Keele Gregory R, Dou Yue, Kodikara Seth P, Jeffery Erin D, Bai Dina, Paulo Joao A, Gygi Steven P, Tian Xiao, Zhang Tian
Research Triangle Institute International, Research Triangle Park, NC 27709, USA.
Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA.
bioRxiv. 2024 Dec 17:2024.12.13.628374. doi: 10.1101/2024.12.13.628374.
Aging results in a progressive decline in physiological function due to the deterioration of essential biological processes, such as transcription and RNA splicing, ultimately increasing mortality risk. Although proteomics is emerging as a powerful tool for elucidating the molecular mechanisms of aging, existing studies are constrained by limited proteome coverage and only observe a narrow range of lifespan. To overcome these limitations, we integrated the Orbitrap Astral Mass Spectrometer with the multiplex tandem mass tag (TMT) technology to profile the proteomes of three brain tissues (cortex, hippocampus, striatum) and kidney in the C57BL/6JN mouse model, achieving quantification of 8,954 to 9,376 proteins per tissue (cumulatively 12,749 across all tissues). Our sample population represents balanced sampling across both sexes and three age groups (3, 12, and 20 months), comprising young adulthood to early late life (approximately 20-60 years of age for human lifespan). To enhance quantitative accuracy, we developed a peptide filtering strategy based on resolution and signal-to-noise thresholds. Our analysis uncovered distinct tissue-specific patterns of protein abundance, with age and sex differences in the kidney, while brain tissues exhibit notable age changes and limited sex differences. In addition, we identified both proteomic changes that are linear with age (, continuous) and that have a non-linear pattern (, non-continuous), revealing complex protein dynamics over the adult lifespan. Integrating our findings with early developmental proteomic data from brain tissues highlighted further divergent age-related trajectories, particularly in synaptic proteins. This study not only provides a robust data analysis workflow for TMT datasets generated using the Orbitrap Astral mass spectrometer but also expands the proteomic landscape of aging, capturing proteins with age and sex effects with unprecedented depth.
衰老会导致生理功能逐渐衰退，这是由于转录和RNA剪接等基本生物学过程的恶化，最终增加了死亡风险。尽管蛋白质组学正成为阐明衰老分子机制的有力工具，但现有研究受到蛋白质组覆盖范围有限的限制，且仅观察到较窄的寿命范围。为了克服这些限制，我们将Orbitrap Astral质谱仪与多重串联质量标签（TMT）技术相结合，对C57BL/6JN小鼠模型中的三种脑组织（皮质、海马体、纹状体）和肾脏的蛋白质组进行分析，每个组织实现了8954至9376种蛋白质的定量分析（所有组织累计12749种）。我们的样本群体代表了对两性和三个年龄组（3个月、12个月和20个月）的均衡采样，涵盖了成年早期到生命晚期（相当于人类寿命中约20 - 60岁）。为了提高定量准确性，我们基于分辨率和信噪比阈值开发了一种肽段过滤策略。我们的分析揭示了不同组织中蛋白质丰度的独特模式，肾脏中存在年龄和性别差异，而脑组织则表现出显著的年龄变化和有限的性别差异。此外，我们识别出了与年龄呈线性（即连续）变化和非线性（即非连续）变化的蛋白质组学变化，揭示了成年期寿命中复杂的蛋白质动态变化。将我们的研究结果与脑组织早期发育蛋白质组学数据相结合，突出了进一步不同的年龄相关轨迹，特别是在突触蛋白方面。这项研究不仅为使用Orbitrap Astral质谱仪生成的TMT数据集提供了强大的数据分析工作流程，还扩展了衰老的蛋白质组学图景，以前所未有的深度捕捉了具有年龄和性别效应的蛋白质。
