cat primary visual cortex. J. Neurosci. 24, 8441–8453 (2004)

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Helmstaedter, M., de Kock, C. P., Feldmeyer, D., Bruno, R. M. & Sakmann, B. Reconstruction of an average cortical column in silico. Brain Res. Rev. 55, 193–203 (2007)

    CAS  PubMed  Google Scholar 

  5. Briggman, K. L., Helmstaedter, M. & Denk, W. Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011)

    ADS  CAS  PubMed  Google Scholar 

  6. Stepanyants, A. & Chklovskii, D. B. Neurogeometry and potential synaptic connectivity. Trends Neurosci. 28, 387–394 (2005)

    CAS  PubMed  Google Scholar 

  7. Mishchenko, Y. et al. Ultrastructural analysis of hippocampal neuropil from the connectomics perspective. Neuron 67, 1009–1020 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Hill, S. L., Wang, Y., Riachi, I., Schurmann, F. & Markram, H. Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits. Proc. Natl Acad. Sci. USA 109, E2885–E2894 (2012)

    ADS  CAS  PubMed  Google Scholar 

  9. Denk, W., Briggman, K. L. & Helmstaedter, M. Structural neurobiology: missing link to a mechanistic understanding of neural computation. Nature Rev. Neurosci. 13, 351–358 (2012)

    CAS  Google Scholar 

  10. Peters, A. Thalamic input to the cerebral cortex. Trends Neurosci. 2, 183–185 (1979)

    Google Scholar 

  11. Markram, H., Lubke, J., Frotscher, M., Roth, A. & Sakmann, B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J. Physiol. (Lond.) 500, 409–440 (1997)

    CAS  Google Scholar 

  12. Fried, S. I., Munch, T. A. & Werblin, F. S. Mechanisms and circuitry underlying directional selectivity in the retina. Nature 420, 411–414 (2002)

    ADS  CAS  PubMed  Google Scholar 

  13. Asari, H. & Meister, M. Divergence of visual channels in the inner retina. Nature Neurosci. 15, 1581–1589 (2012)

    CAS  PubMed  Google Scholar 

  14. Stevens, J. K., Davis, T. L., Friedman, N. & Sterling, P. A systematic approach to reconstructing microcircuitry by electron microscopy of serial sections. Brain Res. 2, 265–293 (1980)

    CAS  PubMed  Google Scholar 

  15. Sterling, P. Microcircuitry of the cat retina. Annu. Rev. Neurosci. 6, 149–185 (1983)

    CAS  PubMed  Google Scholar 

  16. Famiglietti, E. V. Synaptic organization of starburst amacrine cells in rabbit retina: analysis of serial thin sections by electron microscopy and graphic reconstruction. J. Comp. Neurol. 309, 40–70 (1991)

    CAS  PubMed  Google Scholar 

  17. McGuire, B. A., Stevens, J. K. & Sterling, P. Microcircuitry of bipolar cells in cat retina. J. Neurosci. 4, 2920–2938 (1984)

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Briggman, K. L. & Bock, D. D. Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012)

    CAS  PubMed  Google Scholar 

  19. Masland, R. H. The neuronal organization of the retina. Neuron 76, 266–280 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Vaney, D. I., Sivyer, B. & Taylor, W. R. Direction selectivity in the retina: symmetry and asymmetry in structure and function. Nature Rev. Neurosci. 13, 194–208 (2012)

    CAS  Google Scholar 

  21. Euler, T., Detwiler, P. B. & Denk, W. Directionally selective calcium signals in dendrites of starburst amacrine cells. Nature 418, 845–852 (2002)

    ADS  CAS  PubMed  Google Scholar 

  22. Zhou, Z. J. & Lee, S. Synaptic physiology of direction selectivity in the retina. J. Physiol. (Lond.) 586, 4371–4376 (2008)

    CAS  Google Scholar 

  23. Wei, W., Hamby, A. M., Zhou, K. & Feller, M. B. Development of asymmetric inhibition underlying direction selectivity in the retina. Nature 469, 402–406 (2011)

    ADS  CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  25. Helmstaedter, M., Briggman, K. L. & Denk, W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nature Neurosci. 14, 1081–1088 (2011)

    CAS  PubMed  Google Scholar 

  26. Jain, V. et al. Supervised learning of image restoration with convolutional networks. IEEE 11th International Conference on Computer Vision 2, 1–8 (2007)

