ArticleNature methods2025
Improved reconstruction of single-cell developmental potential with CytoTRACE 2.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.
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Who cites it
83 citing papers in PubMed.
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- Profiling maize embryonic leaf development and discovering new genes using high-resolution spatial long-read isoform sequencing.Nature plants · 2026Article
- USP15 mutation associated with autism spectrum disorder alters progenitor fate and neuronal maturation in human brain organoids.Molecules and cells · 2026Article
- Single-cell landscape of human liver sinusoidal γδ T cells in the context of latent human cytomegalovirus infection.Nature communications · 2026Article
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- A single-donor proof-of-concept single-cell analysis maps heterogeneous differentiation trajectories toward cartilage-like states in human urine-derived stem cells.Stem cell research & therapy · 2026Article
23 more citing papers are in PubMed but not listed here.
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14 authors.
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Abstract
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.
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