Abstract
The transcriptional underpinnings of brain development remain poorly understood, particularly in humans and closely related non-human primates. We describe a high-resolution transcriptional atlas of rhesus monkey (Macaca mulatta) brain development that combines dense temporal sampling of prenatal and postnatal periods with fine anatomical division of cortical and subcortical regions associated with human neuropsychiatric disease. Gene expression changes more rapidly before birth, both in progenitor cells and maturing neurons. Cortical layers and areas acquire adult-like molecular profiles surprisingly late in postnatal development. Disparate cell populations exhibit distinct developmental timing of gene expression, but also unexpected synchrony of processes underlying neural circuit construction including cell projection and adhesion. Candidate risk genes for neurodevelopmental disorders including primary microcephaly, autism spectrum disorder, intellectual disability, and schizophrenia show disease-specific spatiotemporal enrichment within developing neocortex. Human developmental expression trajectories are more similar to monkey than rodent, although approximately 9% of genes show human-specific regulation with evidence for prolonged maturation or neoteny compared to monkey.
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Change history
21 July 2016
The OLIG1 and STY7 gene labels in Fig. 6d and legend were reversed.
References
Workman, A. D., Charvet, C. J., Clancy, B., Darlington, R. B. & Finlay, B. L. Modeling transformations of neurodevelopmental sequences across mammalian species. J. Neurosci. 33, 7368–7383 (2013)
Smart, I. H. M., Dehay, C., Giroud, P., Berland, M. & Kennedy, H. Unique morphological features of the proliferative zones and postmitotic compartments of the neural epithelium giving rise to striate and extrastriate cortex in the monkey. Cereb. Cortex 12, 37–53 (2002)
MacLean, E. L. et al. The evolution of self-control. Proc. Natl Acad. Sci. USA 111, E2140–E2148 (2014)
Caldwell, C. A. & Whiten, A. in Primates in Perspective 652–662 (Oxford Univ. Press, 2011)
Izpisua Belmonte, J. C. et al. Brains, genes, and primates. Neuron 86, 617–631 (2015)
Miller, D. J. et al. Prolonged myelination in human neocortical evolution. Proc. Natl Acad. Sci. USA 109, 16480–16485 (2012)
Rakic, P., Bourgeois, J. P., Eckenhoff, M. F., Zecevic, N. & Goldman-Rakic, P. S. Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science 232, 232–235 (1986)
Petanjek, Z. et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc. Natl Acad. Sci. USA 108, 13281–13286 (2011)
Gibbs, R. A. et al. Evolutionary and biomedical insights from the rhesus macaque genome. Science 316, 222–234 (2007)
Bernard, A. et al. Transcriptional architecture of the primate neocortex. Neuron 73, 1083–1099 (2012)
Zeng, H. et al. Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures. Cell 149, 483–496 (2012)
Somel, M. et al. Transcriptional neoteny in the human brain. Proc. Natl Acad. Sci. USA 106, 5743–5748 (2009)
Liu, X. et al. Extension of cortical synaptic development distinguishes humans from chimpanzees and macaques. Genome Res. 22, 611–622 (2012)
Colantuoni, C. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011)
Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012)
Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011)
Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014)
Bakken, T. E. et al. Spatiotemporal dynamics of the postnatal developing primate brain transcriptome. Hum. Mol. Genet. 24, 4327–4339 (2015)
Molyneaux, B. J. et al. DeCoN: genome-wide analysis of in vivo transcriptional dynamics during pyramidal neuron fate selection in neocortex. Neuron 85, 275–288 (2015)
Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015)
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015)
Rakic, P. Neurons in rhesus monkey visual cortex: systematic relation between time of origin and eventual disposition. Science 183, 425–427 (1974)
Hughes, J. F. et al. Strict evolutionary conservation followed rapid gene loss on human and rhesus Y chromosomes. Nature 483, 82–86 (2012)
