Abstract

High-resolution serial-section electron microscopy (ssEM) makes it possible to investigate the dense meshwork of axons, dendrites, and synapses that form neuronal circuits1. However, the imaging scale required to comprehensively reconstruct these structures is more than ten orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons2, some of which span nearly the entire brain. Difficulties in generating and handling data for large volumes at nanoscale resolution have thus restricted vertebrate studies to fragments of circuits. These efforts were recently transformed by advances in computing, sample handling, and imaging techniques1, but high-resolution examination of entire brains remains a challenge. Here, we present ssEM data for the complete brain of a larval zebrafish (Danio rerio) at 5.5 days post-fertilization. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management requirements. The resulting dataset can be analysed to reconstruct neuronal processes, permitting us to survey all myelinated axons (the projectome). These reconstructions enable precise investigations of neuronal morphology, which reveal remarkable bilateral symmetry in myelinated reticulospinal and lateral line afferent axons. We further set the stage for whole-brain structure–function comparisons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. All obtained images and reconstructions are provided as an open-access resource.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    & Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012)

  2. 2.

    & The big and the small: challenges of imaging the brain’s circuits. Science 334, 618–623 (2011)

  3. 3.

    , , & The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. Lond. B 314, 1–340 (1986)

  4. 4.

    et al. The connectome of a decision-making neural network. Science 337, 437–444 (2012)

  5. 5.

    et al. Neuronal connectome of a sensory-motor circuit for visual navigation. eLife 3, e02730 (2014)

  6. 6.

    et al. A multilevel multimodal circuit enhances action selection in Drosophila. Nature 520, 633–639 (2015)

  7. 7.

    et al. The neural circuit for touch sensitivity in Caenorhabditis elegans. J. Neurosci. 5, 956–964 (1985)

  8. 8.

    , , & The interscutularis muscle connectome. PLoS Biol. 7, e32 (2009)

  9. 9.

    et al. Network anatomy and in vivo physiology of visual cortical neurons. Nature 471, 177–182 (2011)

  10. 10.

    , & Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011)

  11. 11.

    et al. Anatomy and function of an excitatory network in the visual cortex. Nature 532, 370–374 (2016)

  12. 12.

    & High-resolution whole-brain staining for electron microscopic circuit reconstruction. Nat. Methods 12, 541–546 (2015)

  13. 13.

    Ultrastructure of retinal rod synapses of the guinea pig eye as revealed by three-dimensional reconstructions from serial sections. J. Ultrastruct. Res. 2, 122–170 (1958)

  14. 14.

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

  15. 15.

    , , & Synaptic circuits involving an individual retinogeniculate axon in the cat. J. Comp. Neurol. 259, 165–192 (1987)

  16. 16.

    , , & Salient features of synaptic organisation in the cerebral cortex. Brain Res. Brain Res. Rev. 26, 113–135 (1998)

  17. 17.

    & Three-dimensional structure and composition of CA3→CA1 axons in rat hippocampal slices: implications for presynaptic connectivity and compartmentalization. J. Neurosci. 18, 8300–8310 (1998)

  18. 18.

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

  19. 19.

    , & The synaptic organization of the claustral projection to the cat’s visual cortex. J. Neurosci. 30, 13166–13170 (2010)

  20. 20.

    & Large-scale imaging in small brains. Curr. Opin. Neurobiol. 32, 78–86 (2015)

  21. 21.

    , & Circuit neuroscience in zebrafish. Curr. Biol. 20, R371–R381 (2010)

  22. 22.

    , , , & Dense EM-based reconstruction of the interglomerular projectome in the zebrafish olfactory bulb. Nat. Neurosci. 19, 816–825 (2016)

  23. 23.

    , & Zebrafish and motor control over the last decade. Brain Res. Rev. 57, 86–93 (2008)

  24. 24.

