Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag
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Abstract
Probing histone modifications at a single-cell level in thousands of cells has been enabled by technologies such as single-cell CUT&Tag. Here we describe nano-CUT&Tag (nano-CT), which allows simultaneous mapping of up to three epigenomic modalities at single-cell resolution using nanobody-Tn5 fusion proteins. Multimodal nano-CT is compatible with starting materials as low as 25,000–200,000 cells and has significantly higher sensitivity and number of fragments per cell than single-cell CUT&Tag. We use nano-CT to simultaneously profile chromatin accessibility, H3K27ac, and H3K27me3 in juvenile mouse brain, allowing for discrimination of more cell types and states than unimodal single-cell CUT&Tag. We also infer chromatin velocity between assay for transposase-accessible chromatin (ATAC) and H3K27ac in the oligodendrocyte lineage and deconvolute H3K27me3 repressive states, finding two sequential waves of H3K27me3 repression at distinct gene modules during oligodendrocyte lineage progression. Given its high resolution, versatility, and multimodal features, nano-CT allows unique insights in epigenetic landscapes in complex biological systems at the single-cell level.
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Integration and co-embedding of spatial CUT&Tag and scCUT&Tag data showed that the epigenetic states in the study data for H3K27me3, H3K27ac and H3K4me3 agreed with the corresponding projection in scCUT&Tag.
“Integration and co-embedding of spatial CUT&Tag and scCUT&Tag data36,37 (Fig. 3d,e and Extended Data Fig. 7a) showed that the epigenetic states in our data for H3K27me3, H3K27ac and H3K4me3 agreed with the corresponding projection in scCUT&Tag.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [37]
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Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Regulation of chromatin by histone modifications
Bannister, A. J., & Kouzarides, T. (2011) · Cell Research · doi:10.1038/cr.2011.22 · Cited by 6,012 · see citing works
A bivalent chromatin structure marks key developmental genes in embryonic stem cells
Bernstein, B. E., Mikkelsen, T. S., Xie, X., Kamal, M., Huebert, D. J., Cuff, J., Fry, B., Meissner, A., Wernig, M., Plath, K., et al. (2006) · Cell · doi:10.1016/j.cell.2006.02.041 · Cited by 5,361 · see citing works
Gene regulation by long non-coding RNAs and its biological functions
Statello, L., Guo, C.-J., Chen, L.-L., & Huarte, M. (2021) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-00315-9 · Cited by 4,991 · see citing works
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · see citing works
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118 · see citing works
Self-citationSlingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E., & Dudoit, S. (2018) · BMC Genomics · doi:10.1186/s12864-018-4772-0 · Cited by 3,159 · 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
Generalizing RNA velocity to transient cell states through dynamical modeling
Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0591-3 · Cited by 2,951 · see citing works
An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues
Corces, M. R., Trevino, A. E., Hamilton, E. G., Greenside, P. G., Sinnott-Armstrong, N. A., Vesuna, S., Satpathy, A. T., Rubin, A. J., Montine, K. S., Wu, B., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4396 · Cited by 2,496 · see citing works
Expanded encyclopaedias of DNA elements in the human and mouse genomes
The ENCODE Project Consortium, Abascal, F., Acosta, R., Addleman, N. J., Adrian, J., Afzal, V., Ai, R., Aken, B., Akiyama, J. A., Jammal, O. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2493-4 · Cited by 2,378 · see citing works
Single-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works
A unique chromatin signature uncovers early developmental enhancers in humans
Rada-Iglesias, A., Bajpai, R., Swigut, T., Brugmann, S. A., Flynn, R. A., & Wysocka, J. (2011) · Nature · doi:10.1038/nature09692 · Cited by 2,233 · see citing works
Transcription factors: from enhancer binding to developmental control
Spitz, F., & Furlong, E. E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3207 · Cited by 2,227 · see citing works
CUT&Tag for efficient epigenomic profiling of small samples and single cells
Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., Ahmad, K., & Henikoff, S. (2019) · Nature Communications · doi:10.1038/s41467-019-09982-5 · Cited by 2,187 · see citing works
The diverse roles of DNA methylation in mammalian development and disease
Greenberg, M., & Bourc’his, D. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0159-6 · Cited by 2,160 · see citing works