Elston, G. N., Oga, T. & Fujita, I. Spinogenesis and pruning scales across functional hierarchies. J. Neurosci. 29, 3271–3275 (2009)
Scott, J. et al. Longitudinal analysis of the developing rhesus monkey brain using magnetic resonance imaging: birth to adulthood. Brain Struct. Funct. 221, 2847–2871 (2016)
Hasegawa, M. et al. Development of myelination in the human fetal and infant cerebrum: a myelin basic protein immunohistochemical study. Brain Dev. 14, 1–6 (1992)
Sorensen, S. A. et al. Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity. Cereb. Cortex 25, 433–449 (2015)
Pletikos, M. et al. Temporal specification and bilaterality of human neocortical topographic gene expression. Neuron 81, 321–332 (2014)
O’Leary, D. D. M. & Chou, S.-J. J. & Sahara, S. Area patterning of the mammalian cortex. Neuron 56, 252–269 (2007)
De la Rossa, A. et al. In vivo reprogramming of circuit connectivity in postmitotic neocortical neurons. Nat. Neurosci. 16, 193–200 (2013)
Rakic, P. Prenatal development of the visual system in rhesus monkey. Phil. Trans. R. Soc. Lond. B 278, 245–260 (1977)
Tropea, D. et al. Gene expression changes and molecular pathways mediating activity-dependent plasticity in visual cortex. Nature Neurosci. 9, 660–668 (2006)
Willsey, A. J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013)
Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013)
Bishop, K. M., Goudreau, G. & O’Leary, D. D. Regulation of area identity in the mammalian neocortex by Emx2 and Pax6. Science 288, 344–349 (2000)
Schoenebeck, J. J. et al. Variation of BMP3 contributes to dog breed skull diversity. PLoS Genet. 8, e1002849 (2012)
Matsumoto, A. et al. LIN7A depletion disrupts cerebral cortex development, contributing to intellectual disability in 12q21-deletion syndrome. PLoS One 9, e92695 (2014)
Bianchi, S. et al. Synaptogenesis and development of pyramidal neuron dendritic morphology in the chimpanzee neocortex resembles humans. Proc. Natl Acad. Sci. USA 110 (Suppl 2), 10395–10401 (2013)
Huttenlocher, P. R. Synaptic density in human frontal cortex — developmental changes and effects of aging. Brain Res. 163, 195–205 (1979)
Rakic, P., Bourgeois, J. P. & Goldman-Rakic, P. S. Synaptic development of the cerebral cortex: implications for learning, memory, and mental illness. Prog. Brain Res. 102, 227–243 (1994)
Huttenlocher, P. R. & Dabholkar, A. S. Regional differences in synaptogenesis in human cerebral cortex. J. Comp. Neurol. 387, 167–178 (1997)
Jackman, S. L., Turecek, J., Belinsky, J. E. & Regehr, W. G. The calcium sensor synaptotagmin 7 is required for synaptic facilitation. Nature 529, 88–91 (2016)
Pouchelon, G. et al. Modality-specific thalamocortical inputs instruct the identity of postsynaptic L4 neurons. Nature 511, 471–474 (2014)
De Marco García, N. V., Karayannis, T. & Fishell, G. Neuronal activity is required for the development of specific cortical interneuron subtypes. Nature 472, 351–355 (2011)
Iossifov, I. et al. Low load for disruptive mutations in autism genes and their biased transmission. Proc. Natl Acad. Sci. USA 112, E5600–E5607 (2015)
de Lacy, N. & King, B. H. Revisiting the relationship between autism and schizophrenia: toward an integrated neurobiology. Annu. Rev. Clin. Psychol. 9, 555–587 (2013)
Stein, J. L. et al. A quantitative framework to evaluate modeling of cortical development by neural stem cells. Neuron 83, 69–86 (2014)
Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015)
Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007)
Bystron, I., Blakemore, C. & Rakic, P. Development of the human cerebral cortex: Boulder Committee revisited. Nature Rev. Neurosci. 9, 110–122 (2008)
Price, J., Russchen, F. & Amaral, D. in Handbook of Chemical Neuroanatomy (eds Hokfelt, B. & Swanson, L. ) 279–381 (Elsevier, 1987)
Berger, B. & Alvarez, C. Neurochemical development of the hippocampal region in the fetal rhesus monkey, III: calbindin-D28K, calretinin and parvalbumin with special mention of cajal-retzius cells and the retrosplenial cortex. J. Comp. Neurol. 366, 674–699 (1996)
Paxinos, G. The Rhesus Monkey Brain in Stereotaxic Coordinates (Academic Press, 2009)
