Yu Xin-Xin, Peng Peng, Wang Yi-Ning, He Mao-Yang, He Shuang, Jin Chen-Tao, Yang Liu, Wang Xi, Zheng Jia-Xi, Gao Jie, Xu Cheng-Ran
State Key Laboratory of Female Fertility Promotion, Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China.
Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
Adv Sci (Weinh). 2025 Aug;12(30):e16326. doi: 10.1002/advs.202416326. Epub 2025 May 29.
Understanding the role and prevalence of bihormonal cells in pancreatic islets and their potential in β-cell restoration is critical but remains ambiguous. Using genetically engineered mouse strains with specific fluorescent markers and advanced imaging flow cytometry, it is found that bihormonal cells are exceedingly rare. Single-cell RNA sequencing reveals that GcgPpy and GcgIns bihormonal cells closely resemble α-cells or PP-cells and α-cells, respectively, indicating they are neither unique lineages nor transitional states. Dual-recombinase lineage tracing further demonstrates that embryonic GcgIns cells resolve into monohormonal α-cells. Applying these insights, the scarcity of bihormonal cells in diabetic mouse models is confirmed, suggesting a limited role in β-cell regeneration. By excluding bihormonal influences, endocrine cell classification is redefined in mouse and human islets through gene coexpression network analysis, identifying distinct subtypes and regulatory modules while uncovering species-specific differences. Additionally, two unique δ-cell subpopulations are identified in human islets. Collectively, this study provides a comprehensive characterization of bihormonal cells, refines endocrine cell taxonomy, and underscores the translational challenges in modeling human islet biology in mice.
了解双激素细胞在胰岛中的作用、患病率及其在β细胞恢复中的潜力至关重要,但仍不明确。利用具有特定荧光标记的基因工程小鼠品系和先进的成像流式细胞术,发现双激素细胞极其罕见。单细胞RNA测序显示,GcgPpy和GcgIns双激素细胞分别与α细胞或PP细胞以及α细胞极为相似,这表明它们既不是独特的谱系,也不是过渡状态。双重组酶谱系追踪进一步证明,胚胎期的GcgIns细胞分化为单激素α细胞。应用这些见解,证实了糖尿病小鼠模型中双激素细胞的稀缺性,表明其在β细胞再生中的作用有限。通过排除双激素的影响,通过基因共表达网络分析重新定义了小鼠和人类胰岛中的内分泌细胞分类,识别出不同的亚型和调控模块,同时揭示了物种特异性差异。此外,在人类胰岛中鉴定出两个独特的δ细胞亚群。总体而言,这项研究全面描述了双激素细胞,完善了内分泌细胞分类法,并强调了在小鼠中模拟人类胰岛生物学的转化挑战。