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.

  • Brief Communication
  • Published:

SNT: a unifying toolbox for quantification of neuronal anatomy

Nature Methods volume 18, pages 374–377 (2021)Cite this article

Subjects

Abstract

SNT is an end-to-end framework for neuronal morphometry and whole-brain connectomics that supports tracing, proof-editing, visualization, quantification and modeling of neuroanatomy. With an open architecture, a large user base, community-based documentation, support for complex imagery and several model organisms, SNT is a flexible resource for the broad neuroscience community. SNT is both a desktop application and multi-language scripting library, and it is available through the Fiji distribution of ImageJ.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: SNT as an end-to-end platform for data retrieval, visualization, quantification and modeling of neuroanatomical data.
Fig. 2: Comprehensive analytical tools enable discovery.

Similar content being viewed by others

Data availability

The data required to generate the figures and analyses described in this manuscript are available at https://github.com/morphonets/SNTManuscript and https://doi.org/10.5281/zenodo.4568540.

Code availability

SNT source code is available at github.com/morphonets/SNT (ref. 18) under the GNU General Public License v3.0. Technical aspects of the software are described in Supplementary Note. The SNT application is available in Fiji by subscription to the ‘Neuroanatomy’ update site (https://imagej.net/SNT#Installation). User documentation, manuals and video-tutorials are available at https://imagej.net/SNT. https://github.com/morphonets/SNT (ref. 18).

References

  1. Ascoli, G. A., Donohue, D. E. & Halavi, M. NeuroMorpho.Org: a central resource for neuronal morphologies. J. Neurosci. 27, 9247–9251 (2007).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  Google Scholar 

  4. 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).

    Article  CAS  Google Scholar 

  5. Günther, U. et al. Scenery: flexible virtual reality visualization on the Java VM. Preprint at https://arxiv.org/abs/1906.06726 (2019).

  6. Chiang, A.-S. et al. Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Curr. Biol. 21, 1–11 (2011).

    Article  CAS  Google Scholar 

  7. Heinze, S. et al. InsectBrainDatabase—a unified platform to manage, share, and archive morphological and functional data. Preprint at bioRxiv https://doi.org/10.1101/2020.11.30.397489 (2020).

  8. Winnubst, J. et al. Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain. Cell 179, 268–281.e13 (2019).

    Article  CAS  Google Scholar 

  9. Milyaev, N. et al. The virtual fly brain browser and query interface. Bioinformatics 28, 411–415 (2012).

    Article  CAS  Google Scholar 

  10. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529 (2017).

    Article  Google Scholar 

  11. Witvliet, D. et al. Connectomes across development reveal principles of brain maturation in C. elegans. Preprint at bioRxiv https://doi.org/10.1101/2020.04.30.066209 (2020).

  12. Zubler, F. & Douglas, R. A framework for modeling the growth and development of neurons and networks. Front. Comput. Neurosci. 3, 25 (2009).

    Article  Google Scholar 

  13. Cussat-Blanc, S., Harrington, K. & Pollack, J. Gene regulatory network evolution through augmenting topologies. IEEE Trans. Evol. Comput. 19, 823–837 (2015).

    Article  Google Scholar 

  14. Scorcioni, R., Polavaram, S. & Ascoli, G. A. L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat. Protoc. 3, 866–876 (2008).

    Article  CAS  Google Scholar 

  15. Ferreira, T. A. et al. Neuronal morphometry directly from bitmap images. Nat. Methods 11, 982–984 (2014).

    Article  CAS  Google Scholar 

  16. Bates, A. S. et al. The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife 9, e53350 (2020).

    Article  Google Scholar 

  17. Claudi, F., Tyson, A. L. & Branco, T. Brainrender: a python based software for visualisation of neuroanatomical and morphological data. Preprint at bioRxiv https://doi.org/10.1101/2020.02.23.961748 (2020).

  18. Ferreira, T. et al. morphonets/SNT: SNT-3.1.114. Zenodo https://doi.org/10.5281/zenodo.4568604 (2021).

  19. Withers, G. CIL:701, Rattus, multipolar neuron. CIL dataset https://doi.org/10.7295/W9CIL701 (2010).

  20. Anderson, T. & Benson, D. CIL:810, Rattus, multipolar neuron. CIL dataset https://doi.org/10.7295/W9CIL810 (2010).

  21. Wang, Q. et al. The allen mouse brain common coordinate framework: a 3D reference atlas. Cell 181, 936–953.e20 (2020).

    Article  CAS  Google Scholar 

  22. Xu, S. et al. Behavioral state coding by molecularly defined paraventricular hypothalamic cell type ensembles. Science 370, eabb2494 (2020).

  23. Feng, G. et al. Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron 28, 41–51 (2000).

    Article  CAS  Google Scholar 

  24. Asano, S. M. et al. Expansion microscopy: protocols for imaging proteins and RNA in cells and tissues. Curr. Protoc. Cell Biol. 80, e56 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Choi, H. M. T. et al. Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust. Development https://doi.org/10.1242/dev.165753 (2018).

