Abstract
'Biological aging clocks' - composite molecular markers thought to capture an individual's biological age - have been traditionally developed through bulk-level analyses of mixed cells and tissues. However, recent evidence highlights the importance of gaining single-cell-level insights into the aging process. Microglia are key immune cells in the brain shown to adapt functionally in aging and disease. Recent studies have generated single-cell RNA sequencing (scRNA-seq) datasets that transcriptionally profile microglia during aging and development. Leveraging such datasets, we develop and compare computational approaches for generating transcriptome-wide summaries to establish robust microglia aging clocks. Our results reveal that unsupervised, frequency-based featurization approaches strike a balance in accuracy, interpretability, and computational efficiency. We further extrapolate and demonstrate applicability of such microglia clocks to readily available bulk RNA-seq data with environmental inputs. Single-cell-derived clocks can yield insights into the determinants of brain aging, ultimately promoting interventions that beneficially modulate health and disease trajectories.