Molecular features driving cellular complexity of human brain evolution – Nature

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  • King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Konopka, G. et al. Human-specific transcriptional networks in the brain. Neuron 75, 601–617 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, X. et al. Extension of cortical synaptic development distinguishes humans from chimpanzees and macaques. Genome Res. 22, 611–622 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science https://doi.org/10.1126/science.aat8077 (2018).

  • Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Miller, D. J. et al. Prolonged myelination in human neocortical evolution. Proc. Natl Acad. Sci. USA 109, 16480–16485 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jakel, S. et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566, 543–547 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jeong, H. et al. Evolution of DNA methylation in the human brain. Nat. Commun. 12, 2021 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Khrameeva, E. et al. Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains. Genome Res. 30, 776–789 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kozlenkov, A. et al. Evolution of regulatory signatures in primate cortical neurons at cell-type resolution. Proc. Natl Acad. Sci. USA 117, 28422–28432 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Krienen, F. M. et al. Innovations present in the primate interneuron repertoire. Nature 586, 262–269 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ma, S. et al. Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science https://doi.org/10.1126/science.abo7257 (2022).

  • Mendizabal, I. et al. Comparative methylome analyses identify epigenetic regulatory loci of human brain evolution. Mol. Biol. Evol. 33, 2947–2959 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, W., Mai, X. & Liu, C. The default mode network and social understanding of others: what do brain connectivity studies tell us. Front. Hum. Neurosci. 8, 74 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, D. et al. Altered functional connectivity of the cingulate subregions in schizophrenia. Transl. Psychiatry 5, e575 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Berto, S. et al. Accelerated evolution of oligodendrocytes in the human brain. Proc. Natl Acad. Sci. USA 116, 24334–24342 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Franjic, D. et al. Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells. Neuron 110, 452–469 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Brown, T. L. & Verden, D. R. Cytoskeletal regulation of oligodendrocyte differentiation and myelination. J. Neurosci. 37, 7797–7799 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Caglayan, E., Liu, Y. & Konopka, G. Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets. Neuron https://doi.org/10.1016/j.neuron.2022.09.010 (2022).

  • Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fumagalli, M. et al. The ubiquitin ligase Mdm2 controls oligodendrocyte maturation by intertwining mTOR with G protein-coupled receptor kinase 2 in the regulation of GPR17 receptor desensitization. Glia 63, 2327–2339 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • den Hoed, J., Devaraju, K. & Fisher, S. E. Molecular networks of the FOXP2 transcription factor in the brain. EMBO Rep. 22, e52803 (2021).

    Article 

    Google Scholar
     

  • Konopka, G. et al. Human-specific transcriptional regulation of CNS development genes by FOXP2. Nature 462, 213–217 (2009).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Doan, R. N. et al. Mutations in human accelerated regions disrupt cognition and social behavior. Cell 167, 341–354 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Franchini, L. F. & Pollard, K. S. Human evolution: the non-coding revolution. BMC Biol. 15, 89 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Capra, J. A., Erwin, G. D., McKinsey, G., Rubenstein, J. L. & Pollard, K. S. Many human accelerated regions are developmental enhancers. Philos. Trans. R. Soc. Lond. B 368, 20130025 (2013).

    Article 

    Google Scholar
     

  • Girskis, K. M. et al. Rewiring of human neurodevelopmental gene regulatory programs by human accelerated regions. Neuron https://doi.org/10.1016/j.neuron.2021.08.005 (2021).

  • Wagnon, J. L. et al. CELF4 regulates translation and local abundance of a vast set of mRNAs, including genes associated with regulation of synaptic function. PLoS Genet. 8, e1003067 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lundgaard, I. et al. Neuregulin and BDNF induce a switch to NMDA receptor-dependent myelination by oligodendrocytes. PLoS Biol. 11, e1001743 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Prufer, K. et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 505, 43–49 (2014).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Arora, V. et al. Increased Grik4 gene dosage causes imbalanced circuit output and human disease-related behaviors. Cell Rep. 23, 3827–3838 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kim, T. K. et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182–187 (2010).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yap, E. L. & Greenberg, M. E. Activity-regulated transcription: bridging the gap between neural activity and behavior. Neuron 100, 330–348 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Berto, S. et al. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. Nat. Neurosci. 24, 554–564 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ducker, G. S. & Rabinowitz, J. D. One-carbon metabolism in health and disease. Cell Metab. 25, 27–42 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yeung, M. S. et al. Dynamics of oligodendrocyte generation and myelination in the human brain. Cell 159, 766–774 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Buchanan, J. et al. Oligodendrocyte precursor cells ingest axons in the mouse neocortex. Proc. Natl Acad. Sci. USA 119, e2202580119 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jorstad, N. L. et al. Comparative transcriptomics reveals human-specific cortical features. Preprint at bioRxiv https://doi.org/10.1101/2022.09.19.508480 (2022).