  26. Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32, 3532–3534 (2016).

    CAS  PubMed  Google Scholar 

  27. Ferreira, T., Hiner, M., Rueden, C., Miura, K. & Eglinger, J. IJ BAR, https://imagej.net/BAR. Zenodo (2017); https://doi.org/10.5281/zenodo.597784

  28. Ollion, J., Cochennec, J., Loll, F., Escudé, C. & Boudier, T. TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization. Bioinformatics 29, 1840–1841 (2013).

    Article  CAS  Google Scholar 

  29. Ferreira, T., Arshadi, C. & Harrington, K. I. S. morphonets/SNTmanuscript 2.0. Zenodo https://doi.org/10.5281/zenodo.4568540 (2021).

  30. Gillette, T. A., Brown, K. M. & Ascoli, G. A. The DIADEM metric: comparing multiple reconstructions of the same neuron. Neuroinformatics 9, 233–245 (2011).

    Article  Google Scholar 

  31. Harrington, K. I. S., Ferreira, T. & Birgiolas, J. morphonets/cx3d: 0.0.4-SNAPSHOT. Zenodo https://doi.org/10.5281/zenodo.4568546 (2021).

  32. Harrington, K. I. S. & scussatb. morphonets/grneat 0.0.5-SNAPSHOT. Zenodo https://doi.org/10.5281/zenodo.4568589 (2021).

  33. Maaten, Lvander & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  34. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

Download references

Acknowledgements

We are extremely thankful to J. Chandrashekar, A. Cardona and P. Tomancak for valuable input. We thank the community of users and contributors of Simple Neurite Tracer, the developers of SciJava, pyimagej and remaining open-source libraries required by SNT, and everyone who helped test the software. We thank W. Rasband, C. Rueden and the ImageJ community for developing and maintaining ImageJ and M. Rozsa and J. Baka for critical reading of the manuscript. We thank the reviewers for constructive feedback that improved SNT. Special thanks to all the laboratories, teams, institutions and initiatives that facilitate public sharing of neuronal data, including 3D InsectBrain, Allen Institute for Brain Science (including BigNeuron), Blue Brain, Cell Image Library, FishAtlas, FlyCircuit, FlyLight, InsectBrain Database, MouseLight, NeuroMorpho, OpenWorm and Virtual Fly Brain. This work was funded by the Howard Hughes Medical Institute. U.G. was funded by CASUS, which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism with tax funds on the basis of the budget approved by the Saxon State Parliament.

Author information

Authors and Affiliations

  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Cameron Arshadi, Mark Eddison, Kyle I. S. Harrington & Tiago A. Ferreira

  2. CASUS—Center for Advanced Systems Understanding, Görlitz, Germany

    Ulrik Günther

  3. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

    Ulrik Günther

  4. Center for Systems Biology, Dresden, Germany

    Ulrik Günther

  5. Virtual Technology and Design, University of Idaho, Moscow, ID, USA

    Kyle I. S. Harrington

  6. Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Kyle I. S. Harrington

Authors
  1. Cameron Arshadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ulrik Günther
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark Eddison
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyle I. S. Harrington
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tiago A. Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.A.F. conceived and supervised the project. T.A.F. and C.A. wrote the core SNT. K.I.S.H. and U.G. implemented Cx3D/sciview integration. K.I.S.H. designed and ran simulations. M.E. performed the ExFISH experiment. T.A.F. analyzed the data. T.A.F. and K.I.S.H. wrote the paper.

Corresponding author

Correspondence to Tiago A. Ferreira.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Michael Hawrylycz, Stanley Heinze and Hermann Cuntz for their contribution to the peer review of this work. Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Supplementary information

Supplementary Information

Supplementary Fig. 1, Note and Glossary.

Reporting Summary

Supplementary Video 1

Example of a programmatic animation: ‘Cumulative’ rendering of the complete MouseLight database in Reconstruction Viewer. The animation was generated on a laptop computer lacking a dedicated graphics processing unit using a single script that downloaded, measured and rendered each cell. See https://github.com/morphonets/SNTManuscript for details. Note that the number of cells in the database has meanwhile surpassed those rendered.

Supplementary Video 2

Showcase example of sciview capabilities, in which segmentation and volumetric data are rendered in the same scene. All data were loaded from the Cremi challenge (https://www.cremi.org) sample dataset ‘A’, with the ten largest volumes (by voxel count) shown in random colors. In addition, half the volume of the electron microscopy raw data is shown as a semi-transparent direct volume rendering.

Supplementary Video 3

Example of image-based modeling using Cx3D and a 3D volumetric image defining a microfluidic circuit designed to assess neurite outgrowth in response to Netrin-1 and Slit2. The simulation shows a single cell (magenta) with chemotaxis and branching preference for a steady-state chemical gradient (low concentration, purple and high concentration, yellow).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arshadi, C., Günther, U., Eddison, M. et al. SNT: a unifying toolbox for quantification of neuronal anatomy. Nat Methods 18, 374–377 (2021). https://doi.org/10.1038/s41592-021-01105-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-021-01105-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

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