Mechanisms of action and regulation of ATP-dependent chromatin-remodelling complexes
Clapier, C. R., Iwasa, J., Cairns, B. R., & Peterson, C. L. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.26 · Cited by 1,305 · see citing works
Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system
Marques, S., Zeisel, A., Codeluppi, S., van Bruggen, D., Mendanha Falcão, A., Xiao, L., Li, H., Häring, M., Hochgerner, H., Romanov, R. A., et al. (2016) · Science · doi:10.1126/science.aaf6463 · Cited by 1,246 · see citing works
Self-citationSingle-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works
Organizational principles of 3D genome architecture
Rowley, M. J., & Corces, V. G. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0060-8 · Cited by 1,171 · see citing works
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
New developments on the Encyclopedia of DNA Elements (ENCODE) data portal
Luo, Y., Hitz, B. C., Gabdank, I., Hilton, J. A., Kagda, M. S., Lam, B. R., Myers, Z., Sud, P., Jou, J., Lin, K. Z., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1062 · Cited by 949 · see citing works
Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells
Arner, E., Daub, C. O., Vitting‐Seerup, K., Andersson, R., Lilje, B., Drabløs, F., Lennartsson, A., Rönnerblad, M., Hrydziuszko, O., Vitezic, M., et al. (2015) · Science · doi:10.1126/science.1259418 · Cited by 570 · see citing works
Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues
Bartosovic, M., Kabbe, M., & Castelo-Branco, G. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00869-9 · Cited by 483 · see citing works
Self-citationFoxg1 Suppresses Early Cortical Cell Fate
Hanashima, C., Li, S. C., Shen, L., Lai, E., & Fishell, G. (2004) · Science · doi:10.1126/science.1090674 · Cited by 381 · see citing works
An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
Zhu, C., Yu, M., Huang, H., Juric, I., Abnousi, A., Hu, R., Lucero, J., Behrens, M. M., Hu, M., & Ren, B. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0323-x · Cited by 344 · see citing works
Joint profiling of histone modifications and transcriptome in single cells from mouse brain
Zhu, C., Zhang, Y., Li, Y. E., Lucero, J., Behrens, M. M., & Ren, B. (2021) · Nature Methods · doi:10.1038/s41592-021-01060-3 · Cited by 301 · see citing works
Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomicsPreprint
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E. A., & Dudoit, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/128843 · Cited by 290 · see citing works
CoBATCH for High-Throughput Single-Cell Epigenomic Profiling
Wang, Q., Xiong, H., Ai, S., Yu, X., Liu, Y., Zhang, J., & He, A. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.07.015 · Cited by 213 · see citing works
Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression
Wu, S. J., Furlan, S. N., Mihalas, A. B., Kaya-Okur, H. S., Feroze, A. H., Emerson, S. N., Zheng, Y., Carson, K., Cimino, P. J., Keene, C. D., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00865-z · Cited by 201 · see citing works
CoBATCH for high-throughput single-cell epigenomic profilingPreprint
Wang, Q., Xiong, H., Ai, S.-S., Yu, X.-H., Liu, Y., Zhang, J., & He, A. (2019) · bioRxiv · doi:10.1101/590661 · Cited by 194 · see citing works
Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification
Ku, W. L., Nakamura, K., Gao, W., Cui, K., Hu, G., Tang, Q., Ni, B., & Zhao, K. (2019) · Nature Methods · doi:10.1038/s41592-019-0361-7 · Cited by 181 · see citing works
A chromatin integration labelling method enables epigenomic profiling with lower input
Harada, A., Maehara, K., Handa, T., Arimura, Y., Nogami, J., Hayashi‐Takanaka, Y., Shirahige, K., Kurumizaka, H., Kimurâ, H., & Ohkawa, Y. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0248-3 · Cited by 155 · see citing works
A toolbox of anti–mouse and anti–rabbit IgG secondary nanobodies
Pleiner, T., Bates, M., & Görlich, D. (2017) · The Journal of Cell Biology · doi:10.1083/jcb.201709115 · Cited by 149 · see citing works
Carter, B., Ku, W. L., Kang, J. Y., Hu, G., Perrie, J., Tang, Q., & Zhao, K. (2019) · Nature Communications · doi:10.1038/s41467-019-11559-1 · Cited by 148 · see citing works
Chromatin potential identified by shared single cell profiling of RNA and chromatinPreprint
Ma, S., Zhang, B. M., LaFave, L. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.17.156943 · Cited by 125 · see citing works
Tedesco, M., Giannese, F., Lazarević, D., Giansanti, V., Rosano, D., Monzani, S., Catalano, I., Grassi, E., Zanella, E. R., Botrugno, O. A., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01031-1 · Cited by 112 · see citing works
Simultaneous quantification of protein–DNA contacts and transcriptomes in single cells
