Back

An Ultrasensitive Luciferase Reporter Reveals Low-Level Extrahepatic mRNA Expression for Enhanced Apparent Biodistribution of Lipid Nanoparticles

Cataldi, V.; Chakraborty, S.; Alani, A. W. G.

2026-09-17 bioengineering
10.64898/2026.09.15.751785 bioRxiv
Show abstract

Reporter mRNAs are widely used to screen lipid nanoparticle (LNP) formulations and characterize functional mRNA expression in vivo, yet conventional firefly luciferase (FLuc) imaging fails to detect low-level expression. Consequently, weak or absent FLuc signal can be misinterpreted as failed LNP delivery or translation, potentially excluding formulations and overlooking extrahepatic or off-target expression. Here, we compared FLuc/D-luciferin with an ultrasensitive NLuc-GFP bioluminescence resonance energy transfer luciferase reporter detected with fluorofurimazine (FFz), using SM-102 and U105 LNPs following intravenous administration in mice. Both reporters showed predominantly hepatic and splenic expression. However, NLuc-GFP/FFz produced substantially greater photon output and detected reporter activity in the brain, heart, kidney, lymph nodes and other tissue where FLuc activity was weak or undetectable. Collectively, we show traditional FLuc imaging can underappreciate mRNA delivery, not necessarily because LNPs fail to reach or deliver mRNA to the tissue but because low-level FLuc expression may fall below its assay sensitivity. This limitation can underappreciate mRNA delivery and unintended protein expression in non-target organs. Therefore, we describe a reproducible workflow encompassing reporter design, LNP formulation and characterization, controlled reporter comparison, ex vivo organ imaging, and a cost-effective DSPE-PEG2000-micelle FFz formulation. This provides a more sensitive framework that improves detection and interpretation of low-level functional expression during preclinical mRNA-LNP screening and safety assessment.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.