Ageas enables time-agnostic cell fate inference from single-cell and spatial multi-omics data
- 1Westlake University
Abstract
Understanding cell fate decisions is fundamental to developmental biology and disease research. However, experimental lineage tracing requires genetic manipulation, which is impractical in many systems, particularly in humans. Computational approaches often rely on time-resolved measurements, which single-cell and spatial omics studies rarely provide. Here, we present Ageas, a time-agnostic transfer learning framework for cell fate inference from single-cell and spatial multi-omics data. Ageas overcomes these limitations by learning fate memory from terminal cell populations and transferring this information to progenitor or intermediate cells, enabling fate bias inference from static molecular snapshots. To enable robust generalization across molecular modalities, Ageas employs a data-adaptive ensemble strategy with automated model selection. In benchmark datasets with lineage-traced single-cell transcriptomic and epigenomic profiles, as well as spatial transcriptomics, Ageas achieves strong performance compared to existing methods. Applying Ageas to a 3D human embryo reveals a spatially organized anterior-posterior gradient of epiblast fate priming, with anterior epiblast cells biased toward ectodermal fates and posterior cells toward primitive streak-derived lineages, accompanied by regionally graded fate-associated regulatory programs. Together, these results establish Ageas as a general framework for decoding cell fate decisions from static molecular snapshots.
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BibTeX
@misc{jiang2026ageas,
title = {{Ageas enables time-agnostic cell fate inference from single-cell and spatial multi-omics data}},
author = {J. Jiang and A. Kong and G. Yu},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.30.748098},
url = {https://www.biorxiv.org/content/10.64898/2026.08.30.748098v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Ageas enables time-agnostic cell fate inference from single-cell and spatial multi-omics data
AU - J. Jiang
AU - A. Kong
AU - G. Yu
PY - 2026
DA - 2026/08/30
PB - bioRxiv
DO - 10.64898/2026.08.30.748098
UR - https://www.biorxiv.org/content/10.64898/2026.08.30.748098v1
AB - Understanding cell fate decisions is fundamental to developmental biology and disease research. However, experimental lineage tracing requires genetic manipulation, which is impractical in many systems, particularly in humans. Computational approaches often rely on time-resolved measurements, which single-cell and spatial omics studies rarely provide. Here, we present Ageas, a time-agnostic transfer learning framework for cell fate inference from single-cell and spatial multi-omics data. Ageas overcomes these limitations by learning fate memory from terminal cell populations and transferring this information to progenitor or intermediate cells, enabling fate bias inference from static molecular snapshots. To enable robust generalization across molecular modalities, Ageas employs a data-adaptive ensemble strategy with automated model selection. In benchmark datasets with lineage-traced single-cell transcriptomic and epigenomic profiles, as well as spatial transcriptomics, Ageas achieves strong performance compared to existing methods. Applying Ageas to a 3D human embryo reveals a spatially organized anterior-posterior gradient of epiblast fate priming, with anterior epiblast cells biased toward ectodermal fates and posterior cells toward primitive streak-derived lineages, accompanied by regionally graded fate-associated regulatory programs. Together, these results establish Ageas as a general framework for decoding cell fate decisions from static molecular snapshots.
ER -