Superresolution live-cell imaging reveals that the localization of TMEM106B to filopodia in oligodendrocytes is compromised by the hypomyelination-related D252N mutation.

Hypomyelination leukodystrophies constitute a group of heritable white matter disorders exhibiting defective myelin development. Initially identified as a lysosomal protein, the TMEM106B D252N mutant has recently been associated with hypomyelination. However, how lysosomal TMEM106B facilitates myelination and how the D252N mutation disrupts that process are poorly understood. We used superresolution Hessian structured illumination microscopy (Hessian-SIM) and spinning disc-confocal structured illumination microscopy (SD-SIM) to find that the wild-type TMEM106B protein is targeted to the plasma membrane, filopodia, and lysosomes in human oligodendrocytes. The D252N mutation reduces the size of lysosomes in oligodendrocytes and compromises lysosome changes upon starvation stress. Most importantly, we detected reductions in the length and number of filopodia in cells expressing the D252N mutant. PLP1 is the most abundant myelin protein that almost entirely colocalizes with TMEM106B, and coexpressing PLP1 with the D252N mutant readily rescues the lysosome and filopodia phenotypes of cells. Therefore, interactions between TMEM106B and PLP1 on the plasma membrane are essential for filopodia formation and myelination in oligodendrocytes, which may be sustained by the delivery of these proteins from lysosomes via exocytosis.

Quantitative structured illumination microscopy via a physical model-based background filtering algorithm reveals actin dynamics.

Despite the prevalence of superresolution (SR) microscopy, quantitative live-cell SR imaging that maintains the completeness of delicate structures and the linearity of fluorescence signals remains an uncharted territory. Structured illumination microscopy (SIM) is the ideal tool for live-cell SR imaging. However, it suffers from an out-of-focus background that leads to reconstruction artifacts. Previous post hoc background suppression methods are prone to human bias, fail at densely labeled structures, and are nonlinear. Here, we propose a physical model-based Background Filtering method for living cell SR imaging combined with the 2D-SIM reconstruction procedure (BF-SIM). BF-SIM helps preserve intricate and weak structures down to sub-70 nm resolution while maintaining signal linearity, which allows for the discovery of dynamic actin structures that, to the best of our knowledge, have not been previously monitored.

Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy.

A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy.

Structured illumination microscopy artefacts caused by illumination scattering.

Despite its wide application in live-cell super-resolution (SR) imaging, structured illumination microscopy (SIM) suffers from aberrations caused by various sources. Although artefacts generated from inaccurate reconstruction parameter estimation and noise amplification can be minimized, aberrations due to the scattering of excitation light on samples have rarely been investigated. In this paper, by simulating multiple subcellular structure with the distinct refractive index from water, we study how different thicknesses of this subcellular structure scatter incident light on its optical path of SIM excitation. Because aberrant interference light aggravates with the increase in sample thickness, the reconstruction of the 2D-SIM SR image degraded with the change of focus along the axial axis. Therefore, this work may guide the future development of algorithms to suppress SIM artefacts caused by scattering in thick samples. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Super-resolution fluorescence-assisted diffraction computational tomography reveals the three-dimensional landscape of the cellular organelle interactome.

The emergence of super-resolution (SR) fluorescence microscopy has rejuvenated the search for new cellular sub-structures. However, SR fluorescence microscopy achieves high contrast at the expense of a holistic view of the interacting partners and surrounding environment. Thus, we developed SR fluorescence-assisted diffraction computational tomography (SR-FACT), which combines label-free three-dimensional optical diffraction tomography (ODT) with two-dimensional fluorescence Hessian structured illumination microscopy. The ODT module is capable of resolving the mitochondria, lipid droplets, the nuclear membrane, chromosomes, the tubular endoplasmic reticulum, and lysosomes. Using dual-mode correlated live-cell imaging for a prolonged period of time, we observed novel subcellular structures named dark-vacuole bodies, the majority of which originate from densely populated perinuclear regions, and intensively interact with organelles such as the mitochondria and the nuclear membrane before ultimately collapsing into the plasma membrane. This work demonstrates the unique capabilities of SR-FACT, which suggests its wide applicability in cell biology in general.