Data availability
A minimal test dataset for our software, along with an example of server configuration, is deposited at https://doi.org/10.5281/zenodo.10988281 (ref. 31). Full-resolution fMOST imaging datasets in this study are deposited at Institute of Neuroscience, Chinese Academy of Sciences, and can be accessed using this server configuration (https://doi.org/10.5281/zenodo.10988281, ref. 31). All reconstructed neurons in the SWC file format, along with complete reconstruction history as LMDB database files, are deposited at https://doi.org/10.5281/zenodo.10988416 (ref. 32). Parameter files of trained U-Net and ResNet are deposited at https://doi.org/10.5281/zenodo.10988756 (ref. 33). Source data are provided with this paper.
Code availability
Gapr is licensed under the GNU General Public License v.3.0 or later. The source code and user guide are available at https://doi.org/10.5281/zenodo.10988621 (ref. 34). For binary packages and future versions of source code and user guide, please refer to http://yanlab.org.cn/gapr/. Custom script files used for data analysis are deposited at https://doi.org/10.5281/zenodo.11005126 (ref. 35).
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Acknowledgements
We thank Y. Sun (Institute of Neuroscience, Chinese Academy of Sciences) for helpful discussions, Q. Luo (Hainan University), H. Gong and A. Li (Huazhong University of Science and Technology) for the help in fMOST imaging, X. Wang and his team (Institute of Neuroscience, Chinese Academy of Sciences) for managing the fMOST data, and Z. Zeng and his team (Chengdu Huizhong Tianzhi Technology Co. Ltd) for manual proofreading using Gapr in this study. This work was supported by the National Science and Technology Innovation 2030—Major Projects (grant nos. 2021ZD0204400 to H.W. and X.X., 2021ZD0200200 to J.Y. and 2021ZD0203203 to X.X.), the Lingang Laboratory grant (no. LG202104-01-06 J.Y.), Shanghai Municipal Science and Technology Major Project grant (no. 2018SHZDZX05 J.Y. and X.X.), the National Natural Science Foundation of China (no. 32221003 J.Y.) and Strategic Priority Research Program of Chinese Academy of Sciences grant (no. XDB32040104 J.Y.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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These authors contributed equally: Lingfeng Gou, Yanzhi Wang.
Authors and Affiliations
Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
Lingfeng Gou,Yanzhi Wang,Le Gao,Yiting Zhong,Lucheng Xie,Haifang Wang,Xi Zha,Yinqi Shao,Huatai Xu,Xiaohong Xu&Jun Yan
University of Chinese Academy of Sciences, Shanghai, China
Yanzhi Wang&Yiting Zhong
Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China
Huatai Xu,Xiaohong Xu&Jun Yan
School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
Jun Yan
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Contributions
The study was designed by L. Gou and J.Y. Gapr was developed by L. Gou. Data management and reconstruction curation was carried out by Y.W. Comparison with other software was carried out by Y.W. and L. Gao. Quality control was performed by Y.W. and L. Gao. fMOST image alignment was carried out by Y.Z. Virus injection and sparse labeling were carried out by L.X., X.Z., Y.S., H.X. and X.X. Data analysis, interpretation and generation of figures were performed by L. Gou, Y.W. and J.Y. Writing, reviewing and editing of the paper were carried out by L. Gou and J.Y. Scientific direction and funding were the responsibilities of J.Y. and H.W.
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Correspondence to Jun Yan.
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Extended data
Extended Data Fig. 1 Comparison between Gapr’s automatic reconstruction (U-Net+neuTube) and the neuTube plugin from the Vaa3D software.
For each dataset, 10 sample cubes containing neurites are randomly selected. With U-Net, both FDR and FNR are significantly reduced (p = 2.0 × 10−11 and 6.8 × 10−4 respectively, with a one-sided Wilcoxon rank-sum test). The horizontal box lines represent the 25th, 50th, and 75th percentiles respectively, and the whiskers extend to values within 1.5 times the interquartile range.
Source data
Extended Data Fig. 2 Demonstration of automatic reconstruction with a large dataset.
Red squares denote active areas for one step of reconstruction. For each step, black lines denote existing edges, and green lines denote newly reconstructed edges. Red dots are active nodes that guide the selection of reconstruction areas. Note that the existing short segment in step 1 is a manually introduced seed.
Extended Data Fig. 3 Confirmation of modification consistency by the gather module, including access control, validation and collision avoidance.
