3D super-resolved multi-angle TIRF via polarization modulation

TL;DR

This paper introduces a polarization modulation technique combined with a fast, open-source algorithm to achieve super-resolved 3D imaging in multi-angle TIRF microscopy, enabling high-resolution live cell imaging with rapid temporal resolution.

Contribution

It presents the first method to enhance lateral resolution in multi-angle TIRF using polarization modulation and a novel accelerated algorithm, improving 3D nanoscale imaging of live cells.

Findings

Enhanced lateral resolution in MA-TIRF imaging.

Achieved 2-second temporal resolution for live cell imaging.

Demonstrated detailed mitochondrial dynamics.

Abstract

Measuring the three dimension nanoscale organization of protein or cellular structures is challenging, especially when the structure is dynamic. Owing to the informative total internal reflection fluorescence (TIRF) imaging under varied illumination angles, multi-angle (MA) TIRF has been examined to offer a nanoscale axial and a sub-second temporal resolution. However, conventional MA-TIRF still performs badly in lateral resolution and fail to characterize the depth-image in densely-distributed regions, leaving a huge contrast between the nanoscale axial resolution and the diffraction limited lateral resolution. Moreover, the previous reconstructions are highly restricted by the efficiency with the increased amount of illumination angles. Here, we for the first time, emphasize the lateral super-resolution in the MA-TIRF and exampled by simply introducing polarization modulation into the illumination procedure. Equipped with a sparsity and accelerated proximal algorithm, we examine a more precise 3D sample structure compared with previous methods, enabling live cell imaging with temporal-resolution of 2 seconds, recovering high-resolution mitochondria fission and fusion process. Since the introduced vortex half wave retarder is an add-on component to the existing MA-TRIF system and the algorithm is the first open sourced and the fastest, we anticipate that the method and algorithm introduced here would be adopted rapidly by the biological community.