GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population | Neuroinformatics Skip to main content
Springer Nature Link
Log in

GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population

  • Software Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ascoli, G. A., Krichmar, J. L., Nasuto, S. J., & Senft, S. L. (2001). Generation, description and storage of dendritic morphology data. Philosophical Transactions of the Royal Society B, 356(1412), 1131–1145.

    Article  CAS  Google Scholar 

  • Bohland, J. W., Wu, C., Barbas, H., Bokil, H., Bota, M., Breiter, H. C., et al. (2009). A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale. PLOS Computational Biology, 5(3), e1000334.

  • Bria, A., Iannello, G., Onofri, L., & Peng, H. (2016). TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images. Nature Methods, 13(3), 192–194.

    Article  CAS  Google Scholar 

  • Brown, K. M., Barrionuevo, G., Canty, A. J., De Paola, V., Hirsch, J. A., Jefferis, G. S., et al. (2011). The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics, 9(2–3), 143–157. doi:https://doi.org/10.1007/s12021-010-9095-5.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chung, K., & Deisseroth, K. (2013). CLARITY for mapping the nervous system. Nature Methods, 10(6), 508–513. doi:https://doi.org/10.1038/nmeth.2481.

    Article  CAS  PubMed  Google Scholar 

  • Defilepe, J. (2010). From the connectome to the synaptome: an epic love story. Science, 330(6008), 1198–1201.

    Article  Google Scholar 

  • Denk, W., Strickler, J., & Webb, W. (1990). Two-photon laser scanning fluorescence microscopy. Science, 248(4951), 73–76.

    Article  CAS  Google Scholar 

  • Donohue, D. E., & Ascoli, G. A. (2011). Automated reconstruction of neuronal morphology: an overview. Brain Research Reviews, 67(1–2), 94–102, doi:10.1016/j.brainresrev.2010.11.003.

  • Economo, M. N., Clack, N. G., Lavis, L. D., Gerfen, C. R., Svoboda, K., Myers, E. W., et al. (2016). A platform for brain-wide imaging and reconstruction of individual neurons. eLife, 5, e10566. doi:https://doi.org/10.7554/eLife.10566.

    Article  PubMed  PubMed Central  Google Scholar 

  • Feng, L., Zhao, T., & Kim, J. (2015) neuTube 1.0: A new design for efficient neuron reconstruction software based on the SWC format. eNeuro, 2(1). https://doi.org/10.1523/eneuro.0049-14.2014.

  • Gong, H., Zeng, S., Yan, C., Lv, X., Yang, Z., Xu, T., et al. (2013). Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution. NeuroImage, 74, 87–98.

    Article  Google Scholar 

  • Han, Y., Kebschull, J. M., Campbell, R. A. A., Cowan, D., Imhof, F., Zador, A. M., et al. (2018). The logic of single-cell projections from visual cortex. Nature, 556(7699), 51–56. doi:https://doi.org/10.1038/nature26159.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Helmstaedter, M., Briggman, K. L., & Denk, W. (2011). High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nature Neuroscience, 14(8), 1081–1088.

    Article  CAS  Google Scholar 

  • Jefferis, G. S., & Livet, J. (2012). Sparse and combinatorial neuron labelling. Current Opinion in Neurobiology, 22(1), 101–110. doi:https://doi.org/10.1016/j.conb.2011.09.010.

    Article  CAS  PubMed  Google Scholar 

  • Knuth, D. E. (1997) The art of computer programming (Vol. 3). London: Pearson Education.

  • Li, A., Gong, H., Zhang, B., Wang, Q., Yan, C., Wu, J., et al. (2010). Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science, 330(6009), 1404–1408. doi:https://doi.org/10.1126/science.1191776.

    Article  CAS  PubMed  Google Scholar 

  • Li, R., Zhu, M., Li, J., Bienkowski, M. S., Foster, N. N., Xu, H., et al. (2019a). Precise segmentation of densely interweaving neuron clusters using G-Cut. Nature Communications, 10(1), 1549. doi:10.1038/s41467-019-09515-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li, S., Quan, T., Xu, C., Huang, Q., Kang, H., Chen, Y., et al. (2019b). Optimization of traced neuron skeleton using lasso-based model. Frontiers in Neuroanatomy, 13, 18–18. doi:https://doi.org/10.3389/fnana.2019.00018.

    Article  PubMed  PubMed Central  Google Scholar 

  • Li, S., Quan, T., Zhou, H., Huang, Q., Guan, T., Chen, Y., et al. (2019c). Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method. Neuroinformatics, 18(2), 199–218. doi:https://doi.org/10.1007/s12021-019-09434-x.

