Transcriptional landscape of the prenatal human brain (original) (raw)

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Acknowledgements

We wish to thank the Allen Institute founders, P. G. Allen and J. Allen, for their vision, encouragement and support. We express our gratitude to past and present Allen Institute staff members R. Adams, A. Alpisa, A. Boe, E. Byrnes, M. Chapin, J. Chen, C. Copeland, N. Dotson, K. Fotheringham, E. Fulfs, M. Gasparrini, T. Gilbert, Z. Haradon, N. Hejazinia, N. Ivanov, J. Kinnunen, A. Kriedberg, J. Laoenkue, S. Levine, V. Menon, E. Mott, N. Motz, J. Pendergraft, L. Potekhina, J. Redmayne-Titley, D. Rosen, C. Simpson, S. Shi, L. Velasquez, U. Wagley, N. Wong and B. Youngstrom for their technical assistance. We would also like to thank J. Augustinack, T. Benner, A. Mayaram, M. Roy, A. van der Kouwe and L. Wald from the Fischl laboratory. Also, we wish to acknowledge Covance Genomics Laboratory (Seattle, Washington) for microarray probe generation, hybridization and scanning. In addition, we express our gratitude to Advanced Bioscience Resources, for providing tissue used for expression profiling and reference atlas generation as well as to the Laboratory of Developmental Biology, University of Washington, for providing tissue used for expression profiling and reference atlas generation. The Laboratory of Developmental Biology work was supported by the National Institutes of Health (NIH) Award Number 5R24HD0008836 from the Eunice Kennedy Shriver National Institute of Child Health & Human Development. The BrainSpan project was supported by Award Number RC2MH089921 (PIs: E. Lein and M. Hawrylycz, Allen Institute for Brain Science) from the National Institute of Mental Health. The content is solely the responsibility of the respective authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institutes of Health.

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Author notes

1. Jeremy A. Miller and Song-Lin Ding: These authors contributed equally to this work.

Authors and Affiliations

1. Allen Institute for Brain Science, Seattle, 98103, Washington, USA
   Jeremy A. Miller, Song-Lin Ding, Susan M. Sunkin, Kimberly A. Smith, Lydia Ng, Aaron Szafer, Amanda Ebbert, Zackery L. Riley, Joshua J. Royall, Kaylynn Aiona, James M. Arnold, Crissa Bennet, Darren Bertagnolli, Krissy Brouner, Stephanie Butler, Shiella Caldejon, Anita Carey, Christine Cuhaciyan, Rachel A. Dalley, Nick Dee, Tim A. Dolbeare, Benjamin A. C. Facer, David Feng, Tim P. Fliss, Garrett Gee, Jeff Goldy, Lindsey Gourley, Benjamin W. Gregor, Guangyu Gu, Robert E. Howard, Jayson M. Jochim, Chihchau L. Kuan, Christopher Lau, Chang-Kyu Lee, Felix Lee, Tracy A. Lemon, Phil Lesnar, Bergen McMurray, Naveed Mastan, Nerick Mosqueda, Nhan-Kiet Ngo, Julie Nyhus, Aaron Oldre, Eric Olson, Jody Parente, Patrick D. Parker, Sheana E. Parry, Melissa Reding, Kate Roll, David Sandman, Melaine Sarreal, Sheila Shapouri, Nadiya V. Shapovalova, Elaine H. Shen, Nathan Sjoquist, Clifford R. Slaughterbeck, Michael Smith, Andy J. Sodt, Derric Williams, Michael J. Hawrylycz, Allan R. Jones, John W. Phillips, Paul Wohnoutka, Chinh Dang, Amy Bernard, John G. Hohmann & Ed S. Lein
2. Division of Genetic Medicine, Department of Pediatrics, University of Washington, 1959 North East Pacific Street, Box 356320, Seattle, Washington 98195, USA,
   Theresa Naluai-Cecchini & Ian A. Glass
3. Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, 02129, Massachusetts, USA
   Allison Stevens, Lilla Zöllei & Bruce Fischl
4. Computer Science and AI Lab, MIT, Cambridge, 02139, Massachusetts, USA
   Allison Stevens & Bruce Fischl
5. Department of Neurobiology and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, 06510, Connecticut, USA
   Mihovil Pletikos & Nenad Šestan
6. Department of Molecular Biophysics and Biochemistry, Program in Computational Biology and Bioinformatics, Yale University, New Haven, 06520, Connecticut, USA
   Mark B. Gerstein
7. Department of Computer Science, Yale University, New Haven, 06520, Connecticut, USA
   Mark B. Gerstein
8. Department of Neurology and Semel Institute David Geffen School of Medicine, Program in Neurogenetics, UCLA, Los Angeles, 90095, California, USA
   Daniel H. Geschwind
9. Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, 98101, Washington, USA
   Robert F. Hevner
10. Department of Neurological Surgery, University of Washington School of Medicine, Seattle, 98105, Washington, USA
    Robert F. Hevner
11. Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, 75390, Texas, USA
    Hao Huang
12. and Department of Psychiatry, Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, 90033, California, USA
    James A. Knowles
13. Department of Pediatrics, Children’s Hospital, Los Angeles, 90027, California, USA
    Pat Levitt
14. Keck School of Medicine, University of Southern California, Los Angeles, 90089, California, USA
    Pat Levitt

