Unravelling the Mystery inside Cells by Using Single-Molecule Fluorescence Imaging

FluoroTensor: Identification and tracking of colocalised molecules and their stoichiometries in multi-colour single molecule imaging via deep learning

The identification of photobleaching steps in single molecule fluorescence imaging is a well-established procedure for analysing the stoichiometries of molecular complexes. Nonetheless, the method is challenging with protein fluorophores because of the high levels of noise, rapid bleaching and highly variable signal intensities, all of which complicate methods based on statistical analyses of intensities to identify bleaching steps. It has recently been shown that deep learning by convolutional neural networks can yield an accurate analysis with a relatively short computational time. We describe here an improved use of such an approach that detects bleaching events even in the first time point of observation, and we have included this within an integrated software package incorporating fluorescence spot detection, colocalisation, tracking, FRET and photobleaching step analyses of single molecules or complexes. This package, known as FluoroTensor, is written in Python with a self-explanatory user interface.

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

Super-resolution microscopy to study membrane nanodomains and transport mechanisms in the plasma membrane

Biological membranes are complex, heterogeneous, and dynamic systems that play roles in the compartmentalization and protection of cells from the environment. It is still a challenge to elucidate kinetics and real-time transport routes for molecules through biological membranes in live cells. Currently, by developing and employing super-resolution microscopy; increasing evidence indicates channels and transporter nano-organization and dynamics within membranes play an important role in these regulatory mechanisms. Here we review recent advances and discuss the major advantages and disadvantages of using super-resolution microscopy to investigate protein organization and transport within plasma membranes.

Integration of secreted signaling molecule sensing on cell monitoring platforms: a critical review

Monitoring cell secretion in complex microenvironments is crucial for understanding cellular behavior and advancing physiological and pathological research. While traditional cell culture methods, including organoids and spheroids, provide valuable models, real-time monitoring of cell secretion of signaling molecules remains challenging. Integrating advanced monitoring technologies into these systems often disrupts the delicate balance of the microenvironment, making it difficult to achieve sensitivity and specificity. This review explored recent strategies for integrating the monitoring of cell secretion of signaling molecules, crucial for understanding and replicating cell microenvironments, within cell culture platforms, addressing challenges such as non-adherent cell models and the focus on single-cell methodologies. We highlight advancements in biosensors, microfluidics, and three-dimensional culture methods, and discuss their potential to enhance real-time, multiplexed cell monitoring. By examining the advantages, limitations, and future prospects of these technologies, we aim to contribute to the development of integrated systems that facilitate comprehensive cell monitoring, ultimately advancing biological research and pharmaceutical development.

Chemo-Enzymatic Generation of Highly Fluorescent Nucleoside Analogs Using Purine-Nucleoside Phosphorylase

Chemo-enzymatic syntheses of strongly fluorescent nucleoside analogs, potentially applicable in analytical biochemistry and cell biology are reviewed. The syntheses and properties of fluorescent ribofuranosides of several purine, 8-azapurine, and etheno-purine derivatives, obtained using various types of purine nucleoside phosphorylase (PNP) as catalysts, as well as α-ribose-1-phosphate (r1P) as a second substrate, are described. In several instances, the ribosylation sites are different to the canonical purine N9. Some of the obtained ribosides show fluorescence yields close to 100%. Possible applications of the new analogs include assays of PNP, nucleoside hydrolases, and other enzyme activities both in vitro and within living cells using fluorescence microscopy.