Priming Human Repopulating Hematopoietic Stem and Progenitor Cells for Cas9/sgRNA Gene Targeting
Bibliographic record
Abstract
Engineered nuclease-mediated gene targeting through homologous recombination (HR) in hematopoietic stem and progenitor cells (HSPCs) has the potential to treat a variety of genetic hematologic and immunologic disorders. Here, we identify critical parameters to reproducibly achieve high frequencies of RNA-guided (single-guide RNA [sgRNA]; CRISPR)-Cas9 nuclease (Cas9/sgRNA) and rAAV6-mediated HR at the β-globin ( HBB ) locus in HSPCs. We identified that by transducing HSPCs with rAAV6 post-electroporation, there was a greater than 2-fold electroporation-aided transduction (EAT) of rAAV6 endocytosis with roughly 70% of the cell population having undergone transduction within 2 hr. When HSPCs are cultured at low densities (1 × 10 5 cells/mL) prior to HBB targeting, HSPC expansion rates are significantly positively correlated with HR frequencies in vitro as well as in repopulating cells in immunodeficient NSG mice in vivo . We also show that culturing fluorescence-activated cell sorting (FACS)-enriched HBB -targeted HSPCs at low cell densities in the presence of the small molecules, UM171 and SR1, stimulates the expansion of gene-edited HSPCs as measured by higher engraftment levels in immunodeficient mice. This work serves not only as an optimized protocol for genome editing HSPCs at the HBB locus for the treatment of β-hemoglobinopathies but also as a foundation for editing HSPCs at other loci for both basic and translational research.
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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) | 0.004 | 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".