[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Regulation of cell-type-specific transcriptomes by microRNA networks during human brain development

Nature Neuroscience volume 21, pages 1784–1792 (2018)Cite this article

Subjects

Abstract

MicroRNAs (miRNAs) regulate many cellular events during brain development by interacting with hundreds of mRNA transcripts. However, miRNAs operate nonuniformly upon the transcriptional profile with an as yet unknown logic. Shortcomings in defining miRNA–mRNA networks include limited knowledge of in vivo miRNA targets and their abundance in single cells. By combining multiple complementary approaches, high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation with an antibody to AGO2 (AGO2-HITS-CLIP), single-cell profiling and computational analyses using bipartite and coexpression networks, we show that miRNA-mRNA interactions operate as functional modules that often correspond to cell-type identities and undergo dynamic transitions during brain development. These networks are highly dynamic during development and over the course of evolution. One such interaction is between radial-glia-enriched ORC4 and miR-2115, a great-ape-specific miRNA, which appears to control radial glia proliferation rates during human brain development.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: High-throughput profiling of miRNA–mRNA interactions.
Fig. 2: Single-cell miRNA expression profiling reveals patterns of cell-type enrichment.
Fig. 3: Dynamic changes in miRNA regulatory networks during development.
Fig. 4: miRNAs contribute to cell-type-specific function.

Similar content being viewed by others

Data availability

The data used in this study are available as part of the publicly available Gene Expression Omnibus database under accession number GSE107468.

References

  1. Tasic, B. Single cell transcriptomics in neuroscience: cell classification and beyond. Curr. Opin. Neurobiol. 50, 242–249 (2018).

    Article  CAS  Google Scholar 

  2. Griffiths, J. A., Scialdone, A. & Marioni, J. C. Using single-cell genomics to understand developmental processes and cell fate decisions. Mol. Syst. Biol. 14, e8046 (2018).

    Article  Google Scholar 

  3. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    Article  CAS  Google Scholar 

  4. Zeng, H. & Sanes, J. R. Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18, 530–546 (2017).

    Article  CAS  Google Scholar 

  5. Kosik, K. S. MicroRNAs and cellular phenotypy. Cell 143, 21–26 (2010).

    Article  CAS  Google Scholar 

  6. Monticelli, S. et al. MicroRNA profiling of the murine hematopoietic system. Genome Biol. 6, R71 (2005).

    Article  Google Scholar 

  7. Fineberg, S. K., Kosik, K. S. & Davidson, B. L. MicroRNAs potentiate neural development. Neuron 64, 303–309 (2009).

    Article  CAS  Google Scholar 

  8. Volvert, M. L., Rogister, F., Moonen, G., Malgrange, B. & Nguyen, L. MicroRNAs tune cerebral cortical neurogenesis. Cell Death Differ. 19, 1573–1581 (2012).

    Article  CAS  Google Scholar 

  9. Berezikov, E. et al. Phylogenetic shadowing and computational identification of human microRNA genes. Cell 120, 21–24 (2005).

    Article  CAS  Google Scholar 

  10. Kapsimali, M. et al. MicroRNAs show a wide diversity of expression profiles in the developing and mature central nervous system. Genome Biol. 8, R173 (2007).

    Article  Google Scholar 

  11. Baudet, M. L. et al. miR-124 acts through CoREST to control onset of Sema3A sensitivity in navigating retinal growth cones. Nat. Neurosci. 15, 29–38 (2011).

    Article  Google Scholar 

  12. Bernstein, E. et al. Dicer is essential for mouse development. Nat. Genet. 35, 215–217 (2003).

    Article  CAS  Google Scholar 

  13. Jonsson, M. E. et al. Comprehensive analysis of microRNA expression in regionalized human neural progenitor cells reveals microRNA-10 as a caudalizing factor. Development 142, 3166–3177 (2015).

    Article  Google Scholar 

  14. Moore, M. J. et al. Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis. Nat. Protoc. 9, 263–293 (2014).

    Article  CAS  Google Scholar 

  15. Pollen, A. A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015).

    Article  CAS  Google Scholar 

  16. Camp, J. G. et al. Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc. Natl. Acad. Sci. USA 112, 15672–15677 (2015).

    Article  CAS  Google Scholar 

  17. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    Article  CAS  Google Scholar 

  18. Liu, X. & Murata, T. Community detection in large-scale bipartite networks. Information and Media Technologies 5, 184–192 (2010).

