Distinct Regulatory Networks Govern Human Hematopoietic Stem Cell Across Development
Bibliographic record
Abstract
Abstract Introduction: Much of our fundamental understanding of stem cell biology comes from studies of hematopoiesis where single cells produce differentiated progeny while still retaining the ability to produce daughter stem cells (self-renewal). The cardinal property of a stem cell, whether normal or malignant, is self-renewal; the key biological process that ensures the ability of the stem cell to maintain long-term clonal growth. However, our understanding of the molecular basis of self-renewal in human hematopoiesis is limited. At the embryonic stage fetal liver is the main source of hematopoiesis; from week 6 of gestation until before birth. At this stage HSCs are in a different microenvironment but capable of self-renewing and differentiation to the full spectrum of blood lineages. While murine studies uncovered several intrinsic differences between fetal and adult HSCs, a comprehensive analysis of human HSC compartment across development is lacking. In this study we have combined HSC purification methods and xenograft quantitative assay in conjunction with low input RNA sequencing and Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) to provide a comprehensive functional and molecular outlook of human stem cell compartment across development. Results: We followed the dynamics of four sub-fractions of CD34+CD38- divided by CD90 and CD49f expression across human blood development: fetal liver (hFL) and adult bone marrow (hBM). Using xenograft model, we identified human long, intermediate and short term HSCs in hFL and hBM. 5 single CD90+CD49f+ hFL cells were capable of sustaining the multilineage graft for over 52 weeks up to tertiary recipient, while BM cells only last for 20 weeks in the primary recipient. The frequency of LT-HSC in the CD90+CD49f+ compartment goes from 1/8 in hFL to 1/50 in hBM. hFL CD90-CD49f+ cells showed an intermediate repopulation capacity up to 44 weeks in secondary recipient. On average 10% of hFL long term HSC (LT-HSC) were in S/G2/M phase, in contrast only 0.4% of BM LT-HSC were in S/G2/M phase indicating that hFL HSCs are 20 times more in cycle compare to BM. We found that 320 genes were expressed differentially between LT-HSC and multipotent progenitors (MPP) in hBM as oppose to only 32 genes found to be differentially expressed in hFL (FDR<0.1). Interestingly, we found only 2 genes in common between these two groups. ERRBS showed an overall increase in methylation of HSC compartment in hBM compare to hFL and gradual demethylation of lineage associated genes in MPP. Conclusion: Our data indicate that there are distinct regulatory networks that govern hFL and hBM HSC self-renewal. We found very little differences in gene expression between all hFL HCS compartments (average 20 genes) compare to hBM (average 224), indicating that by adulthood self-renewal is becoming more restricted to the LT-HSC compartment. Disclosures No relevant conflicts of interest to declare.
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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".