This mechanism allows multiple users to perform modifications to the same dataset concurrently and ensures data consistency without explicitly locking anything.
Extended Data Fig. 4 Collaborative reconstruction of a branch structure by two annotators.
Black lines denote reconstructed edges, while red lines denote new edges to add to the reconstruction result. Annotators prepare the new edges based on their observation of imaging data (thick gray arrows), which typically takes several seconds per step. Thin black arrows show communication between the clients and the server, with a typical round-trip time of less than 100ms. Note that the reconstruction result at the client side may be incomplete. When incompleteness leads to incorrect operations, the reconstruction result is automatically updated. In this example, client B initially only has the reconstruction result of the right-side branch. After rejection by the server, the result for the left-side branch contributed by client A is automatically loaded, such that client B can connect to the correct node at the branch point.
Extended Data Fig. 5 Comparison of visualization efficiency between Gapr, Janelia Workstation and Vaa3D.
We compared these three tools side-by-side with various numbers of nodes loaded.
Extended Data Fig. 6 Reconstruction results of all 15 datasets in sagittal and horizontal views.
Neurons that have been proofread are randomly colored. Gray neurites in dataset no. 192101 have not been manually proofread, as the selected reconstruction procedure is performed.
Extended Data Fig. 7 Examples of loops and errors.
(a) An example of a loop structure. On the right side, a global view of the whole loop is shown. An annotator has reported an error at the correct location for resolution. At this site, the over-connection of the loop is balanced by the under-connection of the other neurite segment. (b) Examples of fixed errors and unresolvable errors. The two unresolvable cases are both tangled neurites. In the first fixed error case, the incorrect path found by the A* algorithm is revised. The later two fixed error cases involve adding missing branches.
Extended Data Fig. 8 Morphology and projection analysis of reconstructed neurons in the mouse brain.
(a) Morphology of cortical Grpr+ interneurons. Five representative neurons demonstrate the morphology of the 5 distinct clusters. Axons are indicated with brighter color. The distribution of dendrite and axon lengths along the cortical layers for all neurons in the corresponding cluster is shown with shaded areas indicating the standard error (s.e.m). Paired one-sided Wilcoxon signed-rank tests were applied and showed axons are deeper than dendrites in most clusters. (b) Correspondence between cortical layers of somata and morphology clusters for cortical Grpr+ interneurons. One-sided Fisher’s exact tests (followed by Benjamini-Hochberg correction) were applied as in Fig. 5c and found significant correspondence of cluster 1 and layer 1 (***, p = 1.72 × 10−4). (c) Difference of morphological features for cortical Grpr+ interneurons with soma located in different regions. Two-sided Wilcoxon rank-sum tests followed by Benjamini-Hochberg correction were applied. (d) Projection strength to targets of each morphology cluster of VMH neurons in Fig. 5j and Fig. 5k. (e) Distinct projection targets between Esr1+ and Nr5a1+ VMH neurons. One-sided Wilcoxon rank-sum tests were applied for each target region.
Source data
Supplementary information
Supplementary Information
Supplementary Tables 1 and 3, Figs. 1–6 and Notes 1 and 2.
Supplementary Table 2
Information for all 15 datasets and their reconstruction processes.
Supplementary Video 1
Screencast of the fix module running in GNU/Linux. The annotator starts at a soma and proofreads the automatically reconstructed neurite segments. The program guides the annotator to locations that need human attention, skipping nodes that have been automatically proofread. This video contains no audio stream.
Supplementary Video 2
Playback of the whole reconstruction process for dataset no. 18925. Summary text is displayed in the top left corner. The number of edits of the current snapshot is displayed in the bottom right corner. This video contains no audio stream.
Supplementary Video 3
Screencast of the proofread module running on an Android smartphone. The annotator proofreads neurite segments in this cube by fixing a few local errors. The proofread module provides only cube-level information, thus can run fluently on mobile devices. This video contains no audio stream.
Source data
Source Data Fig. 2
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Source Data Fig. 3
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Source Data Fig. 4
Source data tables.
Source Data Fig. 5
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Source Data Extended Data Fig. 1
Source data tables.
Source Data Extended Data Fig. 8
Source data tables.
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Gou, L., Wang, Y., Gao, L. et al. Gapr for large-scale collaborative single-neuron reconstruction. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02345-z
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DOI: https://doi.org/10.1038/s41592-024-02345-z
