Monitoring mRNA vaccine antigen expression in vivo using PET/CT
Bibliographic record
Abstract
Noninvasive visualization of the distribution and persistence of mRNA vaccine antigen expression in mammalian systems has implications for the development and evaluation of future mRNA vaccines. Here, we genetically fuse E. coli dihydrofolate reductase (eDHFR) to the delta furin diproline modified SARS-CoV-2 spike glycoprotein (S2P∆f) mRNA vaccine and image its expression in female mice and male non-human primates using [18F]fluoropropyl-trimethoprim ([18F]FP-TMP). Whole body positron emission tomography (PET) imaging revealed transient expression of the vaccine antigen in the injection site and draining lymph nodes (dLNs). Fusion of eDHFR did not impact S2P immunogenicity and no humoral or cellular immune response was detected against eDHFR in either species. In this work, we show that eDHFR can be used as an mRNA-encoded PET reporter gene to monitor the spatiotemporal dynamics of mRNA vaccine antigen expression in vivo. This technique could be applied in clinical translation of future mRNA vaccines or therapeutics. mRNA vaccines have been successfully developed, but a better understanding of in vivo distribution of the encoded antigen may aid further improvements. Here the authors use PET imaging and demonstrate transient expression of the vaccine antigen in the injection site and draining lymph nodes in mice and non-human primates.
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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 | 0.001 | 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".