Any sufficiently complex set of biological data can be used to produce an aging clock via machine learning approaches, generating some combination of values that reflects biological age. This is possible because the burden of damage and dysfunction associated with aging produces characteristic changes in biological data. Novel clocks are published by the research community at a fair pace these days, such as the clock reported in today's open access paper. It was built from transcriptomic data derived from microglia, innate immune cells of the brain. It is a research tool, impractical for medical use given the difficulty of obtaining brain-resident cells from a living individual.
The existence of a clock doesn't tell us anything of the way in which the components of the clock relate to specific forms of damage and dysfunction, only that they may be correlated. A clock could in principle be based on measures that are only sensitive to some of the mechanisms or outcomes of aging - it is impossible to know, given the way the development process works, and the inability to point to any one omics measure, such as level of a specific transcript, and describe accurately how it relates to aging. Thus one cannot trust a clock to accurately assess potential age-slowing and rejuvenation therapies until it has been calibrated against those therapies. This will take some time, and while the growing body of clock data from various studies is very interesting, this calibration has yet to happen in a comprehensive way for any of the clocks developed to date.
Microglia Single-Cell RNA-Seq Enables Robust and Applicable Markers of Biological Aging
"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 in humans and mice, we develop and compare computational approaches for generating transcriptome-wide summaries from microglia to establish robust and applicable aging clocks.
Our results reveal that unsupervised, frequency-based summarization approaches, which encode distributions of cells across molecular subtypes, strike a balance in accuracy, interpretability, and computational efficiency. Notably, our computationally derived microglia markers achieve strong accuracy in predicting chronological age across three diverse single-cell datasets, suggesting that microglia exhibit characteristic changes in gene expression during aging and development that can be computationally summarized to create robust markers of biological aging.
We further extrapolate and demonstrate the applicability of single-cell-based microglia clocks to readily available bulk RNA-seq data with an environmental input (early life stress), indicating the potential for broad utility of our models across genomic modalities and for testing hypotheses about how environmental inputs affect brain age. Such single-cell-derived markers can yield insights into the determinants of brain aging, ultimately promoting interventions that beneficially modulate health and disease trajectories.
View the full article at FightAging
