Evidence map›Paper›PMID 41959436›Full record

ArticlebioRxiv : the preprint server for biology2026

Resolving heterogeneity of targeted lipid nanoparticles through solution-based biophysical analyses.

Hannah C Geisler, Hannah C Safford, Ajay S Thatte, Marshall S Padilla, Elisa Battistini, Hannah M Yamagata, Violet M Ullman, Alex Chan, Benjamin E Nachod, Anushka Agrawal and 5 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Hannah C GeislerDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-6455-8183
Hannah C SaffordDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-2512-8153
Ajay S ThatteDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-7372-8893
Marshall S PadillaDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0003-3607-790X
Elisa BattistiniDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Hannah M YamagataDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-9525-7102
Violet M UllmanDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Alex ChanDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Benjamin E NachodDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Anushka AgrawalDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Maxwell B WatkinsThe Biophysics Collaborative Access Team (BioCAT), Department of Physics, Illinois Institute of Technology, Chicago, IL 60616, USA.ORCID 0000-0003-4559-2049
Jesse B HopkinsThe Biophysics Collaborative Access Team (BioCAT), Department of Physics, Illinois Institute of Technology, Chicago, IL 60616, USA.ORCID 0000-0001-8554-8072
Andrew TsourkasDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-7758-1753
Kushol GuptaDepartment of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0002-7006-2667
Michael J MitchellDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.

Funding

The Biophysics Collaborative Access Team (User Training and Outreach)P30GM138395 · NIGMS · ILLINOIS INSTITUTE OF TECHNOLOGY · PI THOMAS C IRVING · 2021 to 2026
$16.5M
NIGMS NIH HHS P30 GM138395
6 · The paper itself

Abstract

Targeted lipid nanoparticles (tLNPs) represent the next frontier in nucleic acid therapeutics, enabling cell-specific delivery through covalent attachment of targeting ligands that drive receptor-mediated uptake. tLNPs are particularly promising for pregnancy-associated applications where precise on-target delivery is required to minimize maternal toxicity and protect fetal health. Yet, their rational design is limited by an incomplete understanding of how tLNP physicochemical properties influence biological performance. Conventional LNPs already exhibit pronounced heterogeneity in size, composition, and RNA loading, which is further amplified in tLNPs by variability in ligand attachment and surface density. Because traditional analytical methods report only ensemble-averaged properties, the nanoscale diversity of tLNPs remains unresolved. Here, we find that tLNP functional behavior is governed by previously inaccessible, structurally distinct tLNP subpopulations that are not captured by bulk measurements. We utilize asymmetric flow field-flow fractionation integrated with in-line UV spectral analysis, light scattering, and synchrotron small-angle X-ray scattering (AF4-UV-DLS-MALS-SAXS) to resolve ligand-dependent tLNP subpopulations that differ in size, shape, composition, and relative abundance. We find that protein conjugation preserves the internal lipid-RNA nanostructure of base LNPs but substantially increases particle heterogeneity, particularly for larger and multivalent targeting ligands. Despite increased heterogeneity, tLNPs functionalized with higher-avidity ligands achieve more effective targeted placental RNA delivery in mice, suggesting that binding avidity can offset the functional consequences of polydispersity. Chemometric SAXS analyses reveal that only SAXS-resolved tLNP subpopulations, not ensemble-averaged parameters, correlate with targeted placental transfection in vivo, whereas bulk-derived physicochemical metrics more strongly associate with nonspecific hepatic delivery. Together, this work harnesses a separation-coupled biophysical platform to resolve previously inaccessible tLNP subpopulations and demonstrates that subpopulation nanoscale structure, rather than bulk-averaged properties, dictates targeted RNA delivery. These insights provide a mechanistic foundation for rational engineering of next-generation precision targeted RNA LNP therapeutics.

Identifiers

PMID41959436
PMCPMC13060138

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.