    , & Prey capture behavior evoked by simple visual stimuli in larval zebrafish. Front. Syst. Neurosci. 5, 101 (2011)

  25. 25.

    et al. Neural circuits underlying visually evoked escapes in larval zebrafish. Neuron 89, 613–628 (2016)

  26. 26.

    et al. Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits. Front. Neural Circuits 8, 68 (2014)

  27. 27.

    et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015)

  28. 28.

    ., ., ., . & A novel mechanism for mechanosensory based rheotaxis in larval zebrafish. Nature (in the press)

  29. 29.

    , & Segmental homologies among reticulospinal neurons in the hindbrain of the zebrafish larva. J. Comp. Neurol. 251, 147–159 (1986)

  30. 30.

    Nerve fibre topography in the retinal projection to the tectum. Nature 278, 620–624 (1979)

  31. 31.

    , & in Zebrafish: A Practical Approach (eds & ) Ch. 1, 7–37 (Oxford, 2002)

  32. 32.

    , , , & Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10, 413–420 (2013)

  33. 33.

    et al. Optimization of a GCaMP calcium indicator for neural activity imaging. J. Neurosci. 32, 13819–13840 (2012)

  34. 34.

    , , , & W. nacre encodes a zebrafish microphthalmia-related protein that regulates neural-crest-derived pigment cell fate. Development 126, 3757–3767 (1999)

  35. 35.

    , , & Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior. Neuron 81, 1328–1343 (2014)

  36. 36.

    & Lamina-specific axonal projections in the zebrafish tectum require the type IV collagen Dragnet. Nat. Neurosci. 10, 1529–1537 (2007)

  37. 37.

    , , & Ancestry of motor innervation to pectoral fin and forelimb. Nat. Commun. 1, 49 (2010)

  38. 38.

    , & A method for detecting molecular transport within the cerebral ventricles of live zebrafish (Danio rerio) larvae. J. Physiol. (Lond.) 590, 2233–2240 (2012)

  39. 39.

    & Atlas of Early Zebrafish Brain Development: a Tool for Molecular Neurogenetics. 1st edn, 183 (Elsevier, 2005)

  40. 40.

    in Advanced Techniques in Biological Electron Microscopy (ed. ) Ch. 1, 1–34 (Springer, 1973)

  41. 41.

    Whole-Brain Functional and Structural Examination in Larval Zebrafish (Harvard Univ. Press, 2015)

  42. 42.

    , , & Mosaic hoxb4a neuronal pleiotropism in zebrafish caudal hindbrain. PLoS One 4, e5944 (2009)

  43. 43.

    , , & The fuzzy logic of network connectivity in mouse visual thalamus. Cell 165, 192–206 (2016)

  44. 44.

    et al. High-resolution, high-throughput imaging with a multibeam scanning electron microscope. J. Microsc. 259, 114–120 (2015)

  45. 45.

    et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012)

  46. 46.

    , , & As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets. Bioinformatics 26, i57–i63 (2010)

  47. 47.

    , , & Elastic volume reconstruction from series of ultra-thin microscopy sections. Nat. Methods 9, 717–720 (2012)

  48. 48.

    . et al. Registering large volume serial-section electron microscopy image sets for neural circuit reconstruction using FFT signal whitening. Applied Imagery Pattern Recognition Workshop (AIPR) IEEE (in the press)

  49. 49.

    , , & Robust registration of calcium images by learned contrast synthesis. 13th Intl Symp. Biomedical Imaging (ISBI) IEEE, 1123–1126 (2016)

  50. 50.

    et al. Whole-brain activity mapping onto a zebrafish brain atlas. Nat. Methods 12, 1039–1046 (2015)

  51. 51.

    , , & High precision registration between zebrafish brain atlases using symmetric diffeomorphic normalization. Preprint at (2016)

  52. 52.

    et al. A 3D searchable database of transgenic zebrafish Gal4 and Cre lines for functional neuroanatomy studies. Front. Neural Circuits 9, 78 (2015)

  53. 53.

    , , & CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25, 1984–1986 (2009)

  54. 54.

    et al. Quantitative neuroanatomy for connectomics in Drosophila. eLife 5, e12059 (2016)

  55. 55.