Karnovsky, M. J. & Roots, L. A ‘direct-coloring’ thiocholine method for cholinesterases. J. Histochem. Cytochem. 12, 219–221 (1964)
Winrow, C. J. et al. Refined anatomical isolation of functional sleep circuits exhibits distinctive regional and circadian gene transcriptional profiles. Brain Res. 1271, 1–17 (2009)
Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003)
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007)
Miller, J. A. et al. Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-seq. BMC Genomics 15, 154 (2014)
Goecks, J., Nekrutenko, A. & Taylor, J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010)
Blankenberg, D. et al. Galaxy: a web-based genome analysis tool for experimentalists. Curr. Protoc. Mol. Biol. Chapter 19, Unit 19.10.1–21 (2010)
Giardine, B. et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 15, 1451–1455 (2005)
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)
Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014)
Zuber, V. & Strimmer, K. High-dimensional regression and variable selection using CAR scores. Stat. Appl. Genet. Mol. Biol. 10, 1–22 (2011)
Bar-Joseph, Z. et al. Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells. Proc. Natl Acad. Sci. USA 105, 955–960 (2008)
Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nature Neurosci. 19, 335–346 (2016)
Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008)
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008)
Langfelder, P. & Horvath, S. Fast R functions for robust correlations and hierarchical clustering. J. Stat. Softw. 46, http://dx.doi.org/10.18637/jss.v046.i11 (2012)
Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015)
Verloes, A., Drunat, S., Gressens, P. & Passemard, S. in GeneReviews (eds Pagon, R. A. et al.) (Univ. Washington, 2013)
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014)
Karaca, E. et al. Genes that affect brain structure and function identified by rare variant analyses of Mendelian neurologic disease. Neuron 88, 499–513 (2015)
Zapala, M. A. & Schork, N. J. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc. Natl Acad. Sci. USA 103, 19430–19435 (2006)
Stead, J. D. H. et al. Transcriptional profiling of the developing rat brain reveals that the most dramatic regional differentiation in gene expression occurs postpartum. J. Neurosci. 26, 345–353 (2006)
Thompson, C. L. et al. A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain. Neuron 83, 309–323 (2014)
Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44–57 (2009)
Huang, W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009)
Smedley, D. et al. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res. 43, W589–W598 (2015)
Proux, E., Studer, R. A., Moretti, S. & Robinson-Rechavi, M. Selectome: a database of positive selection. Nucleic Acids Res. 37, D404–D407 (2009)
Moretti, S. et al. Selectome update: quality control and computational improvements to a database of positive selection. Nucleic Acids Res. 42, D917–D921 (2014)
Pirooznia, M. et al. SynaptomeDB: an ontology-based knowledgebase for synaptic genes. Bioinformatics 28, 897–899 (2012)
Blue, M. E. & Parnavelas, J. G. The formation and maturation of synapses in the visual cortex of the rat. II. Quantitative analysis. J. Neurocytol. 12, 697–712 (1983)
Acknowledgements
The authors thank the Allen Institute for Brain Science founders, P. G. Allen and J. Allen, for their vision, encouragement, and support. The authors also thank J. Montiel and W. Zhi Wang for their advice on developmental neuroanatomy and experimental design. We also wish to acknowledge the California National Primate Research Center (NIH Award Number RR00169) for providing tissues and Covance Genomics Laboratory (Seattle, Washington) for microarray probe generation, hybridization and scanning. The project was supported by NIH Blueprint for Neuroscience Research contract HHSN-271-2008-0047 (PI: E. Lein, Allen Institute for Brain Science) from the National Institute of Mental Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health or the National Institute of Mental Health.
