Enabling single-molecule localization microscopy in turbid food emulsions

Abstract

Turbidity poses a major challenge for the microscopic characterization of food systems. Local mismatches in refractive indices, for example, lead to significant image deterioration along sample depth. To mitigate the issue of turbidity and to increase the accessible optical resolution in food microscopy, we added adaptive optics (AO) and flat-field illumination to our previously published open microscopy framework, the miCube. In the detection path, we implemented AO via a deformable mirror to compensate aberrations and to modulate the emission wavefront enabling the engineering of point spread functions (PSFs) for single-molecule localization microscopy (SMLM) in three dimensions. As a model system for a non-transparent food colloid such as mayonnaise, we designed an oil-in-water emulsion containing the ferric ion binding protein phosvitin commonly present in egg yolk. We targeted phosvitin with fluorescently labelled primary antibodies and used PSF engineering to obtain two- and three-dimensional images of phosvitin covered oil droplets with sub 100 nm resolution. Our data indicated that phosvitin is homogeneously distributed at the interface. With the possibility to obtain super-resolved images in depth, our work paves the way for localizing biomacromolecules at heterogeneous colloidal interfaces in food emulsions. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 2)’.

Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells

“…An issue with the Tg(CD41:eGFP ) background is the presence of circulating thrombocytes, that move faster than the speed of acquisition and thus generate multiple artefactual 2D objects (only presents in single Z-planes, compared to non-moving cells whose depth spans over 15–20 z-sections). These artefactual objects are removed during the pre-processing steps, by using a Faster Temporal Median algorithm ( Jabermoradi et al, 2022 ). This algorithm, by calculating median value over rolling windows of 20 frames approximate cell depth and substracting it to the pixel values, removes the 2D outliers.…”

Self-assembly of pericentriolar material in interphase cells lacking centrioles

“…Acquired stacks were pre-processed using the Faster Temporal Median ImageJ plugin (https://github.com/HohlbeinLab/FTM2; (Jabermoradi et al, 2021)) with a window size of 100 frames. These stacks were then analyzed using Detection of Molecules (DoM) plugin v.1.2.1 for ImageJ (https://github.com/ekatrukha/DoM_Utrecht), as has been described previously (Chazeau et al, 2016; Tas et al, 2017).…”

“…Acquired stacks were pre-processed using the Faster Temporal Median ImageJ plugin (https://github.com/HohlbeinLab/FTM2; (Jabermoradi et al, 2021)) with a window size of 100 frames.…”

Super-resolution microscopy: a brief history and new avenues

“…Besides biological research, SRM also has the potential to be applied in other fields, such as clinical diagnostics, e.g. using SIM through the eye lens to image the human retina with increased detail [ 117 ], or in food research using AO-assisted SMLM to investigate the characteristics of oil droplets in emulsions [ 118 ].…”