While buffer cocktails remain the most commonly used method for photostabilization and photoswitching of fluorescent markers, intramolecular triplet-state quenchers emerge as an alternative strategy to impart fluorophores with ‘self-healing’ or even functional properties such as photoswitching. In this contribution, we evaluated combinations of both approaches and show that inter- and intramolecular triplet-state quenching processes compete with each other. We find that although the rate of triplet-state quenching is additive, the photostability is limited by the faster pathway. Often intramolecular processes dominate the photophysical situation for combinations of covalently-linked and solution-based photostabilizers and photoswitching agents. Furthermore we show that intramolecular photostabilizers can protect fluorophores from reversible off-switching events caused by solution-additives, which was previously misinterpreted as photobleaching. Our studies also provide practical guidance for usage of photostabilizer–dye conjugates for STORM-type super-resolution microscopy permitting the exploitation of their improved photophysics for increased spatio-temporal resolution. Finally, we provide evidence that the biochemical environment, e.g., proximity of aromatic amino-acids such as tryptophan, reduces the photostabilization efficiency of commonly used buffer cocktails. Not only have our results important implications for a deeper mechanistic understanding of self-healing dyes, but they will provide a general framework to select label positions for optimal and reproducible photostability or photoswitching kinetics in different biochemical environments.

Hydrogen-deuterium exchange (HDX) has become a pivotal method for investigating the structural and dynamic properties of proteins. The versatility and sensitivity of mass spectrometry (MS) made the technique the ideal companion for HDX, and today HDX-MS is addressing a growing number of applications in both academic research and industrial settings. The prolific generation of experimental data has spurred the concurrent development of numerous computational tools, designed to automate parts of the workflow while employing different strategies to achieve common objectives. Various computational methods are available to perform automated peptide searches and identification; different statistical tests have been implemented to quantify differences in the exchange pattern between two or more experimental conditions; alternative strategies have been developed to deconvolve and analyze peptides showing multimodal behavior; and different algorithms have been proposed to computationally increase the resolution of HDX-MS data, with the ultimate aim to provide information at the level of the single residue. This review delves into a comprehensive examination of the merits and drawbacks associated with the diverse strategies implemented by software tools for the analysis of HDX-MS data.

Single-molecule fluorescence microscopy studies of bacteria provide unique insights into the mechanisms of cellular processes and protein machineries in ways that are unrivalled by any other technique. With the cost of microscopes dropping and the availability of fully automated microscopes, the volume of microscopy data produced has increased tremendously. These developments have moved the bottleneck of throughput from image acquisition and sample preparation to data analysis. Furthermore, requirements for analysis procedures have become more stringent given the demand of various journals to make data and analysis procedures available. To address these issues we have developed a new data analysis package for analysis of fluorescence microscopy data from rod-like cells. Our software ColiCoords structures microscopy data at the single-cell level and implements a coordinate system describing each cell. This allows for the transformation of Cartesian coordinates from transmission light and fluorescence images and single-molecule localization microscopy (SMLM) data to cellular coordinates. Using this transformation, many cells can be combined to increase the statistical power of fluorescence microscopy datasets of any kind. ColiCoords is open source, implemented in the programming language Python, and is extensively documented. This allows for modifications for specific needs or to inspect and publish data analysis procedures. By providing a format that allows for easy sharing of code and associated data, we intend to promote open and reproducible research. The source code and documentation can be found via the project’s GitHub page.