Obtaining diffraction patterns from annular dark-field STEM-in-SEM images: Towards a better understanding of image contrast

This contribution demonstrates experimentally how a series of annular dark-field transmission images collected in a scanning electron microscope (SEM) with a basic solid-state detector can be used to quantify electron scattering distributions (i.e., diffraction patterns). The technique is demonstrated at different primary electron energies with a polycrystalline aluminum sample and two amorphous samples comprising vastly different mass-thicknesses. Contrast reversal is demonstrated in both amorphous samples, suggesting that intuitive image contrast interpretation is not always straightforward even for ultrathin, low atomic number samples. We briefly address how the scattering distributions obtained here can be used as an aid to interpret contrast in annular dark-field images, and how to set up imaging conditions to obtain intuitively interpretable contrast from samples with regions of significantly different thickness.

Flow Cytometric Analysis of Protein Aggregates

BACKGROUND

Misfolding of proteins often leads to aggregation. Accumulation of diverse protein aggregates in various cells, tissue and organs is the hallmark of many diseases, such as Alzheimer's disease and Parkinson's disease.

OBJECTIVES

The main objective of this study was to present a novel method of characterization of protein aggregates, associated with differential toxicity with different size and composition in vitro using flow cytometry.

METHODS

A Beckman Coulter Epics XL flow cytometer with argon ion laser operating at 488 nm was used for flow cytometry analysis. The voltage and the gain settings for individual channels were set at high voltage and gain for the detections of autofluorescence, fluorescence of adsorbed Congo red, forward scattering (FSC) and side scattering (SSC) intensities from the aggregates of proteins and nanoparticles. Each sample was analyzed to characterize and quantify the number of aggregates with a limit of maximum 20,000 events. The flow cytometry data were analyzed using Flowing software version 2.5.1 and Origin 8.0.

RESULTS

Autofluorescence and scattering intensities could distinguish between amyloid and nonamyloid aggregates. Dot plots of both side scattering (SSC) and forward scattering (FSC) intensities also showed characteristic fingerprint of both the types of aggregates when compared with those of well known nanoparticles of oxides of Fe and Cu.

CONCLUSION

This work reports a novel, simple and robust flow cytometric method of characterization of protein aggregates of different size and composition which would find wider application in characterization of biomolecular aggregates, in general.