A Novel Osteoclastic Network Determines In Vitro Niche for Mouse and Human Hematopoietic Stem Cells
Bibliographic record
Abstract
Abstract Abstract 1325 New innovative strategies are needed to expand human hematopoietic stem cells (HSCs) for clinical applications. In this perspective, we recently developed an in vitro/in vivo screening strategy, which revealed 18 nuclear factors that enhance HSC activity (Deneault et al., Cell 2009). During these experiments, mouse HSCs were kept for 12 days in mini-cultures that included viral producer cells for each tested factor. Interestingly, 4 of the 18 hits identified in this initial screen, i.e., Fos, Tcfec, Hmgb1 and Sfpi1 operated through non-HSC autonomous (NHA) mechanisms: expanded HSCs were not infected with retrovirus. This suggested that the transfected viral producer cells (feeder cells) produced membrane-bound or soluble molecules that promote expansion of HSCs introduced in these cultures. We now provide evidence that seven additional factors, i.e., Smarcc1, Vps72, Sox4, Klf10, Ski, Prdm16 and Erdr1 significantly enhance HSC activity through NHA mechanisms, hereafter called “NHA factors”. Moreover, we found that Vps72, Fos and Klf10 also promote expansion of human HSCs by NHA mechanisms. Interestingly at least for feeders expressing Klf10, we observed that physical contact between HSCs and these engineered support cells was not necessary, suggesting the presence of secreted molecule(s) in the medium. Expression profiling was next performed using mRNA extracted from feeder cells transduced with each of the NHA factors. Firstly, we shed light on the transcriptional make up and potential convergence of signaling pathways in the engineered feeder cells: our results reveal two independent but interconnected transcriptional regulatory subnetworks. Strikingly, some constituents of the subnetworks, i.e., Sfpi1, Fos, Klf10 and Tcfec (Mitf-related) have previously been shown to play critical roles in the regulation of osteoclasts, which are a myeloid-derived population of cells residing in the HSC bone marrow niche. In addition, all of the NHA factors act in concert to increase Prdm16 expression levels in a range from 2.5- to 54.6-fold. For this reason, Prdm16 clearly holds the position of central hub of the osteoclastic network. However, Tcfec represents the ultimate downstream effector of this pathway as Prdm16 elevates its expression levels by 18.9-fold. In parallel, Prdm16 can also increase the expression of Sfpi1, which in turn can upregulate Tcfec expression up to 874-fold. Moreover, upregulated mRNA targets corresponding to factors that are secreted or associated with the plasma membrane were considered as potential candidate agonists of HSC self-renewal. A high degree of overlap was observed between the sets of proteins produced by feeder cells engineered to overexpress Sfpi1, Fos, Klf10, Tcfec or Ski. These secreted and membrane bound proteins include Agt, Aspn, Ogn, Ptgds, Nckap1l, Rgs16 and Lcn2. Studies are ongoing to characterize the contribution of these newly identified NHA proteins in HSC expansion. Validation of these NHA proteins in human HSC expansion will have a clear potential for translational medicine. 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) | 0.002 | 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".