Authors

1. Jeremy A. Miller
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2. Song-Lin Ding
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3. Susan M. Sunkin
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4. Kimberly A. Smith
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5. Lydia Ng
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8. Zackery L. Riley
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29. Guangyu Gu
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30. Robert E. Howard
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31. Jayson M. Jochim
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32. Chihchau L. Kuan
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33. Christopher Lau
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34. Chang-Kyu Lee
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35. Felix Lee
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36. Tracy A. Lemon
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37. Phil Lesnar
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38. Bergen McMurray
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39. Naveed Mastan
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40. Nerick Mosqueda
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41. Theresa Naluai-Cecchini
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54. Melaine Sarreal
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55. Sheila Shapouri
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56. Nadiya V. Shapovalova
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57. Elaine H. Shen
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58. Nathan Sjoquist
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59. Clifford R. Slaughterbeck
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60. Michael Smith
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63. Lilla Zöllei
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64. Bruce Fischl
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65. Mark B. Gerstein
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66. Daniel H. Geschwind
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67. Ian A. Glass
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68. Michael J. Hawrylycz
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69. Robert F. Hevner
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70. Hao Huang
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71. Allan R. Jones
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72. James A. Knowles
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73. Pat Levitt
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74. John W. Phillips
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75. Nenad Šestan
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79. John G. Hohmann
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80. Ed S. Lein
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Contributions

E.S.L, S.-L.D., K.A.S. and S.M.S. contributed significantly to the overall project design. S.M.S, K.A.S., A.E., A.B., and P.W. managed the tissue and sample processing in the laboratory. K.A., J.M.A., C.B., D.B., K.B., S.B., S.C., A.C., C.C., R.A.D., G.Ge., J.G., L.G., B.W.G., R.E.H., T.A.L., Na.M., N.F.M., N.-K.N., A.O., E.O., J.Pa., P.D.P., S.E.P., M.P., Me.R., J.J.R., K.R., D.S., Me.S., S.S., N.V.S. and Mi.S. contributed to tissue and sample processing. E.H.S., Z.L.R., T.N.-C., and I.A.G. contributed to establishing the tissue acquisition pipeline. N.D., J.N. and A.B. contributed to protocol development. A.S.P., L.Z., B.F., and H.H. contributed to MR and DWI imaging and analysis. J.M.J., C.R.S., and D.W. provided engineering support. S.-L.D., R.A.D., P.D.P., D.S. and J.G.H. contributed to the neuroanatomical design and implementation. S.-L.D., B.A.C.F., Ph.L., B.M., J.J.R., R.F.H., N.Se. and J.G.H. contributed to the reference atlas design, quality control and implementation. L.N., A.S. and C.D. managed the creation of the data pipeline, visualization and mining tools. L.N., A.S., T.A.D., D.F., T.P.F., G.Gu, C.L.K., C.La., F.L., N.Sj. and A.J.S. contributed to the creation of the data pipeline, visualization and mining tools. J.A.M., S.-L.D., R.F.H., C.-K.L., M.J.H., S.M.S. and E.S.L. contributed to data analysis and interpretation. M.B.G., D.H.G., J.A.K., Pa.L., J.W.P., N.Se. and A.R.J. contributed to overall project design and consortium management. E.S.L. and M.J.H. conceived the project, and the manuscript was written by J.A.M. and E.S.L. with input from all other authors.

Corresponding author

Correspondence toEd S. Lein.

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Competing interests

The authors declare no competing financial interests.

Additional information

These data are freely accessible as part of the BrainSpan Atlas of the Developing Human Brain (http://brainspan.org), also available via the Allen Brain Atlas data portal (http://www.brain-map.org).

