IL7 increases targeted lipid nanoparticle–mediated mRNA expression in T cells in vitro and in vivo by enhancing T cell protein translation
Bibliographic record
Abstract
The use of lipid nanoparticles (LNP) to encapsulate and deliver mRNA has become an important therapeutic advance. In addition to vaccines, LNP-mRNA can be used in many other applications. For example, targeting the LNP with anti-CD5 antibodies (CD5/tLNP) can allow for efficient delivery of mRNA payloads to T cells to express protein. As the percentage of protein expressing T cells induced by an intravenous injection of CD5/tLNP is relatively low (4-20%), our goal was to find ways to increase mRNA-induced translation efficiency. We showed that T cell activation using an anti-CD3 antibody improved protein expression after CD5/tLNP transfection in vitro but not in vivo. T cell health and activation can be increased with cytokines, therefore, using mCherry mRNA as a reporter, we found that culturing either mouse or human T cells with the cytokine IL7 significantly improved protein expression of delivered mRNA in both CD4 + and CD8 + T cells in vitro. By pre-treating mice with systemic IL7 followed by tLNP administration, we observed significantly increased mCherry protein expression by T cells in vivo. Transcriptomic analysis of mouse T cells treated with IL7 in vitro revealed enhanced genomic pathways associated with protein translation. Improved translational ability was demonstrated by showing increased levels of protein expression after electroporation with mCherry mRNA in T cells cultured in the presence of IL7, but not with IL2 or IL15. These data show that IL7 selectively increases protein translation in T cells, and this property can be used to improve expression of tLNP-delivered mRNA in vivo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".