Reconstructing Immune Time from a Single Snapshot: The Single-Cell Brownian Bridge Framework for Reverse-Time Evolutionary Inference 2248645

Abstract

Abstract Introduction Modeling the temporal evolution of the immune system remains a central yet unsolved challenge in computational immunology. While single-cell transcriptomics provides unprecedented cellular resolution, most analyses are confined to static distributions that lack true temporal context. We introduce Single-Cell Brownian Bridge (SCBB), a novel stochastic—deep learning framework that infers the hidden temporal dynamics of immune cell evolution from a single timepoint. Methods SCBB establishes a mathematical bridge between static molecular snapshots and continuous biological time. By representing immune state transitions as Brownian bridge diffusions constrained by biologically meaningful endpoints, and embedding these within a forward Markov process, SCBB enables reverse-time learning–the ability to reconstruct developmental or pathological immune trajectories backward from their mature states. This fusion of stochastic process theory and generative modeling transforms static scRNA-seq data into dynamic maps of immune evolution. Results Applied to human and murine immune datasets, SCBB reveals latent differentiation hierarchies, transition probabilities, and emergent attractor states underlying tolerance, aging, and autoimmunity. Conclusion By mathematically grounding biological time within probabilistic manifolds, SCBB represents a conceptual shift from snapshot-based inference to stochastic temporal reconstruction, opening a new avenue for decoding immune system evolution across lifespan and disease. Funding Source n/a Topic Categories Computational and Systems Immunology (COMP)