Multimodal Manifold Learning for Clonally Constrained Trajectory Inference

Irene Bonafonte Pardàs, Myriam Lizotte, Guy Wolf, Benjamin Schubert

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Track: long paper (4–8 pages excluding references)

Keywords: Multimodal Integration, Manifold Learning, Trajectory Inference, Adaptive immune receptors

TL;DR: multimodal trajectory inference combining RNA and immune cell receptor data

Abstract: A central goal of single-cell transcriptomics is to reconstruct dynamic cellular processes from static scRNA-seq snapshots, yet most trajectory inference methods rely on transcriptomic similarity as a proxy for developmental linkage — an assumption that frequently fails. While lineage tracing overcomes this limitation, it requires genetic perturbations and specialized longitudinal designs. In adaptive immune cells, T and B cell receptors (AIRs) naturally encode clonal ancestry and are routinely sequenced alongside the transcriptome, providing lineage information in standard snapshot datasets, but existing trajectory methods are not adapted to exploit this signal. Here, we lay the foundation for incorporating AIR-encoded lineage information into trajectory inference by biasing RNA-based diffusion maps toward AIR-consistent paths, thereby integrating lineage constraints into learned cell-state representations. Across simulations of increasing complexity, our multimodal approach recovers more biologically plausible trajectories than RNA-only baselines. While optimized for lymphocyte differentiation, the framework generalizes to other endogenous lineage barcodes, such as mitochondrial mutations.

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Submission Number: 45