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T.E.B., J.A.M., S.-L.D., L.N., A.S., R.A.D., J.J.R., S.Sha., M.J.H., D.G.A., A.Be. and E.S.L. contributed significantly to the analysis. K.A.S., L.N., A.S., R.A.D., J.G.H., R.F.H., Z.M., C.D., D.G.A., A.Be. and E.S.L. contributed significantly to the experimental and technical design. T.E.B., J.A.M., T.L., S.Sha., and A.Be. contributed significantly to generation of the data. S.M.S., K.A.S., L.N., T.L., P.W., J.G.H., M.J.H., J.W.P., C.D., A.R.J., D.G.A., A.Be. and E.S.L. contributed significantly to supervision and management of the project. K.A., N.De., T.D., J.G., G.Gu., C.L.K., C.L., C.-K.L., P.D.P., T.R., K.R. and D.S. contributed to the analysis. N.De, T.A.D., A.E., J.G., R.A.G., G.Gu., D.R.H., A.H.-S., J.J., C.L.K., C.L., C.-K.L., F.L., J.N., J.R., Z.L.R. and W.B.W. contributed to the experimental and technical design. K.A., J.A., C.B., J.L.B., D.B., K.Bi., A.Bo., K.Br., S.B., E.B., S.Cal., A.C., S.Cat., M.C., J.C., N.De., T.A.D., N.Do., G.Ge., T.L.G., J.G., L.G., B.G., G.Gu., J.H., Z.H., N.H., R.H., M.K., A.K., C.L.K., C.L., C.-K.L., F.L., N.Ma., R.M., J.M., N.Mo., E.M., K.N., J.N., A.O., E.O., J.Pa., S.P., J.Pe., L.P., M.R., Z.L.R., T.R., B.R., K.R., D.R., M.S., N.Sh., S.Shi., N.Sj., A.J.S., R.T., L.V., U.W., W.B.W., C.Wh., J.W., R.Y. and B.L.Y. contributed to generation of the data. A.Bo., E.B., M.C., T.D., T.A.D., A.E., E.F., B.G., M.K., C.L., L.L., N.Ma., S.P., M.R. and A.J.S. contributed to supervision and management of the project. T.E.B., J.A.M. and E.S.L. wrote the manuscript.
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Additional information
Detailed technical protocol documents describing tissue processing and microarray profiling are available at the Allen Brain Atlas portal (http://www.brain-map.org) through the non-human primate link, or directly from the NIH Blueprint NHP Atlas website (http://www.blueprintnhpatlas.org), under the documentation tab. Microarray data can be viewed online by selecting microdissection under the microarray tab and can be downloaded under the download tab.
Reviewer Information Nature thanks P. Carninci and C. Sherwood and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Figure 1 Anatomical parcellations of developing cortical and subcortical regions.
Nissl stained sections of major brain regions sampled in this resource. Green lines demarcate subregions that were isolated by laser capture microdissection and transcriptionally profiled.
Extended Data Figure 2 Canonical cell type marker gene expression across cortical development.
Heat maps of average gene expression in cortical layers at different prenatal (E40–E120) and postnatal ages (0–48 months). Anterior cingulate gyrus (ACG) and primary visual cortex (V1) were sampled at all ages, while primary somatosensory cortex (S1) was sampled in a limited set of layers prenatally. Several additional prefrontal and visual areas were sampled postnatally: orbital gyrus (OG), dorsolateral prefrontal cortex (dlPFC), rectal gyrus (RG) and secondary visual cortex (V2).
Extended Data Figure 3 Sex differential expression is limited to two Y chromosome genes.
a, Quantile–quantile plot of observed versus expected sex differential expression for all 12,441 genes across brain regions during prenatal development. A linear mixed model was fit to all prenatal brain samples with a fixed effect for sex and random effects for brain region, age and donor (see Supplementary Table 11). Genes were ordered by the observed sex effect and plotted versus the expected sex effect based on permutation testing. A 95% confidence interval was calculated (dashed line) based on permutations, and 11 genes in males and no genes in females were more highly expressed than expected by chance. Seven of these 11 genes were nominally significant and included at least two Y chromosome genes (EIF1AY, LOC720563) and potentially a third gene (LOC693361) whose microarray probe maps to an unannotated region of the Y chromosome.
Extended Data Figure 4 Expression rates of change have similar developmental trajectories across all brain regions.
a, Rates of expression change in all available brain subregions and ages. b, Box plots summarizing the number of significantly increasing or decreasing genes between adjacent time points in all subregions. At all ages, the majority of subregions had at least 1,000 genes (red line) that were significantly changing (ANOVA FDR < 0.05, fold change >1.25). c, Regional specificity of increased autophagy may reflect differential timing of synaptic pruning. Enrichment for autophagy of the most dynamically increasing (upper triangle) and decreasing (lower triangle) genes with samples ordered and labelled as in Fig. 3b. For each gene list, the colour corresponds to the proportion of GO terms that are more specific (that is, ‘child’) terms subsumed under autophagy (GO:0006914) based on the GO hierarchy and that are significantly enriched (nominal P < 0.05). Note that autophagy was selectively turned on in occipital cortex after infancy and in hippocampus after juvenility (arrows).
