Dehkordi Shiva Kazempour, Sajedi Sogand, Heshmat Amirreza, Orr Miranda E, Zare Habil
Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA.
Department of Cell Systems and Anatomy, University of Texas Health San Antonio, San Antonio, TX, USA.
NPJ Aging. 2025 Jul 1;11(1):57. doi: 10.1038/s41514-025-00235-y.
Neuronal senescence (i.e., neurescence) is an important hallmark of aging and neurodegeneration, but it remains poorly characterized in the human brain due to the lack of reliable markers. This study aimed to identify neurescence markers based on single-nucleus transcriptome data from postmortem human prefrontal cortex. Using an eigengene approach, we integrated three gene panels: (a) SenMayo, (b) canonical senescence pathway (CSP), and (c) senescence initiating pathway (SIP), to identify neurescence signatures. We found that paired markers outperform single markers; for instance, by combining CDKN2D and ETS2 in a decision tree, a high accuracy of 99% and perfect specificity (100%) were achieved in distinguishing senescent neurons (i.e, neurescent). Differential expression analyses identified 324 genes that are overexpressed in neurescent. These genes showed significant associations with important neurodegeneration-related pathways, including Alzheimer's disease, Parkinson's disease, and Huntington's disease. Interestingly, several of these overexpressed genes are linked to mitochondrial dysfunction and cytoskeletal dysregulation. These findings provide valuable insights into the complexities of neurescence, emphasizing the need for further exploration of histologically viable markers and validation in broader datasets.
神经元衰老(即神经衰老)是衰老和神经退行性变的一个重要标志,但由于缺乏可靠的标志物,在人类大脑中其特征仍不明确。本研究旨在基于死后人类前额叶皮层的单核转录组数据识别神经衰老标志物。我们使用特征基因方法整合了三个基因面板:(a) SenMayo、(b) 经典衰老途径(CSP)和 (c) 衰老起始途径(SIP),以识别神经衰老特征。我们发现配对标志物优于单个标志物;例如,在决策树中结合CDKN2D和ETS2,在区分衰老神经元(即神经衰老)方面实现了99%的高精度和完美的特异性(100%)。差异表达分析确定了324个在神经衰老中过度表达的基因。这些基因与重要的神经退行性变相关途径,包括阿尔茨海默病、帕金森病和亨廷顿病,显示出显著关联。有趣的是,这些过度表达的基因中有几个与线粒体功能障碍和细胞骨架失调有关。这些发现为神经衰老的复杂性提供了有价值的见解,强调了进一步探索组织学上可行的标志物并在更广泛的数据集中进行验证的必要性。