DNA methylation atlas of the mouse brain at single-cell resolution
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Abstract
Abstract Mammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing 1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data 3 enabled prediction of high-confidence enhancer–gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments 4 . By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.
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A different integration method (Allcools) gave similar clustering results.
“A different integration method (Allcools)38 gave similar clustering results”
From Molecular and spatial signatures of mouse brain aging at single-cell resolution (2023) · cites this paper as [38]
The Methods section on clustering-related methods describes integration with Scanorama and subsequent co-clustering, but does not report a comparison with Allcools. A search for Allcools found it in the Methods section on cell-type-specific regulatory elements, where it was used to generate a methylation profile, not to compare clustering results. Page numbers are unavailable in our copy. (LLM, conf=0.74)
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
(2014) · Cited by 33,419 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954 · see citing works
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works
A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.021 · Cited by 9,212 · see citing works
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
From Louvain to Leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635 · see citing works
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572 · see citing works
Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2
Amir, R. E., Van den Veyver, I. B., Wan, M., Tran, C. Q., Francke, U., & Zoghbi, H. Y. (1999) · Nature Genetics · doi:10.1038/13810 · Cited by 4,990 · see citing works
FIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247 · see citing works
Are the Dorsal and Ventral Hippocampus Functionally Distinct Structures?
Fanselow, M. S., & Dong, H.-W. (2010) · Neuron · doi:10.1016/j.neuron.2009.11.031 · Cited by 3,358 · see citing works
Mechanisms and Functional Implications of Adult Neurogenesis
Zhao, C., Deng, W., & Gage, F. H. (2008) · Cell · doi:10.1016/j.cell.2008.01.033 · Cited by 3,007 · see citing works
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,995 · see citing works
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine LearningPreprint
Lemaître, G., Nogueira, F., & Aridas, C. K. (2016) · Journal of machine learning research · doi:10.48550/arxiv.1609.06570 · Cited by 2,733 · see citing works
Adult Neurogenesis in the Mammalian Brain: Significant Answers and Significant Questions
Ming, G.-L., & Song, H. (2011) · Neuron · doi:10.1016/j.neuron.2011.05.001 · Cited by 2,651 · see citing works
Pvclust: an R package for assessing the uncertainty in hierarchical clustering
Suzuki, R., & Shimodaira, H. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl117 · Cited by 2,456 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
The ENCODE Blacklist: Identification of Problematic Regions of the Genome
Amemiya, H. M., Kundaje, A., & Boyle, A. P. (2019) · Scientific Reports · doi:10.1038/s41598-019-45839-z · Cited by 1,831 · see citing works
Global epigenomic reconfiguration during mammalian brain development
Lister, R., Mukamel, E. A., Nery, J. R., Urich, M., Puddifoot, C. A., Johnson, N. D., Lucero, J., Huang, Y., Dwork, A. J., Schultz, M. D., et al. (2013) · Science · doi:10.1126/science.1237905 · Cited by 1,830 · see citing works
Self-citationThe Balanced Accuracy and Its Posterior Distribution
Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010) · 2010 20th International Conference on Pattern Recognition · doi:10.1109/icpr.2010.764 · Cited by 1,816 · see citing works
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775 · see citing works
JASPAR 2020: update of the open-access database of transcription factor binding profiles
Fornes, O., Castro-Mondragon, J. A., Khan, A., van der Lee, R., Zhang, X., Richmond, P. A., Modi, B. P., Correard, S., Gheorghe, M., Baranašić, D., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1001 · Cited by 1,763 · see citing works
Shared and distinct transcriptomic cell types across neocortical areas
Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0654-5 · Cited by 1,746 · see citing works
The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas
Wang, Q., Ding, S., Li, Y., Royall, J. J., Feng, D. H., Lesnar, P., Graddis, N., Naeemi, M., Facer, B. A. C., Ho, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.007 · Cited by 1,410 · see citing works
Self-citationGARCÍA-ALONSO, C. R., Pérez, L. M., & Fernández, J. C. F. (2011) · Annals of Operations Research · doi:10.1007/s10479-011-0841-3 · Cited by 1,357 · see citing works
GOATOOLS: A Python library for Gene Ontology analyses
Klopfenstein, D. V., Zhang, L., Pedersen, B. S., Ramírez, F., Vesztrocy, A. W., Naldi, A., Mungall, C., Yunes, J. M., Botvinnik, O. B., Weigel, M., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-28948-z · Cited by 1,307 · see citing works
Impact of cytosine methylation on DNA binding specificities of human transcription factors
Yin, Y., Morgunova, E., Jolma, A., Kaasinen, E., Sahu, B., Khund-Sayeed, S., Das, P. K., Kivioja, T., Dave, K., Zhong, F., et al. (2017) · Science · doi:10.1126/science.aaj2239 · Cited by 1,257 · see citing works
Putting a spin on the dorsal-ventral divide of the striatum
Voorn, P., Vanderschuren, L. J. M. J., Groenewegen, H. J., Robbins, T. W., & Pennartz, C. M. A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.06.006 · Cited by 1,188 · see citing works
Neuronal Subtype-Specific Genes that Control Corticospinal Motor Neuron Development In Vivo
Arlotta, P., Molyneaux, B. J., Chen, J., Inoue, J., Kominami, R., & Macklis, J. D. (2005) · Neuron · doi:10.1016/j.neuron.2004.12.036 · Cited by 1,157 · see citing works
Interneuron cell types are fit to function
Kepecs, A., & Fishell, G. (2014) · Nature · doi:10.1038/nature12983 · Cited by 1,110 · see citing works
A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
Yao, Z., van Velthoven, C. T. J., Nguyen, T. N., Goldy, J., Sedeno-Cortes, A. E., Baftizadeh, F., Bertagnolli, D., Casper, T., Chiang, M., Crichton, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.021 · Cited by 1,065 · see citing works
The 3D Genome as Moderator of Chromosomal Communication
Dekker, J., & Mirny, L. (2016) · Cell · doi:10.1016/j.cell.2016.02.007 · Cited by 937 · see citing works
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Hie, B., Bryson, B. D., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 853 · see citing works
5-hmC–mediated epigenetic dynamics during postnatal neurodevelopment and aging
Szulwach, K. E., Li, X., Li, Y., Song, C., Wu, H., Dai, Q., Irier, H. A., Upadhyay, A. K., Gearing, M., Levey, A. I., et al. (2011) · Nature Neuroscience · doi:10.1038/nn.2959 · Cited by 818 · see citing works
Self-citationBecoming a new neuron in the adult olfactory bulb
Carleton, A., Petreanu, L., Lansford, R. D., Álvarez-Buylla, A., & Lledo, P. (2003) · Nature Neuroscience · doi:10.1038/nn1048 · Cited by 808 · see citing works
The origin and evolution of cell types
Arendt, D., Musser, J. M., Baker, C. V. H., Bergman, A., Cepko, C., Erwin, D. H., Pavlicev, M., Schlosser, G., Widder, S., Laubichler, M. D., et al. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.127 · Cited by 764 · see citing works
What is the function of the claustrum?
