<i>HLF</i> Expression Defines the Human Hematopoietic Stem Cell State
Bibliographic record
Abstract
Abstract Hematopoietic stem cells (HSCs) sustain blood cell homeostasis throughout life and are able to regenerate all blood lineages following transplantation. Despite this clear functional definition, highly enriched isolation of human HSCs can currently only be achieved through combinatorial assessment of multiple surface antigens. While a number of transgenic HSC reporter mouse strains have been described, no analogous approach to prospectively isolate human HSCs has been reported. To identify genes with the most selective expression in human HSCs, we profiled population- and single-cell transcriptomes of fresh and ex vivo cultured cord blood derived HSPCs as well as peripheral blood, adult bone marrow and fetal liver. Based on these analyses, we propose the master transcription factor HLF ( Hepatic Leukemia Factor ) as one of the most specific HSC marker genes. To directly track its expression in human hematopoietic cells, we developed a genomic HLF reporter strategy, capable of selectively labeling the most immature blood cells on the basis of a single engineered parameter. Most importantly, HLF -expressing cells comprise all of the stem cell activity in culture and in vivo during serial transplantation. Taken together, these results experimentally establish HLF as a defining gene of the human hematopoietic stem cell state and outline a new approach to continuously mark these cells with high fidelity. Key Points In the human blood system, HLF expression is specific to stem cell populations in primary anatomical sites and during ex vivo expansion. CRISPR/rAAV6-mediated integration of a genomic HLF -reporter allows selective and stable genetic labeling of human HSCs ex vivo and in vivo .
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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 | 0.001 |
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".