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.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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).
Custom code, executables and user guides can be accessed at https://github.com/SMART-pipeline.
Belmonte, J. C. I. et al. Brains, genes, and primates. Neuron 86, 617–631 (2015).
Poo, M.-m et al. China Brain Project: basic neuroscience, brain diseases, and brain-inspired computing. Neuron 92, 591–596 (2016).
Markov, N. T. et al. Cortical high-density counterstream architectures. Science 342, 1238406 (2013).
Kleinfeld, D. et al. Large-scale automated histology in the pursuit of connectomes. J. Neurosci. 31, 16125–16138 (2011).
Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
Wang, X.-J. & Kennedy, H. Brain structure and dynamics across scales: in search of rules. Curr. Opin. Neurobiol. 37, 92–98 (2016).
Zeng, H. Mesoscale connectomics. Curr. Opin. Neurobiol. 50, 154–162 (2018).
Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).
Schmahmann, J. & Pandya, D. Fiber Pathways of the Brain (Oxford Univ. Press, 2009).
Lin, M. K. et al. A high-throughput neurohistological pipeline for brain-wide mesoscale connectivity mapping of the common marmoset. eLife 8, e40042 (2019).
Albanese, A. & Chung, K. Neuroimaging: whole-brain imaging reaches new heights (and lengths). eLife 5, e13367 (2016).
Jones, D. K., Knösche, T. R. & Turner, R. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73, 239–254 (2013).
Thomas, C. et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc. Natl Acad. Sci. USA 111, 16574–16579 (2014).
Reveley, C. et al. Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography. Proc. Natl Acad. Sci. USA 112, E2820–E2828 (2015).
Liu, C. et al. A resource for the detailed 3D mapping of white matter pathways in the marmoset brain. Nat. Neurosci. 23, 271–280 (2020).
Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013).
Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958 (2014).
Susaki, E. A. et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157, 726–739 (2014).
Matsumoto, K. et al. Advanced CUBIC tissue clearing for whole-organ cell profiling. Nat. Protoc. 14, 3506–3537 (2019).
Zhao, S. et al. Cellular and molecular probing of intact human organs. Cell 180, 796–812 (2020).
Ueda, H. R. et al. Whole-brain profiling of cells and circuits in mammals by tissue clearing and light-sheet microscopy. Neuron 106, 369–387 (2020).
Tsai, P. S. et al. All-optical histology using ultrashort laser pulses. Neuron 39, 27–41 (2003).
Ragan, T. et al. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat. Methods 9, 255–258 (2012).
Gong, H. et al. High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nat. Commun. 7, 12142 (2016).
Economo, M. N. et al. A platform for brain-wide imaging and reconstruction of individual neurons. eLife 5, e10566 (2016).
Seiriki, K. et al. High-speed and scalable whole-brain imaging in rodents and primates. Neuron 94, 1085–1100 (2017).
Winnubst, J. et al. Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain. Cell 179, 268–281 (2019).
Peng, H. et al. Brain-wide single neuron reconstruction reveals morphological diversity in molecularly defined striatal, thalamic, cortical and claustral neuron types. Preprint at bioRxiv https://doi.org/10.1101/675280 (2020).
Wang, H. et al. Scalable volumetric imaging for ultrahigh-speed brain mapping at synaptic resolution. Natl Sci. Rev. 6, 982–992 (2019).
Bria, A. & Iannello, G. TeraStitcher – a tool for fast automatic 3D-stitching of teravoxel-sized microscopy images. BMC Bioinformatics 13, 316 (2012).
Hörl, D. et al. BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat. Methods 16, 870–874 (2019).
Hayworth, K. J. et al. Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics. Nat. Methods 12, 319–322 (2015).
Ray, J. P. & Price, J. L. The organization of projections from the mediodorsal nucleus of the thalamus to orbital and medial prefrontal cortex in macaque monkeys. J. Comp. Neurol. 337, 1–31 (1993).
Parnaudeau, S., Bolkan, S. S. & Kellendonk, C. The mediodorsal thalamus: an essential partner of the prefrontal cortex for cognition. Biol. Psychiatry 83, 648–656 (2018).