    Google Scholar 

  27. Turaga, S. C. et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511–538 (2010)

    PubMed  MATH  Google Scholar 

  28. Wässle, H., Puller, C., Muller, F. & Haverkamp, S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina. J. Neurosci. 29, 106–117 (2009)

    PubMed  PubMed Central  Google Scholar 

  29. Amthor, F. R., Oyster, C. W. & Takahashi, E. S. Morphology of on-off direction-selective ganglion cells in the rabbit retina. Brain Res. 298, 187–190 (1984)

    CAS  PubMed  Google Scholar 

  30. Strettoi, E., Raviola, E. & Dacheux, R. F. Synaptic connections of the narrow-field, bistratified rod amacrine cell (AII) in the rabbit retina. J. Comp. Neurol. 325, 152–168 (1992)

    CAS  PubMed  Google Scholar 

  31. Macneil, M. A., Heussy, J. K., Dacheux, R. F., Raviola, E. & Masland, R. H. The shapes and numbers of amacrine cells: matching of photofilled with Golgi-stained cells in the rabbit retina and comparison with other mammalian species. J. Comp. Neurol. 413, 305–326 (1999)

    CAS  PubMed  Google Scholar 

  32. Fyk-Kolodziej, B. & Pourcho, R. G. Differential distribution of hyperpolarization-activated and cyclic nucleotide-gated channels in cone bipolar cells of the rat retina. J. Comp. Neurol. 501, 891–903 (2007)

    PubMed  Google Scholar 

  33. Wassle, H. & Riemann, H. J. Mosaic of nerve-cells in mammalian retina. Proc. R. Soc. Lond. B 200, 441–461 (1978)

    ADS  CAS  PubMed  Google Scholar 

  34. 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)

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Levick, W. R. Receptive fields and trigger features of ganglion cells in the visual streak of the rabbits retina. J. Physiol. (Lond.) 188, 285–307 (1967)

    CAS  Google Scholar 

  36. Amthor, F. R., Takahashi, E. S. & Oyster, C. W. Morphologies of rabbit retinal ganglion cells with complex receptive fields. J. Comp. Neurol. 280, 97–121 (1989)

    CAS  PubMed  Google Scholar 

  37. Kolb, H., Nelson, R. & Mariani, A. Amacrine cells, bipolar cells and ganglion cells of the cat retina: a Golgi study. Vision Res. 21, 1081–1114 (1981)

    CAS  PubMed  Google Scholar 

  38. Sivyer, B., Venkataramani, S., Taylor, W. R. & Vaney, D. I. A novel type of complex ganglion cell in rabbit retina. J. Comp. Neurol. 519, 3128–3138 (2011)

    PubMed  PubMed Central  Google Scholar 

  39. Siegert, S. et al. Genetic address book for retinal cell types. Nature Neurosci. 12, 1197–1204 (2009)

    CAS  PubMed  Google Scholar 

  40. 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)

    PubMed  Google Scholar 

  41. Joo, H. R., Peterson, B. B., Haun, T. J. & Dacey, D. M. Characterization of a novel large-field cone bipolar cell type in the primate retina: evidence for selective cone connections. Vis. Neurosci. 28, 29–37 (2011)

    PubMed  Google Scholar 

  42. Seung, H. S. Reading the book of memory: sparse sampling versus dense mapping of connectomes. Neuron 62, 17–29 (2009)

    CAS  PubMed  Google Scholar 

  43. Masland, R. H. The fundamental plan of the retina. Nature Neurosci. 4, 877–886 (2001)

    CAS  PubMed  Google Scholar 

  44. Andres, B. et al. in Computer Vision – ECCV 2012 Lecture Notes in Computer Science (eds Fitzgibbon, A. et al.) 778–791 (Springer, 2012)

  45. Tsukamoto, Y., Morigiwa, K., Ueda, M. & Sterling, P. Microcircuits for night vision in mouse retina. J. Neurosci. 21, 8616–8623 (2001)

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Calkins, D. J. & Sterling, P. Microcircuitry for two types of achromatic ganglion cell in primate fovea. J. Neurosci. 27, 2646–2653 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhang, Y., Kim, I. J., Sanes, J. R. & Meister, M. The most numerous ganglion cell type of the mouse retina is a selective feature detector. Proc. Natl Acad. Sci. USA 109, E2391–E2398 (2012)

