德国马克斯·普朗克分子细胞生物学和遗传学研究所Jesse V. Veenvliet团队的一项最新研究显示，综合分子表型分析揭示了干细胞胚胎模型中形态变异的代谢控制。相关论文发表在2025年4月16日出版的《细胞—干细胞》杂志上。

课题组通过平行记录模拟胚胎干形成的个体结构的转录组状态和形态历史来研究表型变异的根源。机器学习和时间分辨单细胞RNA测序与基于成像的表型分析的集成确定了预测表型结束状态的早期特征。利用这种预测能力揭示了氧化磷酸化和糖酵解的早期不平衡导致异常形态和神经谱系偏差，该团队通过代谢测量证实了这一点。因此，代谢干预改善了表型终态。总的来说，他们的工作建立了不同的代谢状态作为表型变异的驱动因素，并提供了一个广泛适用的框架来绘制和预测类器官和SEMs的表型变异。该策略可以以识别和控制潜在的生物过程为主题，最终提高可重复性。

据悉，在相同的培养条件下，相当大的表型变异限制了基于干细胞的胚胎模型（SEMs）在基础和应用研究中的潜力。引导这种看似随机变化的生物学过程尚不清楚。

附：英文原文

Title: Integrated molecular-phenotypic profiling reveals metabolic control of morphological variation in a stem-cell-based embryo model

Author: Alba Villaronga-Luque, Ryan G. Savill, Natalia López-Anguita, Adriano Bolondi, Sumit Garai, Seher Ipek Gassaloglu, Roua Rouatbi, Kathrin Schmeisser, Aayush Poddar, Lisa Bauer, Tiago Alves, Sofia Traikov, Jonathan Rodenfels, Triantafyllos Chavakis, Aydan Bulut-Karslioglu, Jesse V. Veenvliet

Issue&Volume: 2025-04-16

Abstract: Considerable phenotypic variation under identical culture conditions limits the potential of stem-cell-based embryo models (SEMs) in basic and applied research. The biological processes causing this seemingly stochastic variation remain unclear. Here, we investigated the roots of phenotypic variation by parallel recording of transcriptomic states and morphological history in individual structures modeling embryonic trunk formation. Machine learning and integration of time-resolved single-cell RNA sequencing with imaging-based phenotypic profiling identified early features predictive of phenotypic end states. Leveraging this predictive power revealed that early imbalance of oxidative phosphorylation and glycolysis results in aberrant morphology and a neural lineage bias, which we confirmed by metabolic measurements. Accordingly, metabolic interventions improved phenotypic end states. 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 organoids and SEMs. The strategy can be used to identify and control underlying biological processes, ultimately increasing reproducibility.

DOI: 10.1016/j.stem.2025.03.012

Source: https://www.cell.com/cell-stem-cell/abstract/S1934-5909(25)00102-X