Non-Viral RNA-Lipid Nanoparticles for High Efficiency Genome Editing of CD34+ Hematopoietic Stem and Progenitor Cells for Advanced Cell and Gene Therapies
Bibliographic record
Abstract
Background and Aims: The application of CRISPR-Cas9 gene editing in hematopoietic stem (HSC) and progenitor cells (HSPCs) has the potential to revolutionize the treatment of genetic blood disorders by correcting disease-causing mutations. Previously, we demonstrated the utility of a novel lipid nanoparticle (LNP) reagent for the multiplexed engineering of gene-edited CAR T cells, showing high cell viabilities, rapid onset editing, and potent CAR-mediated killing. In this current work, we demonstrate the capability of LNPs for the efficient and gentle delivery of genetic material to CD34+ HSPCs. The benefits of a non-viral LNP-mediated approach paves the way for the development of next generation gene therapy products in the HSC field. Methods: LNPs encapsulating S.p. Cas9 mRNA and CD33 or CD45 targeted guide RNA (sgRNA) were produced using the scalable NanoAssemblr® NxGen™ microfluidic platform technology ( Fig. 1A). The LNP composition was optimized for the gentle and efficient cargo delivery to HSC/HSPCs. Purified human CD34+ cells were cultured, stimulated, and treated by the direct addition of the RNA-LNPs. Various commercial cell sources were tested including mobilized peripheral blood and cord blood. Gene knockout and viability were assessed using flow cytometry, cell proliferation using an automated cell counter, and differentiation capacity by colony-forming unit (CFU) assays. Results: One-step addition of LNPs to CD34+ HSPCs resulted in 84 ± 6% CD33 and 81 ± 2% CD45 knockout efficiencies (n=6 CD34+ donors, Fig. 1B). After treatment, cells maintained on average 95 ± 3% absolute cell viability, compared to 99% viability of the untreated cells, Fig. 1C. Furthermore, LNP treated HSCs maintained excellent cell proliferation, with over >90% relative cell proliferation to the untreated controls. The CFU assays showed no significant change in relative lineage formation for both the RNA-LNPs and the empty LNP vehicle control ( Fig. 1D). Finally, LNP production was successfully scaled-up using microfluidics from discovery to pre-clinical scales. Conclusions: LNP-mediated CRISPR-Cas9 mRNA delivery is a promising approach for gene editing in HSPCs. The simple and gentle nature of LNP cell treatment allows for multiple genetic engineering steps for simultaneous expression and deletion of proteins for novel gene therapies. Furthermore, LNPs can be easily manufactured using microfluidics, enabling small-scale screening of RNA libraries and rapid scale-up for clinical translation.
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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".