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High-throughput mapping of a whole rhesus monkey brain at micrometer resolution

Nature Biotechnology volume 39, pages 1521–1528 (2021)Cite this article

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Abstract

Whole-brain mesoscale mapping in primates has been hindered by large brain sizes and the relatively low throughput of available microscopy methods. Here, we present an approach that combines primate-optimized tissue sectioning and clearing with ultrahigh-speed fluorescence microscopy implementing improved volumetric imaging with synchronized on-the-fly-scan and readout technique, and is capable of completing whole-brain imaging of a rhesus monkey at 1 × 1 × 2.5 µm3 voxel resolution within 100 h. We also developed a highly efficient method for long-range tracing of sparse axonal fibers in datasets numbering hundreds of terabytes. This pipeline, which we call serial sectioning and clearing, three-dimensional microscopy with semiautomated reconstruction and tracing (SMART), enables effective connectome-scale mapping of large primate brains. With SMART, we were able to construct a cortical projection map of the mediodorsal nucleus of the thalamus and identify distinct turning and routing patterns of individual axons in the cortical folds while approaching their arborization destinations.

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Fig. 1: The SMART approach for high-throughput mapping of a rhesus macaque brain at micron resolution.
Fig. 2: Mesoscopic mapping of the MD projection.
Fig. 3: Organization of axonal fibers in cortical folds.
Fig. 4: Brain-wide tracing of axonal projections.

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Data availability

The complete image datasets (raw and processed) of macaque brains exceed 1 petabyte and are therefore impractical to fully upload to a public data repository. A fraction of the data is available at https://doi.org/10.5281/zenodo.4451992, including image blocks shown in Figs. 3 and 4 for tracing and exploring with Lychnis; or through http://smart.bigconnectome.org, with a browser for viewing at full size the reconstructed two-dimensional images shown in Figs. 1 and 2 and Supplementary Fig. 3. The subsets related to any figure or video in this work are available upon request with feasible data transfer mechanisms (such as physical hard disk drives, cloud storage or onsite visiting). Morphological data of eight mouse MD neurons and two RE neurons used in this work were from the publicly available MouseLight dataset with neuron IDs AA0054 (https://doi.org/10.25378/janelia.5521765), AA0055 (https://doi.org/10.25378/janelia.5521768), AA0094 (https://doi.org/10.25378/janelia.5526661), AA0095 (https://doi.org/10.25378/janelia.5526664), AA0138 (https://doi.org/10.25378/janelia.5527288), AA0353 (https://doi.org/10.25378/janelia.5526664), AA0363 (https://doi.org/10.25378/janelia.7613897), AA0368 (https://doi.org/10.25378/janelia.7613912), AA0370 (https://doi.org/10.25378/janelia.7613921) and AA0371 (https://doi.org/10.25378/janelia.7613924).

Code availability

Custom code, executables and user guides can be accessed at https://github.com/SMART-pipeline.

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Acknowledgements

We thank Y. Song, M. Zhang, S. Zhao, T. Wang, Y. Guo and K. Zhang for technical assistance with sample preparation and imaging, and S. Chen, P. Zhou and D. Bi for suggestions on improving the manuscript. We especially thank M. Poo for advice on this project, and D. Van Essen, H. Kennedy, T. Hayashi, M. Glasser and T. Coalson for critical reading and commenting on the preprint of the paper. This work was supported by grants from the Strategic Priority Research Program of the Chinese Academy of Science (no. XDB32030200 to G.-Q.B.), the National Natural Science Foundation of China (nos. 91732304 to G.-Q.B. and 32000696 to Fang Xu), the Guangdong Basic and Applied Basic Research Foundation (no. 2021A1515010625 to Fang Xu), the Shenzhen Science and Technology Program (no. RCBS20200714114909001 to Fang Xu), the Key-Area Research and Development Program of Guangdong Province (nos. 2018B030331001 to G.-Q.B. and 2018B030338001 to P.-M.L.) and Shenzhen Infrastructure for Brain Analysis and Modeling (no. ZDKJ20190204002 to G.-Q.B.). Fang Xu additionally acknowledges partial support from the Chinese Academy of Sciences International Partnership Program (no. 172644KYSB20170004). Q.Z., L.I.Z., H.-W.D., P.-M.L. and G.-Q.B were also partially supported by the NIH BICCN program (no. U01MH116990).

Author information

Author notes

  1. These authors contributed equally: Fang Xu, Yan Shen, Lufeng Ding, Chao-Yu Yang.

Authors and Affiliations

  1. Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China

    Fang Xu, Hao Wang, Qingyuan Zhu, Cheng Xu, Qianwei Li, Pak-Ming Lau & Guo-Qiang Bi

  2. CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China

    Fang Xu, Fengyi Wu, Yanyang Xiao, Fuqiang Xu, Pak-Ming Lau & Guo-Qiang Bi

  3. CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China

    Yan Shen, Lufeng Ding, Chao-Yu Yang & Pak-Ming Lau

  4. Key Laboratory of Animal Models and Human Disease Mechanism, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China

    Heng Tan & Xintian Hu

  5. Department of Pathology and Pathophysiology, Kunming Medical University, Kunming, China

    Heng Tan

  6. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

    Hao Wang, Pak-Ming Lau & Guo-Qiang Bi

  7. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Rui Xu & Robert Desimone

  8. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China

    Peng Su & Fuqiang Xu

  9. Zilkha Neurogenetic Institute, Center for Neural Circuits & Sensory Processing Disorders, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Li I. Zhang

