High-accuracy neurite reconstruction for high-throughput neuroanatomy

Abstract

Neuroanatomic analysis depends on the reconstruction of complete cell shapes. High-throughput reconstruction of neural circuits, or connectomics, using volume electron microscopy requires dense staining of all cells, which leads even experts to make annotation errors. Currently, reconstruction speed rather than acquisition speed limits the determination of neural wiring diagrams. We developed a method for fast and reliable reconstruction of densely labeled data sets. Our approach, based on manually skeletonizing each neurite redundantly (multiple times) with a visualization-annotation software tool called KNOSSOS, is 50-fold faster than volume labeling. Errors are detected and eliminated by a redundant-skeleton consensus procedure (RESCOP), which uses a statistical model of how true neurite connectivity is transformed into annotation decisions. RESCOP also estimates the reliability of consensus skeletons. Focused reannotation of difficult locations promises a rather steep increase of reliability as a function of the average skeleton redundancy and thus the nearly error-free analysis of large neuroanatomical datasets.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Comparison of volume and skeleton annotation.
Figure 2: Skeletonization by expert annotators.
Figure 3: RESCOP step 1, skeleton-to-skeleton agreement measurement.
Figure 4: RESCOP steps 2 and 3, edge elimination and skeleton recombination.
Figure 5: RESCOP step 4, estimating error rate of RESCOPed skeletons.
Figure 6: Doubly annotated skeletons of 114 putative rod bipolar cells in a block of mouse retina.

References

  1. 1

    Ramón y Cajal, S. Textura del Sistema Nervioso del Hombre y de los Vertebrados (Moya, Madrid, 1899).

  2. 2

    Golgi, C. Sulla struttura della sostanza grigia del cervelo. Gazzetta Medica Italiana Lombardia 33, 244–246 (1873).

    Google Scholar 

  3. 3

    Harris, K.M. & Stevens, J.K. Dendritic spines of CA 1 pyramidal cells in the rat hippocampus: serial electron microscopy with reference to their biophysical characteristics. J. Neurosci. 9, 2982–2997 (1989).

    CAS  Article  Google Scholar 

  4. 4

    Horikawa, K. & Armstrong, W.E. A versatile means of intracellular labeling: injection of biocytin and its detection with avidin conjugates. J. Neurosci. Methods 25, 1–11 (1988).

    CAS  Article  Google Scholar 

  5. 5

    Stretton, A.O. & Kravitz, E.A. Neuronal geometry: determination with a technique of intracellular dye injection. Science 162, 132–134 (1968).

    CAS  Article  Google Scholar 

  6. 6

    Wickersham, I.R. et al. Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639–647 (2007).

    CAS  Article  Google Scholar 

  7. 7

    Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).

    CAS  Article  Google Scholar 

  8. 8

    Sporns, O., Tononi, G. & Kotter, R. The human connectome: A structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).

    Article  Google Scholar 

  9. 9

    Lichtman, J.W., Livet, J. & Sanes, J.R. A technicolour approach to the connectome. Nat. Rev. Neurosci. 9, 417–422 (2008).

    CAS  Article  Google Scholar 

  10. 10

    Helmstaedter, M., Briggman, K.L. & Denk, W. 3D structural imaging of the brain with photons and electrons. Curr. Opin. Neurobiol. 18, 633–641 (2008).

    CAS  Article  Google Scholar 

  11. 11

    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).

    CAS  Article  Google Scholar 

  12. 12

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

    Article  Google Scholar 

  13. 13

    Hayworth, K.J., Kasthuri, N., Schalek, R. & Lichtman, J.W. Automating the collection of ultrathin serial sections for large volume TEM reconstructions. Microsc. Microanal. 12, 86–87 (2006).

    Article  Google Scholar 

  14. 14

    Knott, G., Marchman, H., Wall, D. & Lich, B. Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. J. Neurosci. 28, 2959–2964 (2008).

    CAS  Article  Google Scholar 

  15. 15

    Briggman, K.L. & Denk, W. Towards neural circuit reconstruction with volume electron microscopy techniques. Curr. Opin. Neurobiol. 16, 562–570 (2006).

    CAS  Article  Google Scholar 

  16. 16

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

  17. 17

    Fiala, J.C. Reconstruct: a free editor for serial section microscopy. J. Microsc. 218, 52–61 (2005).

    CAS  Article  Google Scholar 

  18. 18

    Jeong, W.K. et al. Ssecrett and NeuroTrace: interactive visualization and analysis tools for large-scale neuroscience data sets. IEEE Comput. Graph. Appl. 30, 58–70 (2010).

    Article  Google Scholar 

  19. 19

    Trachtenberg, J.T. et al. Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature 420, 788–794 (2002).

    CAS  Article  Google Scholar 

  20. 20

    Stevens, J.K., McGuire, B.A. & Sterling, P. Toward a functional architecture of the retina: serial reconstruction of adjacent ganglion cells. Science 207, 317–319 (1980).

    CAS  Article  Google Scholar 

  21. 21

    Chen, B.L., Hall, D.H. & Chklovskii, D.B. Wiring optimization can relate neuronal structure and function. Proc. Natl. Acad. Sci. USA 103, 4723–4728 (2006).

    CAS  Article  Google Scholar 

  22. 22

    Chklovskii, D.B., Vitaladevuni, S. & Scheffer, L.K. Semi-automated reconstruction of neural circuits using electron microscopy. Curr. Opin. Neurobiol. 20, 667–675 (2010).

    CAS  Article  Google Scholar 

  23. 23

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

    CAS  Article  Google Scholar 

  24. 24

    Jain, V. et al. Supervised learning of image restoration with convolutional networks. IEEE 11th Int. Conf. Comput. Vis. 1–8 (2007).

