Molecular and phenotypic blueprint of human hematopoiesis links proliferation stress to stem cell aging
Bibliographic record
Abstract
Hematopoietic stem/progenitor cells (HSPC) aging has long been associated with myeloid skewing, reduced clonal output, and impaired regenerative capacity, but quantitative immunophenotypic and functional analysis across the human lifespan has been lacking. Here, we provide a comprehensive phenotypic, transcriptional, and functional dissection of human hematopoiesis from youth to advanced age. Although primitive hematopoietic stem cell (HSC) numbers were stable during aging, overall cellularity declined, especially for erythroid and lymphoid lineages. HSPCs from older individuals exhibited repopulating frequencies comparable with those from younger donors in both primary and secondary xenografts; however, aged HSCs displayed impaired differentiation, chromatin and cell cycle dysregulation, and poor tolerance to activation-induced proliferative stress, resulting in DNA damage and senescence-like features after xenotransplantation. Importantly, imposing proliferative stress on young human HSPCs in vivo recapitulated key aging-associated phenotypic and functional declines. Together, our findings identify dysregulated activation responses as a defining feature of HSPC aging and establish proliferative stress-based xenotransplantation models as powerful platforms for investigating age-related hematopoietic dysfunctions.
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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) | 0.001 | 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".