  10. UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Hong-Wei Dong

  11. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China

    Fuqiang Xu, Xintian Hu & Guo-Qiang Bi

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  1. Fang Xu
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Contributions

Fang Xu, L.I.Z., H.-W.D., Fuqiang Xu, X.H., P.-M.L. and G.-Q.B. conceptualized the project. Fang Xu led the project under the supervision of P.-M.L. and G.-Q.B.. Fang Xu, Y.S. and H.W. established the pipeline for macaque whole-brain imaging. Fang Xu, Q.Z., H.W. and C.X. designed and set up the microscope. Y.S. performed sample preparation and acquired data. L.D. developed the software for image acquisition, visualization and neuronal tracing. C.-Y.Y. developed the software for brain reconstruction. H.T. and X.H. injected viruses and prepared brain samples. Fang Xu, Y.S., C.-Y.Y., F.W. and R.X. analyzed data. Y.X. and Q.L. developed tools for image preprocessing. P.S. and Fuqiang Xu validated and provided tracing viruses. H.-W.D. and R.D. provided valuable neuroanatomical insights. Fang Xu, P.-M.L. and G.-Q.B. wrote the manuscript with inputs from all authors.

Corresponding authors

Correspondence to Pak-Ming Lau or Guo-Qiang Bi.

Ethics declarations

Competing interests

The University of Science and Technology of China has filed a patent application related to the imaging method, for which Fang Xu, L.D., C.-Y.Y., H.W., Q.Z., P.-M.L. and G.-Q.B. are named inventors. The remaining authors declare no competing interests.

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Peer review information Nature Biotechnology thanks Moritz Helmstaedter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Organization of immunolabeled dopaminergic fibers.

a, MIP of a 200-μm macaque brain slice stained with anti-GFAP antibody (green), an astrocyte marker, and anti-TH antibody (magenta), a marker for dopaminergic neurons. b, The TH channel is displayed individually. The caudate and putamen feature strong background TH signals. Boxed regions are enlarged in (c-h). c, An example color-coded depth image showing dopaminergic axons traveling in GP and pu. d-e, Example images of dopaminergic neurons distributed in PVN (d) and SN (e). f, Dopaminergic fiber bundles are arranged in thin sheets when traveling in icp. A lonely dopaminergic neuron (arrowhead) is captured in GPi and enlarged in the inset. g, Dopaminergic axons project to cortical areas in different patterns. E.g. in the primary motor area (4), dense axons distributed through all the cortical layers, whereas in somatosensory areas (1-2, 3a/b), dopaminergic axons project mainly in superficial layers. white, the gray/white matter boundaries. h-j, Bright dopaminergic fibers could be identified individually in the cortical areas (i) and the white matter (j). Scale bars: (a-b), 5 mm; (c-f), 200 μm; inset of (f), 50 μm; (g-h), 500 μm; (i-j), 100 μm. Acronyms: 1-2, somatosensory areas 1 and 2; 3a/b, somatosensory areas 3a and 3b; 4, primary motor cortex (or F1, agranular frontal area F1); cd, caudate; cis, cingulate sulcus; cs, central sulcus; GP, globus pallidus; GPe, globus pallidus, external segment; GPi, globus pallidus, internal segment; icp, internal capsule, posterior limb; pu, putamen; PVN, paraventricular hypothalamic nucleus; SN, substantia nigra. Experiments were repeated on at least three monkey brain slices, with similar results obtained each time; representative images from a single slice are shown.

Supplementary information

Supplementary Information

Supplementary Figs. 1–13 and Tables 1–3

Reporting Summary

Supplementary Video 1

On-the-fly VISoR2 imaging of a 300-μm-thick brain slice from a virus-injected adult macaque at 1 × 1 × 2.5-µm3 resolution in 142 s. This video is at 2× playback speed. Fibers labeled with eGFP in the prefrontal area are revealed.

Supplementary Video 2

A stitched image volume spanning four slices of a macaque brain.

Supplementary Video 3

A reconstructed macaque brain. Efferent fibers from the MD injection sites are labeled. This volume was acquired at 1 × 1 × 2.5-µm3 voxel resolution and the reconstructed volume was downsampled to 10 × 10 × 10-µm3 resolution for rendering. The fiber orientation image of this brain is shown from t = 00:05.

Supplementary Video 4

Three-dimensional visualization of a slice image, showing representative fiber terminals (tracks ended mid-slice) and passing-by fibers (tracks traveling through the slice) from the MD to the prefrontal areas.

Supplementary Video 5

Representative axon segments from an ROI surrounding the STS in the temporal lobe, showing distinct turning patterns.

Supplementary Video 6

An axon (no. RM006-4) traced from near the injection site to its arborized terminals in contralateral cortical areas, together with all other fibers shown in Fig. 4, are visualized in the whole-brain framework.

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Xu, F., Shen, Y., Ding, L. et al. High-throughput mapping of a whole rhesus monkey brain at micrometer resolution. Nat Biotechnol 39, 1521–1528 (2021). https://doi.org/10.1038/s41587-021-00986-5

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