    ., . & The Fine Structure of the Nervous System: Neurons and their Supporting Cells (Oxford, 1991)

  56. 56.

    et al. Vivaldi: a domain-specific language for volume processing and visualization on distributed heterogeneous systems. IEEE Trans. Vis. Comput. Graph. 20, 2407–2416 (2014)

  57. 57.

    , & Finding mirror symmetry via registration. Preprint at (2017)

  58. 58.

    , & Brain neurons which project to the spinal cord in young larvae of the zebrafish. J. Comp. Neurol. 205, 112–127 (1982). 10.1002/cne.902050203

  59. 59.

    Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5, 32–38 (1957)

Download references

Acknowledgements

We thank D. D. Bock and K.-H. Huang for preliminary studies; L.-H. Ma, M. B. Ahrens, and D. Schoppik for dissection help; E. Raviola, H. S. Kim, J. A. Buchanan, E. J. Benecchi, and S. Ito for histology guidance; K. J. Hayworth, J. L. Morgan, N. Kasthuri, and R. Schalek for ssEM advice; T. Kazimiers and J. A. Bogovic for software assistance at the Harvard CBS Neuroengineering Core; B. L. Shanny, A. M. Roberson, M. A. Afifi, F. Gao, A. D. Wong, F. Camacho Garcia, C. S. Elkhill, T. J. Cawley, R. J. Plummer, K. M. Runci, A. Haddad, P. E. Lewis, I. Odstrcil, A. H. Cohen, and P. I. Petkova for reconstructions; and R. C. Reid, R. I. Wilson, and J. R. Sanes for valuable input. Support was provided by the NIH through NINDS and NIMH to F.E. (DP1NS082121, RC2NS069407, U01NS090449) and W.-C.A.L. (R21NS085320) and the Harvard CBS Neuroengineering Core (P30NS062685), through NIGMS to MMBioS via the Pittsburgh Supercomputing Center (P41GM103712); by the DARPA SIMPLEX program through SPAWAR to R.B. and J.T.V. (N66001-15-C-4041); by the Korea NRF through MSIP (NRF-2015M3A9A7029725) and MOE (NRF-2014R1A1A2058773) to W.-K.J.; and by the NIH (T32MH20017, T32HL007901) and the NSF (IIA-EAPSI-1317014) to D.G.C.H.

Author information

Author notes

    • David Grant Colburn Hildebrand
    • , Russel Miguel Torres
    • , Owen Randlett
    • , George Scott Plummer
    • , Ruben Portugues
    • , Isaac Henry Bianco
    •  & Wei-Chung Allen Lee

    Present addresses: Laboratory of Neural Systems, Rockefeller University, New York, New York, USA (D.G.C.H.); Allen Institute for Brain Science, Seattle, Washington, USA (R.M.T.); Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA (O.R.); Tufts University School of Medicine, Boston, Massachusetts, USA (G.S.P.); Max Planck Institute of Neurobiology, Martinsried, Germany (R.P.); Department of Neuroscience, Physiology, and Pharmacology, University College London, London, UK (I.H.B.); F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, Massachusetts, USA (W.-C.A.L.).

    • Jeff William Lichtman
    •  & Florian Engert

    These authors contributed equally to this work.

Affiliations

  1. Graduate Program in Neuroscience, Division of Medical Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, Massachusetts, USA

    • David Grant Colburn Hildebrand
  2. Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA

    • David Grant Colburn Hildebrand
    • , Russel Miguel Torres
    • , Owen Randlett
    • , George Scott Plummer
    • , Ruben Portugues
    • , Isaac Henry Bianco
    • , Alexander Franz Schier
    • , Jeff William Lichtman
    •  & Florian Engert
  3. Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA

    • David Grant Colburn Hildebrand
    • , Alexander Franz Schier
    • , Jeff William Lichtman
    •  & Florian Engert
  4. Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA

    • David Grant Colburn Hildebrand
    • , Russel Miguel Torres
    • , Brett Jesse Graham
    •  & Wei-Chung Allen Lee
  5. Image and Data Analysis Core, Harvard Medical School, Boston, Massachusetts, USA

    • David Grant Colburn Hildebrand
    •  & Marcelo Cicconet
  6. School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea

    • Woohyuk Choi
    • , Tran Minh Quan
    • , Jungmin Moon
    •  & Won-Ki Jeong
  7. Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    • Arthur Willis Wetzel
  8. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA

    • Andrew Scott Champion
    •  & Stephan Saalfeld
  9. Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA

    • Alexander David Baden
    • , Kunal Lillaney
    •  & Randal Burns
  10. Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA

    • Joshua Tzvi Vogelstein
  11. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    • Alexander Franz Schier
  12. Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA

    • Alexander Franz Schier
  13. FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, USA

    • Alexander Franz Schier

Authors

  1. Search for David Grant Colburn Hildebrand in:

  2. Search for Marcelo Cicconet in:

  3. Search for Russel Miguel Torres in:

  4. Search for Woohyuk Choi in:

  5. Search for Tran Minh Quan in:

  6. Search for Jungmin Moon in:

  7. Search for Arthur Willis Wetzel in:

  8. Search for Andrew Scott Champion in:

  9. Search for Brett Jesse Graham in:

  10. Search for Owen Randlett in:

  11. Search for George Scott Plummer in:

  12. Search for Ruben Portugues in:

  13. Search for Isaac Henry Bianco in:

  14. Search for Stephan Saalfeld in:

  15. Search for Alexander David Baden in:

  16. Search for Kunal Lillaney in:

  17. Search for Randal Burns in:

  18. Search for Joshua Tzvi Vogelstein in:

  19. Search for Alexander Franz Schier in:

  20. Search for Wei-Chung Allen Lee in:

  21. Search for Won-Ki Jeong in:

  22. Search for Jeff William Lichtman in:

  23. Search for Florian Engert in:

Contributions

F.E. and D.G.C.H. designed experiments. R.P., D.G.C.H., and I.H.B. conducted light microscopy. D.G.C.H. prepared and sectioned samples. D.G.C.H. and G.S.P. completed ssEM. A.W.W., S.S., and D.G.C.H. aligned ssEM. D.G.C.H. and O.R. registered light and ssEM. R.M.T., B.J.G., and D.G.C.H. processed reconstructions. M.C. and D.G.C.H. analysed symmetry. W.C., T.M.Q., J.M., D.G.C.H., and W.-K.J. made visualizations. A.S.C. modified annotation software. A.D.B., K.L., R.B., and J.T.V. provided hosting. F.E., J.W.L., W.-C.A.L., and A.F.S. supplied resources. D.G.C.H., F.E., and J.W.L. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to David Grant Colburn Hildebrand or Florian Engert.

Reviewer Information Nature thanks A. Cardona and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

Videos

  1. 1.

    Survey of sections from the 5.5 dpf larval zebrafish series

    Electron micrographs of 17,963 sections acquired at 758.8 × 758.8 × 60 nm3 per voxel to survey sectioning quality and provide a basis for targeting subsequent high-resolution acquisition. Several sections excluded for faster viewing. Scale bar: 250 µm.

  2. 2.

    Section overview volume rendering

    Volume rendering of aligned 758.8 × 758.8 × 60 nm3 per voxel overviews from 17,963 sections shows how much of the 5.5 dpf larval zebrafish was captured with this series. Magenta and yellow planes correspond to reslice planes in Extended Data Fig. 3c. Green plane corresponds to section outlined in Extended Data Fig. 3a. Scale box: 250 µm.

  3. 3.

    Isotropic ssEM through the anterior quarter of the 5.5 dpf larval zebrafish

    Fly-through of aligned 56.4 × 56.4 × 60 nm3 per voxel electron micrographs acquired for 16,000 transverse sections. The resulting image volume contains the anterior quarter and entire brain of a 5.5 dpf larval zebrafish. Several sections excluded for faster viewing. Scale bar: 50 µm.