Rooijers, K., Markodimitraki, C. M., Rang, F. J., de Vries, S. S., Chialastri, A., de Luca, K. L., Mooijman, D., Dey, S. S., & Kind, J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0150-y · Cited by 110 · see citing works
Simultaneous profiling of multiple chromatin proteins in the same cells
Gopalan, S., Wang, Y., Harper, N. W., Garber, M. G., & Fazzio, T. G. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.09.019 · Cited by 107 · see citing works
Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro
Zhang, B., Srivastava, A., Mimitou, E., Stuart, T., Raimondi, I., Hao, Y., Smibert, P., & Satija, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01250-0 · Cited by 104 · see citing works
Emx2 and Foxg1 Inhibit Gliogenesis and Promote Neuronogenesis
Brancaccio, M., Pivetta, C., Granzotto, M., Filippis, C., & Mallamaci, A. (2010) · Stem Cells · doi:10.1002/stem.443 · Cited by 102 · see citing works
Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility
Meijer, M., Agirre, E., Kabbe, M., van Tuijn, C. A., Heskol, A., Zheng, C., Mendanha Falcão, A., Bartosovic, M., Kirby, L., Calini, D., et al. (2022) · Neuron · doi:10.1016/j.neuron.2021.12.034 · Cited by 98 · see citing works
Self-citationNanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution
Stuart, T., Hao, S., Zhang, B., Mekerishvili, L., Landau, D. A., Maniatis, S., Satija, R., & Raimondi, I. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01588-5 · Cited by 93 · see citing works
Co-ChIP enables genome-wide mapping of histone mark co-occurrence at single-molecule resolution
Weiner, A., Lara‐Astiaso, D., Krupalnik, V., Gafni, O., David, E., Winter, D. R., Hanna, J. H., & Amit, I. (2016) · Nature Biotechnology · doi:10.1038/nbt.3652 · Cited by 88 · see citing works
Expanded encyclopaedias of DNA elements in the human and mouse genomes.
Moore, J. E., Purcaro, M. J., Pratt, H., Epstein, C. B., Noam Shoresh, F., Adrian, J., Kawli, T., Davis, C., Dobin, A., Kaul, R., et al. (2020) · Open Access CRIS of the University of Bern · doi:10.7892/boris.146110 · Cited by 66 · see citing works
High-throughput single-cell epigenomic profiling by targeted insertion of promoters (TIP-seq)
Bartlett, D. A., Dileep, V., Handa, T., Ohkawa, Y., Kimurâ, H., Henikoff, S., & Gilbert, D. M. (2021) · The Journal of Cell Biology · doi:10.1083/jcb.202103078 · Cited by 46 · see citing works
Carter, B., Ku, W. L., Tang, Q., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/571208 · Cited by 28 · see citing works
Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencingPreprint
Ku, W. L., Pan, L., Cao, Y., Gao, W., & Zhao, K. (2021) · Genome Research · doi:10.1101/gr.260893.120 · Cited by 28 · see citing works
Foxg1 Antagonizes Neocortical Stem Cell Progression to Astrogenesis
Falcone, C., Santo, M., Liuzzi, G., Cannizzaro, N., Grudina, C., Valencic, E., Peruzzotti‐Jametti, L., Pluchino, S., & Mallamaci, A. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz031 · Cited by 22 · see citing works
Simultaneous quantification of protein-DNA contacts and transcriptomes in single cellsPreprint
Rooijers, K., Markodimitraki, C. M., Rang, F. J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/529388 · Cited by 18 · see citing works
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Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778 · see citing works
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- Authors
- Bartosovic, M., & Castelo-Branco, G.
- Venue
- Nature Biotechnology
- Published
- 2022-12-19
- Volume
- 41
- Issue
- 6
- Pages
- 794-805
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC10264246
- OpenAlex
- W4311943826
- Cited by
- 146
- References
- 51
- Citations by year
- 2026: 39 · 2025: 41 · 2024: 39 · 2023: 23 · 2022: 3 · 2013: 1
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Biotechnology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Göran Gustafssons Stiftelser
- European Commission (681893, HORIZON2020)
- Cancerfonden (190394 Pj, 2019-)
- Svenska Sällskapet för Medicinsk Forskning (JUB2019)
- Karolinska Institutet
- Knut och Alice Wallenbergs Stiftelse (2019-0089, 2019–0107)
- Vetenskapsrådet (2019-01360, 2015-03558)
- Science for Life Laboratory
- Uppsala Multidisciplinary Center for Advanced Computational Science
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- 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
- Keywords
- single-cell epigenomics, chromatin accessibility, H3K27ac, H3K27me3, histone modifications, oligodendrocyte lineage, cell type deconvolution
- MeSH terms
- Animals, Chromatin, Histones, Protein Processing, Post-Translational, Mice, Gene Regulatory Networks
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