Improved reconstruction of single-cell developmental potential with CytoTRACE 2

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.

• NS
  National Science Foundation

  Award: DGE-1656518
• VF
  V Foundation for Cancer Research
• VA
  Virginia and D.K. Ludwig Fund for Cancer Research
• DE
  Donald E. and Delia B. Baxter Foundation
• KU
  Kirke-, Utdannings- og Forskningsdepartementet

  Award: 334328
• SB
  Stanford Bio-X
• NC
  National Cancer Institute

  Awards: R01CA283317, R01CA255450