Re-Engineered Pseudoviruses for Precise and Robust 3D Mapping of Viral Infection

Engineered vesicular stomatitis virus (VSV) pseudotyping offers an essential method for exploring virus-cell interactions, particularly for viruses that require high biosafety levels. Although this approach has been employed effectively, the current methodologies for virus visualization and labeling can interfere with infectivity and lead to misinterpretation of results. In this study, we introduce an innovative approach combining genetic code expansion (GCE) and click chemistry with pseudotyped VSV to produce highly fluorescent and infectious pseudoviruses (clickVSVs). These clickVSVs enable robust and precise virus-cell interaction studies without compromising the biological function of the viral surface proteins. We evaluated this approach by generating VSVs bearing a unique chemical handle for click labeling and assessing the infectivity in relevant cell lines. Our results demonstrate that clickVSVs maintain their infectivity post-labeling and present an efficiency about two times higher in detecting surface proteins compared to classical immunolabeling. The utilization of clickVSVs further allowed us to visualize and track 3D virus binding and infection in living cells, offering enhanced observation of virus-host interactions. Thus, clickVSVs provide an efficient alternative for virus-associated research under the standard biosafety levels.

Sorzano CO. TrackAnalyzer: A Fiji/ImageJ toolbox for a holistic analysis of tracks

Current live-cell imaging techniques make possible the observation of live events and the acquisition of large datasets to characterize the different parameters of the visualized events. They provide new insights into the dynamics of biological processes with unprecedented spatial and temporal resolutions. Here we describe the implementation and application of a new tool called TrackAnalyzer, accessible from Fiji and ImageJ. Our tool allows running semi-automated single-particle tracking (SPT) and subsequent motion classification, as well as quantitative analysis of diffusion and intensity for selected tracks relying on the graphical user interface (GUI) for large sets of temporal images (X-Y-T or X-Y-C-T dimensions). TrackAnalyzer also allows 3D visualization of the results as overlays of either spots, cells or end-tracks over time, along with corresponding feature extraction and further classification according to user criteria. Our analysis workflow automates the following steps: (1) spot or cell detection and filtering, (2) construction of tracks, (3) track classification and analysis (diffusion and chemotaxis), and (4) detailed analysis and visualization of all the outputs along the pipeline. All these analyses are automated and can be run in batch mode for a set of similar acquisitions.

Dual-color DNA-PAINT single-particle tracking enables extended studies of membrane protein interactions

DNA-PAINT based single-particle tracking (DNA-PAINT-SPT) has recently significantly enhanced observation times in in vitro SPT experiments by overcoming the constraints of fluorophore photobleaching. However, with the reported implementation, only a single target can be imaged and the technique cannot be applied straight to live cell imaging. Here we report on leveraging this technique from a proof-of-principle implementation to a useful tool for the SPT community by introducing simultaneous live cell dual-color DNA-PAINT-SPT for quantifying protein dimerization and tracking proteins in living cell membranes, demonstrating its improved performance over single-dye SPT.

Biased activation of the receptor tyrosine kinase HER2

HER2 belongs to the ErbB sub-family of receptor tyrosine kinases and regulates cellular proliferation and growth. Different from other ErbB receptors, HER2 has no known ligand. Activation occurs through heterodimerization with other ErbB receptors and their cognate ligands. This suggests several possible activation paths of HER2 with ligand-specific, differential response, which has so far remained unexplored. Using single-molecule tracking and the diffusion profile of HER2 as a proxy for activity, we measured the activation strength and temporal profile in live cells. We found that HER2 is strongly activated by EGFR-targeting ligands EGF and TGFα, yet with a distinguishable temporal fingerprint. The HER4-targeting ligands EREG and NRGβ1 showed weaker activation of HER2, a preference for EREG, and a delayed response to NRGβ1. Our results indicate a selective ligand response of HER2 that may serve as a regulatory element. Our experimental approach is easily transferable to other membrane receptors targeted by multiple ligands.

ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks

Single-particle tracking microscopy is a powerful technique to investigate how proteins dynamically interact with their environment in live cells. However, the analysis of tracks is confounded by noisy molecule localization, short tracks, and rapid transitions between different motion states, notably between immobile and diffusive states. Here, we propose a probabilistic method termed ExTrack that uses the full spatio-temporal information of tracks to extract global model parameters, to calculate state probabilities at every time point, to reveal distributions of state durations, and to refine the positions of bound molecules. ExTrack works for a wide range of diffusion coefficients and transition rates, even if experimental data deviate from model assumptions. We demonstrate its capacity by applying it to slowly diffusing and rapidly transitioning bacterial envelope proteins. ExTrack greatly increases the regime of computationally analyzable noisy single-particle tracks. The ExTrack package is available in ImageJ and Python.

ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks

Single-particle tracking microscopy is a powerful technique to investigate how proteins dynamically interact with their environment in live cells. However, the analysis of tracks is confounded by noisy molecule localization, short tracks, and rapid transitions between different motion states, notably between immobile and diffusive states. Here, we propose a probabilistic method termed ExTrack that uses the full spatio-temporal information of tracks to extract global model parameters, to calculate state probabilities at every time point, to reveal distributions of state durations, and to refine the positions of bound molecules. ExTrack works for a wide range of diffusion coefficients and transition rates, even if experimental data deviate from model assumptions. We demonstrate its capacity by applying it to slowly diffusing and rapidly transitioning bacterial envelope proteins. ExTrack greatly increases the regime of computationally analyzable noisy single-particle tracks. The ExTrack package is available in ImageJ and Python.

Current State of Sensors and Sensing Systems Utilized in Beer Analysis

Beer is one of the most consumed beverages in the world. Advances in instrumental techniques have allowed the analysis and characterization of a large number of beers. However, review studies that outline the methodologies used in beer characterization are scarce. Herein, a systematic review investigating the molecular targets and sensometric techniques in beer characterization was performed following the PRISMA protocol. The study reviewed 270 articles related to beer analysis in order to provide a comprehensive summary of the recent advances in beer analysis, including methods using sensors and sensing systems. The results revealed the use of various techniques that include several technologies, such as nanotechnology and electronics, often combined with scientific data analysis tools. To our knowledge, this study is the first of its kind and provides the reader with a faithful overview of what has been done in the sensor field regarding beer characterization.

MPTHub: An Open-Source Software for Characterizing the Transport of Particles in Biorelevant Media

The study of particle transport in different environments plays an essential role in understanding interactions with humans and other living organisms. Importantly, obtained data can be directly used for multiple applications in fields such as fundamental biology, toxicology, or medicine. Particle movement in biorelevant media can be readily monitored using microscopy and converted into time-resolved trajectories using freely available tracking software. However, translation into tangible and meaningful parameters is time consuming and not always intuitive. We developed new software—MPTHub—as an open-access, standalone, user-friendly tool for the rapid and reliable analysis of particle trajectories extracted from video microscopy. The software was programmed using Python and allowed to import and analyze trajectory data, as well as to export relevant data such as individual and ensemble time-averaged mean square displacements and effective diffusivity, and anomalous transport exponent. Data processing was reliable, fast (total processing time of less than 10 s), and required minimal memory resources (up to a maximum of around 150 MB in random access memory). Demonstration of software applicability was conducted by studying the transport of different polystyrene nanoparticles (100–200 nm) in mucus surrogates. Overall, MPTHub represents a freely available software tool that can be used even by inexperienced users for studying the transport of particles in biorelevant media.

Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data

Internalin B–mediated activation of the membrane-bound receptor tyrosine kinase MET is accompanied by a change in receptor mobility. Conversely, it should be possible to infer from receptor mobility whether a cell has been treated with internalin B. Here, we propose a method based on hidden Markov modeling and explainable artificial intelligence that machine-learns the key differences in MET mobility between internalin B–treated and –untreated cells from single-particle tracking data. Our method assigns receptor mobility to three diffusion modes (immobile, slow, and fast). It discriminates between internalin B–treated and –untreated cells with a balanced accuracy of >99% and identifies three parameters that are most affected by internalin B treatment: a decrease in the mobility of slow molecules (1) and a depopulation of the fast mode (2) caused by an increased transition of fast molecules to the slow mode (3). Our approach is based entirely on free software and is readily applicable to the analysis of other membrane receptors.