Asymmetric segregation and self-renewal of hematopoietic stem and progenitor cells with endocytic Ap2a2
Bibliographic record
Abstract
The stem cell-intrinsic model of self-renewal via asymmetric cell division (ACD) posits that fate determinants be partitioned unequally between daughter cells to either activate or suppress the stemness state. ACD is a purported mechanism by which hematopoietic stem cells (HSCs) self-renew, but definitive evidence for this cellular process remains open to conjecture. To address this issue, we chose 73 candidate genes that function within the cell polarity network to identify potential determinants that may concomitantly alter HSC fate while also exhibiting asymmetric segregation at cell division. Initial gene-expression profiles of polarity candidates showed high and differential expression in both HSCs and leukemia stem cells. Altered HSC fate was assessed by our established in vitro to in vivo screen on a subcohort of candidate polarity genes, which revealed 6 novel positive regulators of HSC function: Ap2a2, Gpsm2, Tmod1, Kif3a, Racgap1, and Ccnb1. Interestingly, live-cell videomicroscopy of the endocytic protein AP2A2 shows instances of asymmetric segregation during HSC/progenitor cell cytokinesis. These results contribute further evidence that ACD is functional in HSC self-renewal, suggest a role for Ap2a2 in HSC activity, and provide a unique opportunity to prospectively analyze progeny from HSC asymmetric divisions.
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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".