    Google Scholar 

  19. Florio, M. et al. Human-specific gene ARHGAP11B promotes basal progenitor amplification and neocortex expansion. Science 347, 1465–1470 (2015).

    Article  CAS  Google Scholar 

  20. Yu, B. et al. miR-221 and miR-222 promote Schwann cell proliferation and migration by targeting LASS2 after sciatic nerve injury. J. Cell. Sci. 125, 2675–2683 (2012).

    Article  CAS  Google Scholar 

  21. Maiorano, N. A. & Mallamaci, A. Promotion of embryonic cortico-cerebral neuronogenesis by miR-124. Neural Develop. 4, 40 (2009).

    Article  Google Scholar 

  22. Boumil, R. M. et al. A missense mutation in a highly conserved alternate exon of dynamin-1 causes epilepsy in fitful mice. PLoS Genet. 6, https://doi.org/10.1371/journal.pgen.1001046 (2010).

    Article  Google Scholar 

  23. Buckanovich, R. J., Yang, Y. Y. & Darnell, R. B. The onconeural antigen Nova-1 is a neuron-specific RNA-binding protein, the activity of which is inhibited by paraneoplastic antibodies. J. Neurosci. 16, 1114–1122 (1996).

    Article  CAS  Google Scholar 

  24. Nowakowski, T. J. et al. MicroRNA-92b regulates the development of intermediate cortical progenitors in embryonic mouse brain. Proc. Natl. Acad. Sci. USA 110, 7056–7061 (2013).

    Article  CAS  Google Scholar 

  25. Magri, L. et al. c-Myc-dependent transcriptional regulation of cell cycle and nucleosomal histones during oligodendrocyte differentiation. Neuroscience 276, 72–86 (2014).

    Article  CAS  Google Scholar 

  26. Kawakami, Y. et al. Impaired neurogenesis in embryonic spinal cord of Phgdh knockout mice, a serine deficiency disorder model. Neurosci. Res. 63, 184–193 (2009).

    Article  CAS  Google Scholar 

  27. Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323 (2017).

    Article  CAS  Google Scholar 

  28. Hadjighassem, M. R. et al. Human Freud-2/CC2D1B: a novel repressor of postsynaptic serotonin-1A receptor expression. Biol. Psychiatry 66, 214–222 (2009).

    Article  CAS  Google Scholar 

  29. Deshar, R., Cho, E. B., Yoon, S. K. & Yoon, J. B. CC2D1A and CC2D1B regulate degradation and signaling of EGFR and TLR4. Biochem. Biophys. Res. Commun. 480, 280–287 (2016).

    Article  CAS  Google Scholar 

  30. Fededa, J. P. et al. MicroRNA-34/449 controls mitotic spindle orientation during mammalian cortex development. EMBO J. 35, 2386–2398 (2016).

    Article  CAS  Google Scholar 

  31. Wu, J. et al. Two miRNA clusters, miR-34b/c and miR-449, are essential for normal brain development, motile ciliogenesis, and spermatogenesis. Proc. Natl. Acad. Sci. USA 111, E2851–E2857 (2014).

    Article  CAS  Google Scholar 

  32. Sousa, A. M. M. et al. Molecular and cellular reorganization of neural circuits in the human lineage. Science 358, 1027–1032 (2017).

    Article  CAS  Google Scholar 

  33. Boudreau, R. L. et al. Transcriptome-wide discovery of microRNA binding sites in human brain. Neuron 81, 294–305 (2014).

    Article  CAS  Google Scholar 

  34. Wu, Y. E., Parikshak, N. N., Belgard, T. G. & Geschwind, D. H. Genome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder. Nat. Neurosci. 19, 1463–1476 (2016).

    Article  CAS  Google Scholar 

  35. Abu-Elneel, K. et al. Heterogeneous dysregulation of microRNAs across the autism spectrum. Neurogenetics 9, 153–161 (2008).

    Article  CAS  Google Scholar 

  36. He, M. et al. Cell-type-based analysis of microRNA profiles in the mouse brain. Neuron 73, 35–48 (2012).

    Article  CAS  Google Scholar 

  37. Liu, J. et al. A reciprocal antagonism between miR-376c and TGF-beta signaling regulates neural differentiation of human pluripotent stem cells. FASEB J. 28, 4642–4656 (2014).

    Article  CAS  Google Scholar 

  38. Nowakowski, T. J., Pollen, A. A., Sandoval-Espinosa, C. & Kriegstein, A. R. Transformation of the radial glia scaffold demarcates two stages of human cerebral cortex development. Neuron 91, 1219–1227 (2016).

