Lipid Nanoparticle-Mediated Gene Editing of Human Primary T Cells and Off-Target Analysis of the CRISPR-Cas9 Indels
Bibliographic record
Abstract
Background and Aims: The CRISPR/Cas9 system has emerged as a powerful tool for gene editing of primary cells. In our previous work, we demonstrated a novel lipid nanoparticle (LNP) reagent for the multi-step engineering of gene-edited CAR T cells. We showed high cell viabilities and potent CAR-T mediated killing, even after multiple genetic manipulations. Here, we extend this work by assessing potential off-target editing effects in both LNP-treated T cells and T-cells where the CRISPR reagents were delivered by electroporation. Further, we evaluated multiple Cas9 variants and guide RNA targets. Methods: LNPs encapsulating wild type or high fidelity S.p. Cas9 mRNA and various TRAC and CD52 targeted guide RNAs (sgRNAs) were produced using our scalable NanoAssemblrTM microfluidics platform. Concurrently, electroporation was performed to deliver equivalent cargoes. Purified primary T cells were cultured, activated, and expanded in serum-free media in plates, flasks, or small bioreactors. The LNPs were added to cells by direct addition for gene editing. Gene expression and cell viability were measured using flow cytometry or colorimetric assays. Multi-target performance of CRISPR-Cas9 editing was evaluated through rhAmpSeq-based targeted next-generation sequencing (NGS) and indels analyzed for with CRISPRAltRations. Results: TCR or CD52 targeted Cas9 mRNA-LNP addition or electroporation yielded high single and double knockout efficiencies. High-throughput NGS analysis showed strong agreeance to flow cytometry for on-target analysis. We tested a range of sgRNA targets, wild-type and high-fidelity Cas9 mRNAs, and determined off-target editing for all targets and variants investigated. Similar results were obtained when comparing different LNP batch sizes (micro to milligram RNA) and cell culture vessels (0.1 to 45 million cells), demonstrating scalability of both LNP production and cell treatment. Conclusions: The results from this study further support the utility RNA-LNPs for the genetic engineering of primary T cells. The simple and gentle nature of LNP cell treatment allows for multiple genetic engineering steps for simultaneous expression and deletion of proteins for the next cell therapies. These LNPs can be easily manufactured from small-scale screening of RNA libraries to 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".