Conclusive Identification of Senescent T Cells Reveals Their Abundance in Aging Humans
Bibliographic record
Abstract
ABSTRACT Aging leads to a progressive functional decline of the immune system, which renders the elderly increasingly susceptible to disease and infection. The degree to which immune cell senescence contributes to this functional decline, however, remains unclear since methods to accurately identify and isolate senescent immune cells are missing. By measuring senescence-associated ß-galactosidase activity, a hallmark of senescent cells, we demonstrate here that healthy humans develop senescent T lymphocytes in peripheral blood with advancing age. Particularly senescent CD8+ T cells increased in abundance with age, ranging from 30% of the total CD8+ T cell population in donors in their 20s and reaching levels of 64% in donors in their 60s. Senescent CD8+ T cell populations displayed features of telomere dysfunction-induced senescence as well as p16-mediated senescence, developed in various T cell differentiation states and established gene expression signatures consistent with the senescence state observed in other cell types. On the basis of our results we propose that cellular senescence of T lymphocytes is a major contributing factor to the observed decline of immune cell function with advancing age and that immune cell senescence, therefore, plays a significant role in the increased susceptibility of the elderly to age-associated diseases and infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".