Molecular hallmarks of heterochronic parabiosis at single-cell resolution
Bibliographic record
Abstract
The ability to slow or reverse biological ageing would have major implications for mitigating disease risk and maintaining vitality1. Although an increasing number of interventions show promise for rejuvenation2, their effectiveness on disparate cell types across the body and the molecular pathways susceptible to rejuvenation remain largely unexplored. Here we performed single-cell RNA sequencing on 20 organs to reveal cell-type-specific responses to young and aged blood in heterochronic parabiosis. Adipose mesenchymal stromal cells, haematopoietic stem cells and hepatocytes are among those cell types that are especially responsive. On the pathway level, young blood invokes new gene sets in addition to reversing established ageing patterns, with the global rescue of genes encoding electron transport chain subunits pinpointing a prominent role of mitochondrial function in parabiosis-mediated rejuvenation. We observed an almost universal loss of gene expression with age that is largely mimicked by parabiosis: aged blood reduces global gene expression, and young blood restores it in select cell types. Together, these data lay the groundwork for a systemic understanding of the interplay between blood-borne factors and cellular integrity. A transcriptomics study demonstrates cell-type-specific responses to differentially aged blood and shows young blood to have restorative and rejuvenating effects that may be invoked through enhanced mitochondrial function.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".