ArticleNature communications2025
DynaTag for efficient mapping of transcription factors in low-input samples and at single-cell resolution.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Immune signaling as a determinant of cellular identity and tissue function.Frontiers in immunology · 2026Review
- Response to the commentary by Melidis et al. on "Untargeted CUT&Tag reads are enriched at accessible chromatin and restrict identification of potential G4-forming sequences in G4-targeted CUT&Tag experiments".Nucleic acids research · 2025Article
- Dysregulation of Central-Medial Amygdala Histone Modifiers in Preclinical Models of Ethanol Exposure.Addiction biology · 2025Review
- DynaTag for efficient mapping of transcription factors in low-input samples and at single-cell resolution.Nature communications · 2025Article
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Authors and funding
10 authors.
Funding
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Abstract
Systematic discovery of transcription factor (TF) landscapes in low-input samples and at single cell level is a major challenge in the fields of molecular biology, genetics, and epigenetics. Here, we present cleavage under Dynamic targets and Tagmentation (DynaTag), enabling robust mapping of TF-DNA interactions using a physiological salt solution during sample preparation. DynaTag uncovers occupancy alterations for 15 TFs in stem cell and cancer tissue models. We highlight changes in TF-DNA binding for NANOG, MYC, and OCT4, during stem-cell differentiation, at both bulk and single-cell resolutions. DynaTag surpasses CUT&RUN and ChIP-seq in signal-to-background ratio and resolution. Furthermore, using tumours of a small cell lung cancer model derived from a single female donor, DynaTag reveals increased chromatin occupancy of FOXA1, MYC, and the mutant p53 R248Q at enriched gene pathways (e.g. epithelial-mesenchymal transition), following chemotherapy treatment. Collectively, we believe that DynaTag represents a significant technological advancement, facilitating precise characterization of TF landscapes across diverse biological systems and complex models.
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Registered trials
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