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.
Full-text reasoning
From the deep-tier full-text analysis of this preprint.
Reasoning review — 8 key claims, 3 well-supported, 5 with gaps
- supported FLOWSATATE is a novel framework for single-cell trajectory inference leveraging time-resolved RNA labeling and Optimal Transport.
- supported FLOWSTATE integrates total and labeled RNA to model cell dynamics as a gradient flow in an inferred potential landscape.
- partial The learned potential enables identification of key genes/TFs driving cell fate and prediction of future cellular states. gap: The manuscript claims these capabilities but provides no data or analysis to demonstrate their accuracy, reliability, or quantitative performance within the excerpt.
- unsupported FLOWSTATE improves inference of cellular dynamics and predicts unseen states compared to state-of-the-art methods. gap: The manuscript claims improvement over state-of-the-art methods and successful prediction but presents no data, metrics, or comparative results to substantiate these claims.
- unsupported FLOWSTATE can generalize to unseen data and recover coherent trajectories. gap: The manuscript claims the ability to generalize and recover coherent trajectories but provides no evidence or results from the stated benchmarking to support this.
- partial FLOWSTATE recovered known effects of demethylating treatment and identified candidate genes (e.g., FLI1) in HCT116 colorectal cells. gap: The manuscript states "known effects" were recovered and "candidate genes" identified, but provides no specific details, data, or validation for these findings within the excerpt.
- unsupported FLOWSTATE suggests the Slit/Robo signaling axis is established earlier in motor neuron commitment during neuronal differentiation. gap: The manuscript makes a specific biological claim about the timing of a signaling axis but provides no data, analysis, or comparison to existing literature to support this assertion.
- supported FLOWSTATE is available as a Python package with code for reproducibility.
Claims and gaps are read from the full text by a language model, shown for transparency; they do not affect ranking or selection.
Lifecycle
- biorxiv v1 2026-06-15 source ↗
Discussion
Moderated digest of third-party discussion on Bluesky — substantive endorsement and critique.
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AI x Bio Discovery @aixbiobot.bsky.social · 787 followers neutral
Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data [new] ...using time-res RNA labeling & Optimal Transport to model cell dynamics in a potential landscape, identifying drivers and predicting future states.
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#SingleCell preprints @prepub-singlecell.bsky.social · 721 followers neutral
Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data #SingleCell 🧪🧬🖥️ https://www.biorxiv.org/content/10.64898/2026.06.11.731638v1
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bioRxivpreprint @biorxivpreprint.bsky.social · 8895 followers neutral
Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data https://www.biorxiv.org/content/10.64898/2026.06.11.731638v1
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bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · 4920 followers neutral
Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data https://www.biorxiv.org/content/10.64898/2026.06.11.731638v1