Evidence map›Paper›PMID 41025580›Full record

ArticleJournal of extracellular vesicles2025

Lipoprotein Association Fluorometry (LAF) as a Semi-Quantitative Characterization Tool to Assess Extracellular Vesicle-Lipoprotein Binding.

Raluca Ghebosu, Jenifer Pendiuk Goncalves, Nur Indah Fitri, Dalila Iannotta, Mohammad Farouq Sharifpour, Elaina Coleborn, Alex Loukas, Fernando Souza-Fonseca-Guimaraes, Joy Wolfram

Abstract read
In one paragraph

Article in Journal of extracellular vesicles, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Rethinking Extracellular Vesicle Signaling.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
  5. Integrative Approaches to Treating Cellular Senescence in Kidney Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  6. Article
  7. Article
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

9 authors.

Raluca GhebosuAustralian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0001-8839-3512
Jenifer Pendiuk GoncalvesAustralian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-6609-1481
Nur Indah FitriAustralian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0009-0003-4107-8269
Dalila IannottaSchool of Chemical Engineering, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-9189-4382
Mohammad Farouq SharifpourAustralian Institute of Tropical Health and Medicine, James Cook University, Cairns, Queensland, Australia.ORCID https://orcid.org/0000-0002-7950-955X
Elaina ColebornFrazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
Alex LoukasAustralian Institute of Tropical Health and Medicine, James Cook University, Cairns, Queensland, Australia.
Fernando Souza-Fonseca-GuimaraesFrazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
Joy WolframAustralian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0003-0579-9897

Funding

Extracellular vesicle-based senotherapeutics for aging diabetic kidneydiseaseR01AG076537 · NIA · MAYO CLINIC JACKSONVILLE · PI LaTonya J Hickson · 2023 to 2026
$2.3M
NIA NIH HHS R01 AG076537
6 · The paper itself

Abstract

Extracellular vesicles (EVs) are biological nanoparticles that play important roles in (patho)physiological processes and are promising new therapeutic and diagnostic tools. Recent evidence suggests that other circulating biological nanoparticles, primarily lipoproteins, bind to EVs, changing their biological identity. Such binding has been demonstrated with complex qualitative techniques, such as cryogenic transmission electron microscopy. There is a need to rapidly and simply quantify EV-lipoprotein binding, as such complexes could have major implications for EV biology and medical applications. This study developed lipoprotein association fluorometry (LAF; based on fluorescent lipophilic indocarbocyanine dyes), as a first-of-its-kind, simple and quick assay to assess EV binding to lipoproteins. The LAF assay was validated with synthetic nanoparticles, small molecules, polymers and proteins that display known interactions with lipoproteins. The LAF assay demonstrates that EVs from various human and non-human (nematode and bacteria) sources bind to very-low-density lipoprotein (VLDL) and low-density lipoprotein (LDL). Notably, EVs derived from cancerous cells displayed substantially increased binding to VLDL, LDL and plasma compared to EVs from normal cells. Additionally, the LAF assay revealed that EVs from metastatic cancer cells bound to VLDL to a greater extent than those from corresponding patient-matched non-metastatic cancer cells. On the contrary, EVs displayed minimal binding to high-density lipoprotein (HDL). Taken together, the LAF assay is capable of measuring EV-lipoprotein binding in a simple, rapid and semi-quantitative manner, leading to new opportunities to probe EV biology and develop novel therapeutics, and diagnostics.

Indexed as

Extracellular VesiclesFluorometryLipoproteinsAnimalsFluorescent DyesHumansLipoproteins, LDLLipoproteins, VLDLNanoparticlesProtein BindingFluorescent DyesLipoproteinsLipoproteins, LDLLipoproteins, VLDLbad cholesterolcoronaexosomesLDLmicrovesiclesVLDL

Identifiers

PMID41025580
PMCPMC12481431

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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.