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bioRxiv · 10.64898/2026.06.09.731114

DyMoTree decodes early cell state transitions and drivers from single-cell transcriptomes using a tree-structured neural network

Wang, J.·Xu, Y.·Tu, K.·Qiang, M.·Wang, S.·Guo, C.·Li, R.·Wang, G.
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Abstract

Inferring early cell fate from single-cell RNA-sequencing data is essential for identifying cellular origins and fate plasticity in development and disease. However, existing methods often fail to exploit tree-structured lineage trajectories, limiting the accuracy and interpretability of fate mapping. Here we present DyMoTree, a computational framework that models cell fate decisions as nonlinear mappings between progenitor and terminal cell states under explicit lineage constraints. By integrating lineage graphs with a tree-structured neural architecture, DyMoTree learns lineage-resolved cell-state transition maps from single-cell transcriptomes, enabling robust inference of early fate bias and identification of fate-specific progenitor substates and driver genes. Across simulations, lineage-tracing experiments, and in vivo systems, DyMoTree outperformed existing methods in resolving early fate biases. Applications to mouse embryogenesis, lung adenocarcinoma progression, and CAR-T immunotherapy revealed regulatory programs underlying developmental and disease-associated transitions. DyMoTree provides a general framework for modeling lineage-resolved cell-state dynamics underlying development and disease progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/731114v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@11e715dorg.highwire.dtl.DTLVardef@1a4c695org.highwire.dtl.DTLVardef@e98805org.highwire.dtl.DTLVardef@1e14054_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Wang, J., Xu, Y., Tu, K., Qiang, M., Wang, S., Guo, C., Li, R., Wang, G.. 2026-06-11. DyMoTree decodes early cell state transitions and drivers from single-cell transcriptomes using a tree-structured neural network. https://doi.org/10.64898/2026.06.09.731114

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