Lipid nanoparticles allow efficient and harmless ex vivo gene editing of human hematopoietic cells
Bibliographic record
Abstract
Ex vivo gene editing in T cells and hematopoietic stem/progenitor cells (HSPCs) holds promise for treating diseases. Gene editing encompasses the delivery of a programmable editor RNA or ribonucleoprotein, often achieved ex vivo via electroporation, and when aiming for homology-driven correction of a DNA template, often provided by viral vectors together with a nuclease editor. Although HSPCs activate a robust p53-dependent DNA damage response upon nuclease-based editing, the responses triggered in T cells remain poorly characterized. Here, we performed comprehensive multiomics analyses and found that electroporation is the main culprit of cytotoxicity in T cells, causing death and cell cycle delay, perturbing metabolism, and inducing an inflammatory response. Nuclease RNA delivery using lipid nanoparticles (LNPs) nearly abolished cell death and ameliorated cell growth, improving tolerance to the procedure and yielding a higher number of edited cells compared with using electroporation. Transient transcriptomic changes upon LNP treatment were mostly caused by cellular loading with exogenous cholesterol, whose potentially detrimental impact could be overcome by limiting exposure. Notably, LNP-based HSPC editing dampened p53 pathway induction and supported higher clonogenic activity and similar or higher reconstitution by long-term repopulating HSPCs compared with electroporation, reaching comparable editing efficiencies. Overall, LNPs may allow efficient and harmless ex vivo gene editing in hematopoietic cells for the treatment of human diseases.
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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".