“Biomarkers of aging offer insights into how diseases and interventions affect biological systems. However, most current biomarkers are based on bulk cell measurements, making it difficult to distinguish between changes driven by shifts in cell type composition (systemic effects) versus intrinsic changes within individual cells.”
Aging reshapes the immune system in two fundamental ways: it alters the proportions of different immune cell types circulating in the blood, and it induces molecular changes within each individual cell. For years, researchers have struggled to disentangle these two intertwined processes using standard “bulk” measurements, which average signals across millions of cells and obscure what is happening at the single-cell level.
A new research paper, titled “Single-cell transcriptomics reveal intrinsic and systemic T cell aging in COVID-19 and HIV” published in Volume 18 of Aging-US by researchers at the Buck Institute for Research on Aging in California, the University of Southern California, and the University of Copenhagen, introduces an innovative solution.
The team of Alan Tomusiak, Sierra Lore, Morten Scheibye-Knudsen, and corresponding author Eric Verdin, developed a novel tool called Tictock (T immune cell transcriptomic clock) that uses single-cell RNA sequencing to separately measure systemic and cell-intrinsic components of immune aging, and then applied it to understand how COVID-19 and HIV affect T cells.
The Tictock Model
The challenge the researchers addressed is akin to a chicken-and-egg problem. When we see a change in the average gene expression of a T cell population with age, is it because the cells themselves are aging, or because the composition of the population has shifted to contain more aged cell types?
To solve this, the researchers built Tictock, a two-part model using a massive dataset of two million peripheral blood mononuclear cells from 166 individuals. The first component is an automated cell type predictor that classifies T cells into six canonical subsets with 97% accuracy. It identifies naïve CD8+ T cells, central memory CD8+ cells, effector memory CD8+ cells, naïve CD4+ cells, central memory CD4+ cells, and regulatory T cells based on the expression of key marker genes like CD4, CD8A, CCR7, and FOXP3.
The second component consists of six distinct age-prediction models—one trained specifically for each T cell subset. By applying the cell type predictor first, the researchers can isolate a pure population of, say, naïve CD8+ T cells, and then apply the age model for that specific cell type to calculate its “transcriptomic age.” This dual-layer design allows Tictock to separate the signal of aging cell populations from the signal of aging within a cell.
Evidence from Laboratory and Human Studies
The researchers first validated their model by confirming known trends in immune aging. They observed a significant increase in the CD4/CD8 ratio with age, a well-established phenomenon. More specifically, they found a sharp decline in the proportion of naïve CD8+ cytotoxic T cells as people grow older, which aligns with decades of immunological research.
Having validated the tool, the authors then applied Tictock to two disease contexts: acute COVID-19 and HIV infection managed with antiretroviral therapy (HIV+ART). The results revealed distinct patterns. In acute COVID-19, the model detected a significant change in cell type composition—a systemic shift toward increased proportions of CD8+ cytotoxic T cells, likely reflecting the body’s acute immune response to the virus.
However, both diseases shared a striking commonality at the cell-intrinsic level. In people with acute COVID-19 and in those with HIV+ART, Tictock detected a significant increase in the transcriptomic age of naïve CD8+ T cells. In other words, these naïve cells appeared biologically older than expected for the individual’s chronological age. This accelerated aging signature was specific; it was not observed in other T cell subsets like CD4+ helper cells.
Insights into Mechanisms
To understand what was driving these age predictions, the team analyzed the 209 genes that were consistently included across the six different cell-type age models. Gene Ontology enrichment analysis revealed that these shared genes were heavily involved in fundamental cellular processes, including components of the cytosolic small and large ribosomal subunits and pathways related to TNF receptor binding.
This points to a central role for protein synthesis machinery and inflammatory signaling in T cell aging. The authors also discovered a correlation between aging and mean transcript length within cells, suggesting that changes in RNA processing or stability may be a general feature of the aging process at the single-cell level. Across these examples, the recurring theme is the power of single-cell resolution to reveal distinct layers of aging—systemic shifts in cell populations versus intrinsic molecular aging within specific cell types.
Implications for Future Research
The development of Tictock opens several avenues for future investigation. One immediate application is as a tool to measure how different interventions, such as drugs or lifestyle changes, affect immune aging. Because the model can distinguish between effects on cell composition and effects on cell-intrinsic age, it could provide a more nuanced readout of whether a therapy is truly rejuvenating immune cells or simply altering their proportions.
The finding that both a chronic viral infection (HIV) and an acute viral infection (COVID-19) accelerate aging in naïve CD8+ T cells raises important questions about the long-term consequences of severe infections. It suggests that the immune system may carry a “memory” of these encounters in the form of prematurely aged T cells, which could impact future immune responses.
Future Perspectives and Conclusion
Tictock does not claim to be a universal clock for all tissues or all immune cells. Rather, it offers a proof-of-concept for a powerful approach: using single-cell transcriptomics to build interpretable biomarkers that can disentangle the multiple layers of a complex process like aging. By integrating automated cell typing with cell-type-specific age predictors, the model clarifies how systemic and intrinsic factors combine to shape the aging immune system.
This perspective suggests that immune aging is not a single process but a composite of changes at different levels of biological organization. Continued research will be needed to determine how broadly this model applies to other cell types and other diseases, and how it might guide future efforts to monitor and modulate immune health in older adults and in people living with chronic viral infections.
Click here to read the full research paper published in Aging-US.
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