Crick, F. C., & Koch, C. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1661 · Cited by 742 · see citing works
Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain
Mo, A., Mukamel, E. A., Davis, F. P., Luo, C., Henry, G. L., Picard, S., Urich, M. A., Nery, J. R., Sejnowski, T. J., Lister, R., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.018 · Cited by 739 · see citing works
Self-citationDistribution, recognition and regulation of non-CpG methylation in the adult mammalian brain
Guo, J. U., Su, Y., Shin, J. H., Shin, J., Li, H., Xie, B., Zhong, C., Hu, S., Le, T., Fan, G., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3607 · Cited by 714 · see citing works
Human body epigenome maps reveal noncanonical DNA methylation variation
Schultz, M. D., He, Y., Whitaker, J. W., Hariharan, M., Mukamel, E. A., Leung, D., Rajagopal, N., Nery, J. R., Urich, M. A., Chen, H., et al. (2015) · Nature · doi:10.1038/nature14465 · Cited by 690 · see citing works
Self-citationDynamic DNA methylation: In the right place at the right time
Luo, C., Hajkova, P., & Ecker, J. R. (2018) · Science · doi:10.1126/science.aat6806 · Cited by 668 · see citing works
Self-citationPurhonen, J., Banerjee, R., McDonald, A. E., Fellman, V., & Kallijärvi, J. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa516 · Cited by 657 · see citing works
Transcription factors as readers and effectors of DNA methylation
Zhu, H., Wang, G., & Qian, J. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.83 · Cited by 649 · see citing works
Basic helix-loop-helix factors in cortical development.
Ross, S. E., Greenberg, M., & Stiles, C. (2003) · Neuron · doi:10.1016/s0896-6273(03)00365-9 · Cited by 637 · see citing works
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601 · see citing works
Self-citationA fast algorithm for matrix balancing
Knight, P. A., & Ruiz, D. (2012) · IMA Journal of Numerical Analysis · doi:10.1093/imanum/drs019 · Cited by 599 · see citing works
Area Patterning of the Mammalian Cortex
O’Leary, D. D. M., Chou, S., & Sahara, S. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.010 · Cited by 574 · see citing works
Disruption of DNA-methylation-dependent long gene repression in Rett syndrome
Gabel, H. W., Kinde, B., Stroud, H., Gilbert, C. S., Harmin, D. A., Kastan, N. R., Hemberg, M., Ebert, D. H., & Greenberg, M. E. (2015) · Nature · doi:10.1038/nature14319 · Cited by 569 · see citing works
Nieto, M., Monuki, E. S., Tang, H., Imitola, J., Haubst, N., Khoury, S. J., Cunningham, J., Götz, M., & Walsh, C. A. (2004) · The Journal of Comparative Neurology · doi:10.1002/cne.20322 · Cited by 548 · see citing works
Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex
Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · Cited by 545 · see citing works
Self-citationA viral strategy for targeting and manipulating interneurons across vertebrate species
Dimidschstein, J., Chen, Q., Tremblay, R., Rogers, S. L., Saldi, G.-A., Guo, L., Xu, Q., Liu, R., Lu, C., Chu, J., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4430 · Cited by 541 · see citing works
Self-citationWidespread Expression of BDNF But Not NT3 by Target Areas of Basal Forebrain Cholinergic Neurons
PHILLIPS, H. S., Hains, J. M., Laramee, G. R., Rosenthal, A., & Winslow, J. W. (1990) · Science · doi:10.1126/science.1688328 · Cited by 532 · see citing works
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data
Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S., & Kluger, Y. (2019) · Nature Methods · doi:10.1038/s41592-018-0308-4 · Cited by 521 · see citing works
Hochgerner, H., Zeisel, A., Lönnerberg, P., & Linnarsson, S. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0056-2 · Cited by 482 · see citing works
Characterization of Foxp2 and Foxp1 mRNA and protein in the developing and mature brain
Ferland, R. J., Cherry, T. J., Preware, P. O., Morrisey, E. E., & Walsh, C. A. (2003) · The Journal of Comparative Neurology · doi:10.1002/cne.10654 · Cited by 477 · see citing works
Epigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues
Hon, G. C., Rajagopal, N., Shen, Y., McCleary, D. F., Yue, F., Dang, M. D., & Ren, B. (2013) · Nature Genetics · doi:10.1038/ng.2746 · Cited by 457 · see citing works
Self-citationHipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons
Cembrowski, M. S., Wang, L., Sugino, K., Shields, B. C., & Spruston, N. (2016) · eLife · doi:10.7554/elife.14997 · Cited by 422 · see citing works
Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
Paul, A., Crow, M., Raudales, R., He, M., Gillis, J., & Huang, Z. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.032 · Cited by 421 · see citing works
A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex
Yao, Z., Liu, H., Xie, F., Fischer, S., Adkins, R. S., Aldridge, A. I., Ament, S. A., Bartlett, A., Behrens, M. M., Van den Berge, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03500-8 · Cited by 398 · see citing works
Self-citationAn integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types
Yao, Z., Liu, H., Xie, F., Fischer, S., Booeshaghi, A. S., Adkins, R. S., Aldridge, A. I., Ament, S. A., Pinto-Duarte, A., Bartlett, A., et al. (2020) · doi:10.1101/2020.02.29.970558 · Cited by 397 · see citing works
Self-citationInnovations present in the primate interneuron repertoire
Krienen, F. M., Goldman, M., Zhang, Q., C. H. del Rosario, R., Florio, M., Machold, R., Saunders, A., Levandowski, K., Zaniewski, H., Schuman, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2781-z · Cited by 381 · see citing works
Innovations in Primate Interneuron Repertoire
Krienen, F. M., Goldman, M., Zhang, Q., del Rosario, R., Florio, M., Machold, R., Saunders, A., Levandowski, K., Zaniewski, H., Schuman, B., et al. (2019) · doi:10.1101/709501 · Cited by 372 · see citing works
An atlas of dynamic chromatin landscapes in mouse fetal development
