Transient yet Robust Expression of Proteins in the Mouse Liver via Intravenous Injection of Lipid Nanoparticle-encapsulated Nucleoside-modified mRNA
Bibliographic record
Abstract
With the recent availability of the SARS-CoV-2 mRNA-based vaccines, public attention has been drawn to this new technology and how it may be applied to other indications. Temporal activation of key hepatic regenerative pathways can induce liver regeneration, overcoming the lack of donor organs for liver transplantation and ineffectiveness of alternative treatments. Recombinant protein therapies and genetic therapies that target these pathways require frequent and repeated injections or, when integrated into the genome, may lead to deleterious effects. In contrast, nucleoside-modified mRNA encapsulated in lipid nanoparticles (mRNA-LNP) are non-integrative and induce transient yet robust expression of proteins that could serve as an ideal therapeutic tool to treat specific liver diseases. For instance, our recent publication in Nature Communications used mRNA-LNP to express hepatic mitogens, hepatocyte growth factor, and epidermal growth factor to induce liver regeneration following both acute and chronic liver injuries. Initial testing with firefly luciferase mRNA-LNP transfection and in vivo imaging confirmed specific hepatotropic delivery. In this protocol, we describe in detail the necessary steps to deliver mRNA-LNP to the murine liver and, following intravenous injection of eGFP mRNA-LNP, verify transfection efficiency using flow cytometry and liver cell specificity using immunofluorescence analyses. This procedure presents an unprecedented tool that can be customized with mRNA-LNP encoding any protein of interest to be expressed by virtually all hepatocytes, ~70% endothelial cells, and ~40% Kupffer cells for promoting liver function and/or regeneration.Graphic abstract:Experimental Design of mRNA-LNP IV Injection and Analysis of Liver Cell Specificity and Efficiency of Transfection (Created with BioRender.com)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".