    Article  CAS  Google Scholar 

  39. Guernsey, D. L. et al. Mutations in origin recognition complex gene ORC4 cause Meier-Gorlin syndrome. Nat. Genet. 43, 360–364 (2011).

    Article  CAS  Google Scholar 

  40. de Munnik, S. A. et al. Meier-Gorlin syndrome: growth and secondary sexual development of a microcephalic primordial dwarfism disorder. Am. J. Med. Genet. A 158A, 2733–2742 (2012).

    Article  Google Scholar 

  41. Marin, R. M., Sulc, M. & Vanicek, J. Searching the coding region for microRNA targets. RNA 19, 467–474 (2013).

    Article  CAS  Google Scholar 

  42. Ramachandran Iyer, E. P. et al. Barcoded oligonucleotides ligated on RNA amplified for multiplex and parallel in-situ analyses. Preprint at bioRxiv https://doi.org/10.1101/281121 (2018).

  43. Faridani, O. R. et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).

    Article  CAS  Google Scholar 

  44. Srinivasan, K. et al. A network of genetic repression and derepression specifies projection fates in the developing neocortex. Proc. Natl. Acad. Sci. USA 109, 19071–19078 (2012).

    Article  CAS  Google Scholar 

  45. Hevner, R. F. et al. Tbr1 regulates differentiation of the preplate and layer 6. Neuron 29, 353–366 (2001).

    Article  CAS  Google Scholar 

  46. Marin, O., Anderson, S. A. & Rubenstein, J. L. Origin and molecular specification of striatal interneurons. J. Neurosci. 20, 6063–6076 (2000).

    Article  CAS  Google Scholar 

  47. Forman, J. J., Legesse-Miller, A. & Coller, H. A. A search for conserved sequences in coding regions reveals that the let-7 microRNA targets Dicer within its coding sequence. Proc. Natl. Acad. Sci. USA 105, 14879–14884 (2008).

    Article  CAS  Google Scholar 

  48. Hafner, M. et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141, 129–141 (2010).

    Article  CAS  Google Scholar 

  49. Schnall-Levin, M., Zhao, Y., Perrimon, N. & Berger, B. Conserved microRNA targeting in Drosophila is as widespread in coding regions as in 3′ UTRs. Proc. Natl. Acad. Sci. USA 107, 15751–15756 (2010).

    Article  CAS  Google Scholar 

  50. Schnall-Levin, M. et al. Unusually effective microRNA targeting within repeat-rich coding regions of mammalian mRNAs. Genome Res. 21, 1395–1403 (2011).

    Article  CAS  Google Scholar 

  51. Saito, T. In vivo electroporation in the embryonic mouse central nervous system. Nat. Protoc. 1, 1552–1558 (2006).

    Article  CAS  Google Scholar 

  52. Rani, N. et al. A primate lncRNA mediates notch signaling during neuronal development by sequestering miRNA. Neuron 90, 1174–1188 (2016).

    Article  CAS  Google Scholar 

  53. Pollen, A. A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).

    Article  CAS  Google Scholar 

  54. Hansen, D. V. et al. Non-epithelial stem cells and cortical interneuron production in the human ganglionic eminences. Nat. Neurosci. 16, 1576–1587 (2013).

    Article  CAS  Google Scholar 

  55. Takahashi, T., Nowakowski, R. S. & Caviness, V. S. Jr. The cell cycle of the pseudostratified ventricular epithelium of the embryonic murine cerebral wall. J. Neurosci. 15, 6046–6057 (1995).

    Article  CAS  Google Scholar 

  56. Meister, G. et al. Human Argonaute2 mediates RNA cleavage targeted by miRNAs and siRNAs. Mol. Cell 15, 185–197 (2004).

    Article  CAS  Google Scholar 

  57. Liu, J. et al. Argonaute2 is the catalytic engine of mammalian RNAi. Science 305, 1437–1441 (2004).

    Article  CAS  Google Scholar 

  58. Moreau, M. P., Bruse, S. E., Jornsten, R., Liu, Y. & Brzustowicz, L. M. Chronological changes in microRNA expression in the developing human brain. PLoS One 8, e60480 (2013).

