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NeuroGPS-Tree: automatic reconstruction of large-scale neuronal populations with dense neurites

Nature Methods volume 13, pages 51–54 (2016)Cite this article

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Abstract

The reconstruction of neuronal populations, a key step in understanding neural circuits, remains a challenge in the presence of densely packed neurites. Here we achieved automatic reconstruction of neuronal populations by partially mimicking human strategies to separate individual neurons. For populations not resolvable by other methods, we obtained recall and precision rates of approximately 80%. We also demonstrate the reconstruction of 960 neurons within 3 h.

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Figure 1: Integrating information at different scales to progressively detect spurious links in neuronal population reconstructions.
Figure 2: Reconstruction performance of NeuroGPS-Tree.
Figure 3: Reconstruction of a neuronal population from dense and large-scale data using NeuroGPS-Tree.

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Acknowledgements

We thank the members of the Britton Chance Center for Biomedical Photonics for advice and help in experiments. We also thank S.L. Hill and Y. Wang for suggesting Sholl analysis, as well as H. Peng and G.A. Ascoli for help with software usability and paper quality. This work is supported by the National Basic Research Program of China (grant 2011CB910401), the National Natural Science Foundation of China (grants 81327802 and 91432116), the Science Fund for Creative Research Group of China (grant 61421064), the National Key Scientific Instrument & Equipment Development Program of China (grant 2012YQ030260) and the the Director Fund of the Wuhan National Laboratory for Optoelectronics.

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Authors and Affiliations

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

    Tingwei Quan, Hang Zhou, Jing Li, Shiwei Li, Anan Li, Yuxin Li, Xiaohua Lv, Qingming Luo, Hui Gong & Shaoqun Zeng

  2. Ministy of Education Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China

    Tingwei Quan, Hang Zhou, Jing Li, Shiwei Li, Anan Li, Yuxin Li, Xiaohua Lv, Qingming Luo, Hui Gong & Shaoqun Zeng

  3. Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Tingwei Quan, Hang Zhou, Jing Li, Shiwei Li, Anan Li, Yuxin Li, Xiaohua Lv, Qingming Luo, Hui Gong & Shaoqun Zeng

  4. School of Mathematics and Statistics, Hubei University of Education, Wuhan, China

    Tingwei Quan

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  1. Tingwei Quan
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Contributions

S.Z. and H.G. conceived of the project. S.Z. and T.Q. designed the model and wrote the manuscript. T.Q. developed the algorithm. H.Z. wrote the software. J.L., H.Z. and S.L. performed image analysis and processing. A.L. and Y.L. constructed the computing platform for image preprocessing. S.Z., H.G., X.L. and Q.L. produced data. All authors revised the paper.

Corresponding authors

Correspondence to Hui Gong or Shaoqun Zeng.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–23, Supplementary Notes 1 and 2 and Supplementary Discussion (PDF 4305 kb)

Source data to Supplementary Figure 4

Source data to Supplementary Figure 10

Source data to Supplementary Figure 13

Source data to Supplementary Figure 23

Supplementary Software

NeuroGPS-Tree software (ZIP 46847 kb)

NeuroGPS-Tree_single tree

The manual reconstruction of a single neuron is faithful to the data set. (MOV 9752 kb)

NeuroGPS-Tree_slice

Maximum-intensity projections of a series of 3D image sections with the same thickness (10 μm) are used to show dense populations. (MOV 30863 kb)

NeuroGPS-Tree_Neuronal Population

Reconstruction of neuronal population from the image volume with NeuroGPS-Tree and individual neuronal trees are identified in different pseudo-colors. (MOV 26924 kb)

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

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