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

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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.

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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.

Data availability

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


  1. 1.

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

  2. 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).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 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).

  11. 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).

  12. 12.

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

  13. 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).

  14. 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).

  15. 15.

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

  16. 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).

  17. 17.

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

  18. 18.

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

  19. 19.

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

  20. 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).

  21. 21.

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

  22. 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, (2010).

  23. 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).

  24. 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).

  25. 25.

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

  26. 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).

  27. 27.

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

  28. 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).

  29. 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).

  30. 30.

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

  31. 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).

  32. 32.

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

  33. 33.

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

  34. 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).

  35. 35.

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

  36. 36.

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

  37. 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).

  38. 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).

  39. 39.

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

  40. 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).

  41. 41.

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

  42. 42.

    Ramachandran Iyer, E. P. et al. Barcoded oligonucleotides ligated on RNA amplified for multiplex and parallel in-situ analyses. Preprint at bioRxiv (2018).

  43. 43.

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

  44. 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).

  45. 45.

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

  46. 46.

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

  47. 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).

  48. 48.

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

  49. 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).

  50. 50.

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

  51. 51.

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

  52. 52.

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

  53. 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).

  54. 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).

  55. 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).

  56. 56.

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

  57. 57.

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

  58. 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).

  59. 59.

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

  60. 60.

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

  61. 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).

  62. 62.

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

  63. 63.

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

  64. 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).

  65. 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).

  66. 66.

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

  67. 67.

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

  68. 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).

  69. 69.

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

  70. 70.

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

  71. 71.

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

  72. 72.

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

  73. 73.

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

  74. 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).

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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

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.

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

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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.

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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) doi:10.1038/s41593-018-0265-3

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