Four-color single-molecule imaging system for tracking GPCR dynamics with fluorescent HiBiT peptide

Single-molecule imaging provides information on diffusion dynamics, oligomerization, and protein-protein interactions in living cells. To simultaneously monitor different types of proteins at the single-molecule level, orthogonal fluorescent labeling methods with different photostable dyes are required. G-protein-coupled receptors (GPCRs), a major class of drug targets, are prototypical membrane receptors that have been studied using single-molecule imaging techniques. Here we developed a method for labeling cell-surface GPCRs inspired by the HiBiT system, which utilizes the high affinity complementation between LgBiT and HiBiT fragments of the NanoLuc luciferase. We synthesized four fluorescence-labeled HiBiT peptides (F-FiBiTs) with a different color dye (Setau-488, TMR, SaraFluor 650 and SaraFluor 720). We constructed a multicolor total internal reflection fluorescence microscopy system that allows us to track four color dyes simultaneously. As a proof-of-concept experiment, we labeled an N-terminally LgBiT-fused GPCR (Lg-GPCR) with a mixture of the four F-FiBiTs and successfully tracked each dye within a cell at the single-molecule level. The F-FiBiT-labeled Lg-GPCRs showed agonist-dependent changes in the diffusion dynamics and accumulation into the clathrin-coated pits as observed with a conventional method using a C-terminally HaloTag-fused GPCR. Taking advantage of luciferase complementation by the F-FiBiT and Lg-GPCRs, the F-FiBiT was also applicable to bioluminescence plate-reader-based assays. By combining existing labeling methods such as HaloTag, SNAP-tag, and fluorescent proteins, the F-FiBiT method will be useful for multicolor single-molecule imaging and will enhance our understanding of GPCR signaling at the single-molecule level.

Application of single-molecule analysis to singularity phenomenon of cells

Single-molecule imaging in living cells is an effective tool for elucidating the mechanisms of cellular phenomena at the molecular level. However, the analysis was not designed for throughput and requires high expertise, preventing it from reaching large scale, which is necessary when searching for rare cells that induce singularity phenomena. To overcome this limitation, we have automated the imaging procedures by combining our own focusing device, artificial intelligence, and robotics. The apparatus, called automated in-cell single-molecule imaging system (AiSIS), achieves a throughput that is a hundred-fold higher than conventional manual imaging operations, enabling the analysis of molecular events by individual cells across a large population. Here, using AiSIS, we demonstrate the single-molecule imaging of molecular behaviors and reactions related to tau protein aggregation, which is considered a singularity phenomenon in neurological disorders. Changes in the dynamics and kinetics of molecular events were observed inside and on the basal membrane of cells after the induction of aggregation. Additionally, to detect rare cells based on the molecular behavior, we developed a method to identify the state of individual cells defined by the quantitative distribution of molecular mobility and clustering. Using this method, cellular variations in receptor behavior were shown to decrease following ligand stimulation. This cell state analysis based on large-scale single-molecule imaging by AiSIS will advance the study of molecular mechanisms causing singularity phenomena.

Cholesterol suppresses spontaneous activation of EGFR-mediated signal transduction

Epidermal growth factor receptor (EGFR)-mediated signal transduction controls cell growth and proliferation. The signaling pathway is regulated so that it is activated only by external EGF stimuli, but the mechanisms that prevent EGF-independent spontaneous activation of EGFR-mediated signaling are unknown. Here we report cholesterol depletion activates EGFR-mediated signaling without EGF. We applied automated single-molecule imaging to EGFR and characterized the lateral diffusion and cluster formation on cholesterol-depleted and cholesterol-supplemented membranes. In cells in which cholesterol was depleted by methyl-β-cyclodextrin (MβCD) treatment, EGFR exhibited a reduction in lateral diffusion, an acceleration of cluster formation, and autophosphorylation without EGF. Concurrently, extracellular signal-regulated kinase (ERK), which is regulated by EGFR-mediated signaling, exhibited phosphorylation and nuclear translocation without EGF. These cholesterol depletion-induced changes were similar, albeit less efficient, to those that occurred with EGF stimulation in normal cells without MβCD, indicating the spontaneous activation of EGFR signaling. The exogenous supplementation of cholesterol suppressed the MβCD-induced spontaneous activation of EGFR and ERK nuclear translocation. Single-molecule imaging of EGFR in a large number of cells revealed cell-to-cell heterogeneity, with a sub-population showing a high ability for spontaneous activation. These results provide evidence that EGFR-mediated signaling is properly regulated by cholesterol metabolism to prevent uncontrolled spontaneous activation.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Deep learning in single-molecule imaging and analysis: recent advances and prospects