    Article  CAS  Google Scholar 

  59. Berezikov, E. et al. Diversity of microRNAs in human and chimpanzee brain. Nat. Genet. 38, 1375–1377 (2006).

    Article  CAS  Google Scholar 

  60. Somel, M. et al. MicroRNA-driven developmental remodeling in the brain distinguishes humans from other primates. PLoS Biol. 9, e1001214 (2011).

    Article  CAS  Google Scholar 

  61. Arcila, M. L. et al. Novel primate miRNAs coevolved with ancient target genes in germinal zone-specific expression patterns. Neuron 81, 1255–1262 (2014).

    Article  CAS  Google Scholar 

  62. Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323 e1330 (2016).

    Article  CAS  Google Scholar 

  63. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  64. Friedländer, M. R., Mackowiak, S. D., Li, N., Chen, W. & Rajewsky, N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 40, 37–52 (2011).

    Article  Google Scholar 

  65. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  66. Uren, P. J. et al. Site identification in high-throughput RNA–protein interaction data. Bioinformatics 28, 3013–3020 (2012).

    Article  CAS  Google Scholar 

  67. Chi, S. W., Zang, J. B., Mele, A. & Darnell, R. B. Ago HITS-CLIP decodes miRNA-mRNA interaction maps. Nature 460, 479 (2009).

    Article  CAS  Google Scholar 

  68. Lewis, B. P., Burge, C. B. & Bartel, D. P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005).

    Article  CAS  Google Scholar 

  69. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  Google Scholar 

  70. Liu, S. J. et al. Single-cell analysis of long non-coding RNAs in the developing human neocortex. Genome Biol. 17, 67 (2016).

    Article  Google Scholar 

  71. Banerjee-Basu, S. & Packer, A. SFARIGene: an evolving database for the autism research community. Dis. Models Mech. 3, 133–135 (2010).

    Article  Google Scholar 

  72. Barber, M. J. Modularity and community detection in bipartite networks. Phys. Rev. E 76, 066102 (2007).

    Article  Google Scholar 

  73. Dormann, C. F., Gruber, B. & Fründ, J. Introducing the bipartite package: analysing ecological networks. Interaction 1, 0.2413793 (2008).

    Google Scholar 

  74. Seal, A. & Wild, D. J. Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links. BMC Bioinformatics 19, 265 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank S. Wang, C. Sandoval-Espinosa, E. Guzman, A. Bhaduri and N. Li for providing research resources, technical help and helpful comments during manuscript preparation. N.R. acknowledges support (Ramanujan Fellowship SB/S2/RJN-030/2017) from Science and Engineering Research Board, Department of Science & Technology, India. This research was supported by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (K.S.K.) and National Institutes of Health (NIH) awards U54NS100717 (K.S.K.), MH105989 (A.R.K.) and R01NS075998 (A.R.K). This work was supported by a grant from the Simons Foundation (SFARI 491371; T.J.N).

Author information

Author notes

  1. These authors contributed equally: T. J. Nowakowski, N. Rani, M. Golkaram, H. R. Zhou.

Authors and Affiliations

  1. Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA

    Tomasz J. Nowakowski, Beatriz Alvarado, Alex A. Pollen & Arnold R. Kriegstein

  2. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA

    Tomasz J. Nowakowski

  3. Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA

    Neha Rani, Hongjun R. Zhou, Kylie Huch & Kenneth S. Kosik

  4. Department of Biological Sciences and Bioengineering, Indian Institute of Technology, Kanpur, India

    Neha Rani

  5. Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA

    Mahdi Golkaram & Linda R. Petzold

  6. Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA

    Hongjun R. Zhou, Kylie Huch & Kenneth S. Kosik

  7. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Beatriz Alvarado & Arnold R. Kriegstein

  8. New Technologies, Fluidigm Corporation, South San Francisco, CA, USA

    Jay A. West & Anne Leyrat

  9. Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA

    Alex A. Pollen

  10. Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA, USA

    Linda R. Petzold

Authors
  1. Tomasz J. Nowakowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neha Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahdi Golkaram
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongjun R. Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Beatriz Alvarado
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kylie Huch
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jay A. West
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anne Leyrat
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alex A. Pollen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Arnold R. Kriegstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Linda R. Petzold
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Kenneth S. Kosik
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.S.K., N.R., T.J.N., H.R.Z., L.R.P. and A.R.K. designed and supervised the study. J.A.W., A.L. and B.A. designed and optimized single-cell miRNA and mRNA PCR protocol. B.A., N.R., T.J.N., A.A.P. and B.A. performed experiments. H.R.Z., M.G., K.H., B.A., N.R. and T.J.N. performed data analysis. T.J.N., N.R. and K.S.K. wrote the paper with contribution from all authors.

