LOTUS: lineage-aware optimal transport reconstructs continuous clonal fate dynamics

Abstract

Destructive sampling in single-cell sequencing obscures ancestor-descendant relationships, limiting the continuous reconstruction of cell-fate dynamics. Here we present Lineage-aware optimal transport for unbalanced single-cell dynamics (LOTUS), a framework that integrates time-resolved transcriptomic snapshots with clonal lineage information from engineered barcodes or endogenous T cell receptor clonotypes. LOTUS couples clone-level optimal transport with neural unbalanced transport to model continuous cellular state change, population growth and clonal propagation under lineage-consistent constraints. In branching simulations, LOTUS reduced lineage-inconsistent transport relative to benchmark methods, lowering off-target routing from 4.94-14.84% to 1.10%. On LARRY lineage-barcode scRNA-seq of hematopoiesis, LOTUS reconstructed branching trajectories, improved early neutrophil-monocyte fate-boundary prediction, and inferred multi-fate progenitors and fate-biased meta-clones, linking their distinct fate potentials to inferred epigenetic priming. On CellTagging scRNA-seq of fibroblast reprogramming, it distinguished reprogrammed from failed trajectories and prioritized fate-associated perturbation genes. In longitudinal scRNA-seq and TCR-seq of malaria-responsive memory (CD45RA-) CD4+ T cells, it preserved endogenous clonotype structure and reconstructed TR1 dynamics and regulatory networks. LOTUS thus converts lineage-resolved single-cell snapshots into interpretable, continuous models of fate dynamics and their regulatory programs.