Wu Peiyi, Kong Lingtao, Zhou Yifang, Deng Caijiu, Wang Ziyi, Shen Yuxin, Wang Lei, Tuo Zhengjiao, Liu Yuang, Wang Yucheng, Zhou Yuning, Sun Qikun, Tang Yanqing
Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China.
Mol Psychiatry. 2025 Aug 2. doi: 10.1038/s41380-025-03133-7.
Adolescent major depressive disorder (AMDD) is a heterogeneous condition with rising global prevalence and limited treatment efficacy. This study integrates morphometric similarity networks (MSN) and spatial transcriptomics to identify neurobiologically distinct AMDD subtypes and their underlying molecular mechanisms. Using the HYDRA algorithm, we delineate two subtypes: AMDD1, characterized by reduced MSN strength in frontoparietal networks, heightened impulsivity, and preserved cognition; and AMDD2, marked by elevated MSN strength in limbic-visual circuits, severe emotional dysregulation, and rumination. Transcriptomic analyses reveal subtype-specific gene expression patterns, with AMDD1 associated with synaptic pruning deficits and AMDD2 linked to GABAergic inhibition deficits. Cell-type mapping highlights astrocytic dysregulation in AMDD1 and microglial activation in AMDD2, while pathway enrichment identifies distinct molecular networks, including endocannabinoid signaling in AMDD1 and MAPK-driven neuroinflammation in AMDD2. Developmental trajectory analysis uncovers critical windows for intervention, with AMDD1 showing delayed cerebellar maturation and AMDD2 exhibiting early hippocampal-striatal priming. These findings advance a precision framework for AMDD, linking spatially patterned gene expression to neurodevelopmental trajectories and offering targeted therapeutic strategies tailored to subtype-specific mechanisms. By bridging molecular, cellular, and network-level insights, this study provides a transformative approach to understanding and treating adolescent depression.
青少年重度抑郁症（AMDD）是一种异质性疾病，全球患病率不断上升，治疗效果有限。本研究整合形态计量相似性网络（MSN）和空间转录组学，以识别神经生物学上不同的AMDD亚型及其潜在分子机制。使用HYDRA算法，我们划分出两种亚型：AMDD1，其特征是额顶叶网络中MSN强度降低、冲动性增强且认知功能保留；以及AMDD2，其特征是边缘-视觉回路中MSN强度升高、严重情绪失调和反复思考。转录组分析揭示了亚型特异性基因表达模式，AMDD1与突触修剪缺陷相关，AMDD2与GABA能抑制缺陷相关。细胞类型图谱突出了AMDD1中的星形胶质细胞失调和AMDD2中的小胶质细胞激活，而通路富集则确定了不同的分子网络，包括AMDD1中的内源性大麻素信号传导和AMDD2中的MAPK驱动的神经炎症。发育轨迹分析揭示了关键的干预窗口期，AMDD1表现出小脑成熟延迟，AMDD2表现出早期海马-纹状体启动。这些发现推进了AMDD的精准框架，将空间模式化的基因表达与神经发育轨迹联系起来，并提供针对亚型特异性机制的靶向治疗策略。通过桥接分子、细胞和网络水平的见解，本研究为理解和治疗青少年抑郁症提供了一种变革性方法。