  4. 4.

    Mauthner cell reconstruction

    Fly-through of aligned 56.4 × 56.4 × 60 nm3 per voxel electron micrographs centred on part of a skeleton reconstruction of the left Mauthner cell (magenta dot) from the 5.5 dpf larval zebrafish ssEM dataset. The video begins at the Mauthner lateral dendrite, proceeds through its soma and axon cap, crosses to the contralateral side, and ends in the spinal cord. Extracting vertical line segments from the centre of these micrographs yielded the contour reslices in Fig. 1a−c. Several sections excluded for faster viewing. Scale bar: 5 µm.

  5. 5.

    Correspondence across light and ssEM datasets

    Fly-through of matched horizontal reslice planes through the 56.4 × 56.4 × 60 nm3 per voxel ssEM dataset (upper) and co-registered 0.637 × 0.637 × 1 µm3 per voxel in vivo two-photon elavl3:GCaMP5G fluorescence dataset (lower) for the 5.5 dpf larval zebrafish. Scale bar: 100 µm.

  6. 6.

    Lateral line primary sensory afferent reconstruction

    Fly-through of the aligned multi-resolution 5.5 dpf larval zebrafish ssEM data centred on part of a skeleton reconstruction of an afferent neuron that innervates a right dorsal neuromast (orange dot). This video contains data from three separate image volumes, starting at 4.0 × 4.0 × 60 nm3 per voxel where the afferent receives input from a neuromast hair cell. The field of view expands to follow the myelinated afferent at 56.4 × 56.4 × 60 nm3 per voxel throughout the periphery. It next magnifies to 18.8 × 18.8 × 60 nm3 per voxel before reaching the soma in the posterior lateral line ganglion and ends in the hindbrain. Scale bar: 1 µm (size changes depending on magnification).

  7. 7.

    Projectome reconstruction

    Volume rendering of the projectome reconstruction formed by annotation of all myelinated axons from the multi-resolution 5.5 dpf larval zebrafish ssEM dataset. The reconstructions are presented within a semi-transparent rendering of the aligned ssEM image volume so that it is possible to reference the positions of the myelinated axons. All reconstructions were manually annotated from the ssEM data. Colours assigned randomly. Scale bar: 100 µm.

  8. 8.

    Bilateral symmetry in the lateral line system

    Volume rendering of lateral line system reconstructions from the multi-resolution 5.5 dpf larval zebrafish ssEM dataset reveal bilateral symmetry in most neuromasts (blue) and the myelinated axons that innervate them. The myelinated axons innervating identified neuromasts (purple) and members of the posterior lateral line nerve (yellow) that likely innervate neuromasts outside the imaged volume are both displayed. Neuromasts have a bilaterally symmetrical counterpart in all but one case (orange) and the myelinated axons innervating them overlap substantially when viewed from the side. Scale bar: 100 µm.

  9. 9.

    Bilateral symmetry in the reticulospinal system

    Volume rendering of the Z-Brain spinal backfill label (red) and reticulospinal axon reconstructions (blue) embedded within semi-transparent 5.5 dpf larval zebrafish ssEM data. Also illustrated is the plane of symmetry (yellow) that was extracted from myelinated axons of identified reticulospinal neurons whose projections formed part of the MLF. Cross-modal registration of the Z-Brain and Zebrafish Brain Browser reference atlases allowed for anatomical characterization of domains within the ssEM dataset and aided in reticulospinal neuron identification. Scale bar: 100 µm.

  10. 10.

    Analysis of symmetry in myelinated axon neighbour relations

    Slice-by-slice 2D symmetry analysis of myelinated axon neighbour relations within the MLF. Angle and distance differences (upper right) were calculated from cross-sectional slices (upper left) through a subset of myelinated axon reconstructions (lower), linearized, and aggregated (middle) for display. The video shows how some panels in Fig. 4 and Extended Data Fig. 9 were constructed.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature22356

Further reading

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.