TOGGLE delineates fate and function within individual cell types via single cell transcriptomics
Journal:
bioRxiv
Published Date:
Mar 27, 2026
Abstract
Cells that appear transcriptionally identical can maintain vastly different functions or fate, an enduring blind spot in single-cell transcriptomics. Here we introduce TOGGLE, a self-supervised graph diffusion framework that delineates fine-grained functional heterogeneity within phenotypically stable cell populations. By combining deep diffusion learning with reinforcement-guided clustering and a BERT-inspired masking strategy, TOGGLE reconstructs hidden trajectories of cell fate without prior labels or temporal information. Applied across multiple single-cell RNA-seq datasets, TOGGLE achieves up to 90 % accuracy in unsupervised fate prediction, surpassing existing trajectory algorithms such as WOT and Cospar. It distinguishes ferroptotic, apoptotic, and intermediate neuronal states in ischemic stroke, with predictions experimentally validated in animal models. In neural stem cells TOGGLE reveals epigenetic memory of metabolic activity, linking local DNA demethylation and chromatin accessibility to mitochondrial RNA expression. We further introduce the Graph Diffusion Functional Map, which isolates subtle RNA functional groupings otherwise obscured by conventional dimensionality reduction. TOGGLE thus establishes a generalizable framework for mapping functional identity and epigenetic dynamics at single-cell resolution, providing new insights into cellular memory, regeneration, and disease mechanisms.