  • Berg, M. et al. FastCAR: Fast Correction for Ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets. Preprint at bioRxiv https://doi.org/10.1101/2022.07.19.500594 (2022).

  • McLean, C. Y. et al. Human-specific loss of regulatory DNA and the evolution of human-specific traits. Nature 471, 216–219 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hickey, S. L., Berto, S. & Konopka, G. Chromatin decondensation by FOXP2 promotes human neuron maturation and expression of neurodevelopmental disease genes. Cell Rep. 27, 1699–1711 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, C. C. et al. Discovering chromatin motifs using FAIRE sequencing and the human diploid genome. BMC Genomics 14, 310 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ataman, B. et al. Evolution of osteocrin as an activity-regulated factor in the primate brain. Nature 539, 242–247 (2016).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pruunsild, P., Bengtson, C. P. & Bading, H. Networks of cultured iPSC-derived neurons reveal the human synaptic activity-regulated adaptive gene program. Cell Rep. 18, 122–135 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Qiu, J. et al. Evidence for evolutionary divergence of activity-dependent gene expression in developing neurons. Elife https://doi.org/10.7554/eLife.20337 (2016).

  • Hrvatin, S. et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21, 120–129 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fleming, S. J., Marioni, J. C. & Babadi, M. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Preprint at bioRxiv https://doi.org/10.1101/791699v2 (2019).

  • Howe, K. L. et al. Ensembl 2021. Nucleic Acids Res. 49, D884–D891 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Picard Toolkit (Broad Institute, 2019); http://broadinstitute.github.io/picard/.

  • Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  • Kuhn, R. M., Haussler, D. & Kent, W. J. The UCSC genome browser and associated tools. Brief. Bioinform. 14, 144–161 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lareau, C. A., Ma, S., Duarte, F. M. & Buenrostro, J. D. Inference and effects of barcode multiplets in droplet-based single-cell assays. Nat. Commun. 11, 866 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Statist. Softw. 67, 1–48 (2015).

    Article 

    Google Scholar
     

  • Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, Y., Lun, A. T. & Smyth, G. K. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res 5, 1438 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gontarz, P. et al. Comparison of differential accessibility analysis strategies for ATAC-seq data. Sci. Rep. 10, 10150 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mendizabal, I. et al. Cell type-specific epigenetic links to schizophrenia risk in the brain. Genome Biol. 20, 135 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • van Arensbergen, J., van Steensel, B. & Bussemaker, H. J. In search of the determinants of enhancer-promoter interaction specificity. Trends Cell Biol. 24, 695–702 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cavalcante, R. G. & Sartor, M. A. annotatr: genomic regions in context. Bioinformatics 33, 2381–2383 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Schep, A. motifmatchr: Fast motif matching in R. R version 1.4.0. (2018).

  • Kolde, R. pheatmap: Pretty heatmaps. R version 4.1.1. https://cran.r-project.org/web/packages/pheatmap/index.html (2012).

  • Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gittelman, R. M. et al. Comprehensive identification and analysis of human accelerated regulatory DNA. Genome Res. 25, 1245–1255 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Blanchette, M. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–715 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinform. 12, 41–51 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mafessoni, F. et al. A high-coverage Neandertal genome from Chagyrskaya Cave. Proc. Natl Acad. Sci. USA 117, 15132–15136 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Prufer, K. et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science 358, 655–658 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article 
    PubMed Central 

    Google Scholar
     

  • The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article 

    Google Scholar
     

  • Ghandi, M. et al. gkmSVM: an R package for gapped-kmer SVM. Bioinformatics 32, 2205–2207 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     



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