美国加州大学Maike Sander、Kyle J. Gaulton和Sebastian Preissl团队合作的最新研究，通过将遗传学与跨疾病状态的单细胞多组学分析相结合揭示了2型糖尿病（T2D）患者β细胞功能障碍的机制。相关论文于2023年5月25日发表在《自然—遗传学》杂志上。

研究人员将来自单个β细胞染色质可及性、基因表达和功能测量信息与遗传关联数据相结合，以揭示T2D患者中致病基因的调控变化。利用机器学习对34名非糖尿病、前T2D和T2D供体的染色质可及性数据进行分析，研究人员鉴定了两种转录和功能不同的β细胞亚群，它们在T2D进展期间经历了丰度变化。亚型特异性染色质可及性在T2D风险变异中出现富集，表明亚型特异性基因对T2D发生有因果关系。

两种β细胞亚型均表现出应激反应性转录程序激活和T2D功能障碍，这可能是由T2D相关代谢环境诱导的。该研究结果证明了多模态单细胞测序与机器学习相结合在表征复杂疾病机制方面具有很高的应用前景。

据了解，胰岛β细胞功能失调是2型糖尿病的标志，但缺乏对潜在机制（包括基因失调）的全面了解。

附：英文原文

Title: Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes

Author: Wang, Gaowei, Chiou, Joshua, Zeng, Chun, Miller, Michael, Matta, Ileana, Han, Jee Yun, Kadakia, Nikita, Okino, Mei-Lin, Beebe, Elisha, Mallick, Medhavi, Camunas-Soler, Joan, dos Santos, Theodore, Dai, Xiao-Qing, Ellis, Cara, Hang, Yan, Kim, Seung K., MacDonald, Patrick E., Kandeel, Fouad R., Preissl, Sebastian, Gaulton, Kyle J., Sander, Maike

Issue&Volume: 2023-05-25

Abstract: Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

DOI: 10.1038/s41588-023-01397-9

Source: https://www.nature.com/articles/s41588-023-01397-9