2025-12-12 カロリンスカ研究所(KI)
<関連情報>
- https://news.ki.se/new-algorithm-maps-how-cells-develop
- https://www.pnas.org/doi/10.1073/pnas.2516046122
MultistageOT: 多段階最適輸送は単一細胞データのスナップショットから軌道を推測します MultistageOT: Multistage optimal transport infers trajectories from a snapshot of single-cell data
Magnus Tronstad, Johan Karlsson, and Joakim S. Dahlin
Proceedings of the National Academy of Sciences Published:December 11, 2025
DOI:https://doi.org/10.1073/pnas.2516046122
Significance
Cell differentiation is a fundamental biological process whose dysregulation leads to disease. Single-cell sequencing offers unique insight into the differentiation process, but data analysis remains a major modeling challenge—particularly in complex branching systems e.g. hematopoiesis (blood cell development). Here, we extend optimal transport theory to address a previously inaccessible modeling problem: inferring developmental progression of differentiating cells from a single snapshot of an in vivo process. We achieve this by deriving a multistage transport model. Our approach accurately reconstructs cell fate decision in hematopoiesis. Moreover, it infers rare bipotent cell states and uniquely detects individual outlier cells that diverge from the main differentiation paths. We thus introduce a powerful mathematical framework that enables more granular analyses of cell differentiation.
Abstract
Single-cell RNA-sequencing captures a temporal slice, or a snapshot, of a cell differentiation process. A major bioinformatical challenge is the inference of differentiation trajectories from a single snapshot, and methods that account for outlier cells that are unrelated to the differentiation process have yet to be established. We present MultistageOT (https://github.com/dahlinlab/MultistageOT), a generalized optimal transport-based framework that models cell differentiation in a single snapshot as a series of intermediate cell transitions. MultistageOT employs multiple transport stages to establish temporal progression within the snapshot—overcoming limitations with the classic bimarginal formulation of optimal transport. Moreover, our multistage framework uses global information across all cells and differentiation stages to infer coherent trajectories from initial to terminal states. This allows MultistageOT to infer individual outlier cells that are unrelated to the analyzed differentiation process—an essential mechanism for preventing the inference of spurious or biologically implausible trajectories. We benchmark MultistageOT on snapshot data of cell differentiation, showing significantly improved fate prediction accuracy over state-of-the-art bimarginal optimal transport and demonstrating MultistageOT’s unique ability to detect outlier cells.