    CAS  PubMed  Google Scholar 

  48. Ölveczky, B. P., Baccus, S. A. & Meister, M. Segregation of object and background motion in the retina. Nature 423, 401–408 (2003)

    ADS  PubMed  Google Scholar 

  49. Turaga, S. C., Briggman, K., Helmstaedter, M., Denk, W. & Seung, H. S. Maximin affinity learning of image segmentation. Adv. Neural Info. Proc. Syst. 22, 1–8 (2009)

    Google Scholar 

  50. Studer, D. & Gnaegi, H. Minimal compression of ultrathin sections with use of an oscillating diamond knife. J. Microsc. 197, 94–100 (2000)

    CAS  PubMed  Google Scholar 

  51. Binding, J., Mikula, S. & Denk, W. Low-dosage maximum-a-posteriori focusing and stigmation. Microsc. Microanal. 19, 38–55 (2013)

    ADS  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank J. Diamond, T. Euler, R. Masland, M. Meister and J. Sanes for discussions, J. Kornfeld and F. Svara for programming and continually improving KNOSSOS, M. Müller and J. Tritthardt for programming and building instrumentation, C. Roome for IT support, and A. Borst, M. Fee, T. Gollisch and A. Karpova for comments on the manuscript. We especially thank F. Isensee for help with synapse identification. We thank P. Bastians, A. Biasotto, F. Drawitsch, H. Falk, A. Gable, M. Grohmann, A. Gäbelein, J. Hanne, F. Isensee, H. Jakobi, M. Kotchourko, E. Möller, J. Pollmann, C. Röhrig, A. Rommerskirchen, L. Schreiber, C. Willburger, H. Wissler and J. Youm for reconstruction management and annotator training, and N. Abazova, S. Abele, O. Aderhold, C. Altbürger, T. Amberger, K. Aninditha, A. Antunes, E. Atsiatorme, H. Augenstein, I. Bartsch, I. Barz, P. Bastians, J. Bauer, H. Bauersachs, R. Bay, J. Becker, M. Beez, S. Bender, M. Berberich, I. Bertlich, J. Bewersdorf, A. Biasotto, P. Biti, M. Bittmann, K. Bretzel, J. Briegel, E. Buckler, A. Buntjer, C. Burkhardt, S. Bühler, S. Daum, N. Demir, E. Demirel, S. Dettmer, M. Diemer, J. Dietrich, S. Dittrich, C. Domnick, F. Drawitsch, C. Eck, L. Ehm, S. Ehrhardt, T. Eliguezel, K. Ernst, O. Eryilmaz, F. Euler, H. Falk, K. Fischer, K. Foerster, R. Foitzik, A. Foltin, R. Foltin, S. Freiß, A. Gable, P. Gallandi, K. Garbe, A. Gebhardt, F. Gebhart, S. Gottwalt, A. Greis, M. Grohmann, A. Gromann, S. Gröbner, E. Grün, M. Grün, K. Guo, A. Gäbelein, K. Haase, J. Hammerich, J. Hanne, B. Hauber, M. Hensen, F. Hentzschel, M. Herberz, M. Heumannskämper, C. Hilbert, L. Hofmann, P. Hofmann, T. Hondrich, U. Häusler, M. Höreth, J. Hügle, F. Isensee, A. Ivanova, F. Jahnke, H. Jakobi, M. Joel, M. Jonczyk, A. Joschko, A. Jünger, K. Kappler, S. Kaspar, C. Kehrel, J. Kern, K. Keßler, S. Khoury, M. Kiapes, M. Kirchberger, A. Klein, C. Klein, S. Klein, J. Kratzer, C. Kraut, P. Kremer, P. Kretzer, F. Kröller, D. Krüger, M. Kuderer, S. Kull, S. Kwakman, S. Laiouar, L. Lebelt, H. Lesch, R. Lichtenberger, J. Liermann, C. Lieven, J. Lin, B. Linser, S. Lorger, J. Lott, D. Luft, L. Lust, J. Löffler, C. Marschall, B. Martin, D. Maton, B. Mayer, S. Mayorca, de. Ituarte, M. Meleux, C. Meyer, M. Moll, T. Moll, L. Mroszewski, E. Möller, M. Müller, L. Münster, N. Nasresfahani, J. Nassal, M. Neuschwanger, C. Nguyen, J. Nguyen, N. Nitsche, S. Oberrauch, F. Obitz, D. Ollech, C. Orlik, T. Otolski, S. Oumohand, A. Palfi, J. Pesch, M. Pfarr, S. Pfarr, M. Pohrath, J. Pollmann, M. Prokscha, S. Putzke, E. Rachmad, M. Reichert, J. Reinhardt, M. Reitz, J. Remus, M. Richter, M. Richter, J. Ricken, N. Rieger, F. Rodriguez. Jahnke, A. Rommerskirchen, M. Roth, I. Rummer, J. Rätzer, C. Röhrig, J. Röther, V. Saratov, E. Sauter, T. Schackel, M. Schamberger, M. Scheller, J. Schied, M. Schiedeck, J. Schiele, K. Schleich, M. Schlösser, S. Schmidt, C. Schneeweis, K. Schramm, M. Schramm, L. Schreiber, D. Schwarz, A. Schürholz, L. Schütz, A. Seitz, C. Sellmann, E. Serger, J. Sieber, L. Silbermann, I. Sonntag, T. Speck, Y. Söhngen, T. Tannig, N. Tisch, V. Tran, J. Trendel, M. Uhrig, D. Vecsei, F. Viehweger, V. Viehweger, R. Vogel, A. Vogel, J. Volz, P. Weber, K. Wegmeyer, J. Wiederspohn, E. Wiegand, R. Wiggers, C. Willburger, H. Wissler, V. Wissdorf, S. Wörner, J. Youm, A. Zegarra, J. Zeilfelder, F. Zickgraf and T. Ziegler for cell reconstruction. This work was supported by the Max-Planck Society and the DFG (Leibniz prize to W.D.). H.S.S. is grateful for support from the Gatsby Charitable Foundation.

