King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975).
Konopka, G. et al. Human-specific transcriptional networks in the brain. Neuron 75, 601–617 (2012).
Liu, X. et al. Extension of cortical synaptic development distinguishes humans from chimpanzees and macaques. Genome Res. 22, 611–622 (2012).
Sousa, A. M. M. et al. Molecular and cellular reorganization of neural circuits in the human lineage. Science 358, 1027–1032 (2017).
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).
Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).
Miller, D. J. et al. Prolonged myelination in human neocortical evolution. Proc. Natl Acad. Sci. USA 109, 16480–16485 (2012).
Jakel, S. et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566, 543–547 (2019).
Jeong, H. et al. Evolution of DNA methylation in the human brain. Nat. Commun. 12, 2021 (2021).
Khrameeva, E. et al. Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains. Genome Res. 30, 776–789 (2020).
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).
Krienen, F. M. et al. Innovations present in the primate interneuron repertoire. Nature 586, 262–269 (2020).
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).
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).
Wang, D. et al. Altered functional connectivity of the cingulate subregions in schizophrenia. Transl. Psychiatry 5, e575 (2015).
Berto, S. et al. Accelerated evolution of oligodendrocytes in the human brain. Proc. Natl Acad. Sci. USA 116, 24334–24342 (2019).
Franjic, D. et al. Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells. Neuron 110, 452–469 (2022).
Brown, T. L. & Verden, D. R. Cytoskeletal regulation of oligodendrocyte differentiation and myelination. J. Neurosci. 37, 7797–7799 (2017).
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).
Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).
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).
den Hoed, J., Devaraju, K. & Fisher, S. E. Molecular networks of the FOXP2 transcription factor in the brain. EMBO Rep. 22, e52803 (2021).
Konopka, G. et al. Human-specific transcriptional regulation of CNS development genes by FOXP2. Nature 462, 213–217 (2009).
Doan, R. N. et al. Mutations in human accelerated regions disrupt cognition and social behavior. Cell 167, 341–354 (2016).
Franchini, L. F. & Pollard, K. S. Human evolution: the non-coding revolution. BMC Biol. 15, 89 (2017).
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).
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).
Lundgaard, I. et al. Neuregulin and BDNF induce a switch to NMDA receptor-dependent myelination by oligodendrocytes. PLoS Biol. 11, e1001743 (2013).
Prufer, K. et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 505, 43–49 (2014).
Arora, V. et al. Increased Grik4 gene dosage causes imbalanced circuit output and human disease-related behaviors. Cell Rep. 23, 3827–3838 (2018).
Kim, T. K. et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182–187 (2010).
Yap, E. L. & Greenberg, M. E. Activity-regulated transcription: bridging the gap between neural activity and behavior. Neuron 100, 330–348 (2018).
Berto, S. et al. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. Nat. Neurosci. 24, 554–564 (2021).
Ducker, G. S. & Rabinowitz, J. D. One-carbon metabolism in health and disease. Cell Metab. 25, 27–42 (2017).
Yeung, M. S. et al. Dynamics of oligodendrocyte generation and myelination in the human brain. Cell 159, 766–774 (2014).
Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016).
Buchanan, J. et al. Oligodendrocyte precursor cells ingest axons in the mouse neocortex. Proc. Natl Acad. Sci. USA 119, e2202580119 (2022).
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).
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).
Yang, C. C. et al. Discovering chromatin motifs using FAIRE sequencing and the human diploid genome. BMC Genomics 14, 310 (2013).
Ataman, B. et al. Evolution of osteocrin as an activity-regulated factor in the primate brain. Nature 539, 242–247 (2016).
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).
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).
Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).
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).
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).
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).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
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).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
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).
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871 (2018).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Statist. Softw. 67, 1–48 (2015).
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).
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).
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).
Gontarz, P. et al. Comparison of differential accessibility analysis strategies for ATAC-seq data. Sci. Rep. 10, 10150 (2020).
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).
Mendizabal, I. et al. Cell type-specific epigenetic links to schizophrenia risk in the brain. Genome Biol. 20, 135 (2019).
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).
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).
Cavalcante, R. G. & Sartor, M. A. annotatr: genomic regions in context. Bioinformatics 33, 2381–2383 (2017).
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).
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).
Gittelman, R. M. et al. Comprehensive identification and analysis of human accelerated regulatory DNA. Genome Res. 25, 1245–1255 (2015).
Blanchette, M. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–715 (2004).
Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinform. 12, 41–51 (2011).
Mafessoni, F. et al. A high-coverage Neandertal genome from Chagyrskaya Cave. Proc. Natl Acad. Sci. USA 117, 15132–15136 (2020).
Prufer, K. et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science 358, 655–658 (2017).
The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Ghandi, M. et al. gkmSVM: an R package for gapped-kmer SVM. Bioinformatics 32, 2205–2207 (2016).
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