Single-cell immune aging clocks reveal inter-individual heterogeneity during infection and vaccination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Aging affects human immune system functionality, increasing susceptibility to immune-mediated diseases. While gene expression programs accurately reflect immune function, their relationship with biological immune aging and health status remains unclear. Here we developed robust, cell-type-specific aging clocks (sc-ImmuAging) for the myeloid and lymphoid immune cell populations in circulation within peripheral blood mononuclear cells, using single-cell RNA-sequencing data from 1,081 healthy individuals aged from 18 to 97 years. Application of sc-ImmuAging to transcriptome data of patients with COVID-19 revealed notable age acceleration in monocytes, which decreased during recovery. Furthermore, inter-individual variations in immune aging induced by vaccination were identified, with individuals exhibiting elevated baseline interferon response genes showing age rejuvenation in CD8 + T cells after BCG vaccination. sc-ImmuAging provides a powerful tool for decoding immune aging dynamics, offering insights into age-related immune alterations and potential interventions to promote healthy aging.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it