Fabrication of Monolayer Graphene-Coated Grids for Cryoelectron Microscopy.


Journal

Journal of visualized experiments : JoVE
ISSN: 1940-087X
Titre abrégé: J Vis Exp
Pays: United States
ID NLM: 101313252

Informations de publication

Date de publication:
08 09 2023
Historique:
medline: 26 9 2023
pubmed: 25 9 2023
entrez: 25 9 2023
Statut: epublish

Résumé

Cryogenic electron microscopy (cryoEM) has emerged as a powerful technique for probing the atomic structure of macromolecular complexes. Sample preparation for cryoEM requires preserving specimens in a thin layer of vitreous ice, typically suspended within the holes of a fenestrated support film. However, all commonly used sample preparation approaches for cryoEM studies expose the specimen to the air-water interface, introducing a strong hydrophobic effect on the specimen that often results in denaturation, aggregation, and complex dissociation. Further, preferred hydrophobic interactions between regions of the specimen and the air-water interface impact the orientations adopted by the macromolecules, resulting in 3D reconstructions with anisotropic directional resolution. Adsorption of cryoEM specimens to a monolayer of graphene has been shown to help mitigate interactions with the air-water interface while minimizing the introduction of background noise. Graphene supports also offer the benefit of substantially lowering the required concentration of proteins required for cryoEM imaging. Despite the advantages of these supports, graphene-coated grids are not widely used by the cryoEM community due to the prohibitive expense of commercial options and the challenges associated with large-scale in-house production. This paper describes an efficient method for preparing batches of cryoEM grids that have nearly full coverage of monolayer graphene.

Identifiants

pubmed: 37747197
doi: 10.3791/65702
doi:

Substances chimiques

Graphite 7782-42-5
Water 059QF0KO0R

Types de publication

Journal Article Video-Audio Media

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Benjamin Basanta (B)

Department of Structural and Computational Biology, Scripps Research.

Wenqian Chen (W)

Department of Structural and Computational Biology, Scripps Research.

Daniel E Pride (DE)

Department of Structural and Computational Biology, Scripps Research.

Gabriel C Lander (GC)

Department of Structural and Computational Biology, Scripps Research; glander@scripps.edu.

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Classifications MeSH