Gorkin, D. U., Barozzi, I., Zhao, Y., Zhang, Y., Huang, H., Lee, A. Y., Li, B., Chiou, J., Wildberg, A., Ding, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2093-3 · Cited by 364 · see citing works
Self-citationEpigenetic influences on brain development and plasticity
Fagiolini, M., Jensen, C. L., & Champagne, F. A. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.05.009 · Cited by 362 · see citing works
Simultaneous profiling of 3D genome structure and DNA methylation in single human cells
Lee, D.-S., Luo, C., Zhou, J., Chandran, S., Rivkin, A., Bartlett, A., Nery, J. R., Fitzpatrick, C., O’Connor, C., Dixon, J. R., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0547-z · Cited by 340 · see citing works
Self-citationPreissl, S., Fang, R., Huang, H., Zhao, Y., Raviram, R., Gorkin, D. U., Zhang, Y., Sos, B. C., Afzal, V., Dickel, D. E., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0079-3 · Cited by 332 · see citing works
Self-citationRediscovering area CA2: unique properties and functions
Dudek, S. M., Alexander, G. M., & Farris, S. (2016) · Nature reviews. Neuroscience · doi:10.1038/nrn.2015.22 · Cited by 329 · see citing works
Cubelos, B., Sebastián‐Serrano, Á., Beccari, L. L., Calcagnotto, M. E., Cisneros, E., Kim, S., Dopazo, A., Álvarez‐Dolado, M., Redondo, J. M., Bovolenta, P., et al. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.038 · Cited by 301 · see citing works
Chen, L., Chen, K., Lavery, L. A., Baker, S. A., Shaw, C. A., Li, W., & Zoghbi, H. Y. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1505909112 · Cited by 301 · see citing works
From profiles to function in epigenomics
Stricker, S. H., Köferle, A., & Beck, S. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.138 · Cited by 290 · see citing works
Chromatin structure dynamics during the mitosis-to-G1 phase transition
Zhang, H., Emerson, D. J., Gilgenast, T. G., Titus, K. R., Lan, Y., Huang, P., Zhang, D., Wang, H., Keller, C. A., Giardine, B., et al. (2019) · Nature · doi:10.1038/s41586-019-1778-y · Cited by 271 · see citing works
MethylC-seq library preparation for base-resolution whole-genome bisulfite sequencing
Urich, M. A., Nery, J. R., Lister, R., Schmitz, R. J., & Ecker, J. R. (2015) · Nature Protocols · doi:10.1038/nprot.2014.114 · Cited by 268 · see citing works
Self-citationThe diversity of GABAergic neurons and neural communication elements
Huang, Z. J., & Paul, A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0195-4 · Cited by 201 · see citing works
Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex
Mich, J. K., Graybuck, L. T., Hess, E. E., Mahoney, J. T., Kojima, Y., Ding, Y., Somasundaram, S., Miller, J. A., Kalmbach, B. E., Radaelli, C., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108754 · Cited by 193 · see citing works
Robust single-cell DNA methylome profiling with snmC-seq2
Luo, C., Rivkin, A., Zhou, J., Sandoval, J. P., Kurihara, L., Lucero, J., Castanon, R., Nery, J. R., Pinto-Duarte, A., Bui, B., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06355-2 · Cited by 189 · see citing works
Self-citationAn LRRTM4-HSPG Complex Mediates Excitatory Synapse Development on Dentate Gyrus Granule Cells
Siddiqui, T. J., Tari, P. K., Connor, S. A., Zhang, P., Dobie, F. A., She, K., Kawabe, H., Wang, Y. T., Brose, N., & Craig, A. M. (2013) · Neuron · doi:10.1016/j.neuron.2013.06.029 · Cited by 186 · see citing works
Early-Life Gene Expression in Neurons Modulates Lasting Epigenetic States
Stroud, H., Su, S. C., Hrvatin, S., Greben, A. W., Renthal, W. R., Boxer, L. D., Nagy, M. A., Hochbaum, D. R., Kinde, B., Gabel, H. W., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.047 · Cited by 186 · see citing works
Maintenance of postmitotic neuronal cell identity
Deneris, E. S., & Hobert, O. (2014) · Nature Neuroscience · doi:10.1038/nn.3731 · Cited by 185 · see citing works
An atlas of gene regulatory elements in adult mouse cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Qiu, Y., Poirion, O. B., Li, B., Chiou, J., Liu, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03604-1 · Cited by 185 · see citing works
Self-citationRobust single-cell Hi-C clustering by convolution- and random-walk-based imputation
Zhou, J., Ma, J., Chen, Y., Cheng, C., Bao, B., Peng, J., Sejnowski, T. J., Dixon, J. R., & Ecker, J. R. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1901423116 · Cited by 180 · see citing works
Self-citationGenetic-Based Dissection Unveils the Inputs and Outputs of Striatal Patch and Matrix Compartments
Smith, J. B., Klug, J. R., Ross, D. L., Howard, C. D., Hollon, N. G., Ko, V. I., Hoffman, H., Callaway, E. M., Gerfen, C. R., & Jin, X. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.046 · Cited by 176 · see citing works
Self-citationEfficient Algorithms for t-distributed Stochastic Neighborhood EmbeddingPreprint
Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S., & Kluger, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.09005 · Cited by 172 · see citing works
Single nucleus multi-omics identifies human cortical cell regulatory genome diversity
Luo, C., Liu, H., Xie, F., Armand, E. J., Siletti, K., Bakken, T. E., Fang, R., Doyle, W. I., Stuart, T., Hodge, R. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100107 · Cited by 167 · see citing works
Self-citationSpatiotemporal DNA methylome dynamics of the developing mouse fetus
He, Y., Hariharan, M., Gorkin, D. U., Dickel, D. E., Luo, C., Castanon, R., Nery, J. R., Lee, A. Y., Zhao, Y., Huang, H., et al. (2020) · Nature · doi:10.1038/s41586-020-2119-x · Cited by 162 · see citing works
Self-citationZhang, T., Keown, C. L., Wen, X., Li, J., Vousden, D. A., Anacker, C., Bhattacharyya, U., Ryan, R., Diorio, J. C., O’Toole, N., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02748-x · Cited by 153 · see citing works
Self-citationGradients in the Brain: The Control of the Development of Form and Function in the Cerebral Cortex
Sansom, S. N., & Livesey, F. J. (2009) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a002519 · Cited by 148 · see citing works
