Authors
Yu-Chen Cheng, Hyemin Gu, Thomas O McDonald, Wenbo Wu, Shubham Tripathi, Cristina Guarducci, Douglas Russo, Daniel L Abravanel, Madeline Bailey, Yue Wang, Yun Zhang, Yannis Pantazis, Herbert Levine, Rinath Jeselsohn, Markos A Katsoulakis, Franziska Michor
Published in
Cell systems. Pages 101710. Aug 28, 2026. Epub Aug 28, 2026.
Abstract
Single-cell RNA sequencing (scRNA-seq) profiles cellular heterogeneity but captures only static snapshots, limiting inference of gene expression dynamics. We developed PROFET (particle-based reconstruction of generative force-matched expression trajectories), a framework that reconstructs continuous, nonlinear single-cell trajectories from sparsely sampled scRNA-seq time series. PROFET combines a particle-based gradient-flow algorithm with simulation-free force matching to accurately infer cellular dynamics. Across mouse and human in vitro datasets and an in vivo axolotl regeneration dataset, PROFET achieved 2.6-12.5× lower prediction error than ten state-of-the-art trajectory inference methods. Applying PROFET to newly generated scRNA-seq data from a palbociclib-treated MCF7 cell line and three published breast cancer patient datasets, we reconstructed treatment-response trajectories and identified a resistant cell subpopulation exhibiting large phenotypic shifts and enrichment of the surface markers UNC5B, TLR3, PCDH19, PROCR, SLITRK6, and SEMA6B. PROFET provides a biologically grounded framework for reconstructing cell-state dynamics from static single-cell data across development, regeneration, and therapeutic response. A record of this paper's transparent peer review process is included in the supplemental information.
PMID:
42664975
Bibliographic data and abstract were imported from PubMed on 29 Aug 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 43
- Comments 0