Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data
Abstract
Single-cell RNA sequencing provides high-resolution snapshots of cellular states but lacks direct information about transcriptional dynamics. Metabolic RNA labeling addresses this limitation by distinguishing newly synthesized RNA, offering insight into the direction of cell state changes, and providing valuable information when attempting to recover the underlying continuous dynamics from static snapshots of cell distributions. However, existing trajectory inference methods do not fully exploit this additional signal. Here, we propose FLOWSATATE, a framework for single-cell trajectory inference that leverages time-resolved RNA labeling within an Optimal Transport setting. We model cell dynamics as a gradient flow in an inferred potential landscape parameterized by a neural network, integrating both total and labeled RNA across time points. The learned potential enables identification of key genes and transcription factors driving cell fate decisions and supports prediction of future cellular states. We benchmark our approach on its ability to generalize unseen data and recover coherent trajectories. We also apply it to study colorectal cancer response to demethylation treatment as well as neuronal differentiation of embryonic stem cells.
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BibTeX
@misc{audit2026inferring,
title = {{Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data}},
author = {Anna Audit and Gabriel Peyré and Laura Cantini},
howpublished = {HAL},
year = {2026},
month = jun,
doi = {10.64898/2026.06.11.731638},
url = {https://hal.science/hal-05797152v1},
note = {CC BY-NC 4.0},
}
RIS
TY - UNPB
TI - Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data
AU - Anna Audit
AU - Gabriel Peyré
AU - Laura Cantini
PY - 2026
DA - 2026/06/15
PB - HAL
DO - 10.64898/2026.06.11.731638
UR - https://hal.science/hal-05797152v1
AB - Single-cell RNA sequencing provides high-resolution snapshots of cellular states but lacks direct information about transcriptional dynamics. Metabolic RNA labeling addresses this limitation by distinguishing newly synthesized RNA, offering insight into the direction of cell state changes, and providing valuable information when attempting to recover the underlying continuous dynamics from static snapshots of cell distributions. However, existing trajectory inference methods do not fully exploit this additional signal. Here, we propose FLOWSATATE, a framework for single-cell trajectory inference that leverages time-resolved RNA labeling within an Optimal Transport setting. We model cell dynamics as a gradient flow in an inferred potential landscape parameterized by a neural network, integrating both total and labeled RNA across time points. The learned potential enables identification of key genes and transcription factors driving cell fate decisions and supports prediction of future cellular states. We benchmark our approach on its ability to generalize unseen data and recover coherent trajectories. We also apply it to study colorectal cancer response to demethylation treatment as well as neuronal differentiation of embryonic stem cells.
ER -