Single-cell gene expression (transcriptomics) data are becoming robust and abundant, and are increasingly used to track organisms along their life-course. This allows investigation into how aging affects cellular transcriptomes, and how changes in transcriptomes may underlie aging, including chronic inflammation (inflammaging), immunosenescence and cellular senescence. We compiled and tabulated aging-related single-cell datasets published to date, collected and discussed relevant findings, and inspected some of these datasets ourselves. We specifically note insights that cannot (or not easily) be based on bulk data. For example, in some datasets, the fraction of cells expressing p16 (CDKN2A), one of the most prominent markers of cellular senescence, was reported to increase, in addition to its upregulated mean expression over all cells. Moreover, we found evidence for inflammatory processes in most datasets, some of these driven by specific cells of the immune system. Further, single-cell data are specifically useful to investigate whether transcriptional heterogeneity (also called noise or variability) increases with age, and many (but not all) studies in our review report an increase in such heterogeneity. Finally, we demonstrate some stability of marker gene expression patterns across closely similar studies and suggest that single-cell experiments may hold the key to provide detailed insights whenever interventions (countering aging, inflammation, senescence, disease, etc.) are affecting cells depending on cell type.

单细胞基因表达（转录组学）数据变得越来越强大和丰富，并且越来越多地用于跟踪生物体的生命历程。这使得我们能够研究衰老如何影响细胞转录组，以及转录组的变化如何导致衰老，包括慢性炎症（炎症）、免疫衰老和细胞衰老。我们编制并列出了迄今为止发布的与衰老相关的单细胞数据集，收集和讨论了相关发现，并亲自检查了其中一些数据集。我们特别注意到不能（或不容易）基于批量数据的见解。例如，在一些数据集中，除了所有细胞的平均表达上调外，表达 p16（CDKN2A）（细胞衰老最显着的标志物之一）的细胞比例据报道有所增加。此外，我们在大多数数据集中发现了炎症过程的证据，其中一些是由免疫系统的特定细胞驱动的。此外，单细胞数据对于研究转录异质性（也称为噪声或变异性）是否随着年龄的增长而增加特别有用，并且我们综述中的许多（但不是全部）研究报告了这种异质性的增加。最后，我们在非常相似的研究中证明了标记基因表达模式的一些稳定性，并表明单细胞实验可能是提供详细见解的关键，每当干预措施（对抗衰老、炎症、衰老、疾病等）影响细胞时，单细胞实验可能是提供详细见解的关键，具体取决于细胞类型。