Extended data figures and tables

Extended Data Figure 1 Representative Nissl sections for laser microdissection (LMD) of 16 pcw and 21 pcw brains.

a, b, Nissl-stained sections were annotated and used to determine LMD region boundaries for 16 pcw (a) and 21 pcw (b) brains. Regions from adjacent sections on PEN membrane slides were captured using these annotations as guidelines. Labels show full name and abbreviation for representative planes of section through presumptive neocortical regions. Panel b is a higher resolution modified version of the bottom row in Fig. 1d of the main manuscript.

Extended Data Figure 2 Overview of magnetic resonance imaging data acquired from the post-mortem, formalin-fixed human fetal brain samples.

Diffusion-weighted MRI were acquired for each sample using a steady state free precession sequence (b = 730 s per mm2; 44 directions), yielding maps of apparent diffusion coefficient (ADC) (first row) and of fractional anisotropy (FA) (second row). Whole-brain deterministic tractography results (third row) represent visualization of tractography output data filtered by a coronal slice filter. Structural data were acquired for each sample using a multi-echo flash sequence with images acquired at alpha = 40 providing optimal contrast to identify cortical and subcortical structures of interest (fourth row).

Extended Data Figure 3 White matter fibre tracts in fetal human brain.

a–d, Orientation-encoded diffusion tensor imaging (DTI) colormaps in the left panel (a) and the three-dimensionally reconstructed fetal white matter fibres in the right panels (b–d) for fetal brains at 15 pcw (upper row), 16 pcw (middle row) and 19 pcw (lower row). The orientation-encoded DTI colormaps are in axial planes at the anterior commissure level. The red, pink, green and purple fibres in the right panels are cc in (b), cp and icp in (c) and pvf in (d), respectively. The transparent whole brain and yellow thalamus are also shown as anatomical guidance in (b–d). The scale bars are shown in the left panel (a). The fibre name abbreviations are: cc, corpus callosum; cp, cerebral peduncle; icp, inferior cerebellar peduncle; pvf, periventricular fibres (transient fibres coursing around the germinal matrix and only existing in the prenatal brain).

Extended Data Figure 4 Module eigengene expression of remaining modules in the cortical network.

Module eigengene expression of remaining 38 modules averaged across brain and layer. Each box corresponds to average module eigengene expression of all samples in that layer (rows) and brain (columns). Red corresponds to higher expression.

Extended Data Figure 5 Temporal patterning of whole cortex WGCNA modules across early to mid-gestational periods in BrainSpan RNA-seq cortical data.

RNA-seq RPKM values for 8–22 pcw specimens in the BrainSpan database for genes assigned to WGCNA modules (Fig. 3 in main manuscript) were correlated with age. For each module (Fig. 3a–c; x axis), the average correlation (± standard error of the mean) between expression of genes in that module and age (y axis) is plotted. Many of the modules show increases (positive correlation) or decreases (negative correlation) with age. In particular, modules C38 (decreasing with age) and C22 (increasing with age) presented in the main manuscript (see Fig. 3b, left column) show consistent trends with age in both data sets.

Extended Data Figure 6 Gene sets corresponding to GABAergic interneurons and proliferating layers also are highly expressed in the ganglionic eminences.

To examine the relationship between genes enriched in the cortical ventricular zone, including gene modules associated with GABAergic interneurons and mitotically active proliferative cells, WGCNA was performed on the combined cortical and GE samples (referred to as the ‘GE network’). a, Genes from module C31 in the whole cortex WGCNA (GABAergic interneurons) are assigned primarily to three modules in the GE network. GE31a has a similar pattern in cortex as C31, is highly expressed in GE and is enriched in genes associated with GABAergic interneurons. Other genes from C31 were assigned to modules with other cortical patterns and functional ontological associations (GE31b, GE31c). b, Genes from module C38 in the whole cortex WGCNA also divide primarily into three GE modules that are enriched in both the cortical germinal layers and the GE. These modules are enriched for genes expressed in astrocytes, potentially reflecting expression in radial glia, and are associated with cell cycle. For all plots, module eigengene (ME) expression is averaged across brain and layer (as in Fig. 3b), also including LGE, MGE and CGE (referred to here collectively as GE). Numbers in parentheses below each plot show the number of genes from module C31 in a, or C38 in b, out of the total number of module genes in the newly generated network. One representative enrichment category for each module is shown with enrichment P value.