Corresponding authors

Correspondence to Tomasz J. Nowakowski or Kenneth S. Kosik.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Validation of the interaction between miRNA and mRNA identified by HITS-CLIP.

(a) Example of AGO2-HITS-CLIP peaks mapping to a previously validated interaction between miR-9 and the 3′ UTR of HES1 (ref. 65). (b) Distribution of HITS-CLIP reads. Several long noncoding RNAs (lncRNAs) were also identified among AGO2-bound transcripts (Supplementary Table 2), including 9 lncRNAs previously annotated as cell type specific60. (c) Luciferase reporter assay showing the down-regulation of luciferase, fused to respective genes, measured 48 h after the overexpression of respective miRNA(s) in HEK293T cells (see Methods for details). (d) Experimental design testing selected miRNA-mRNA interactions in human cells using fluorescent reporters harboring target sites identified in HITS-CLIP (same sequences as those used in c). (e) Representative images of fluorescent primary cells transfected with ORC4 MRE reporters and miRNA inhibitors. (f) Quantifications of average fluorescence change following miR inhibition relative to control (no miRNA inhibitor) for wild type reporter (wt) and a reporter lacking the MRE (mt). *- p<0.05, **- p<0.01, *** - p<0.001, **** - p<0.0001, calculated using two-tailed Student’s t-test. The number of biological replicates (individuals) is indicated above each graph. (g) We further tested the impact of nonspecific anti-miR transfections on the ORC4 WT and MT miR-2115 CDS reporter. Fluorescent reporter signal (GFP density) was normalized to loading control CAG-dsRed reporter transfection, to increase the accuracy of our analysis. The experiment was performed on three biological specimens, GW15, GW17, and GW19. We fist examined the presence of an effect using Tukey’s range test, followed by post-hoc student’s t test, P-value **- p<0.01.

Supplementary Figure 2 miRNA–mRNA node degree distribution.

(a-b) each node in mode I (target genes, a) has significantly less connections with the opposite mode than mode II (targeting miRNA, b). (c) miRNA abundance shows a long-tailed distribution. (d) Abundance of miRNA members of each module is illustrated. Modules tend to either recruit several low-abundant miRNAs or few high-abundant miRNAs.

Supplementary Figure 3 Bipartite network analysis.

(a-b) Both GW15-16 and GW19-20 networks illustrate a scale-free topology (Dark, median and light grey lines refer to exponential, power and truncated power law, respectively. p < 0.001). (c-d) Overlap rate matrix after hierarchical clustering for GW15-16 (c) and GW19-20 (d). For each gestational stage two replicates are produced to ensure the robustness of the community detection algorithm. (e-f) Dynamic branch cutting of panel c and d.

Supplementary Figure 4 Relationship between miRNA and target mRNA abundance.

(a) Plot represents coefficient of variation (CV)66 for every mRNA profiled using sc-qPCR in relation to the number of HITS-CLIP reads reflecting interaction levels. Red points indicate genes not detected in HITS-CLIP. (b) Violin plots showing Pearson’s correlation coefficient between miRNA and mRNA abundance for miRNA-mRNA pairs of interactions identified by HITS-CLIP. Correlation coefficient is calculated across single cell qPCR data (Supplementary Table 7).

Supplementary Figure 5 Expression of miR-2115 is enriched in late cortical development.

hybridization for miR-2115 in human cortex reveals increasing expression levels during development, reflecting sequencing results. Bottom panels, also shown in Fig. 3, show magnified view of the cortical OSVZ, scale bar is 50um.

Supplementary Figure 6 Expression of miR-362 is enriched in early cortical development.

In situ hybridization for miR-362 in human cortex reveals declining expression levels during development, reflecting sequencing results. Bottom panels, also shown in Fig. 3, show magnified view of the cortical OSVZ.

Supplementary Figure 7 Evolution of miRNA-2115 in the primate lineage.