Extended Data Figure 5 Variable synchrony of biological processes between brain regions.
a, Example of variable timing of GO process activity (black boxes) between regions, resulting in different age overlaps (mini-table below). Note that E50 was the earliest age for which we could calculate expression change. AM, amygdala; BG, basal ganglia; HP, hippocampus; NCX, neocortex. b, Average pairwise age overlaps (black, solid line) for all increasing (top) and decreasing (bottom) GO processes were greater than expected by chance (black, dotted line). c, Rank ordered timing of GO processes in Fig. 4c with weighted average rank for each region (asterisks). d, Developmental expression of mature oligodendrocyte markers in four myelin-enriched brain subregions with early increased expression in globus pallidus (arrows) for MOG and ERMN, but not MAL and ASPA.
Extended Data Figure 6 Neurogenesis and gliogenesis in S1 occur at a time course intermediate between V1 and ACG.
a, Genes with enriched expression in ACG relative to V1 between E70–E90 also show enriched expression in S1 relative to V1, suggesting that the timing of primary sensory regions is non-uniform in L5/L6 (top), subventricular zone (SZ) (middle), and ventricular zone (VZ) (bottom). Each plot shows the average enrichment (log2 fold change) in S1 vs V1 (y axis) compared with the average enrichment in ACG vs V1 (x axis) between E70–E90 for all genes significantly enriched in ACG in at least two of the three ages between E70–E90 (Fig. 5a). b, Marker genes for cell types (compare with Fig. 5e–h) show expression patterns in S1 which are either consistent with ACG (that is, GAD1) or intermediate between V1 and ACG (that is, AQP4). c, Genes with enriched expression for V1 (FGFR3) or ACG (CBLN2) across development show intermediate expression in S1, suggesting that these genes may show cortical gradients rather than specific expression in V1 or ACG. d, Genes with common expression patterning in V1 and ACG can show different patterns in S1, suggesting that rate of neuron and glia development is not the whole story. For example, SPARCL1 and ABCA8 both show increased expression with time in VZ with V1 showing a delay relative to ACG; however, these two genes show different temporal delays in S1.
Extended Data Figure 7 Evolutionary conservation of developmental expression.
a, Median pairwise correlations of expression trajectories in prefrontal cortex within human (black) and between human and rhesus monkey (red) decrease for less variable genes in rhesus monkey (genes ordered by standard deviation of expression across ages). b, Left: proportion of genes assigned to different conservation categories is robust to correlation threshold. Right: developmentally dynamic genes are more highly conserved (genes ordered by same method as in a). For each set of genes, the average ± standard deviation of the proportion of genes in each conservation category was estimated using correlation thresholds ranging from 0 to 0.9. c, Left: segmented linear fits of expression for example gene with estimated breakpoints in each species (dashed lines). Right: distribution of breakpoint ages for 179 decreasing genes with good fits to the model in frontal and primary visual cortex. Colours and symbols are consistent in c and d. d, Segmented linear fits with breakpoint estimation of synaptic density for prefrontal and primary visual cortex based on previously published studies (see Methods). e, Breakpoint comparison of 179 increasing genes including 81 synapse related genes (red) between cortical areas within species. Genes that fall on the lines peak at the same age in primary visual and prefrontal cortex. f, g, Comparison of breakpoint timing between human, rhesus monkey and rat in prefrontal cortex (f) and additional brain regions (g). Genes that plateau in expression after their breakpoint in human (grey points), and genes that significantly decrease (blue symbols) or increase (red symbols) expression with 95% confidence intervals (grey lines) of breakpoints. Black lines correspond to equal (solid) ± a window (dashed) of developmental ages between species. pcd, post-conceptional days.
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Bakken, T., Miller, J., Ding, SL. et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367–375 (2016). https://doi.org/10.1038/nature18637
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DOI: https://doi.org/10.1038/nature18637
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