Evidence map›Paper›PMID 42116239›Full record

ArticleSmall methods2026

Direct Measurement of Protein Corona Thickness on Nanomaterials Using Dual-Color Stochastic Optical Reconstruction Microscopy (STORM).

Bradley T Cech, Sophie M Sullivan, Christopher C Landry

Abstract read
In one paragraph

Article in Small methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bradley T CechDepartment of Chemistry, University of Vermont, Burlington, Vermont, USA.
Sophie M SullivanDepartment of Chemistry, University of Vermont, Burlington, Vermont, USA.
Christopher C LandryDepartment of Chemistry, University of Vermont, Burlington, Vermont, USA.ORCID https://orcid.org/0000-0002-3092-8104

Funding

Vermont INBRE Administrative Supplement 2024 AWD 118P20GM103449 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Julie Dragon · 2012 to 2026
$58.4M
National Science Foundation DMR-1828371NIGMS NIH HHS P20 GM103449
6 · The paper itself

Abstract

Nanoparticles introduced to biological media rapidly adsorb proteins to form a protein corona that alters their surface identity and governs biological interactions. While protein corona composition has been extensively studied, its spatial structure, particularly thickness at the single-particle level, remains poorly resolved. Here, we establish and validate a strategy using dual-color stochastic optical reconstruction microscopy (STORM) for the direct measurement of hard corona thickness on individual nanoparticles. The nanoparticle surface and retained protein corona were labeled with spectrally distinct fluorophores, allowing the radial separation between the nanoparticle and adsorbed protein layer to be determined from single-particle localization distributions. Applied to dense silica, mesoporous silica, and polystyrene nanoparticles, the method revealed a remarkably consistent hard corona thickness of approximately 50 nm, with particle-level standard deviations ranging from 12 to 27 nm. Sensitivity analysis, localization precision assessment, control experiments, and day-to-day reproducibility measurements supported the robustness of the measurement strategy. Proteomic analysis showed similar molecular weight distributions across all nanoparticle classes, consistent with the comparable external surface chemistries used here.

Indexed as

MicroscopyNanoparticlesNanostructuresProtein CoronaMicroscopy, FluorescencePolystyrenesProteomicsReproducibility of ResultsSilicon DioxideStochastic ProcessesPolystyrenesProtein CoronaSilicon Dioxidebio‐nano interfacenanoparticlesprotein coronaSTORM imagingsuper‐resolution microscopy

Identifiers

PMID42116239
PMCPMC13505707

What OpenQuestion holds

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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.