Cited by 5RNA Interference and Gene DeliveryNanoparticle-Based Drug DeliveryBarrier Structure and Function Studies

High-Throughput In Vivo Screening Identifies Structural Factors Driving mRNA Lipid Nanoparticle Delivery to the Brain

Han, Emily L., Kim, Dongyoon, Murray, Amanda M., Mrksich, Kaitlin, Hamilton, Alex G., Tang, Sophia, Yoo, Soyeon, Zhu, Audrey T. +5 more·2026·ACS Nano

Abstract

Achieving systemic nonviral delivery of large nucleic acids such as mRNA to the brain is challenging due to high off-target delivery and the blood-brain barrier (BBB), a cellular barrier which prevents most nucleic acids in circulation from entering the brain. Ionizable lipid nanoparticles (LNPs) are a promising class of nanocarriers to facilitate the delivery of mRNA, as their highly modular nature enables fine-tuning of the LNP formulation for targeted delivery applications. In this work, we explore the role of ionizable lipid chemical structure and lipid molar ratios within the LNP formulation on mRNA delivery to and transfection of the brain. We utilize a high-throughput in vivo screening approach based on mRNA barcoding to study a large library of LNPs made with systematically varied ionizable lipid structures, amounts of ionizable lipid, and amounts of lipid-polyethylene glycol (PEG). We find that ionizable lipids with longer tail structures and linear amine cores can facilitate mRNA delivery to the mouse brain, and ultimately identify a specific ionizable lipid, C14-306, that facilitates brain transfection coupled with reduced liver transfection compared to an FDA-approved benchmark formulation. Furthermore, the lead LNP formulated with C14-306 is able to increase neuronal transfection and facilitate Cre-mediated recombination in the brain. Finally, safety analyses demonstrate that the lead LNP does not induce BBB leakage, increases in serum inflammatory cytokine levels, or increases in serum liver enzyme levels. Overall, our work highlights the utility of molecular barcoding for high-throughput screening of LNPs for delivery to the brain and suggests several design principles to guide the engineering of next-generation brain-tropic LNPs.

Integrity signals

Registry

Crossref · 10.1021/acsnano.5c19138

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Cites

Flagged first, then by citations

Cited by

Still being checked.

Authors
Han, E. L., Kim, D., Murray, A. M., Mrksich, K., Hamilton, A. G., Tang, S., Yoo, S., Zhu, A. T., Tong, E. R., Palanki, R., Hall, M. L., Bedingfield, S. K., & Mitchell, M. J.
Affiliations
  • University of Pennsylvania (US)
  • Department of Bioengineering
  • Eli Lilly (United States) (US)
  • Abramson Cancer Center (US)
Corresponding author
Mitchell, Michael J.
Venue
ACS Nano
Published
2026-01-20
Volume
20
Issue
4
Pages
3807-3820
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W7124948137
Cited by
5
References
53
Citations by year
2026: 5
FWCI
9.03
Type
journal article
Language
en
Open access
closed
Locations
  • ACS Nano (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Burroughs Wellcome Fund
  • Eli Lilly and Company
  • National Science Foundation (1845298)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
  • Physical Sciences › Materials Science › Biomaterials › Nanoparticle-Based Drug Delivery
  • Life Sciences › Neuroscience › Neurology › Barrier Structure and Function Studies
Keywords
mRNA lipid nanoparticles, ionizable lipids, brain drug delivery, blood-brain barrier, neuronal transfection, Cre-mediated recombination, non-viral gene delivery
MeSH terms
Animals, Blood-Brain Barrier, Brain, Humans, Lipids, Liposomes, Mice, Inbred C57BL, Polyethylene Glycols, RNA, Messenger, Transfection, Mice, Nanoparticles, High-Throughput Screening Assays

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review