Improved reconstruction of single-cell developmental potential with CytoTRACE 2.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41145665.
- Also identified by DOI 10.1038/s41592-025-02857-2 and PMC identifier 12615260.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Single-Cell Analysis
- Cell Differentiation
- Sequence Analysis, RNA
- Deep Learning