A Functional Screen to Identify Novel Effectors of Hematopoietic Stem Cell Activity
Bibliographic record
Abstract
Abstract The maintenance of blood homeostasis depends on hematopoietic stem cells (HSCs), which rely on two critical properties, namely multipotency and self-renewal. The former enables differentiation into multiple lineages, the latter ensures preservation of fate upon cellular division. By definition, a self-renewal division implies that a HSC is permissive to cell cycle entry, while restrained from engaging in differentiation, apoptosis or senescence pathways. Despite the tremendous progress made towards the identification of the molecular circuitry that governs ESC fate, genes controlling this process in adult HSCs have proven more difficult to unmask. This is principally due to our inability to maintain or expand HSC ex vivo as homogenous populations, to the absence of a stringent surrogate marker to follow the HSC multipotent state and to changes in cell phenotype observed shortly upon facing the selective pressures of in vitro culture conditions, impeding HSC tracking in this context. We now report the results of a novel in vitro to in vivo functional screen, which identified a series of nuclear factors that induced high levels of HSC activity similar to that previously achieved with Hoxb4. We created a database consisting of 689 nuclear factors considered as potential candidate regulators of HSC activity. This list was mostly derived from microarray gene expression profiling of normal and leukemia stem cells including our recently generated FLA2 leukemia (1 in 1.5 cells are leukemia stem cells, G.S. et coll., in preparation). It was also enriched by genes obtained following a review of the literature on stem cell self-renewal. Genes in this database were next ranked from 1 (lowest priority) to 10 (highest priority) based on 3 factors: differential expression between primitive and more mature cellular fractions (e.g., LT-HSC-enriched: 3 points), expression levels (high, highest priority: max 3 points) and the consistency of findings between datasets (max 4 points). Genes with a score of 6 and above (n=139) were selected for functional studies, of which 104 were tested in HSCs, using a high-throughput overexpression in vitro to in vivo assay tailored to circumvent current limitations imposed by the biology of HSCs. In total, 18 new determinants have emerged, 11 of which act in a cell autonomous manner, namely Ski, Smarcc1, Vps72, Trim27, Sox4, Klf10, Prdm16, Erdr1, Cnbp, Xbp1 and Hnrpdl, while the remaining provide a non-autonomous influence on HSC activity, i.e, Fos, Hmgb1, Tcfec, Sfpi1, Zfp472, Hdac1 and Pml. Clonal and phenotypic analyses of hematopoietic tissues derived from selected recipients confirmed that the majority of these factors induced HSC expansion in vitro without perturbing their differentiation in vivo. Epistatic analyses further reveals that 3 of the most potent candidates, namely Ski, Prdm16 and Klf10 may exploit both mechanisms, i.e., cell and non-cell autonomous. The utilization of this novel screening method together with the creation of a database enriched for potential determinants and candidate regulators of adult stem cell activity can now be exploited to devise regulatory networks in these cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".