Highly efficient CD4+ T cell targeting and genetic recombination using engineered CD4+ cell-homing mRNA-LNPs
Bibliographic record
Abstract
Nucleoside-modified messenger RNA (mRNA)-lipid nanoparticles (LNPs) are the basis for the first two EUA (Emergency Use Authorization) COVID-19 vaccines. The use of nucleoside-modified mRNA as a pharmacological agent opens immense opportunities for therapeutic, prophylactic and diagnostic molecular interventions. In particular, mRNA-based drugs may specifically modulate immune cells, such as T lymphocytes, for immunotherapy of oncologic, infectious and other conditions. The key challenge, however, is that T cells are notoriously resistant to transfection by exogenous mRNA. Here, we report that conjugating CD4 antibody to LNPs enables specific targeting and mRNA interventions to CD4+ cells, including T cells. After systemic injection in mice, CD4-targeted radiolabeled mRNA-LNPs accumulated in spleen, providing ∼30-fold higher signal of reporter mRNA in T cells isolated from spleen as compared with non-targeted mRNA-LNPs. Intravenous injection of CD4-targeted LNPs loaded with Cre recombinase-encoding mRNA provided specific dose-dependent loxP-mediated genetic recombination, resulting in reporter gene expression in about 60% and 40% of CD4+ T cells in spleen and lymph nodes, respectively. T cell phenotyping showed uniform transfection of T cell subpopulations, with no variability in uptake of CD4-targeted mRNA-LNPs in naive, central memory, and effector cells. The specific and efficient targeting and transfection of mRNA to T cells established in this study provides a platform technology for immunotherapy of devastating conditions and HIV cure.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".