Each month, we will highlight a paper published in Aging-US chosen as the “Editors’ Choice.” These selections are handpicked by our editors and accompanied by a brief summary, showcasing research with significant impact and novel insights in aging and age-related diseases.
Biomarkers of aging help researchers understand how diseases influence the body over time. However, most current biomarkers rely on measurements from mixed cell populations, making it difficult to distinguish between changes caused by shifts in cell types and aging processes occurring within individual cells.
Applying this tool, the researchers found that acute COVID-19 was associated with increased proportions of CD8⁺ cytotoxic T cells, while T cell composition remained relatively stable in individuals with HIV receiving antiretroviral therapy (HIV+ART). Despite these differences, both conditions showed signs of accelerated transcriptomic aging, particularly in naïve CD8⁺ T cells.
Further analysis identified shared aging-related genes and biological pathways linked to ribosomal components and TNF receptor binding. These findings demonstrate how single-cell transcriptomic biomarkers can help separate systemic immune changes from cell-intrinsic aging processes, providing new tools to measure immune aging in disease.
Click here to read the full research paper published in Aging-US.
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“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.
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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Aging-US is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).
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“Aging is a multifaceted process influenced by intrinsic and extrinsic factors, with lipid alterations playing a critical role in brain aging and neurological disorders.”
A new study published recently as the cover of Aging Volume 17, Issue 6, describes a new method to estimate how fast the brain is aging. By analyzing lipids, or fat molecules, in brain tissue, researchers from the National University of Singapore and Hanze University of Applied Sciences created a biological “clock” called DoliClock. This innovation highlights how conditions such as autism, schizophrenia, and Down syndrome are associated with accelerated brain aging.
Understanding Brain Aging
As people grow older, their brains naturally change. However, in many neurological disorders, these changes seem to appear earlier and progress more rapidly. Disorders like autism, schizophrenia, and Down syndrome reduce quality of life and contribute to premature death. Scientists have long searched for better ways to measure biological age in the brain to understand these processes and develop strategies to slow them down.
Most existing methods for estimating biological age rely on genetic markers, such as DNA methylation, which are chemical modifications of DNA. While useful, these approaches may not fully capture the complexity of aging, especially in the brain. Lipids, which are essential components of brain cells and play important roles in energy storage and signaling, offer another perspective.
The Study: Building a Lipid-Based Aging Clock
A team led by first author Djakim Latumalea and corresponding author Brian K. Kennedy introduced DoliClock, a model that predicts brain age using lipid profiles from the prefrontal cortex. This region of the brain, located just behind the forehead, plays a key role in decision-making, memory, and emotional regulation.
The researchers focused on a class of lipids called dolichols, which are involved in vital cellular processes such as protein transport and glycosylation. These lipids tend to accumulate in brain tissue as people age, making them promising markers for measuring biological aging.
Results: Lipids Reflect the Pace of Aging
The DoliClock model showed that dolichol levels in the brain increased gradually with age. This change became particularly noticeable around the age of 40, suggesting a shift in how the brain regulates lipid metabolism during midlife. In addition to dolichols, the researchers observed an increase in entropy, a measure of disorder in lipid composition, which also intensified around this age.
When applied to brain samples from individuals with neurological disorders, DoliClock revealed significant differences. Samples from people with autism, schizophrenia, and Down syndrome showed higher predicted biological ages compared to their actual ages. This finding indicates that these disorders are associated with accelerated brain aging. The results align with previous studies using other biological clocks but add a new layer of understanding by focusing on lipid metabolism.
The Impact: A New Window into Brain Aging
DoliClock represents an important step in aging research because it demonstrates how lipid profiles can serve as markers of biological age. Unlike genetic markers, which may not fully capture brain-specific changes, lipidomic data directly reflect the brain’s structure and metabolic state. Dolichols, in particular, emerged as strong indicators of aging and may also play a role in the development of neurological disorders. This lipid-based clock could help scientists better understand the brain aging process and identify individuals at risk of premature decline.
Future Perspectives and Conclusion
DoliClock opens new possibilities for studying the molecular basis of brain aging. Although the current study used post-mortem brain tissue, future research could adapt this approach for use with more accessible samples. Similar lipid signatures might eventually be detectable in blood or cerebrospinal fluid, offering a non-invasive way to monitor brain health. Such tools could support early diagnosis and help track the effectiveness of treatments designed to slow brain aging.
Investigating how interventions such as dietary changes or medications affect lipid-based aging markers could also lead to new strategies for promoting healthy brain aging, making DoliClock a promising foundation for further exploration in aging research and brain health.
Click here to read the full research paper published in Aging.
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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).
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