(a) Phylogeny tree of upper primates (Bonobo excluded due to poor read sequence quality in region of interest). Star represents presence of miR-2115. I) Alignment of SPINK8 intron 3 - Grey represents agreement to consensus. Black represents disagreement to consensus. Lines indicate gaps compared to consensus. II) Sequence of miR-2115 in higher primates. Highlighting indicates disagreement to consensus. miR-2115 is predicted to be present in Orangutans but is absent in Gibbons. b) Intron 3 is significantly smaller in Gibbons than higher primates (~¼ the length) and more closely matches Intron 3 of mice. There is a ~7100 nucleotide section present in Hominidae but absent in Gibbons, likely arising from an insertion occurring in Hominidae after the divergence of Gibbons. This insertion likely carried miR-2115. There is a 367 nucleotide section directly downstream of miR-2115 present in Orangutans but not Great Apes, indicating it was lost in the Great Apes common ancestor after the divergence of Orangutans. There is a 1832 nucleotide section present only in Orangutans occurring after their divergence from the Great Apes. The great mobility in this intron is likely not due to transposable elements judging from the lack of inverted repeats in the area. (c) Expression of novel miRNAs that were not annotated in latest miRBase67 across the samples. In this study, we found 36 of them expressed in prenatal brain samples. Of the expressed miRNAs, 31 of them were either human-specific or primate-specific, and 4 of them were specific expressed at GW15. Red dots indicate differentially expressed miRNAs between GW15-16 and GW19-20 by DEseq2.

Supplementary Figure 8 Expression of miR-1286 is enriched in human occipital cortex.

In situ hybridization for miR-1286 in human prefrontal (PFC) and visual cortex (V1) reveals strong signal in the occipital cortex. Bottom panels show magnified view of the cortical OSVZ.

Supplementary Figure 9 Expression of miR-142 is enriched in human occipital cortex.

In situ hybridization for miR-142 in human prefrontal (PFC) and visual cortex (V1) reveals strong signal in the occipital cortex. Bottom panels show magnified view of the cortical OSVZ.

Supplementary Figure 10 Expression of miR-548aa enriched in human occipital cortex.

In situ hybridization for miR-548aa in human prefrontal and visual cortex reveals strong signal in the occipital cortex.

Supplementary Figure 11 Bipartite network modules are preserved between GW15-16 samples and GW19-20.

(a) Module preservation statistics shown on x-axis suggest a significant preservation of modules (modules with higher interaction levels show higher preservation as well). Modules turquoise and blue are the two least preserved modules, suggesting developmental stage-specific changes. GW15-16 module names are used to compare GW15-16 modules with their homologues in GW19-20 (see Methods for details). Module assignments are listed in Supplementary Table 6. (b) Module preservation between prenatal (rows) and adult human brain samples (columns), based on data from a HITS-CLIP analysis in the adult human cerebral cortex31,32. *-FDR<0.1, **-FDR<0.05, ***-FDR<0.01.

Supplementary Figure 12 miRNA regulation of autism spectrum disorder-linked genes.

(a) Bipartite network module magenta is significantly enriched for genes annotated associated with ASD. (b) Expression of two miRNAs associated with ASD33 assayed using sc-qPCR expression. Notably, expression of miR-137 and miR-218 are both highly detected in neurons, with miR-218 also being expressed in progenitors. (c) In situ hybridization for miR-137 and miR-218 in primary tissue sections reveals expression of both miRNAs in the cortical plate. In particular, miR-137 appears to be expressed in maturing excitatory neurons at midgestation. ** FDR<0.05.

Supplementary Figure 13 miRNA-2115 engineered to target ORC4 influences radial glia development.

(a) Schematic shows mouse in vivo experimental design. Mouse cortex two days after electroporation at E13.5. White arrowhead indicates example of a dividing GFP+ radial glia cell, while yellow arrowheads indicate dividing intermediate progenitor cells. **-p<0.01, unpaired student t-test. (b) Related to Figure 4d—examines the impact of microRNA perturbation on radial glia number. We compared the proportions of SOW2 positive cells in control GFP expressing cells versus specific microRNA overexpression (miR1 or miR-2115) or specific anti-miR conditions. This analysis showed strong impact of miR-2115 on radial glia development. Interestingly, inhibition of miR-9 also lead to an increased proportion of SOX2 positive cells, consistent with previous studies68. This data was generated for three biological replicates (individuals, GW15, 17, and 19). Statistical significance was confirmed using Tukey’s test, with post hoc student t-test. P - *<0.05.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13

Reporting Summary

Supplementary Tables

Supplementary Tables 1–12

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nowakowski, T.J., Rani, N., Golkaram, M. et al. Regulation of cell-type-specific transcriptomes by microRNA networks during human brain development. Nat Neurosci 21, 1784–1792 (2018). https://doi.org/10.1038/s41593-018-0265-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-018-0265-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing