» Articles » PMID: 35332340

Characterizing Cellular Heterogeneity in Chromatin State with ScCUT&Tag-pro

Overview
Journal Nat Biotechnol
Specialty Biotechnology
Date 2022 Mar 25
PMID 35332340
Authors
Affiliations
Soon will be listed here.
Abstract

Technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization, but the sparsity of the measurements and integrating multiple binding maps represent substantial challenges. Here we introduce single-cell (sc)CUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce single-cell ChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states and identify extensive and cell-type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

Citing Articles

Reducing batch effects in single cell chromatin accessibility measurements by pooled transposition with MULTI-ATAC.

Conrad D, Phong K, Korotkevich E, McGinnis C, Zhu Q, Chow E bioRxiv. 2025; .

PMID: 40027737 PMC: 11870453. DOI: 10.1101/2025.02.14.638353.


Genome-coverage single-cell histone modifications for embryo lineage tracing.

Liu M, Yue Y, Chen X, Xian K, Dong C, Shi M Nature. 2025; .

PMID: 40011786 DOI: 10.1038/s41586-025-08656-1.


Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics.

Huan C, Li J, Li Y, Zhao S, Yang Q, Zhang Z BME Front. 2025; 6():0084.

PMID: 39810754 PMC: 11725630. DOI: 10.34133/bmef.0084.


Single-cell mapping of regulatory DNA:Protein interactions.

Chi W, Yoon S, Mekerishvili L, Ganesan S, Potenski C, Izzo F bioRxiv. 2025; .

PMID: 39803441 PMC: 11722406. DOI: 10.1101/2024.12.31.630903.


Advances and applications in single-cell and spatial genomics.

Wang J, Ye F, Chai H, Jiang Y, Wang T, Ran X Sci China Life Sci. 2025; .

PMID: 39792333 DOI: 10.1007/s11427-024-2770-x.


References
1.
Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N . mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009; 6(5):377-82. DOI: 10.1038/nmeth.1315. View

2.
Jaitin D, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I . Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014; 343(6172):776-9. PMC: 4412462. DOI: 10.1126/science.1247651. View

3.
Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View

4.
Hashimshony T, Wagner F, Sher N, Yanai I . CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012; 2(3):666-73. DOI: 10.1016/j.celrep.2012.08.003. View

5.
Picelli S, Bjorklund A, Faridani O, Sagasser S, Winberg G, Sandberg R . Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013; 10(11):1096-8. DOI: 10.1038/nmeth.2639. View