  25. 25

    Andres, B., Köthe, U., Helmstaedter, M., Denk, W. & Hamprecht, F. Segmentation of SBFSEM volume data of neural tissue by hierarchical classification. in Pattern Recognition: Lecture Notes in Computer Science (ed. Rigoll, G.) 142–152 (Springer-Verlag, 2008).

  26. 26

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

    Article  Google Scholar 

  27. 27

    Martin, D., Fowlkes, C., Tal, D. & Malik, J. A Database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. IEEE 8th Int. Conf. Comput. Vis. 2, 416–423 (2001).

    Google Scholar 

  28. 28

    Warfield, S.K., Zou, K.H. & Wells, W.M. Validation of image segmentation by estimating rater bias and variance. Philos. Transact. A Math. Phys. Eng. Sci. 366, 2361–2375 (2008).

    Article  Google Scholar 

  29. 29

    Warfield, S.K., Zou, K.H. & Wells, W.M. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23, 903–921 (2004).

    Article  Google Scholar 

  30. 30

    Wang, W. et al. A classifier ensemble based on performance level estimation. IEEE Int. Symposium on Biomed. Imaging: from Nano to Micro, 342–345 (2009).

  31. 31

    Körding, K. Decision theory: what “should” the nervous system do? Science 318, 606–610 (2007).

    Article  Google Scholar 

  32. 32

    Crook, S., Gleeson, P., Howell, F., Svitak, J. & Silver, R.A. MorphML: level 1 of the NeuroML standards for neuronal morphology data and model specification. Neuroinformatics 5, 96–104 (2007).

    Article  Google Scholar 

Download references

Acknowledgements

We thank B. Andres, F. Hamprecht, U. Köthe, V. Jain, S. Seung and S. Turaga for many fruitful discussions and comments on the manuscript, J. Bollmann and A. Schaefer for helpful comments on the manuscript, C. Roome for information technology support, J. Kornfeld and F. Svara for programming KNOSSOS, and J. Hanne, H. Jakobi and H. Wissler for help with annotator training. We thank N. Abazova, E. Abs, A. Antunes, P. Bastians, J. Bauer, M. Beez, M. Beining, S. Bender, S. Best, L. Brosi, M. Bucher, E. Buckler, J. Buhmann, C. Burkhardt, F. Drawitsch, L. Ehm, S. Ehm, C. Fianke, R. Foltin, S. Freiss, M. Funk, A. Gebhardt, M. Gruen, K. Haase, J. Hammerich, J. Hanne, B. Hauber, M. Hensen, L. Hofmann, P. Hofmann, M. Hülser, F. Isensee, H. Jakobi, M. Jonczyk, A. Joschko, A. Juenger, S. Kaspar, K. Kessler, A. Khan, M. Kiapes, A. Klein, C. Klein, S. Laiouar, T. Lang, L. Lebelt, H. Lesch, C. Lieven, D. Luft, E. Moeller, A. Muellner, M. Mueller, D. Ollech, A. Oppold, T. Otolski, S. Oumohand, S. Pfarr, M. Pohrath, A. Poos, S. Putzke, J. Reinhardt, A. Rommerskirchen, M. Roth, J. Sambel, K. Schramm, C. Sellmann, J. Sieber, I. Sonntag, M. Stahlberg, T. Stratmann, J. Trendel, F. Trogisch, M. Uhrig, A. Vogel, J. Volz, C. Weber, P. Weber, K. Weiss, L. Weisshaar, E. Wiegand, T. Wiegand, M. Wiese, R. Wiggers, C. Willburger and A. Zegarra for neurite skeletonizations. This work was funded by the Max Planck Society.

Author information

Affiliations

Authors

Contributions

M.H. and W.D. designed the study and devised the analysis algorithms; K.L.B. carried out the SBEM experiments; M.H., K.L.B. and W.D. specified the KNOSSOS software; M.H. analyzed the data; M.H., K.L.B. and W.D. wrote the paper.

Corresponding author

Correspondence to Moritz Helmstaedter.

Ethics declarations

Competing interests

Moritz Helmstaedter and Winfried Denk have applied for a patent (Published Patent Application US 20100183217). Winfried Denk receives IP license income from Gatan Inc. for serial blockface imaging.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 (PDF 576 kb)

Supplementary Movie 1

Skeleton tracing with KNOSSOS. 3 orthogonal views are displayed (xy, top left, yz, top right, xz, bottom left) and a 3-dimensional view of the dataset bounding box and the skeleton being created (bottom right). The 19MB version requires a video player compatible with DivX-encoded files. The 68MB version can be viewed with a generic player but is shorter. (ZIP 51786 kb)

Supplementary Image Stack 1 (ZIP 2890 kb)

Supplementary Image Stack 2 (ZIP 2896 kb)

Supplementary Image Stack 3 (ZIP 2908 kb)

Supplementary Image Stack 4 (ZIP 2451 kb)

Supplementary Image Stack 5 (ZIP 2857 kb)

Supplementary Image Stack 6 (ZIP 2909 kb)

Supplementary Image Stack 7 (ZIP 2909 kb)

Supplementary Image Stack 8 (ZIP 2910 kb)

Supplementary Image Stack 9 (ZIP 2883 kb)

Supplementary Image Stack 10 (ZIP 2885 kb)

Supplementary Image Stack 11 (ZIP 2916 kb)

Supplementary Image Stack 12 (ZIP 2900 kb)

Supplementary Image Stack 13 (ZIP 54012 kb)

Supplementary Image Stack 14 (ZIP 56713 kb)

Supplementary Image Stack 15 (ZIP 6820 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Helmstaedter, M., Briggman, K. & Denk, W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci 14, 1081–1088 (2011). https://doi.org/10.1038/nn.2868

Download citation

Further reading

Search

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