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<p>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 <Emphasis Type="ItalicUnderline">Dyna</Emphasis>mic targets and <Emphasis Type="ItalicUnderline">Tag</Emphasis>mentation (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&amp;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.</p>

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DynaTag for efficient mapping of transcription factors in low-input samples and at single-cell resolution

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月28日

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  • Pascal Hunold,
  • Giulia Pizzolato,
  • Nadia Heramvand,
  • Laura Kaiser,
  • Giulia Barbiera,
  • Olivia van Ray,
  • Roman Thomas,
  • Julie George,
  • Martin Peifer,
  • Robert Hänsel-Hertsch

摘要

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.