    Article  Google Scholar 

  • Li, S., Quan, T., Zhou, H., Yin, F., Li, A., Fu, L., et al. (2019d). Identifying weak signals in inhomogeneous neuronal images for large-scale tracing of sparsely distributed neurites. Neuroinformatics. doi:https://doi.org/10.1007/s12021-018-9414-9.

    Article  PubMed  PubMed Central  Google Scholar 

  • Li, S., Zhou, H., Quan, T., Li, J., Li, Y., Li, A., et al. (2016). SparseTracer: the reconstruction of discontinuous neuronal morphology in noisy images. Neuroinformatics, 15(2), 133–149.

    Article  Google Scholar 

  • Li, Y., Gong, H., Yang, X., Yuan, J., Jiang, T., Li, X., et al. (2017). TDat: An efficient platform for processing petabyte-scale whole-brain volumetric images. Frontiers in Neural Circuits, 11, 51. doi:https://doi.org/10.3389/fncir.2017.00051.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lichtman, J. W., & Denk, W. (2011). The big and the small: challenges of imaging the brain’s circuits. Science, 334(6056), 618–623.

    Article  CAS  Google Scholar 

  • Luisi, J., Narayanaswamy, A., Galbreath, Z., & Roysam, B. (2011). The FARSIGHT trace editor: an open source tool for 3-D inspection and efficient pattern analysis aided editing of automated neuronal reconstructions. Neuroinformatics, 9(2–3), 305–315. doi:https://doi.org/10.1007/s12021-011-9115-0.

    Article  PubMed  Google Scholar 

  • Magliaro, C., Callara, A. L., Vanello, N., & Ahluwalia, A. (2017). A manual segmentation tool for three-dimensional neuron datasets. [Methods]. Frontiers in Neuroinformatics, 11(36). https://doi.org/10.3389/fninf.2017.00036.

  • Megjhani, M., Rey-Villamizar, N., Merouane, A., Lu, Y., Mukherjee, A., Trett, K., et al. (2015). Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors. Bioinformatics, 31. https://doi.org/10.1093/bioinformatics/btv109.

  • Meijering, E. (2010). Neuron tracing in perspective. Cytometry. Part A, 77(7), 693–704. doi:https://doi.org/10.1002/cyto.a.20895.

    Article  Google Scholar 

  • Narasimhan, A., Venkataraju, K. U., Mizrachi, J., Albeanu, D. F., & Osten, P. (2017) A high resolution whole brain imaging using oblique light sheet tomography. bioRxiv, 132423.

  • Osten, P., & Margrie, T. W. (2013). Mapping brain circuitry with a light microscope. Nature Methods, 10(6), 515–523. doi:https://doi.org/10.1038/nmeth.2477.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peng, H., Hawrylycz, M., Roskams, J., Hill, S., Spruston, N., Meijering, E., et al. (2015). BigNeuron: Large-scale 3D neuron reconstruction from optical microscopy images. Neuron, 87(2), 252–256. https://doi.org/10.1016/j.neuron.2015.06.036

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peng, H., Meijering, E., & Ascoli, G. A. (2015b). From DIADEM to BigNeuron. Neuroinformatics, 13(3), 259–260. doi:10.1007/s12021-015-9270-9.

    Article  PubMed  PubMed Central  Google Scholar 

  • Peng, H., Ruan, Z., Long, F., Simpson, J. H., & Myers, E. W. (2010). V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology, 28(4), 348¬353.

  • Peng, H., Zhou, Z., Meijering, E., Zhao, T., Ascoli, G. A., & Hawrylycz, M. (2017). Automatic tracing of ultra-volumes of neuronal images. Nature Methods, 14(4), 332–333. doi:https://doi.org/10.1038/nmeth.4233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pietzsch, T., Saalfeld, S., Preibisch, S., & Tomancak, P. (2015). BigDataViewer: visualization and processing for large image data sets. Nature Methods, 12(6), 481–483.

  • Quan, T., Zhou, H., Li, J., Li, S., Li, A., Li, Y., et al. (2016). NeuroGPS-Tree: automatic reconstruction of large-scale neuronal populations with dense neurites. Nature Methods, 13(1), 51–54. doi:https://doi.org/10.1038/nmeth.3662.

    Article  CAS  PubMed  Google Scholar 

  • Radojević, M., & Meijering, E. (2019). Automated neuron reconstruction from 3D fluorescence microscopy images using sequential Monte Carlo estimation. Neuroinformatics, 17(3), 423–442. doi:https://doi.org/10.1007/s12021-018-9407-8.

