Back

Modified self-amplifying RNAs mediate robust and prolonged gene expression in the mammalian brain

Freire, J.; McGee, J. E.; Shaw, D.; Zhou, Y.; Porter, C.; Dang, L.; San Antonio, E.; Yu, Z.; Li, K.; Wong, W.; Grinstaff, M.; Han, X.

2025-10-31 neuroscience Community evaluation
10.1101/2025.10.30.685635 bioRxiv
Show abstract

In self-amplifying ribonucleic acid (saRNA), substitution of cytidine with 5-hydroxymethylcytidine (hm5C) reduces innate immune responses and prolongs protein expression. Administration routes to date for hm5C modified saRNA encapsulated within lipid nanoparticle (LNPs) include intramuscular, as a potent low dose vaccine, but expression levels, patterns, and cell tropism in other key organs are lacking but critical for advancing RNA treatments/technology. Here we report the protein expression and cell type tropism of modified saRNA-LNPs, encoding fluorescent proteins, when injected in the mouse brain or applied to human cortical brain slices. saRNA encapsulated in an LNP formulation comprising ALC-0315 (present in Comirnaty(R)) efficiently mediates robust and long-lasting protein expression in mouse brain cells beyond five weeks, with detectable expression in some neurons at three months. hm5C saRNA substantially outperforms N1m{Psi} mRNA. In addition to transfecting astrocytes and neurons at the injection site, saRNA-LNPs labels neurons retrogradely. Excitingly, the saRNA-LNPs afford robust protein expression in human cortical brain slices, obtained during standard surgical procedures for epilepsy treatment, with expression emerging within twenty-four hours and lasting beyond six days. Thus, saRNA-LNPs are an exciting nonviral gene delivery method that effectively transfects brain cells and will catalyze new opportunities for mechanistic neuroscience research and therapeutic development.

Matching journals

The top 5 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.