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

2017 
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.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    41
    References
    19
    Citations
    NaN
    KQI
    []