Geschwind, D. H. & Konopka, G. Neuroscience in the era of functional genomics and systems biology. Nature 461, 908–915 (2009).
Berg, J. et al. Human neocortical expansion involves glutamatergic neuron diversification. Nature 598, 151–158 (2021).
Halvorsen, M. et al. Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia. Nat. Commun. 11, 1842 (2020).
Sanders, S. J. et al. Whole genome sequencing in psychiatric disorders: the WGSPD consortium. Nat. Neurosci. 20, 1661–1668 (2017).
Trost, B. et al. Genomic architecture of autism from comprehensive whole-genome sequence annotation. Cell 185, 4409–4427 e4418 (2022).
Bhaduri, A. et al. An atlas of cortical arealization identifies dynamic molecular signatures. Nature 598, 200–204 (2021).
Healy, L. M., Zia, S. & Plemel, J. R. Towards a definition of microglia heterogeneity. Commun. Biol. 5, 1114 (2022).
Yang, A. C. et al. A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk. Nature 603, 885–892 (2022).
Batiuk, M. Y. et al. Upper cortical layer-driven network impairment in schizophrenia. Sci. Adv. 8, eabn8367 (2022).
Gandal, M. J. et al. Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD. Nature 611, 532–539 (2022).
Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).
Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).
Morabito, S. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat. Genet. 53, 1143–1155 (2021).
Ziffra, R. S. et al. Single-cell epigenomics reveals mechanisms of human cortical development. Nature 598, 205–213 (2021).
Boldog, E. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 21, 1185–1195 (2018).
Ma, S. et al. Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science 377, eabo7257 (2022).
Caglayan, E. et al. Molecular features driving cellular complexity of human brain evolution. Nature 620, 145–153 (2023). References 16,17 present comparative single-cell transcriptomics studies of human and non-human primate brain to uncover cellular innovations, including cell-type-specific changes related to FOXP2, a key gene in brain development and disease.
Allen, D. E. et al. Fate mapping of neural stem cell niches reveals distinct origins of human cortical astrocytes. Science 376, 1441–1446 (2022).
Delgado, R. N. et al. Individual human cortical progenitors can produce excitatory and inhibitory neurons. Nature 601, 397–403 (2022).
You, Z. et al. Mapping of clonal lineages across developmental stages in human neural differentiation. Cell Stem Cell 30, 473–487 e479 (2023).
Chung, C. et al. Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development. Nat. Genet. 55, 209–220 (2023).
Miller, M. B. et al. Somatic genomic changes in single Alzheimer’s disease neurons. Nature 604, 714–722 (2022).
Kim, C. K., Adhikari, A. & Deisseroth, K. Integration of optogenetics with complementary methodologies in systems neuroscience. Nat. Rev. Neurosci. 18, 222–235 (2017).
Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34, 199–203 (2016). This study presents the utility of a method to simultaneously profile electrophysiological activity and transcriptional identity of neurons.
The Brain Initiative Cell Census Network. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021). The Brain Initiative Cell Census Network assembled single-cell tools and multiomic profiling to enable the study of cell types in mammalian systems with some of the first atlas resources of the human brain.
Scala, F. et al. Phenotypic variation of transcriptomic cell types in mouse motor cortex. Nature 598, 144–150 (2021).
Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 598, 137–143 (2021).
Munoz-Castaneda, R. et al. Cellular anatomy of the mouse primary motor cortex. Nature 598, 159–166 (2021).
Zhang, Z. et al. Epigenomic diversity of cortical projection neurons in the mouse brain. Nature 598, 167–173 (2021).
Joglekar, A. et al. A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain. Nat. Commun. 12, 463 (2021).
Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).
Ding, S. L. et al. Comprehensive cellular-resolution atlas of the adult human brain. J. Comp. Neurol. 524, 3127–3481 (2016).
Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
Fang, R. et al. Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH. Science 377, 56–62 (2022). This study compares cortical cell types and organization using spatial transcriptomic approaches, identifying differences in cell subtype composition and spatial arrangements.
Speir, M. L. et al. UCSC Cell Browser: visualize your single-cell data. Bioinformatics 37, 4578–4580 (2021).
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
Tabula Sapiens, C. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).
Tryka, K. A. et al. NCBI’s Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Res. 42, D975–D979 (2014).
Schwarz, N. et al. Long-term adult human brain slice cultures as a model system to study human CNS circuitry and disease. eLife 8, e48417 (2019).
Ting, J. T. et al. A robust ex vivo experimental platform for molecular-genetic dissection of adult human neocortical cell types and circuits. Sci. Rep. 8, 8407 (2018). References 39,40 describe methods for culturing human brain tissue from surgical patients and enable viral tracing of cell types and accessible intracortical connections.
Luo, C. et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genom 2, 100107 (2022).
Li, Y. E. et al. A comparative atlas of single-cell chromatin accessibility in the human brain. Science 382, eadf7044 (2023).
Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).
Caglayan, E., Liu, Y. & Konopka, G. Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets. Neuron 110, 4043–4056 e4045 (2022).
DeWeerdt, S. How to map the brain. Nature 571, S6–S8 (2019).
Sherwood, C. C. et al. Invariant synapse density and neuronal connectivity scaling in primate neocortical evolution. Cereb. Cortex 30, 5604–5615 (2020).
Alzu’bi, A., Homman-Ludiye, J., Bourne, J. A. & Clowry, G. J. Thalamocortical afferents innervate the cortical subplate much earlier in development in primate than in rodent. Cereb. Cortex 29, 1706–1718 (2019).
Lopez-Bendito, G. Development of the thalamocortical interactions: past, present and future. Neuroscience 385, 67–74 (2018).
Wang, L. et al. A cross-species proteomic map reveals neoteny of human synapse development. Nature 622, 112–119 (2023).
Laszlo, Z. I. et al. Synaptic proteomics reveal distinct molecular signatures of cognitive change and C9ORF72 repeat expansion in the human ALS cortex. Acta Neuropathol. Commun. 10, 156 (2022).
Hesse, R. et al. Comparative profiling of the synaptic proteome from Alzheimer’s disease patients with focus on the APOE genotype. Acta Neuropathol. Commun. 7, 214 (2019).
Carlyle, B. C. et al. Synaptic proteins associated with cognitive performance and neuropathology in older humans revealed by multiplexed fractionated proteomics. Neurobiol. Aging 105, 99–114 (2021).
Graham, L. C. et al. Regional molecular mapping of primate synapses during normal healthy aging. Cell Rep. 27, 1018–1026.e1014 (2019).
Niu, M. et al. Droplet-based transcriptome profiling of individual synapses. Nat. Biotechnol. 41, 1332–1344 (2023).
Hobson, B. D. & Herzog, E. Methodological concerns and lack of evidence for single-synapse RNA-seq. Nat. Biotechnol. 41, 1221–1224 (2023).
Niu, M. & Zong, C. Reply to: Methodological concerns and lack of evidence for single-synapse RNA-seq. Nat. Biotechnol. 41, 1225–1228 (2023).
Perez, J. D. et al. Subcellular sequencing of single neurons reveals the dendritic transcriptome of GABAergic interneurons. eLife 10, e63092 (2021). This paper demonstrated that subcellular, single-cell-resolution transcriptional profiling provides additional information in interneurons.
Finke, S. & Conzelmann, K. K. Replication strategies of rabies virus. Virus Res. 111, 120–131 (2005).
Wickersham, I. R. et al. Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639–647 (2007).
Saunders, A. et al. Ascertaining cells’ synaptic connections and RNA expression simultaneously with barcoded rabies virus libraries. Nat. Commun. 13, 6993 (2022).
Clark, I. C. et al. Barcoded viral tracing of single-cell interactions in central nervous system inflammation. Science 372, eabf1230 (2021).
Yuan, L., Chen, X., Zhan, H., Gilbert, H. L. & Zador, A. M. Massive multiplexing of spatially resolved single neuron projections with axonal BARseq. Preprint at bioRxiv https://doi.org/10.1101/2023.02.18.528865 (2023).
Graybuck, L. T. et al. Enhancer viruses for combinatorial cell-subclass-specific labeling. Neuron 109, 1449–1464.e1413 (2021).
Mich, J. K. et al. Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex. Cell Rep. 34, 108754 (2021).
Vanderhaeghen, P. & Polleux, F. Developmental mechanisms underlying the evolution of human cortical circuits. Nat. Rev. Neurosci. 24, 213–232 (2023).
Linaro, D. et al. Xenotransplanted human cortical neurons reveal species-specific development and functional integration into mouse visual circuits. Neuron 104, 972–986.e976 (2019).
Revah, O. et al. Maturation and circuit integration of transplanted human cortical organoids. Nature 610, 319–326 (2022).
Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V. & Oldham, M. C. Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat. Neurosci. 21, 1171–1184 (2018).
Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323 (2017). This single-cell profiling of the developing human cortex serves as a reference dataset for cell types and identified differences in transcriptional profiles across cortical regions during development.
Bhaduri, A. et al. Cell stress in cortical organoids impairs molecular subtype specification. Nature 578, 142–148 (2020).
Pollen, A. A. et al. Establishing cerebral organoids as models of human-specific brain evolution. Cell 176, 743–756.e717 (2019).
Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).
Feregrino, C. & Tschopp, P. Assessing evolutionary and developmental transcriptome dynamics in homologous cell types. Dev. Dyn. 251, 1472–1489 (2022).
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods https://doi.org/10.1016/j.crmeth.2023.100498 (2023).
Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).
Costa, I. G. Dissecting gene regulation with multimodal sequencing. Nat. Methods 20, 1282–1284 (2023).
Wu, S. J. et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat. Biotechnol. 39, 819–824 (2021).
Park, Y. P. & Kellis, M. CoCoA-diff: counterfactual inference for single-cell gene expression analysis. Genome Biol. 22, 228 (2021).
Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).
Brendel, M. et al. Application of deep learning on single-cell RNA sequencing data analysis: a review. Genomics Proteomics Bioinformatics 20, 814–835 (2022).
Ma, Q. & Xu, D. Deep learning shapes single-cell data analysis. Nat. Rev. Mol. Cell Biol. 23, 303–304 (2022).
Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023).
Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e1817 (2016).
Weiss, C. V. et al. The cis-regulatory effects of modern human-specific variants. eLife 10, e63713 (2021).
Whalen, S. et al. Machine learning dissection of human accelerated regions in primate neurodevelopment. Neuron 111, 857–873.e858 (2023).
Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).
Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022).
Fu, J. M. et al. Rare coding variation provides insight into the genetic architecture and phenotypic context of autism. Nat. Genet. 54, 1320–1331 (2022).
Mollon, J., Almasy, L., Jacquemont, S. & Glahn, D. C. The contribution of copy number variants to psychiatric symptoms and cognitive ability. Mol. Psychiatry 28, 1480–1493 (2023).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
Smith, S. M. et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat. Neurosci. 24, 737–745 (2021). References 90,91 harness the large UK Biobank resource to integrate genetic information with other features of the human brain including brain imaging and other phenotypes.
Bogdan, R., Hatoum, A. S., Johnson, E. C. & Agrawal, A. The genetically informed neurobiology of addiction (GINA) model. Nat. Rev. Neurosci. 24, 40–57 (2023).
Konopka, G. Cognitive genomics: linking genes to behavior in the human brain. Netw. Neurosci. 1, 3–13 (2017).
Richiardi, J. et al. Correlated gene expression supports synchronous activity in brain networks). Science 348, 1241–1244 (2015).
Wang, G. Z. et al. Correspondence between resting-state activity and brain gene expression. Neuron 88, 659–666 (2015). References 94,95 integrated transcriptional profiling and brain imaging to begin to link gene expression and human brain function.
Berto, S. et al. Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder. Nat. Commun. 13, 3328 (2022).
Romero-Garcia, R. et al. Schizotypy-related magnetization of cortex in healthy adolescence is colocated with expression of schizophrenia-related genes. Biol. Psychiatry 88, 248–259 (2020).
Seidlitz, J. et al. Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nat. Commun. 11, 3358 (2020).
Buch, A. M. et al. Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nat. Neurosci. 26, 650–663 (2023).
Kong, X. Z. et al. Gene expression correlates of the cortical network underlying sentence processing. Neurobiol. Lang. 1, 77–103 (2020).
Berto, S. et al. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. Nat. Neurosci. 24, 554–564 (2021).
Eugene, E. et al. An organotypic brain slice preparation from adult patients with temporal lobe epilepsy. J. Neurosci. Methods 235, 234–244 (2014).
de la Torre-Ubieta, L. et al. The dynamic landscape of open chromatin during human cortical neurogenesis. Cell 172, 289–304.e218 (2018).
Markenscoff-Papadimitriou, E. et al. A chromatin accessibility atlas of the developing human telencephalon. Cell 182, 754–769.e718 (2020). References 103,104 utilize the characterization of epigenetic state to describe the regulatory programs found during human brain development.
Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e1322 (2019).
Xu, J. et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. eLife 8, e45105 (2019).
Meyer, M. et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc. Natl Acad. Sci. USA 112, 851–856 (2015).
Radecki, G., Nargeot, R., Jelescu, I. O., Le Bihan, D. & Ciobanu, L. Functional magnetic resonance microscopy at single-cell resolution in Aplysia californica. Proc. Natl Acad. Sci. USA 111, 8667–8672 (2014).
Velmeshev, D. et al. Single-cell analysis of prenatal and postnatal human cortical development. Science 382, eadf0834 (2023).
Zhu, K. et al. Multi-omic profiling of the developing human cerebral cortex at the single-cell level. Sci. Adv. 9, eadg3754 (2023).
Johansen, N., et al. Interindividual variation in human cortical cell type abundance and expression. Science 382, eadf2359 (2023).
Jorstad, N. L. et al. Transcriptomic cytoarchitecture reveals principles of human neocortex organization. Science 382, eadf6812 (2023).
Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023).
Mathys, H. et al. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology. Cell 186, 4365–4385 (2023).
Fan, X. et al. Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis. Cell Res. 28, 730–745 (2018).
Zhong, S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018).
Polioudakis, D. et al. A single-cell transcriptomic atlas of human neocortical development during mid-gestation. Neuron 103, 785–801 e788 (2019).
Fan, X. et al. Single-cell transcriptome analysis reveals cell lineage specification in temporal-spatial patterns in human cortical development. Sci. Adv. 6, eaaz2978 (2020).
Eze, U. C., Bhaduri, A., Haeussler, M., Nowakowski, T. J. & Kriegstein, A. R. Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia. Nat. Neurosci. 24, 584–594 (2021).
Smith, R. S. et al. Early role for a Na+,K+-ATPase (ATP1A3) in brain development. Proc. Natl Acad. Sci. USA 118, e2023333118 (2021).
Ramos, S. I. et al. An atlas of late prenatal human neurodevelopment resolved by single-nucleus transcriptomics. Nat. Commun. 13, 7671 (2022).
Braun, E. et al. Comprehensive cell atlas of the first-trimester developing human brain. Science 382, eadf1226 (2023).
Herring, C. A. et al. Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution. Cell 185, 4428–4447.e4428 (2022).
Cameron, D. et al. Single-nuclei RNA sequencing of 5 regions of the human prenatal brain implicates developing neuron populations in genetic risk for schizophrenia. Biol. Psychiatry 93, 157–166 (2023).
Krienen, F. M. et al. Innovations present in the primate interneuron repertoire. Nature 586, 262–269 (2020).
Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).
Hodes, R. J. & Buckholtz, N. Accelerating Medicines Partnership: Alzheimer’s disease (AMP-AD) knowledge portal aids Alzheimer’s drug discovery through open data sharing. Expert Opin. Ther. Targets 20, 389–391 (2016).
Schirmer, L. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019).
Bressan, E. et al. The Foundational Data Initiative for Parkinson disease: enabling efficient translation from genetic maps to mechanism. Cell Genom. 3, 100261 (2023).
