Abstract
Electron microscopy plays an important role in the analysis of functional nano-to-microstructures. Substrates and staining procedures present common sources of variation for the analysis. However, systematic investigations on the impact of these sources on data interpretation are lacking. Here we pinpoint key determinants associated with reproducibility issues in the imaging of archetypal protein assemblies, protein shells, and filaments. The effect of staining on the morphological characteristics of the assemblies was assessed to reveal differential features for anisotropic (filaments) and isotropic (shells) forms. Commercial substrates and coatings under the same staining conditions gave comparable results for the same model assembly, while highlighting intrinsic sample variations including the density and heterogenous distribution of assemblies on the substrate surface. With no aberrant or disrupted structures observed, and putative artefacts limited to substrate-associated markings, the study emphasizes that reproducible imaging must correlate with an optimal combination of substrate stability, stain homogeneity, accelerating voltage, and magnification.
Highlights
Biological processes and systems employ micromachines to deliver a variety of functions ranging from intra- and inter-cellular gene transfer [1] to antimicrobial host defense [2] and tissue repair [3]
The other type is represented by protein filaments of different lengths, which are exemplified in models 3 and 4 [9,10,12]
Different substrate coatings were found to experimental design allowed us to reveal the morphology of resulting assemblies depending on the two sources of variation
Summary
Biological processes and systems employ micromachines to deliver a variety of functions ranging from intra- and inter-cellular gene transfer [1] to antimicrobial host defense [2] and tissue repair [3] These are hierarchical supramolecular structures that assemble from perfectly matched subunits or building blocks. The reproducibility of their assembly continues to inspire artificial designs, which aim to acquire a level of control over a desired function and often explore properties that are not characteristic of their naturally occurring counterparts [4]. Variations in analysis persist [5]
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