Author information

Authors and Affiliations

Authors

Contributions

M.H. and W.D. designed the study. K.L.B. prepared the samples and acquired the data using a microtome designed by W.D. M.H. analysed the data, with minor contributions from W.D. S.C.T., V.J. and H.S.S. developed the boundary classifier. M.H., K.L.B. and W.D. wrote the paper.

Corresponding author

Correspondence to Moritz Helmstaedter.

Ethics declarations

Competing interests

W.D. receives licensing income from Gatan Inc.

Supplementary information

Supplementary Information

This file contains full descriptions of Supplementary Data sets 1-8. (PDF 523 kb)

Supplementary Data

This file contains Supplementary Data 1, a gallery of cell types, depth profiles, and contact area plots (see Supplementary Information for detailed description). (PDF 27686 kb)

Supplementary Data

This zipped file contains Supplementary Data files 2, 4, 5, 7 and 8 (see Supplementary Information for detailed description). (ZIP 26213 kb)

Supplementary Data

This zipped file contains Supplementary Data files 3a and b, which contain volume data samples from the conventionally stained sample (see Supplementary Information for detailed description). (ZIP 27921 kb)

Supplementary Data

This zipped file contains Supplementary Data files 3c, d and e containing volume data samples of EM data from the main data set (e2006), X-direction component of the classifier output for the same region, and segmentation before skeleton-based object collection (see Supplementary Information for detailed description). (ZIP 27295 kb)

Supplementary Data

This zipped file contains Supplementary Data 6 a and b, 6a contains a gallery of 36 Ganglion cells, 6b contains Gallery of 190 small-field Amacrine cells (see Supplementary Information for detailed description). (ZIP 22023 kb)

Supplementary Data

This zipped file contains Supplementary Data 6c, which contains a gallery of 163 medium- and wide-field Amacrine cells (see Supplementary Information for detailed description). (ZIP 16569 kb)

Supplementary Data

This zipped file contains Supplementary Data 6d which contains a gallery of 307 cone bipolar cells (see Supplementary Information for detailed description). (ZIP 26832 kb)

Supplementary Data

This zipped file contains Supplementary Data 6e and f, 6e contains a gallery of 144 rod bipolar cells and 6f contains a gallery of 110 cells from the “orphan” category (see Supplementary Information for detailed description). (ZIP 21656 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Helmstaedter, M., Briggman, K., Turaga, S. et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174 (2013). https://doi.org/10.1038/nature12346

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature12346

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing