A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
received:
05
03
2020
accepted:
26
03
2021
entrez:
7
10
2021
pubmed:
8
10
2021
medline:
3
11
2021
Statut:
ppublish
Résumé
Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain
Identifiants
pubmed: 34616066
doi: 10.1038/s41586-021-03500-8
pii: 10.1038/s41586-021-03500-8
pmc: PMC8494649
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
103-110Subventions
Organisme : NIMH NIH HHS
ID : U19 MH114821
Pays : United States
Organisme : NIMH NIH HHS
ID : R24 MH114815
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01 DC013817
Pays : United States
Organisme : NIDCD NIH HHS
ID : U01 DC013817
Pays : United States
Organisme : NIH HHS
ID : DC013817
Pays : United States
Organisme : NIH HHS
ID : GM114267
Pays : United States
Organisme : NIMH NIH HHS
ID : RF1 MH123199
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH121282
Pays : United States
Organisme : NIMH NIH HHS
ID : R24 MH114788
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA014195
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01 DC019370
Pays : United States
Organisme : NIMH NIH HHS
ID : U24 MH114827
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM114267
Pays : United States
Organisme : NIMH NIH HHS
ID : U19 MH114830
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021. The Author(s).
Références
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).
pubmed: 30096314
pmcid: 6086934
doi: 10.1016/j.cell.2018.06.021
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030.e16 (2018).
pubmed: 30096299
pmcid: 6447408
doi: 10.1016/j.cell.2018.07.028
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
pubmed: 30382198
pmcid: 6456269
doi: 10.1038/s41586-018-0654-5
Yamawaki, N., Borges, K., Suter, B. A., Harris, K. D. & Shepherd, G. M. G. A genuine layer 4 in motor cortex with prototypical synaptic circuit connectivity. eLife 3, e05422 (2014).
pubmed: 25525751
pmcid: 4290446
doi: 10.7554/eLife.05422
Zeng, H. & Sanes, J. R. Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18, 530–546 (2017).
pubmed: 28775344
doi: 10.1038/nrn.2017.85
Ramon y Cajal, S. Histologie du système nerveux de l’homme et des vertébrés. Maloine Paris 2, 153–173 (1911).
Armand, E. J., Li, J., Xie, F., Luo, C. & Mukamel, E. A. Single-cell sequencing of brain cell transcriptomes and epigenomes. Neuron 109, 11–26 (2021).
pubmed: 33412093
doi: 10.1016/j.neuron.2020.12.010
pmcid: 7808568
Paul, A. et al. Transcriptional architecture of synaptic communication delineates GABAergic neuron identity. Cell 171, 522–539.e20 (2017).
pubmed: 28942923
pmcid: 5772785
doi: 10.1016/j.cell.2017.08.032
Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).
pubmed: 28798132
pmcid: 5570439
doi: 10.1126/science.aan3351
Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).
pubmed: 23828890
pmcid: 3785061
doi: 10.1126/science.1237905
Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).
pubmed: 29434377
pmcid: 5862073
doi: 10.1038/s41593-018-0079-3
Mukamel, E. A. & Ngai, J. Perspectives on defining cell types in the brain. Curr. Opin. Neurobiol. 56, 61–68 (2019).
pubmed: 30530112
doi: 10.1016/j.conb.2018.11.007
Waddington, C. H. The Strategy of the Genes (Routledge, 1957).
Luo, C. et al. Robust single-cell DNA methylome profiling with snmC-seq2. Nat. Commun. 9, 3824 (2018).
pubmed: 30237449
pmcid: 6147798
doi: 10.1038/s41467-018-06355-2
Vanderburg, C. et al. Fresh frozen mouse brain preparation (for single nuclei sequencing). protocols.io https://doi.org/10.17504/protocols.io.bcbrism6 (2020).
Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).
pubmed: 31435019
pmcid: 6919571
doi: 10.1038/s41586-019-1506-7
Miller, J. A. et al. Common cell type nomenclature for the mammalian brain. eLife 9, e59928 (2020).
pubmed: 33372656
pmcid: 7790494
doi: 10.7554/eLife.59928
Economo, M. N. et al. Distinct descending motor cortex pathways and their roles in movement. Nature 563, 79–84 (2018).
pubmed: 30382200
doi: 10.1038/s41586-018-0642-9
Brodmann, K. Brodmann’s: Localisation in the Cerebral Cortex (Springer Science & Business Media, 2007).
Jabaudon, D., Shnider, S. J., Tischfield, D. J., Galazo, M. J. & Macklis, J. D. RORβ induces barrel-like neuronal clusters in the developing neocortex. Cereb. Cortex 22, 996–1006 (2012).
pubmed: 21799210
doi: 10.1093/cercor/bhr182
Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature https://doi.org/10.1038/s41586-021-03705-x (2021).
Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).
pubmed: 30586455
pmcid: 6306246
doi: 10.1371/journal.pone.0209648
Tripathi, V. et al. The nuclear-retained noncoding RNA MALAT1 regulates alternative splicing by modulating SR splicing factor phosphorylation. Mol. Cell 39, 925–938 (2010).
pubmed: 20797886
pmcid: 4158944
doi: 10.1016/j.molcel.2010.08.011
Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018).
pubmed: 29491377
pmcid: 5830442
doi: 10.1038/s41467-018-03282-0
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).
pubmed: 28114287
pmcid: 5330805
doi: 10.1038/nmeth.4150
Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).
pubmed: 28346451
pmcid: 5410170
doi: 10.1038/nmeth.4236
Preissl, S., Wang, X. & Ren, B. Sequencing open chromatin of single cell nuclei: snATAC-seq. protocols.io, https://doi.org/10.17504/protocols.io.pjudknw (2018).
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).
pubmed: 31178122
pmcid: 6716797
doi: 10.1016/j.cell.2019.05.006
Luo, C. et al. Single nucleus multi-omics links human cortical cell regulatory genome diversity to disease risk variants. Preprint at https://doi.org/10.1101/2019.12.11.873398 (2019).
Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).
pubmed: 30166440
pmcid: 6571013
doi: 10.1126/science.aau0730
Vruwink, M., Schmidt, H. H., Weinberg, R. J. & Burette, A. Substance P and nitric oxide signaling in cerebral cortex: anatomical evidence for reciprocal signaling between two classes of interneurons. J. Comp. Neurol. 441, 288–301 (2001).
pubmed: 11745651
doi: 10.1002/cne.1413
Peukert, D., Weber, S., Lumsden, A. & Scholpp, S. Lhx2 and Lhx9 determine neuronal differentiation and compartition in the caudal forebrain by regulating Wnt signaling. PLoS Biol. 9, e1001218 (2011).
pubmed: 22180728
pmcid: 3236734
doi: 10.1371/journal.pbio.1001218
Xie, W. et al. Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153, 1134–1148 (2013).
pubmed: 23664764
pmcid: 3786220
doi: 10.1016/j.cell.2013.04.022
He, Y. et al. Improved regulatory element prediction based on tissue-specific local epigenomic signatures. Proc. Natl Acad. Sci. USA 114, E1633–E1640 (2017).
pubmed: 28193886
pmcid: 5338528
doi: 10.1073/pnas.1618353114
Harris, K. D. et al. Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics. PLoS Biol. 16, e2006387 (2018).
pubmed: 29912866
pmcid: 6029811
doi: 10.1371/journal.pbio.2006387
Barkas, N. et al. Wiring together large single-cell RNA-seq sample collections. Preprint at https://doi.org/10.1101/460246 (2018).
Zhu, C., Preissl, S. & Ren, B. Single-cell multimodal omics: the power of many. Nat. Methods 17, 11–14 (2020).
pubmed: 31907462
doi: 10.1038/s41592-019-0691-5
Hertler, B., Hosp, J. A., Blanco, M. B. & Luft, A. R. Substance P signalling in primary motor cortex facilitates motor learning in rats. PLoS One 12, e0189812 (2017).
pubmed: 29281692
pmcid: 5744944
doi: 10.1371/journal.pone.0189812
McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
Harris, J. A. et al. Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation. Front. Neural Circuits 8, 76 (2014).
pubmed: 25071457
pmcid: 4091307
doi: 10.3389/fncir.2014.00076
Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).
pubmed: 20023653
doi: 10.1038/nn.2467
Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).
pubmed: 26727548
pmcid: 4985242
doi: 10.1038/nn.4216
Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).
pubmed: 17151600
doi: 10.1038/nature05453
Martin, C. et al. Frozen tissue nuclei extraction (for 10xV3 snSEQ). protocols.io, https://doi.org/10.17504/protocols.io.bi62khge (2020).
Bortolin, L., Goldman, M. & McCarroll, S. Extraction of nuclei from brain tissue. protocols.io, https://doi.org/10.17504/protocols.io.2srged6 (2020).
Luo, C. & Ecker, J. R. Methyl-C sequencing of single cell nuclei: snmC-seq2. protocols.io, https://doi.org/10.17504/protocols.io.pjvdkn6 (2018).
Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021)
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).
pubmed: 23950696
pmcid: 3738458
doi: 10.1371/journal.pcbi.1003118
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e4 (2019).
pubmed: 30954475
pmcid: 6853612
doi: 10.1016/j.cels.2019.03.003
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).
pubmed: 31375813
pmcid: 7299161
doi: 10.1038/s41587-019-0206-z
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).
pubmed: 30954476
pmcid: 6625319
doi: 10.1016/j.cels.2018.11.005
Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-seq applications. Bioinformatics 27, 1571–1572 (2011).
pubmed: 21493656
pmcid: 3102221
doi: 10.1093/bioinformatics/btr167
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
pubmed: 30914743
pmcid: 6435756
doi: 10.1038/s41598-019-41695-z
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
pubmed: 29608179
pmcid: 6700744
doi: 10.1038/nbt.4096
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).
doi: 10.1088/1742-5468/2008/10/P10008
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018).
pubmed: 29961576
pmcid: 6771278
doi: 10.1016/j.cell.2018.05.061
Schultz, M. D. et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523, 212–216 (2015).
pubmed: 26030523
pmcid: 4499021
doi: 10.1038/nature14465
Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).
pubmed: 18798982
pmcid: 2592715
doi: 10.1186/gb-2008-9-9-r137
Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).
pubmed: 30361341
pmcid: 6408149
doi: 10.1126/science.aav1898
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE Blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).
pubmed: 31249361
pmcid: 6597582
doi: 10.1038/s41598-019-45839-z
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pubmed: 20110278
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
He, Y. et al. Spatiotemporal DNA methylome dynamics of the developing mouse fetus. Nature 583, 752–759 (2020).
pubmed: 32728242
pmcid: 7398276
doi: 10.1038/s41586-020-2119-x
Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2020).
pubmed: 31701148
doi: 10.1093/nar/gkaa516
Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).
pubmed: 21330290
pmcid: 3065696
doi: 10.1093/bioinformatics/btr064
Hon, G. C. et al. Epigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues. Nat. Genet. 45, 1198–1206 (2013).
pubmed: 23995138
pmcid: 4095776
doi: 10.1038/ng.2746
Wingender, E., Schoeps, T. & Dönitz, J. TFClass: an expandable hierarchical classification of human transcription factors. Nucleic Acids Res. 41, D165–D170 (2013).
pubmed: 23180794
doi: 10.1093/nar/gks1123