Identification of a human hematopoietic stem cell subset that retains memory of inflammatory stress
Bibliographic record
Abstract
Abstract Inflammation activates many blood cell types, driving aging and malignancy. Yet, hematopoietic stem cells (HSCs) survive a lifetime of infection to sustain life-long blood production. To understand HSC adaptation to inflammation, we developed xenograft inflammation-recovery models and performed single cell multiomics on isolated human HSC. Two transcriptionally and epigenetically distinct HSC subsets expressing canonical HSC programs were identified. Only one showed sustained transcriptional and epigenetic changes after recovery from inflammatory treatments. This HSC inflammatory memory (HSC-iM) program is enriched in memory T cells and HSCs from recovered COVID-19 patients. Importantly, HSC-iM accumulates with age and with clonal hematopoiesis. Overall, heritable molecular alterations in a subset of human HSCs, an adaptation to long-term inflammatory stress, may predispose to heightened age-related risk of blood cancer and infection. One-Sentence Summary Inflammation across a lifetime rewires human HSCs to produce a distinct HSC subset with both beneficial and deleterious fitness consequences.
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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 | 0.000 |
| 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 | 0.000 |
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 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".