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.

Authors

Institutions

Publication Details

Journal
Nature Methods
Published
2025-10-27
DOI
https://doi.org/10.1038/s41592-025-02857-2
Citations
28
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
22.57
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improved reconstruction of single-cell developmental potential with CytoTRACE 2

Aaron M. Newman, Jeremy Philip D’Silva, Janella C. Schwab, Erin L. Brown et al.
28 citations
Nature Methods
Single-cell and spatial transcriptomics
22.57
article

Improved reconstruction of single-cell developmental potential with CytoTRACE 2

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

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.

Nature MethodsVol. 22(11)
Oslo University Hospital (NO), Mayo Clinic (US), University of Oslo (NO), California Institute for Regenerative Medicine (US), Institute for Stem Cell Biology and Regenerative Medicine (IN), Dana-Farber Cancer Institute (US), Chan Zuckerberg Initiative (United States) (US), Mayo Clinic in Arizona (US), Stanford University (US)
Openalex Percentile: Top 0%
Single-cell and spatial transcriptomics
22.57
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.