Lagger, S., Connelly, J. C., Schweikert, G., Webb, S., Selfridge, J., Ramsahoye, B. H., Yu, M., DeSousa, D., Seiser, C., He, C., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006793 · Cited by 142 · see citing works
A genuine layer 4 in motor cortex with prototypical synaptic circuit connectivity
Yamawaki, N., Borges, K., Suter, B. A., Harris, K. D., & Shepherd, G. M. G. (2014) · eLife · doi:10.7554/elife.05422 · Cited by 141 · see citing works
A scalable platform for the development of cell-type-specific viral drivers
Hrvatin, S., Tzeng, C. P., Nagy, M. A., Stroud, H., Koutsioumpa, C., Wilcox, O. F., Assad, E. G., Green, J., Harvey, C. D., Griffith, E. C., et al. (2019) · eLife · doi:10.7554/elife.48089 · Cited by 118 · see citing works
TFClass: expanding the classification of human transcription factors to their mammalian orthologs
Wingender, E., Schoeps, T., Haubrock, M., Krull, M., & Dönitz, J. (2017) · Nucleic Acids Res. · doi:10.1093/nar/gkx987 · Cited by 114 · see citing works
Spatial diversity in gene expression for VDCCγ subunit family in developing and adult mouse brains
Fukaya, M., Yamazaki, M., Sakimura, K., & Watanabe, M. (2005) · Neuroscience Research · doi:10.1016/j.neures.2005.08.009 · Cited by 110 · see citing works
Functional enhancer elements drive subclass-selective expression from mouse to primate neocortexPreprint
Mich, J., Graybuck, L. T., Hess, E. E., et al. (2019) · bioRxiv · doi:10.1101/555318 · Cited by 99 · see citing works
Smith, J. B., Alloway, K. D., Hof, P. R., Orman, R., Reser, D. H., Watakabe, A., & Watson, G. D. R. (2018) · The Journal of Comparative Neurology · doi:10.1002/cne.24537 · Cited by 97 · see citing works
Oishi, K., Aramaki, M., & Nakajima, K. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1515949113 · Cited by 83 · see citing works
Probabilistic preference learning with the Mallows rank modelPreprint
Vitelli, V., Sørensen, Ø., Crispino, M., Frigessi, A., & Arjas, E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.7945 · Cited by 81 · see citing works
Epigenomic Diversity of Cortical Projection Neurons in the Mouse Brain
Zhang, Z., Zhou, J., Tan, P., Pang, Y., Rivkin, A., Kirchgessner, M. A., Williams, E., Lee, C.-T., Liu, H., Franklin, A. D., et al. (2020) · doi:10.1101/2020.04.01.019612 · Cited by 75 · see citing works
Self-citationAntonio, A. S., Liban, K., Ikrar, T., Tsyganovskiy, E., & Xu, X. (2013) · The Journal of Comparative Neurology · doi:10.1002/cne.23486 · Cited by 72 · see citing works
Epigenomic diversity of cortical projection neurons in the mouse brain
Zhang, Z., Zhou, J., Tan, P., Pang, Y., Rivkin, A. C., Kirchgessner, M. A., Williams, E., Lee, C.-T., Liu, H., Franklin, A. D., et al. (2021) · Nature · doi:10.1038/s41586-021-03223-w · Cited by 71 · see citing works
Self-citationImproved regulatory element prediction based on tissue-specific local epigenomic signatures
He, Y., Gorkin, D. U., Dickel, D. E., Nery, J. R., Castanon, R., Lee, A. Y., Shen, Y., Visel, A., Pennacchio, L. A., Ren, B., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618353114 · Cited by 70 · see citing works
Self-citationDevelopment and Evolution of the Claustrum
Puelles, L. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-404566-8.00004-0 · Cited by 58 · see citing works
Perspectives on defining cell types in the brain
Mukamel, E. A., & Ngai, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.11.007 · Cited by 56 · see citing works
Self-citationThe indusium griseum in the mouse: Architecture, Timm's histochemistry and some afferent connections
Adamek, G. D., Shipley, M. T., & Sanders, M. S. (1984) · Brain Research Bulletin · doi:10.1016/0361-9230(84)90147-3 · Cited by 54 · see citing works
Lavery, L. A., Ure, K., Wan, Y., Luo, C., Trostle, A. J., Wang, W., Jin, H., Lopez, J., Lucero, J. D., Durham, M. A., et al. (2020) · eLife · doi:10.7554/elife.52981 · Cited by 51 · see citing works
Self-citationA comparison of Notch, Hes and Grg expression during murine embryonic and post-natal development.
Allen, T. D., & Lobe, C. G. (1999) · PubMed · Cited by 50 · see citing works
Systematic mapping of chromatin state landscapes during mouse developmentPreprint
Gorkin, D. U., Barozzi, I., Zhang, Y.-X., Lee, A.-Y., Li, B., Zhao, Y., Wildberg, A., Ding, B., Zhang, B., Wang, M.-C., et al. (2017) · bioRxiv · doi:10.1101/166652 · Cited by 33 · see citing works
Self-citationLuo, C., Liu, H., Xie, F.-M., et al. (2019) · bioRxiv · doi:10.1101/2019.12.11.873398 · Cited by 27 · see citing works
Self-citationSpatiotemporal DNA Methylome Dynamics of the Developing Mammalian FetusPreprint
He, Y., Hariharan, M., Gorkin, D. U., et al. (2017) · bioRxiv · doi:10.1101/166744 · Cited by 22 · see citing works
PESCA: A scalable platform for the development of cell-type-specific viral driversPreprint
Hrvatin, S., Tzeng, C. P., Nagy, M. A., Stroud, H., Koutsioumpa, C., Wilcox, O. F., Assad, E. G., Green, J., Harvey, C. D., Griffith, E. C., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/570895 · Cited by 6 · see citing works
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Comparative cellular analysis of motor cortex in human, marmoset and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03465-8 · Cited by 812 · see citing works
Self-citationA multimodal cell census and atlas of the mammalian primary motor cortex
BRAIN Initiative Cell Census Network (BICCN), BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, Callaway, E. M., Dong, H.-W., Ecker, J. R., Hawrylycz, M. J., Huang, Z. J., Lein, E. S., Ngai, J., Osten, P., et al. (2021) · Nature · doi:10.1038/s41586-021-03950-0 · Cited by 522 · see citing works
Self-citationMolecularly defined and spatially resolved cell atlas of the whole mouse brain
Zhang, M., Pan, X., Jung, W., Halpern, A. R., Eichhorn, S. W., Lei, Z., Cohen, L., Smith, K. A., Tasic, B., Yao, Z., et al. (2023) · Nature · doi:10.1038/s41586-023-06808-9 · Cited by 506 · see citing works
What is a cell type and how to define it?
Zeng, H. (2022) · Cell · doi:10.1016/j.cell.2022.06.031 · Cited by 439 · see citing works
Molecular and spatial signatures of mouse brain aging at single-cell resolution
Allen, W. E., Blosser, T. R., Sullivan, Z. A., Dulac, C., & Zhuang, X. (2023) · Cell · doi:10.1016/j.cell.2022.12.010 · Cited by 423 · see citing works
“(E) As in Figure 4E, but using imputed gene expression data derived from Allcools integration.”
InfluentialSun, Y., Koyama, Y., & Shimada, S. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.903455 · Cited by 304 · see citing works
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works
Self-citationDNA methylation in mammalian development and disease
Smith, Z. D., Hetzel, S., & Meissner, A. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00760-8 · Cited by 280 · see citing works
Characterizing cis-regulatory elements using single-cell epigenomics
Preissl, S., Gaulton, K. J., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-022-00509-1 · Cited by 251 · see citing works
Self-citationFunctional enhancer elements drive subclass-selective expression from mouse to primate neocortex
Mich, J. K., Graybuck, L. T., Hess, E. E., Mahoney, J. T., Kojima, Y., Ding, Y., Somasundaram, S., Miller, J. A., Kalmbach, B. E., Radaelli, C., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108754 · Cited by 193 · see citing works
An atlas of gene regulatory elements in adult mouse cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Qiu, Y., Poirion, O. B., Li, B., Chiou, J., Liu, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03604-1 · Cited by 185 · see citing works
Self-citationSingle nucleus multi-omics identifies human cortical cell regulatory genome diversity
Luo, C., Liu, H., Xie, F., Armand, E. J., Siletti, K., Bakken, T. E., Fang, R., Doyle, W. I., Stuart, T., Hodge, R. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100107 · Cited by 167 · see citing works
Self-citationUltrafast bisulfite sequencing detection of 5-methylcytosine in DNA and RNA
Dai, Q., Ye, C., Irkliyenko, I., Wang, Y., Sun, H., Gao, Y., Liu, Y., Beadell, A. V., Perea, J., Goel, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02034-w · Cited by 165 · see citing works
Iadecola, C., Smith, E. E., Anrather, J., Gu, C., Mishra, A., Misra, S., Pérez‐Pinzón, M. A., Shih, A. Y., Sorond, F. A., van Veluw, S. J., et al. (2023) · Stroke · doi:10.1161/str.0000000000000431 · Cited by 156 · see citing works
Single-cell DNA methylation and 3D genome architecture in the human brain
Tian, W., Zhou, J., Bartlett, A., Zeng, Q., Liu, H., Castanon, R. G., Kenworthy, M., Altshul, J., Valadon, C., Aldridge, A., et al. (2023) · Science · doi:10.1126/science.adf5357 · Cited by 154 · see citing works
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Lee, B. R., Dalley, R., Miller, J. A., Chartrand, T., Close, J., Mann, R., Mukora, A., Ng, L., Alfiler, L., Baker, K., et al. (2023) · Science · doi:10.1126/science.adf6484 · Cited by 125 · see citing works
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Evrony, G. D., Hinch, A. G., & Luo, C. (2021) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-111320-090436 · Cited by 120 · see citing works
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Zemke, N. R., Armand, E. J., Wang, W., Lee, S. Y., Zhou, J., Li, Y. E., Liu, H., Tian, W., Nery, J. R., Castanon, R., et al. (2023) · Nature · doi:10.1038/s41586-023-06819-6 · Cited by 114 · see citing works
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Zu, S., Li, Y. E., Wang, K., Armand, E. J., Mamde, S., Amaral, M. L., Wang, Y., Chu, A., Xie, Y., Miller, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06824-9 · Cited by 99 · see citing works
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Ben‐Simon, Y., Hooper, M. J., Narayan, S., Daigle, T. L., Dwivedi, D., Way, S. W., Oster, A., Stafford, D. A., Mich, J. K., Taormina, M. J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.002 · Cited by 81 · see citing works
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Self-citationSingle-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 81 · see citing works
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Ruf-Zamojski, F. M., Zhang, Z., Zamojski, M., Smith, G. R., Mendelev, N., Liu, H., Nudelman, G., Moriwaki, M., Pinças, H., Castanon, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22859-w · Cited by 73 · see citing works
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Zhang, Z., Zhou, J., Tan, P., Pang, Y., Rivkin, A. C., Kirchgessner, M. A., Williams, E., Lee, C.-T., Liu, H., Franklin, A. D., et al. (2021) · Nature · doi:10.1038/s41586-021-03223-w · Cited by 71 · see citing works
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LaSalle, J. M. (2023) · Molecular Psychiatry · doi:10.1038/s41380-022-01917-9 · Cited by 79 · see citing works
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Zhou, T., Zhang, R., Jia, D., Doty, R. T., Munday, A. D., Gao, D., Xin, L., Abkowitz, J. L., Duan, Z., & Ma, J. (2024) · Nature Genetics · doi:10.1038/s41588-024-01745-3 · Cited by 77 · see citing works
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Nichols, R. V., O’Connell, B. L., Mulqueen, R. M., Thomas, J., Woodfin, A. R., Acharya, S. N., Mandel, G., Pokholok, D., Steemers, F. J., & Adey, A. (2022) · Nature Communications · doi:10.1038/s41467-022-35374-3 · Cited by 69 · see citing works
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Xie, Y., Zhu, C., Wang, Z., Tastemel, M., Chang, L., Li, Y. E., & Ren, B. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01060-1 · Cited by 69 · see citing works
Self-citationIntroduction to Single-Cell DNA Methylation Profiling Methods
Ahn, J., Heo, S., Lee, J., & Bang, D. (2021) · Biomolecules · doi:10.3390/biom11071013 · Cited by 67 · see citing works
Lyons, C. E., Razzoli, M., & Bartolomucci, A. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105359 · Cited by 74 · see citing works
Clonal tracing with somatic epimutations reveals dynamics of blood ageing
Scherer, M., Singh, I., Braun, M. M., Szu‐Tu, C., Sánchez-Sánchez, P., Lindenhofer, D., Jakobsen, N. A., Körber, V., Kardorff, M., Nitsch, L., et al. (2025) · Nature · doi:10.1038/s41586-025-09041-8 · Cited by 72 · see citing works
Temporally distinct 3D multi-omic dynamics in the developing human brain
Heffel, M. G., Zhou, J., Zhang, Y., Lee, D.-S., Hou, K., Pastor-Alonso, O., Abuhanna, K. D., Galasso, J., Kern, C., Tai, C., et al. (2024) · Nature · doi:10.1038/s41586-024-08030-7 · Cited by 71 · see citing works
Self-citationChang, L., Xie, Y., Taylor, B., Wang, Z., Sun, J., Armand, E. J., Mishra, S. V., Xu, J., Tastemel, M., Lie, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02447-1 · Cited by 70 · see citing works
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Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 69 · see citing works
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Chien, J.-F., Liu, H., Wang, B.-A., Luo, C., Bartlett, A., Castanon, R., Johnson, N. D., Nery, J. R., Osteen, J. K., Li, J., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.05.013 · Cited by 63 · see citing works
Self-citationTechnologies and applications of single-cell DNA methylation sequencing
Liu, F., Wang, Y., Gu, H., & Wang, X. (2023) · Theranostics · doi:10.7150/thno.82582 · Cited by 63 · see citing works
Brain-wide correspondence of neuronal epigenomics and distant projections
Zhou, J., Zhang, Z., Wu, M., Liu, H., Pang, Y., Bartlett, A., Peng, Z., Ding, W., Rivkin, A. C., Lagos, W. N., et al. (2023) · Nature · doi:10.1038/s41586-023-06823-w · Cited by 53 · see citing works
Self-citationJoint single-cell profiling resolves 5mC and 5hmC and reveals their distinct gene regulatory effects
Fabyanic, E. B., Hu, P., Qiu, Q., Berríos, K. N., Connolly, D. R., Wang, T., Flournoy, J., Zhou, Z., Kohli, R. M., & Wu, H. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01909-2 · Cited by 53 · see citing works
A guide to the BRAIN Initiative Cell Census Network data ecosystem
Hawrylycz, M., Martone, M. E., Ascoli, G. A., Bjaalie, J. G., Dong, H., Ghosh, S., Gillis, J. A., Hertzano, R. P., Haynor, D. R., Hof, P. R., et al. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002133 · Cited by 55 · see citing works
Self-citationMapping cancer biology in space: applications and perspectives on spatial omics for oncology
Lee, S., Kim, G., Lee, J., Lee, A. C., & Kwon, S. (2024) · Molecular Cancer · doi:10.1186/s12943-024-01941-z · Cited by 56 · see citing works
MeCP2 Is an Epigenetic Factor That Links DNA Methylation with Brain Metabolism
Vuu, Y. M., Roberts, C.-T., & Rastegar, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24044218 · Cited by 53 · see citing works
A Single-Nucleus Atlas of Seed-to-Seed Development in ArabidopsisPreprint
Lee, T. A., Nobori, T., Illouz‐Eliaz, N., Xu, J., Jow, B., Nery, J. R., & Ecker, J. R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.23.533992 · Cited by 55 · see citing works
Self-citationUltrafast and interpretable single-cell 3D genome analysis with Fast-Higashi
Zhang, R., Zhou, T., & Ma, J. (2022) · Cell Systems · doi:10.1016/j.cels.2022.09.004 · Cited by 46 · see citing works
Cross-disorder and disease-specific pathways in dementia revealed by single-cell genomics
Rexach, J. E., Cheng, Y., Chen, L., Polioudakis, D., Lin, L., Mitri, V., Elkins, A., Han, X., Yamakawa, M., Yin, A., et al. (2024) · Cell · doi:10.1016/j.cell.2024.08.019 · Cited by 56 · see citing works
Ament, S. A., Adkins, R. S., Carter, R., Chrysostomou, E., Colantuoni, C., Crabtree, J., Creasy, H. H., Degatano, K., Felix, V., Gandt, P., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac962 · Cited by 48 · see citing works
Comparative epigenome analysis using Infinium DNA methylation BeadChips
Ding, W., Kaur, D., Horvath, S., & Zhou, W. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac617 · Cited by 47 · see citing works
NSD1 deposits histone H3 lysine 36 dimethylation to pattern non-CG DNA methylation in neurons
Hamagami, N., Wu, D. Y., Clemens, A. W., Nettles, S. A., Li, A., & Gabel, H. W. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.04.001 · Cited by 44 · see citing works
Yuan, W., Ma, S., Brown, J. R., Kim, K., Murek, V., Trastulla, L., Meissner, A., Lodato, S., Shetty, A. S., Levin, J. Z., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01123-4 · Cited by 41 · see citing works
Nair, V. D., Pinças, H., Smith, G. R., Zaslavsky, E., Ge, Y., Amper, M. A. S., Vasoya, M., Chikina, M. D., Sun, Y., Raja, A. N., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2023.100421 · Cited by 44 · see citing works
Predictive and robust gene selection for spatial transcriptomics
Covert, I., Gala, R., Wang, T., Svoboda, K., Sümbül, U., & Lee, S. (2023) · Nature Communications · doi:10.1038/s41467-023-37392-1 · Cited by 40 · see citing works
Spatial joint profiling of DNA methylome and transcriptome in tissues
Lee, C. N., Fu, H., Cardilla, A., Zhou, W., & Deng, Y. (2025) · Nature · doi:10.1038/s41586-025-09478-x · Cited by 41 · see citing works
Single-Cell Mapping of GLP-1 and GIP Receptor Expression in the Dorsal Vagal Complex
Ludwig, M. Q., Todorov, P. V., Egerod, K. L., Olson, D. P., & Pers, T. H. (2021) · Diabetes · doi:10.2337/dbi21-0003 · Cited by 40 · see citing works
Zhou, W., Johnson, B. K., Morrison, J., Beddows, I., Eapen, J., Katsman, E., Semwal, A., Habib, W. A., Heo, L., Laird, P. W., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae097 · Cited by 38 · see citing works
From morphology to single-cell molecules: high-resolution 3D histology in biomedicine
Xu, X., Su, J., Zhu, R., Li, K., Zhao, X., Fan, J., & Mao, F. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02240-x · Cited by 41 · see citing works
Emerging Insights into the Distinctive Neuronal Methylome
Clemens, A. W., & Gabel, H. W. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.07.009 · Cited by 34 · see citing works
scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells
Bianchi, A., Scherer, M., Zaurín, R., Quililan, K., Velten, L., & Beekman, R. (2022) · Genome biology · doi:10.1186/s13059-022-02796-7 · Cited by 33 · see citing works
DNA Epigenetics in Addiction Susceptibility
Kaplan, G., Xu, H., Abreu, K., & Feng, J. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.806685 · Cited by 34 · see citing works
Mammalian DNA methylome dynamics: mechanisms, functions and new frontiers
Wei, A., & Wu, H. (2022) · Development · doi:10.1242/dev.182683 · Cited by 33 · see citing works
Analyzing single-cell bisulfite sequencing data with MethSCAn
Kremer, L. P. M., Braun, M. M., Ovchinnikova, S., Küchenhoff, L., Cerrizuela, S., Martín-Villalba, A., & Anders, S. (2024) · Nature Methods · doi:10.1038/s41592-024-02347-x · Cited by 28 · see citing works
Cell type–specific 3D-genome organization and transcription regulation in the brain
Liu, S., Wang, C. Y., Zheng, P., Jia, B. B., Zemke, N. R., Ren, P., Park, H. L., Ren, B., & Zhuang, X. (2025) · Science Advances · doi:10.1126/sciadv.adv2067 · Cited by 30 · see citing works
Self-citationChatterton, Z., Lamichhane, P., Rastegar, D. A., Fitzpatrick, L., Lebhar, H., Marquis, C. P., Halliday, G. M., & Kwok, J. B. (2023) · Cell & Bioscience · doi:10.1186/s13578-022-00938-9 · Cited by 29 · see citing works
Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing
Yuan, C. U., Quah, F. X., & Hemberg, M. (2024) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2024.101255 · Cited by 25 · see citing works
Multimodal spatial transcriptomic characterization of mouse kidney injury and repair
Xuanyuan, Q., Wu, H., Sundaramoorthi, H., Isnard, P., Chen, C., Rahmani, W. M., & Humphreys, B. D. (2025) · Nature Communications · doi:10.1038/s41467-025-62599-9 · Cited by 28 · see citing works
Computational Methods for Single-Cell DNA Methylome Analysis
Iqbal, W., & Zhou, W. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.05.007 · Cited by 23 · see citing works
Farzad, N., Enninful, A., Bao, S., Zhang, D., Deng, Y., & Fan, R. (2024) · Nature Protocols · doi:10.1038/s41596-024-01013-y · Cited by 22 · see citing works
Epigenetics of neural differentiation: Spotlight on enhancers
Giacoman-Lozano, M., Meléndez-Ramírez, C., Martínez-Ledesma, E., Durán, R. C., & Velasco, I. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1001701 · Cited by 22 · see citing works
Tang, L., Huang, Z., Mei, H., & Hu, Y. (2023) · Military Medical Research · doi:10.1186/s40779-023-00486-4 · Cited by 24 · see citing works
Atlas-scale single-cell DNA methylation profiling with sciMETv3
Nichols, R. V., Rylaarsdam, L. E., O’Connell, B. L., Shipony, Z., Iremadze, N., Acharya, S. N., & Adey, A. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100726 · Cited by 21 · see citing works
Non‐Viral Gene Delivery Systems: Current Advances and Therapeutic Applications
Wang, F., Liang, L., Wang, C., Zhang, C., Ye, Y., Dou, L., Shi, L., Zhou, D., & Sun, X. (2025) · Chemistry - An Asian Journal · doi:10.1002/asia.202500684 · Cited by 21 · see citing works
Uniform volumetric single-cell processing for organ-scale molecular phenotyping
Yun, D. H., Park, Y.-G., Cho, J. H., Kamentsky, L. D., Evans, N. B., DiNapoli, N., Xie, K., Choi, S. W., Albanese, A., Tian, Y., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02533-4 · Cited by 21 · see citing works
scSPLAT, a scalable plate-based protocol for single cell WGBS library preparation
Raine, A., Lundmark, A., Annett, A., Wiman, A.-C., Cavalli, M., Wadelius, C., Bergin, C., & Nordlund, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-09798-2 · Cited by 20 · see citing works
SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
Li, X., Lee, L., Abnousi, A., Yu, M., Liu, W., Huang, L., Li, Y., & Hu, M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.046 · Cited by 20 · see citing works
Self-citationPredictive analyses of regulatory sequences with EUGENe
Klie, A., Laub, D., Talwar, J. V., Stites, H., Jores, T., Solvason, J. J., Farley, E. K., & Carter, H. K. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00544-w · Cited by 23 · see citing works
Zheng, Y., Zhao, C. N., Song, Q., Xu, L., Zhang, B., Hu, G., Kong, X., Li, S., Li, X., Shen, Y., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113496 · Cited by 20 · see citing works
scGHOST: identifying single-cell 3D genome subcompartments
Xiong, K., Zhang, R., & Ma, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02230-9 · Cited by 20 · see citing works
Mich, J. K., Sunil, S., Johansen, N., Martinez, R. A., Liu, J., Gore, B. B., Mahoney, J. T., Leytze, M., Ben‐Simon, Y., Bertagnolli, D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.20.558718 · Cited by 21 · see citing works
Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex
Johansen, N., Kempynck, N., Zemke, N. R., Somasundaram, S., Winter, S. D., Hooper, M. J., Dwivedi, D., Lohia, R., Wehbe, F., Li, B., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100879 · Cited by 19 · see citing works
Self-citationCatacalos-Goad, C., Krohannon, A., Somalraju, S., Meyer, K. D., Janga, S. C., & Chakrabarti, K. (2022) · PLoS Pathogens · doi:10.1371/journal.ppat.1010972 · Cited by 18 · see citing works
Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes
Xie, F., Armand, E. J., Yao, Z., Liu, H., Bartlett, A., Behrens, M. M., Li, Y. E., Lucero, J. D., Luo, C., Nery, J. R., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100342 · Cited by 20 · see citing works
Self-citationHu, H., & Quon, G. (2024) · Nature Communications · doi:10.1038/s41467-024-53971-2 · Cited by 18 · see citing works
Epigenetic and 3D genome reprogramming during the aging of the human hippocampus
Zemke, N. R., Lee, S. Y., Mamde, S., Yang, B., Berchtold, N. C., Garduño, B. M., Indralingam, H. S., Bartosik, W. M., Lau, P. K., Dong, K., et al. (2026) · Science · doi:10.1126/science.adt8307 · Cited by 27 · see citing works
Self-citationSingle-Cell DNA Methylation Analysis in Cancer
O’Neill, H., Lee, H. J., Gupta, I., Rodger, E. J., & Chatterjee, A. (2022) · Cancers · doi:10.3390/cancers14246171 · Cited by 19 · see citing works
The single-cell opioid responses in the context of HIV (SCORCH) consortium
Ament, S. A., Campbell, R. R., Lobo, M. K., Receveur, J. P., Agrawal, K., Borjabad, A., Byrareddy, S. N., Chang, L., Clarke, D., Emani, P. S., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02620-7 · Cited by 20 · see citing works
Self-citationInter and intra-tumoral heterogeneity as a platform for personalized therapies in medulloblastoma
Danilenko, M., Clifford, S. C., & Schwalbe, E. C. (2021) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2021.107828 · Cited by 18 · see citing works
Tang, N., Li, J., Gu, A., Li, M., & Liu, Y. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00330-2 · Cited by 17 · see citing works
Drug targeting in psychiatric disorders — how to overcome the loss in translation?
Khodosevich, K. V., Dragičević, K., & Howes, O. D. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00847-7 · Cited by 17 · see citing works
Moore, J. R., Nemera, M. T., D’Souza, R. D., Hamagami, N., Clemens, A. W., Beard, D. C., Urman, A., Razia, Y., Mendoza, V. R., Law, T. E., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01947-w · Cited by 21 · see citing works
Yang, M., Wu, Z., Huang, L.-L., Abbas, F., & Wang, H. (2022) · Cells · doi:10.3390/cells11233919 · Cited by 15 · see citing works
Differential usage of DNA modifications in neurons, astrocytes, and microglia
Tooley, K. B., Chucair‐Elliott, A. J., Ocañas, S. R., Machalinski, A. H., Pham, K. D., Hoolehan, W., Kulpa, A. M., Stanford, D. R., & Freeman, W. M. (2023) · Epigenetics & Chromatin · doi:10.1186/s13072-023-00522-6 · Cited by 13 · see citing works
Distinctive aspects of the placental epigenome and theories as to how they arise
Pastor, W. A., & Kwon, S. Y. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04568-9 · Cited by 15 · see citing works
Single-cell DNA methylome and 3D genome atlas of human subcutaneous adipose tissue
Chen, Z. J., Das, S. S., Kar, A., Lee, S. H., Abuhanna, K. D., Alvarez, M., Sukhatme, M. G., Wang, Z., Gelev, K. Z., Heffel, M. G., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02300-4 · Cited by 15 · see citing works
Self-citationDisruption of the gene regulatory programme in neurodevelopmental disorders
Griffin, A., Mahesh, A., & Tiwari, V. K. (2022) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2022.194860 · Cited by 13 · see citing works
sciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden
Acharya, S. N., Nichols, R. V., Rylaarsdam, L. E., O’Connell, B. L., Braun, T. P., & Adey, A. (2024) · Genome biology · doi:10.1186/s13059-024-03306-7 · Cited by 13 · see citing works
Mapping chromatin loops in single cells
Yu, M., Li, Y., & Hu, M. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.03.007 · Cited by 13 · see citing works
Self-citationUnderstanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization
Liu, W., Zhong, W., Chen, J. C., Huang, B., Hu, M., & Li, Y. (2022) · Genes · doi:10.3390/genes13040586 · Cited by 11 · see citing works
Self-citationDose-dependent sensitivity of human 3D chromatin to a heart disease-linked transcription factorPreprint
Grant, Z. L., Kuang, S., Zhang, S., Horrillo, A. J., Rao, K. S., Kameswaran, V., Joubran, C., Lau, P. K., Dong, K., Yang, B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.09.632202 · Cited by 13 · see citing works
Self-citation
- Authors
- Liu, H., Zhou, J., Tian, W., Luo, C., Bartlett, A., Aldridge, A., Lucero, J., Osteen, J. K., Nery, J. R., Chen, H., Rivkin, A., Castanon, R. G., Clock, B., Li, Y. E., Hou, X., Poirion, O. B., Preissl, S., Pinto-Duarte, A., O’Connor, C., Boggeman, L., Fitzpatrick, C., Nunn, M., Mukamel, E. A., Zhang, Z., Callaway, E. M., Ren, B., Dixon, J. R., Behrens, M. M., & Ecker, J. R.
- Affiliations
- Salk Institute for Biological Studies (US)
- Ludwig Cancer Research (US)
- University of California San Diego (US)
- Corresponding author
- Ecker, Joseph R.
- ORCID
- Liu, Hanqing
- Zhou, Jingtian
- Luo, Chongyuan
- Bartlett, Anna
- Aldridge, Andrew
- Lucero, Jacinta
- Osteen, Julia K.
- Nery, Joseph R.
- Chen, Huaming
- Rivkin, Angeline
- Castanon, Rosa G.
- Clock, Ben
- Li, Yang Eric
- Hou, Xiaomeng
- Poirion, Olivier B.
- Preissl, Sebastian
- Pinto-Duarte, Antonio
- O’Connor, Carolyn
- Fitzpatrick, Conor
- Nunn, Michael
- Mukamel, Eran A.
- Zhang, Zhuzhu
- Callaway, Edward M.
- Ren, Bing
- Dixon, Jesse R.
- Behrens, M. Margarita
- Ecker, Joseph R.
- Venue
- Nature
- Published
- 2021-10-06
- Volume
- 598
- Issue
- 7879
- Pages
- 120-128
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8494641
- OpenAlex
- W3203959602
- Cited by
- 289
- References
- 102
- Citations by year
- 2026: 50 · 2025: 66 · 2024: 59 · 2023: 52 · 2022: 46 · 2021: 14 · 2020: 2
- FWCI
- 14.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (NCI CCSG: P30 014195, P30 014195, 1s10od023689-01a1)
- National Institute of Mental Health (U19MH114831, U19MH114830, F30MH011483)
- National Human Genome Research Institute (R01HG010634)
- National Cancer Institute (S10-OD023689, CCSG:P30 014195, NIH-NCI CCSG: P30 014195, P30014195, P30CA014195)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- DNA methylation, mouse brain, neuronal cell types, excitatory and inhibitory neurons, enhancer-gene interactions, chromatin accessibility, hippocampus, cerebral cortex, olfactory bulb, striatum, globus pallidus, transcription factors, regulatory elements
- MeSH terms
- Epigenome, Animals, Atlases as Topic, Brain, Chromatin, Cytosine, Enhancer Elements, Genetic, Hippocampus, Male, Mice, Inbred C57BL, Models, Biological, Neural Pathways, Neurons, Dentate Gyrus, DNA Methylation, Gene Expression Profiling, Mice, Epigenomics, Single-Cell Analysis, Datasets as Topic
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