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bioRxiv · 10.1101/2024.07.08.602444

Unveiling Clonal Cell Fate and Differentiation Dynamics: A Hybrid NeuralODE-Gillespie Approach

Gao, M.·Barile, M.·Chabra, S.·Haltalli, M.·Calderbank, E.·Chao, Y.·Laurenti, E.·Gottgens, B.·Huang, Y.
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Abstract

Recent lineage tracing single-cell techniques (LT-scSeq), e.g., the Lineage And RNA RecoverY (LARRY) barcoding system, have enabled clonally resolved interpretation of differentiation trajectories. However, the heterogeneity of clone-specific kinetics remains understudied, both quantitatively and in terms of interpretability, thus limiting the power of bar-coding systems to unravel how heterogeneous stem cell clones drive overall cell population dynamics. Here, we present CLADES, a NeuralODE-based framework to faithfully estimate clone-specific kinetics of cell states from newly generated and publicly available human cord blood LARRY LT-scSeq data. By incorporating a stochastic simulation algorithm (SSA) and differential expression gene (DEGs) analysis, CLADES yields cell division dynamics across differentiation timecourses and fate bias predictions for the early progenitor cells. Moreover, clone-level quantitative behaviours can be grouped into characteristic types by pooling individual clones into meta-clones. By benchmarking with CoSpar, we found that CLADES improves fate bias prediction accuracy at the meta-clone level. In conclusion, we report a broadly applicable approach to robustly quantify differentiation kinetics using meta-clones while providing valuable insights into the fate bias of cellular populations for any organ system maintained by a pool of heterogeneous stem and progenitor cells.

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Gao, M., Barile, M., Chabra, S., Haltalli, M., Calderbank, E., Chao, Y., Laurenti, E., Gottgens, B., Huang, Y.. 2024-07-11. Unveiling Clonal Cell Fate and Differentiation Dynamics: A Hybrid NeuralODE-Gillespie Approach. https://doi.org/10.1101/2024.07.08.602444

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