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<p>While single-cell RNA sequencing has advanced our understanding of cell fate, identifying molecular hallmarks of potency—a cell’s ability to differentiate into other cell types—remains a challenge. Here we introduce CytoTRACE 2, an interpretable deep learning framework for predicting absolute developmental potential from single-cell RNA sequencing data. Across diverse platforms and tissues, CytoTRACE 2 outperformed previous methods in predicting developmental hierarchies, enabling detailed mapping of single-cell differentiation landscapes and expanding insights into cell potency.</p>

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Improved reconstruction of single-cell developmental potential with CytoTRACE 2

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月27日

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  • Minji Kang,
  • Gunsagar S. Gulati,
  • Erin L. Brown,
  • Zhen Qi,
  • Susanna Avagyan,
  • Jose Juan Almagro Armenteros,
  • Rachel Gleyzer,
  • Wubing Zhang,
  • Chloé B. Steen,
  • Jeremy Philip D’Silva,
  • Janella Schwab,
  • Michael F. Clarke,
  • Aadel A. Chaudhuri,
  • Aaron M. Newman

摘要

While single-cell RNA sequencing has advanced our understanding of cell fate, identifying molecular hallmarks of potency—a cell’s ability to differentiate into other cell types—remains a challenge. Here we introduce CytoTRACE 2, an interpretable deep learning framework for predicting absolute developmental potential from single-cell RNA sequencing data. Across diverse platforms and tissues, CytoTRACE 2 outperformed previous methods in predicting developmental hierarchies, enabling detailed mapping of single-cell differentiation landscapes and expanding insights into cell potency.