Giguere, M. & Goldman-Rakic, P. S. Mediodorsal nucleus: areal, laminar, and tangential distribution of afferents and efferents in the frontal lobe of rhesus monkeys. J. Comp. Neurol. 277, 195–213 (1988).
Friedman, D. P. & Murray, E. A. Thalamic connectivity of the second somatosensory area and neighboring somatosensory fields of the lateral sulcus of the macaque. J. Comp. Neurol. 252, 348–373 (1986).
Bria, A., Iannello, G., Onofri, L. & Peng, H. TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images. Nat. Methods 13, 192–194 (2016).
Wang, Y. et al. TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain. Nat. Commun. 10, 3474 (2019).
Gao, R. et al. Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution. Science 363, eaau8302 (2019).
Luo, L., Callaway, E. M. & Svoboda, K. Genetic dissection of neural circuits: a decade of progress. Neuron 98, 256–281 (2018).
Lin, R. et al. Cell-type-specific and projection-specific brain-wide reconstruction of single neurons. Nat. Methods 15, 1033–1036 (2018).
Friedmann, D. et al. Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network. Proc. Natl Acad. Sci. USA 117, 11068–11075 (2020).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Levinthal, D. J. & Strick, P. L. Multiple areas of the cerebral cortex influence the stomach. Proc. Natl Acad. Sci. USA 117, 13078–13083 (2020).
Jungmann, A., Leuchs, B., Rommelaere, J., Katus, H. A. & Müller, O. J. Protocol for efficient generation and characterization of adeno-associated viral vectors. Hum. Gene Ther. Methods 28, 235–246 (2017).
Wu, S. H. et al. Comparative study of the transfection efficiency of commonly used viral vectors in rhesus monkey (Macaca mulatta) brains. Zool. Res. 38, 88–95 (2017).
Jing, W. et al. A new MRI approach for accurately implanting microelectrodes into deep brain structures of the rhesus monkey (Macaca mulatta). J. Neurosci. Methods 193, 203–209 (2010).
Goldberg, I. G. et al. The Open Microscopy Environment (OME) data model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 6, R47 (2005).
Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010).
Ziv, J. & Lempel, A. Compression of individual sequences via variable-rate coding. IEEE Trans. Inf. Theory 24, 530–536 (1978).
Welch, T. A. A technique for high-performance data compression. Computer 17, 8–19 (1984).
Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010).
Schilling, K. G. et al. Histological validation of diffusion MRI fiber orientation distributions and dispersion. Neuroimage 165, 200–221 (2018).
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).
Saleem, K. S. & Logothetis, N. K. A Combined MRI and Histology Atlas of the Rhesus Monkey Brain in Stereotaxic Coordinates (Academic Press, 2012).
Van Essen, D. C., Glasser, M. F., Dierker, D. L. & Harwell, J. Cortical parcellations of the macaque monkey analyzed on surface-based atlases. Cereb. Cortex 22, 2227–2240 (2011).
Seidlitz, J. et al. A population MRI brain template and analysis tools for the macaque. Neuroimage 170, 121–131 (2018).
Schroeder, W., Martin, K. & Lorensen, B. The Visualization Toolkit: An Object-oriented Approach to 3D Graphics (Kitware, 2006).
Peng, H. et al. Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis. Nat. Commun. 5, 4342 (2014).
Peng, H., Bria, A., Zhou, Z., Iannello, G. & Long, F. Extensible visualization and analysis for multidimensional images using Vaa3D. Nat. Protoc. 9, 193–208 (2014).
Fan, Q., Efrat, A., Koltun, V., Krishnan, S. & Venkatasubramanian, S. Hardware-assisted natural neighbor interpolation. Proc. Seventh Workshop on Algorithm Engineering and Experiments (ALENEX) (Society for Industrial and Applied Mathematics, 2005).
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).
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.
Peer review information Nature Biotechnology thanks Moritz Helmstaedter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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 Figs. 1–13 and Tables 1–3
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.
A stitched image volume spanning four slices of a macaque brain.
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.
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.
Representative axon segments from an ROI surrounding the STS in the temporal lobe, showing distinct turning patterns.
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.
About this article
Cite this article
Xu, F., Shen, Y., Ding, L. et al. High-throughput mapping of a whole rhesus monkey brain at micrometer resolution. Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-00986-5