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Free keywords:
embryo models, gastruloids, morphospace, glycolysis, oxidative phosphorylation, single-
cell RNA-seq, machine learning, neuro-mesodermal progenitors, somitogenesis, organoids
Abstract:
Mammalian stem-cell-based models of embryo development (stembryos) hold great promise in basic
and applied research. However, considerable phenotypic variation despite identical culture conditions
limits their potential. The biological processes underlying this seemingly stochastic variation are poorly
understood. Here, we investigate the roots of this phenotypic variation by intersecting transcriptomic
states and morphological history of individual stembryos across stages modeling post-implantation and
early organogenesis. Through machine learning and integration of time-resolved single-cell RNA-
sequencing with imaging-based quantitative phenotypic profiling, we identify early features predictive
of the phenotypic end-state. Leveraging this predictive power revealed that early imbalance of oxidative
phosphorylation and glycolysis results in aberrant morphology and a neural lineage bias that can be
corrected by metabolic interventions. Collectively, our work establishes divergent metabolic states as
drivers of phenotypic variation, and offers a broadly applicable framework to chart and predict
phenotypic variation in organoid systems. The strategy can be leveraged to identify and control
underlying biological processes, ultimately increasing the reproducibility of in vitro systems.
