Nature Biotechnology
\n (2026) Cite this article<\/p>\n
Simultaneous mapping of chromatin states and transcriptomes in rare cell populations is challenging, as most methods require thousands of cells and are limited in their ability to capture multiple molecular layers accurately within the same cell. Here we introduce OneCell CUT&Tag, a method that provides matched high-resolution epigenome, full-transcriptome and surface marker quantification from every cell, with input as low as one cell, without relying on computational aggregation into metacells. Using this approach, we uncover epigenomic priming of basal cells in the mammary gland and capture the dynamics of basal-to-luminal transdifferentiation, suggesting that epigenomic and transcriptional remodeling do not occur in complete synchrony during cell-fate conversion. Adaptable to diverse samples and tissues, this method also reveals the role of H3K27me3 in shaping zygotic expression programs. By matching multiple layers of molecular information within individual cells, OneCell CUT&Tag reveals how complementary regulatory layers shape cellular identity and state, enabling the study of rare biological samples in development and disease.<\/p>\n
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Preprocessed data used to perform analysis were submitted to Zenodo51. Raw sequencing files were submitted to the Gene Expression Omnibus under accession number GSE290486.<\/p>\n
All code and pipelines related to the data analysis were deposited to GitHub: the unified single-cell epigenomics preprocessing pipeline (https:\/\/github.com\/bioinfo-pf-curie\/scEpigenome), the scRNA pipeline (https:\/\/github.com\/bioinfo-pf-curie\/scRNA-SmartSeq3) and all code for the downstream data analysis and figure generation (https:\/\/github.com\/vallotlab\/OneCell_CUTTag).<\/p>\n
Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 39, 825\u2013835 (2021).<\/p>\n
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