    Article  PubMed  Google Scholar 

  • Ragan, T., Kadiri, L. R., Venkataraju, K. U., Bahlmann, K., Sutin, J., Taranda, J., et al. (2012). Serial two-photon tomography for automated ex vivo mouse brain imaging. Nature Methods, 9(3), 255–248. doi:https://doi.org/10.1038/nmeth.1854.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rey-Villamizar, N. (2014). Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python. Frontiers in Neuroinformatics. doi:https://doi.org/10.3389/fninf.2014.00039.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sholl, D. A. (1953). Dendritic organization in the neurons of the visual and motor cortices of the cat. Journal of Anatomy, 87(4), 387.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Silvestri, L., Bria, A., Sacconi, L., Iannello, G., & Pavone, F. S. (2012). Confocal light sheet microscopy: micron-scale neuroanatomy of the entire mouse brain. Optics Express, 20(18), 20582–20598. doi:https://doi.org/10.1364/OE.20.020582.

    Article  CAS  PubMed  Google Scholar 

  • Sun, P., Jin, S., Tao, S., Wang, J., Li, A., Li, N., et al. (2020). Highly efficient and super-bright neurocircuit tracing using vector mixing-based virus cocktail. bioRxiv, 705772. https://doi.org/10.1101/705772.

  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58(1), 267–288.

    Google Scholar 

  • Tomer, R., Khairy, K., Amat, F., & Keller, P. J. (2012). Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy. Nature Methods, 9(7), 755.

    Article  CAS  Google Scholar 

  • Wang, Y., Li, Q., Liu, L., Zhou, Z., Ruan, Z., Kong, L., et al. (2019). TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain. Nature Communications, 10(1), 1–9.

    Article  Google Scholar 

  • Winnubst, J., Bas, E., Ferreira, T. A., Wu, Z., Economo, M. N., Edson, P., et al. (2019) Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain. Cell, 179(1), 268–281. e213.

  • Yang, W. H., Feng, J. S., Yang, J. C., Zhao, F., Liu, J. Y., Guo, Z. M., et al. (2017). Deep edge guided recurrent residual learning for image super-resolution. IEEE Transactions on Image Processing, 26(12), 5895–5907. doi:https://doi.org/10.1109/Tip.2017.2750403.

    Article  Google Scholar 

  • Zhao, T., Xie, J., Amat, F., Clack, N., Ahammad, P., Peng, H., et al. (2011). Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics, 9(2–3), 247–261. doi:https://doi.org/10.1007/s12021-011-9120-3.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Drs. Yunyun Han, Pavel Osten and Arun Narasimhan for providing testing datasets. We also thank the Optical Bioimaging Core Facility of WNLO-HUST for the support in data acquisition, and the Analytical and Testing Center of HUST for spectral measurements. We thank the help from Li Jing and Su Lei in the design of the software tool. We thank Li Shoucheng, Li Yingfei, Zhao Ming, Wang Hao, Chen Cheng, Zhao Yu, Huang Lu, Kong Xinyi, Li Hanying, Zhou Wuxian, Tian Tian, He Sijie, Wang Danni and Gong Yaqiong for their efforts in testing the software and in tracing neurons.

Author information

Author notes

  1. Hang Zhou, Shiwei Li and Anan Li equally contributed to this work.

Authors and Affiliations

  1. Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China

    Hang Zhou, Shiwei Li, Anan Li, Qing Huang, Feng Xiong, Ning Li, Jiacheng Han, Hongtao Kang, Yijun Chen, Yun Li, Huimin Lin, Yu-Hui Zhang, Xiaohua Lv, Xiuli Liu, Hui Gong, Qingming Luo, Shaoqun Zeng & Tingwei Quan

  2. MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China

    Hang Zhou, Shiwei Li, Anan Li, Qing Huang, Feng Xiong, Ning Li, Jiacheng Han, Hongtao Kang, Yijun Chen, Yun Li, Huimin Lin, Yu-Hui Zhang, Xiaohua Lv, Xiuli Liu, Hui Gong, Qingming Luo, Shaoqun Zeng & Tingwei Quan

  3. School of Mathematics and Economics, Hubei University of Education, 430205, Wuhan, Hubei, China

    Tingwei Quan

Authors
  1. Hang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qing Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Feng Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ning Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jiacheng Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hongtao Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yijun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Huimin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yu-Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Xiaohua Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Xiuli Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Hui Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Qingming Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Shaoqun Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Tingwei Quan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tingwei Quan.

Ethics declarations

Conflict of Interest.

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Electronic supplementary material

ESM 1

(DOCX 2.72 MB)

Video S2

(MOV 22.7 MB)

Video S4

(MOV 6.20 MB)

Video S6

(MOV 7.54 MB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, H., Li, S., Li, A. et al. GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population. Neuroinform 19, 305–317 (2021). https://doi.org/10.1007/s12021-020-09484-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-020-09484-6

Keywords

Search

Navigation