Evidence map›Paper›PMID 28419915›Full record

ArticleMicron (Oxford, England : 1993)2017

Enhanced imaging of lipid rich nanoparticles embedded in methylcellulose films for transmission electron microscopy using mixtures of heavy metals.

Jalal Asadi, Sophie Ferguson, Hussain Raja, Christian Hacker, Phedra Marius, Richard Ward, Christos Pliotas, James Naismith, John Lucocq

Open access · hybridAbstract read
In one paragraph

Article in Micron (Oxford, England : 1993), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
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

20 citing papers in PubMed, 44 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Charge-Stabilized Nanodiscs as a New Class of Lipid Nanoparticles.Advanced materials (Deerfield Beach, Fla.) · 2024
    Article
  7. Keratinocyte-Derived Exosomes in Painful Diabetic Neuropathy.bioRxiv : the preprint server for biology · 2024
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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 at 1 institution in 1 country.

Jalal AsadiSchool of Medicine, University of St Andrews, St. Andrews, Fife, KY16 9TF, UK.
Sophie FergusonSchool of Medicine, University of St Andrews, St. Andrews, Fife, KY16 9TF, UK.
Hussain RajaSchool of Medicine, University of St Andrews, St. Andrews, Fife, KY16 9TF, UK.
Christian HackerSchool of Medicine, University of St Andrews, St. Andrews, Fife, KY16 9TF, UK.
Phedra MariusBiomedical Sciences Research Complex, North Haugh, University of St. Andrews, St. Andrews, Scotland, UK.
Richard WardBiomedical Sciences Research Complex, North Haugh, University of St. Andrews, St. Andrews, Scotland, UK.
Christos PliotasBiomedical Sciences Research Complex, North Haugh, University of St. Andrews, St. Andrews, Scotland, UK.
James NaismithBiomedical Sciences Research Complex, North Haugh, University of St. Andrews, St. Andrews, Scotland, UK.
John LucocqSchool of Medicine, University of St Andrews, St. Andrews, Fife, KY16 9TF, UK. Electronic address: jml7@st-andrews.ac.uk.
University of St Andrews · GB

Funding

Wellcome Trust 092552Wellcome Trust 100209
6 · The paper itself

Abstract

Synthetic and naturally occurring lipid-rich nanoparticles are of wide ranging importance in biomedicine. They include liposomes, bicelles, nanodiscs, exosomes and virus particles. The quantitative study of these particles requires methods for high-resolution visualization of the whole population. One powerful imaging method is cryo-EM of vitrified samples, but this is technically demanding, requires specialized equipment, provides low contrast and does not reveal all particles present in a population. Another approach is classical negative stain-EM, which is more accessible but is difficult to standardize for larger lipidic structures, which are prone to artifacts of structure collapse and contrast variability. A third method uses embedment in methylcellulose films containing uranyl acetate as a contrasting agent. Methylcellulose embedment has been widely used for contrasting and supporting cryosections but only sporadically for visualizing lipid rich vesicular structures such as endosomes and exosomes. Here we present a simple methylcellulose-based method for routine and comprehensive visualization of synthetic lipid rich nanoparticles preparations, such as liposomes, bicelles and nanodiscs. It combines a novel double-staining mixture of uranyl acetate (UA) and tungsten-based electron stains (namely phosphotungstic acid (PTA) or sodium silicotungstate (STA)) with methylcellulose embedment. While the methylcellulose supports the delicate lipid structures during drying, the addition of PTA or STA to UA provides significant enhancement in lipid structure display and contrast as compared to UA alone. This double staining method should aid routine structural evaluation and quantification of lipid rich nanoparticles structures.

Indexed as

LipidsLiposomesMetals, HeavyMethylcelluloseMicroscopy, Electron, TransmissionNanoparticlesNegative StainingOrganometallic CompoundsPhosphotungstic AcidSilicatesSpecimen HandlingStaining and LabelingTungsten CompoundsLipidsLiposomesMetals, HeavyMethylcelluloseOrganometallic CompoundsPhosphotungstic AcidSilicatessilicotungstic acidTungsten Compoundsuranyl acetateBicellesLipidsLiposomesMembranesMethylcelluloseNanodiscsNegative stainPhospholipidsPhosphotungstic acidSodium silicotungstateUranyl acetate

Identifiers

PMID28419915
PMCPMC5465805
OpenAlexW2606710583

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.