Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification
Open accessAbstract
Our method for analyzing histone modifications, scChIC-seq (single-cell chromatin immunocleavage sequencing), involves targeting of the micrococcal nuclease (MNase) to a histone mark of choice by tethering to a specific antibody. Cleaved target sites are then selectively PCR amplified. We show that scChIC-seq reliably detects H3K4me3 and H3K27me3 target sites in single human white blood cells. The resulting data are used for clustering of blood cell types.
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Single-cell characterization of histone post-translational modifications has not been achieved for highly complex adult organs such as the brain.
“However, for highly complex adult organs such as the brain, this single-cell characterization has not been achieved.”
From Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues (2021) · cites this paper as [10]
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Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382 · see citing works
High-Resolution Profiling of Histone Methylations in the Human Genome
Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007) · Cell · doi:10.1016/j.cell.2007.05.009 · Cited by 6,845 · see citing works
Self-citationSoftware for Computing and Annotating Genomic Ranges
Lawrence, M. F., Huber, W. K., Pagès, H., Aboyoun, P., Carlson, M. R. J., Gentleman, R. C., Morgan, M. T., & Carey, V. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003118 · Cited by 4,284 · 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
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795 · see citing works
An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites
Skene, P. J., & Henikoff, S. (2017) · eLife · doi:10.7554/elife.21856 · Cited by 1,737 · see citing works
chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data
Schep, A. N., Wu, B., Buenrostro, J. D., & Greenleaf, W. J. (2017) · Nature Methods · doi:10.1038/nmeth.4401 · Cited by 1,680 · see citing works
SC3: consensus clustering of single-cell RNA-seq data
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500 · see citing works
Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · see citing works
Corces, M. R., Buenrostro, J. D., Wu, B., Greenside, P. G., Chan, S. M., Koenig, J. L., Snyder, M. P., Pritchard, J. K., Kundaje, A., Greenleaf, W. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3646 · Cited by 1,151 · see citing works
Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity
Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G. (2014) · Nature Methods · doi:10.1038/nmeth.3035 · Cited by 1,125 · see citing works
Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state
Rotem, A., Ram, O., Shoresh, N., Sperling, R. A., Goren, A., Weitz, D. A., & Bernstein, B. E. (2015) · Nature Biotechnology · doi:10.1038/nbt.3383 · Cited by 854 · see citing works
Chromatin state dynamics during blood formation
Lara-Astiaso, D., Weiner, A., Lorenzo-Vivas, E., Zaretsky, I., Jaitin, D. A., David, E., Keren-Shaul, H., Mildner, A., Winter, D., Jung, S., et al. (2014) · Science · doi:10.1126/science.1256271 · Cited by 776 · see citing works
An accurate and robust imputation method scImpute for single-cell RNA-seq data
Li, W. V., & Li, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03405-7 · Cited by 682 · see citing works
ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors
Schmidl, C., Rendeiro, A. F., Sheffield, N. C., & Bock, C. (2015) · Nature Methods · doi:10.1038/nmeth.3542 · Cited by 509 · see citing works
An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations
Brind’Amour, J., Liu, S., Hudson, M. B., Chen, C. C. L., Karimi, M. M., & Lorincz, M. C. (2015) · Nature Communications · doi:10.1038/ncomms7033 · Cited by 379 · see citing works
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Jin, W., Tang, Q., Wan, M., Cui, K., Zhang, Y., Ren, G., Ni, B., Sklar, J., Przytycka, T. M., Childs, R., et al. (2015) · Nature · doi:10.1038/nature15740 · Cited by 332 · see citing works
Self-citationThe Human Epigenome Browser at Washington University
Zhou, X., Maricque, B. B., Xie, M., Li, D., Sundaram, V., Martin, E. A., Koebbe, B. C., Nielsen, C. B., Hirst, M., Farnham, P., et al. (2011) · Nature Methods · doi:10.1038/nmeth.1772 · Cited by 309 · see citing works
Blecher-Gonen, R., Barnett-Itzhaki, Z., Jaitin, D., Amann-Zalcenstein, D., Lara-Astiaso, D., & Amit, I. (2013) · Nature Protocols · doi:10.1038/nprot.2013.023 · Cited by 289 · see citing works
Xu, S., Grullon, S., Ge, K., & Peng, W. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-0512-6_5 · Cited by 249 · see citing works
Pott, S. (2017) · eLife · doi:10.7554/elife.23203 · Cited by 230 · see citing works
Schmid, M., Durussel, T., & Laemmli, U. K. (2004) · Molecular Cell · doi:10.1016/j.molcel.2004.09.007 · Cited by 212 · see citing works
Whole-genome chromatin profiling from limited numbers of cells using nano-ChIP-seq
Adli, M., & Bernstein, B. (2011) · Nature Protocols · doi:10.1038/nprot.2011.402 · Cited by 183 · see citing works
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Lai, B.-B., Gao, W.-W., Cui, K.-R., Xie, W.-L., Tang, Q.-S., Jin, W.-F., Hu, G.-Q., Ni, B., & Zhao, K.-J. (2018) · Nature · doi:10.1038/s41586-018-0567-3 · Cited by 149 · see citing works
Self-citationA microfluidic device for epigenomic profiling using 100 cells
Cao, Z., Chen, C., He, B., Tan, K., & Lu, C. (2015) · Nature Methods · doi:10.1038/nmeth.3488 · Cited by 111 · see citing works
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Lay, F. D., Kelly, T. K., & Jones, P. A. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7481-8_14 · Cited by 31 · see citing works
Zhao, C., Hu, S. S., Huo, X., & Zhang, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180583 · Cited by 28 · see citing works
A single-cell chromatin immunocleavage sequencing (scChIC-seq)
Nakamura, K., Nakamura, K., Zhao, K., et al. (2019) · Protocol Exchange · doi:10.1038/protex.2019.011 · Cited by 1 · see citing works
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Chromatin accessibility profiling by ATAC-seq
Grandi, F. C., Modi, H., Kampman, L., & Corces, M. R. (2022) · Nature Protocols · doi:10.1038/s41596-022-00692-9 · Cited by 557 · see citing works
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- Authors
- Ku, W. L., Nakamura, K., Gao, W., Cui, K., Hu, G., Tang, Q., Ni, B., & Zhao, K.
- Affiliations
- National Institutes of Health (US)
- Army Medical University (CN)
- Corresponding author
- Ni, Bing
- Zhao, Keji
- Venue
- Nature Methods
- Published
- 2019-03-28
- Volume
- 16
- Issue
- 4
- Pages
- 323-325
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7187538
- OpenAlex
- W2923284916
- Cited by
- 181
- References
- 30
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 20 · 2023: 24 · 2022: 28 · 2021: 43 · 2020: 25 · 2019: 13
- FWCI
- 9.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- Japan Society for the Promotion of Science (18K06679)
- National Heart, Lung, and Blood Institute (ZIAHL005801)
- 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
- histone modifications, staphylococcal nuclease, H3K4me3, H3K27me3, single-cell epigenomics, leukocytes
- MeSH terms
- Animals, Antibodies, Chromatin, DNA, Histones, Humans, Leukocytes, Male, Micrococcal Nuclease, Nucleosomes, Promoter Regions, Genetic, Protein Processing, Post-Translational, Software, Reproducibility of Results, Polymerase Chain Reaction, Genome, Sequence Analysis, DNA, Computational Biology, NIH 3T3 Cells, Histone Code, Chromatin Immunoprecipitation, Mice, Epigenomics, High-Throughput Nucleotide Sequencing
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