Single-molecule microscopy is advantageous in characterizing heterogeneous dynamics at the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest from single-molecule data. As a new class of computer algorithms that simulate the human brain to extract data features, deep learning networks excel in task parallelism and model generalization, and are well-suited for handling nonlinear functions and extracting weak features, which provide a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advances in the application of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.

Three live-imaging techniques for comprehensively understanding the initial trigger for insulin-responsive intracellular GLUT4 trafficking

Quantitative features of GLUT4 glucose transporter's behavior deep inside cells remain largely unknown. Our previous analyses with live-cell imaging of intracellular GLUT4 trafficking demonstrated two crucial early events responsible for triggering insulin-responsive translocation processes, namely, heterotypic fusion and liberation. To quantify the regulation, interrelationships, and dynamics of the initial events more accurately and comprehensively, we herein applied three analyses, each based on our distinct dual-color live-cell imaging approaches. With these approaches, heterotypic fusion was found to be the first trigger for insulin-responsive GLUT4 redistributions, preceding liberation, and to be critically regulated by Akt substrate of 160 kDa (AS160) and actin dynamics. In addition, demonstrating the subcellular regional dependence of GLUT4 dynamics revealed that liberated GLUT4 molecules are promptly incorporated into the trafficking itinerary of transferrin receptors. Our approaches highlight the physiological significance of endosomal "GLUT4 molecule trafficking" rather than "GLUT4 vesicle delivery" to the plasma membrane in response to insulin.

Budded baculoviruses as a receptor display system to quantify ligand binding with TIRF microscopy

Studying mechanisms of receptor-ligand interactions has remained challenging due to several limitations of different measurement methods. Here we present a total internal reflection fluorescence microscopy-based method that maintains the right balance between retaining the receptors in the natural lipid environment, sufficient throughput for ligand screening, high sensitivity, and offering more detailed view into the ligand-binding process. The novel method combines G protein-coupled receptor display in budded baculovirus particles and the immobilization of the particles to a functionalized coverslip. We adapted and validated the functionalized coverslip preparation process to achieve selective immobilization of budded baculovirus particles. The selectivity of budded baculovirus immobilization was validated with budded baculovirus particles displaying either Frizzled 6 receptors labeled with mCherry or neuropeptide Y Y1 receptors. To scale the system for ligand binding assays, we developed both open-source multiwell systems and image analysis software SPOTNIC for flexible assay design. The neuropeptide Y Y1 receptor was used for further receptor-ligand binding studies with high-affinity TAMRA labeled fluorescent ligand UR-MC026. The affinities of the fluorescent ligand and four unlabeled ligands (BIBO3304, UR-MK299, PYY, pNPY) were obtained with the developed method and followed a similar trend with both the parallel measurements with fluorescence anisotropy method and the data published earlier. The novel method could be extended for various advanced assays utilizing multidimensional detection modes, integrating super-resolution methods for single molecule detection and microfluidic devices for kinetic measurements.

Workflows of the Single-Molecule Imaging Analysis in Living Cells: Tutorial Guidance to the Measurement of the Drug Effects on a GPCR

Single-molecule imaging (SMI) is a powerful method to measure the dynamics of membrane proteins on the cell membrane. The single-molecule tracking (SMT) analysis provides information about the diffusion dynamics, the oligomer size distribution, and the particle density change. The affinity and on/off-rate of a protein-protein interaction can be estimated from the dual-color SMI analysis. However, it is difficult for trainees to determine quantitative information from the SMI movies. The present protocol guides the detailed workflows to measure the drug-activated dynamics of a G protein-coupled receptor (GPCR) and metabotropic glutamate receptor 3 (mGluR3), by using the total internal reflection fluorescence microscopy (TIRFM). This tutorial guidance comprises an open-source software, named smDynamicsAnalyzer, with which one can easily analyze the SMT dataset by just following the workflows after building a designated folder structure ( https://github.com/masataka-yanagawa/IgorPro8-smDynamicsAnalyzer ).