Integrating engineered point spread functions into the phasor-based single-molecule localization microscopy framework

Enabling Spectrally Resolved Single-Molecule Localization Microscopy at High Emitter Densities

Single-molecule localization microscopy (SMLM) is a powerful super-resolution technique for elucidating structure and dynamics in the life- and material sciences. Simultaneously acquiring spectral information (spectrally resolved SMLM, sSMLM) has been hampered by several challenges: an increased complexity of the optical detection pathway, lower accessible emitter densities, and compromised spatio-spectral resolution. Here we present a single-component, low-cost implementation of sSMLM that addresses these challenges. Using a low-dispersion transmission grating positioned close to the image plane, the +1stdiffraction order is minimally elongated and is analyzed using existing single-molecule localization algorithms. The distance between the 0th and 1st order provides accurate information on the spectral properties of individual emitters. This method enables a 5-fold higher emitter density while discriminating between fluorophores whose peak emissions are less than 15 nm apart. Our approach can find widespread use in single-molecule applications that rely on distinguishing spectrally different fluorophores under low photon conditions.

Enabling single-molecule localization microscopy in turbid food emulsions

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)’.

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) is an advanced microscopy method that uses the blinking of fluorescent molecules to determine the position of these molecules with a resolution below the diffraction limit (∼5-40 nm). While SMLM imaging itself is becoming more popular, the computational analysis surrounding the technique is still a specialized area and often remains a "black box" for experimental researchers. Here, we provide an introduction to the required computational analysis of SMLM imaging, post-processing and typical data analysis. Importantly, user-friendly, ready-to-use and well-documented code in Python and MATLAB with exemplary data is provided as an interactive experience for the reader, as well as a starting point for further analysis. Our code is supplemented by descriptions of the computational problems and their implementation. We discuss the state of the art in computational methods and software suites used in SMLM imaging and data analysis. Finally, we give an outlook into further computational challenges in the field.