Sun Eric D, Zhou Olivia Y, Hauptschein Max, Rappoport Nimrod, Xu Lucy, Navarro Negredo Paloma, Liu Ling, Rando Thomas A, Zou James, Brunet Anne
Department of Biomedical Data Science, Stanford University, CA, USA.
Department of Genetics, Stanford University, CA, USA.
bioRxiv. 2024 Jul 19:2024.07.16.603809. doi: 10.1101/2024.07.16.603809.
Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk. Brain aging is complex and accompanied by many cellular changes. However, the influence that aged cells have on neighboring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in aging tissues have not yet been developed. Here, we generate spatiotemporal data at single-cell resolution for the mouse brain across lifespan, and we develop the first machine learning models based on spatial transcriptomics ('spatial aging clocks') to reveal cell proximity effects during brain aging and rejuvenation. We collect a single-cell spatial transcriptomics brain atlas of 4.2 million cells from 20 distinct ages and across two rejuvenating interventions-exercise and partial reprogramming. We identify spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Using spatial aging clocks and deep learning models, we find that T cells, which infiltrate the brain with age, have a striking pro-aging proximity effect on neighboring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating effect on neighboring cells. By developing computational tools to identify mediators of these proximity effects, we find that pro-aging T cells trigger a local inflammatory response likely via interferon-γ whereas pro-rejuvenating neural stem cells impact the metabolism of neighboring cells possibly via growth factors (e.g. vascular endothelial growth factor) and extracellular vesicles, and we experimentally validate some of these predictions. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.
衰老与认知功能衰退以及神经退行性疾病风险增加相关。大脑衰老过程复杂，伴随着许多细胞变化。然而，衰老细胞对邻近细胞的影响以及这种影响如何导致组织衰退尚不清楚。更普遍地说，在衰老组织中系统解决这个问题的工具尚未开发出来。在这里，我们生成了小鼠大脑在整个生命周期内单细胞分辨率的时空数据，并开发了首个基于空间转录组学的机器学习模型（“空间衰老时钟”），以揭示大脑衰老和恢复活力过程中的细胞邻近效应。我们收集了来自20个不同年龄以及两种恢复活力干预措施（运动和部分重编程）的420万个细胞的单细胞空间转录组学大脑图谱。我们确定了衰老、恢复活力和疾病的空间及细胞类型特异性转录组指纹，包括罕见细胞类型。使用空间衰老时钟和深度学习模型，我们发现随着年龄增长渗入大脑的T细胞对邻近细胞具有显著的促衰老邻近效应。令人惊讶的是，神经干细胞对邻近细胞具有强大的促恢复活力作用。通过开发计算工具来识别这些邻近效应的介导因子，我们发现促衰老T细胞可能通过干扰素-γ触发局部炎症反应，而促恢复活力的神经干细胞可能通过生长因子（如血管内皮生长因子）和细胞外囊泡影响邻近细胞的代谢，并且我们通过实验验证了其中一些预测。这些结果表明，罕见细胞可以对其邻居产生巨大影响，并且可以成为对抗组织衰老的靶点。我们预计这些空间衰老时钟不仅将允许对衰老和疾病干预措施的疗效进行可扩展评估，而且还将成为研究许多空间背景下细胞间相互作用的新工具。
