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The ultimate goal of biological super-resolution fluorescence microscopy is to provide three-dimensional resolution at the size scale of a fluorescent marker. Here we show that by localizing individual switchable fluorophores with a probing donut-shaped excitation beam, MINFLUX nanoscopy can provide resolutions in the range of 1 to 3 nm for structures in fixed and living cells. This progress has been facilitated by approaching each fluorophore iteratively with the probing-donut minimum, making the resolution essentially uniform and isotropic over scalable fields of view. MINFLUX imaging of nuclear pore complexes of a mammalian cell shows that this true nanometer-scale resolution is obtained in three dimensions and in two color channels. Relying on fewer detected photons than standard camera-based localization, MINFLUX nanoscopy is poised to open a new chapter in the imaging of protein complexes and distributions in fixed and living cells. Advances in MINFLUX nanoscopy enable multicolor imaging over large fields of view, bringing true nanometer-scale fluorescence imaging to labeled structures in fixed and living cells.

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but standard analysis algorithms require sparse emitters, which limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE (deep context dependent), a computational tool that can localize single emitters at high density in three dimensions with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 datasets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to acquire fast dynamic live-cell SMLM data with reduced light exposure and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many laboratories to reduce imaging times and increase localization density in SMLM.DECODE uses deep learning for localizing single emitters in high-density two-dimensional and three-dimensional single-molecule localization microscopy data. DECODE outperforms available methods and enables fast live-cell SMLM of dynamic processes.

Multicolor single-molecule localization microscopy (λSMLM) is a powerful technique to reveal the relative nanoscale organization and potential colocalization between different molecular species. While several standard analysis methods exist for pixel-based images, λSMLM still lacks such a standard. Moreover, existing methods only work on 2D data and are usually sensitive to the relative molecular organization, a very important parameter to consider in quantitative SMLM. Here, we present an efficient, parameter-free colocalization analysis method for 2D and 3D λSMLM using tessellation analysis. We demonstrate that our method allows for the efficient computation of several popular colocalization estimators directly from molecular coordinates and illustrate its capability to analyze multicolor SMLM data in a robust and efficient manner. Multicolour single-molecule localization microscopy lacks a standard analysis method. Here Levet et al. introduce Coloc-Tesseler, a parameter-free colocalisation analysis method based on tessellation analysis for the efficient analysis of multicolour SMLM data.

Photoactivatable fluorophores are important for single-particle tracking and super-resolution microscopy. Here we present a photoactivatable fluorophore that forms a bright silicon rhodamine derivative through a light-dependent protonation. In contrast to other photoactivatable fluorophores, no caging groups are required, nor are there any undesired side-products released. Using this photoactivatable fluorophore, we create probes for HaloTag and actin for live-cell single-molecule localization microscopy and single-particle tracking experiments. The unusual mechanism of photoactivation and the fluorophore’s outstanding spectroscopic properties make it a powerful tool for live-cell super-resolution microscopy. Activatable fluorophores are of interest for a wide range of applications but the need for caging groups complicates their development and application. Here, the authors report on a photoactivatable silicon rhodamine derivative and its application in live cell imaging and single-particle tracking.

We present a real-time fitter for 3D single-molecule localization microscopy using experimental point spread functions (PSFs) that achieves minimal uncertainty in 3D on any microscope and is compatible with any PSF engineering approach. We used this method to image cellular structures and attained unprecedented image quality for astigmatic PSFs. The fitter compensates for most optical aberrations and makes accurate 3D super-resolution microscopy broadly accessible, even on standard microscopes without dedicated 3D optics.

Quantitative data analysis is important for any single-molecule localization microscopy (SMLM) workflow to extract biological insights from the coordinates of the single fluorophores. However, current approaches are restricted to simple geometries or require identical structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model to localization coordinates. It extracts meaningful parameters from individual structures and can select the most suitable model. In addition to analyzing complex, heterogeneous and dynamic structures for in situ structural biology, we demonstrate how LocMoFit can assemble multi-protein distribution maps of six nuclear pore components, calculate single-particle averages without any assumption about geometry or symmetry, and perform a time-resolved reconstruction of the highly dynamic endocytic process from static snapshots. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials to enable any user to increase the information content they can extract from their SMLM data. Localization Model Fit (LocMoFit) is an open-source tool for extracting meaningful parameters from individual structures in localization microscopy data. The framework was used for quantitative analysis of diverse biological structures.

Quantitative fluorescence and superresolution microscopy are often limited by insufficient data quality or artifacts. In this context, it is essential to have biologically relevant control samples to benchmark and optimize the quality of microscopes, labels and imaging conditions. Here, we exploit the stereotypic arrangement of proteins in the nuclear pore complex as in situ reference structures to characterize the performance of a variety of microscopy modalities. We created four genome edited cell lines in which we endogenously labeled the nucleoporin Nup96 with mEGFP, SNAP-tag, HaloTag or the photoconvertible fluorescent protein mMaple. We demonstrate their use (1) as three-dimensional resolution standards for calibration and quality control, (2) to quantify absolute labeling efficiencies and (3) as precise reference standards for molecular counting. These cell lines will enable the broader community to assess the quality of their microscopes and labels, and to perform quantitative, absolute measurements.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.