Extended Data Figure 7 FISH of hub genes in ventricular-zone-enriched modules shows expected laminar enrichment and largely non-overlapping subcellular distributions.

a, Fluorescent in situ hybridization (FISH) in proliferative layers of 15 pcw human cortex for three genes in modules G7 and G8 in the germinal layers network shown in Fig. 3 of the manuscript (see Fig. 3d–f)—SPATA13, NR2E1 and DTL. All three genes show enrichment in the ventricular zone compared to the subventricular zone as expected based on microarray data. Nuclei are labelled with DAPI (blue). b. High-magnification images in the ventricular zone show double labelling for each pair of genes (with fluor reversal, lower row) and show complex subcellular distributions. SPATA13, NR2E1 and (to a lesser extent) DTL appear to be expressed in most cells in the ventricular zone, but these genes are typically expressed in non-overlapping punctate cytoplasmic locations (excluded from DAPI-stained nuclei in blue). b is at ×50 magnification relative to a.

Extended Data Figure 8 Laminar gene expression of putative SP markers for human and mouse in prenatal human cortex.

a, Novel human subplate-enriched genes showing at least eightfold enrichment in subplate in all four prenatal human brains. CDH18, a known subplate marker in mouse, is presented as a positive control. b, Genes with differences in subplate expression between mouse and human. These genes have been reported as subplate-enriched in mouse studies but do not show human subplate enrichment. Labelling as in Fig. 4a of the main manuscript. Microarray data are plotted as the average ±s.e.m. for each layer in each of the four brains analysed (colours).

Extended Data Figure 9 Areal gradients are consistent with patterns in BrainSpan RNA-seq cortical data, particularly for post-mitotic layers.

RNA-seq RPKM values for 8–22 pcw specimens in the BrainSpan database were used to assess rostral caudal patterning for all genes in prenatal development. Specifically, gene expression was correlated with a template of frontal cortex samples (1) versus samples from other cortical areas (0), such that positive correlations correspond to rostral enrichment. The same density plot of the resulting correlations is plotted for each layer in black. For each layer (except the subpial granular zone), density plots for the subset of rostral (red) and caudal (green) genes identified in this study (Fig. 5h) are shown. Note the significant offset of density curves for rostral and caudal genes in MZ, CPo, CPi, and IZ (and other layers to a lesser extent), indicating good agreement in areal gradient genes between studies.

Extended Data Figure 10 Areal and laminar expression patterning of FOXP2.

a, Summarized expression levels of FOXP2 across each lobe, layer and brain. b, FOXP2 shows enrichment in parietal and temporal regions overlapping Wernicke’s area in subplate at all three time points. c, FOXP2 shows enrichment in frontal cortex in germinal zones. Red corresponds to higher expression.

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Supplementary information

Supplementary Information

This file contains Supplementary Methods and Supplementary Table 1, which gives additional biological insights into the prenatal human brain. (PDF 274 kb)

Supplementary Table 2

Complete ontology for the BrainSpan project, showing the subset of structures and layers assayed in this study. Further details in the "Key" tab of the spreadsheet. (XLS 531 kb)

Supplementary Table 3 and 4

Layer of maximal expression (with statistics) for each gene in each brain (Supplementary Table 3). These data were used for Figure 2d. Enrichment analysis for laminar genes at 21pcw (Supplementary Table 4). Significantly enriched gene ontology and brain-related categories are listed. Further details in the "Key" tab of the spreadsheet. (XLS 6409 kb)

Supplementary Table 5

Module assignments and module membership for each gene in the cortical network. Genes listed in Figure 3b were chosen from this table. Further details in the "Key" tab of the spreadsheet. (XLS 27162 kb)

Supplementary Table 6

Enrichment analysis for genes in each cortical network module. Significantly enriched DAVID categories and relevant brain-related categories, including cell type enrichment are listed. Details are described in the worksheet labeled "Key". (XLS 1368 kb)

Supplementary Table 7

Enrichment analysis for genes in each germinal network module. Significantly enriched DAVID categories and relevant brain-related categories, including cell type enrichment are listed. Details are described in the worksheet labeled "Key". (XLS 450 kb)

Supplementary Table 8

150 marker genes for human and/or mouse subplate, along with evidence for defining these genes as SP markers. Genes listed in Figure 4 were selected from this table. Further details in the "Key" tab of the spreadsheet. (XLS 81 kb)

Supplementary Table 9

All genes identified as showing frontal to temporal gradient patterning in the developing human neocortex are included. Subsets of these genes, which are associated with human accelerated conserved noncoding sequences (haCNSs) or that are consistent with mouse, are also highlighted. Further details in the "Key" tab of the spreadsheet. (XLS 217 kb)

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Miller, J., Ding, SL., Sunkin, S. et al. Transcriptional landscape of the prenatal human brain.Nature 508, 199–206 (2014). https://doi.org/10.1038/nature13185

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• Received: 25 March 2013
• Accepted: 26 February 2014
• Published: 02 April 2014
• Issue Date: 10 April 2014
• DOI: https://doi.org/10.1038/nature13185