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SNT: a unifying toolbox for quantification of neuronal anatomy

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

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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.

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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.

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).

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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
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  5. Tiago A. Ferreira
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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.

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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).

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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

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