CRISPR/Cas12a-mediated labeling of MET receptor enables quantitative single-molecule imaging of endogenous protein organization and dynamics

Long-Term Single-Molecule Tracking in Living Cells using Weak-Affinity Protein Labeling

Single-particle tracking (SPT) has become a powerful tool to monitor the dynamics of membrane proteins in living cells. However, permanent labeling strategies for SPT suffer from photobleaching as a major limitation, restricting observation times, and obstructing the study of long-term cellular processes within single living cells. Here, we use exchangeable HaloTag Ligands (xHTLs) as an easy-to-apply labeling approach for live-cell SPT and demonstrate extended observation times of individual living cells of up to 30 minutes. Using the xHTL/HaloTag7 labeling system, we measure the ligand-induced activation kinetics of the epidermal growth factor receptor (EGFR) in single living cells. We generate spatial maps of receptor diffusion in cells, report non-uniform distributions of receptor mobility, and the formation of spatially confined 'hot spots' of EGFR activation. Furthermore, we measured the mobility of an ER-luminal protein in living cells and found diffusion coefficients that correlated with the ER nano-structure. This approach represents a general strategy to monitor protein mobility in a functional context and for extended observation times in single living cells.

Single-molecule imaging and molecular dynamics simulations reveal early activation of the MET receptor in cells

Embedding of cell-surface receptors into a membrane defines their dynamics but also complicates experimental characterization of their signaling complexes. The hepatocyte growth factor receptor MET is a receptor tyrosine kinase involved in cellular processes such as proliferation, migration, and survival. It is also targeted by the pathogen Listeria monocytogenes, whose invasion protein, internalin B (InlB), binds to MET, forming a signaling dimer that triggers pathogen internalization. Here we use an integrative structural biology approach, combining molecular dynamics simulations and single-molecule Förster resonance energy transfer (smFRET) in cells, to investigate the early stages of MET activation. Our simulations show that InlB binding stabilizes MET in a conformation that promotes dimer formation. smFRET reveals that the in situ dimer structure closely resembles one of two previously published crystal structures, though with key differences. This study refines our understanding of MET activation and provides a methodological framework for studying other plasma membrane receptors.

Advancements and Practical Considerations for Biophysical Research: Navigating the Challenges and Future of Super-resolution Microscopy

The introduction of super-resolution microscopy (SRM) has significantly advanced our understanding of cellular and molecular dynamics, offering a detailed view previously beyond our reach. Implementing SRM in biophysical research, however, presents numerous challenges. This review addresses the crucial aspects of utilizing SRM effectively, from selecting appropriate fluorophores and preparing samples to analyzing complex data sets. We explore recent technological advancements and methodological improvements that enhance the capabilities of SRM. Emphasizing the integration of SRM with other analytical methods, we aim to overcome inherent limitations and expand the scope of biological insights achievable. By providing a comprehensive guide for choosing the most suitable SRM methods based on specific research objectives, we aim to empower researchers to explore complex biological processes with enhanced precision and clarity, thereby advancing the frontiers of biophysical research.

From Monomers to Hexamers: A Theoretical Probability of the Neighbor Density Approach to Dissect Protein Oligomerization in Cells

Deciphering the oligomeric state of proteins within cells is pivotal to understanding their role in intricate cellular processes. With the recent advances in single-molecule localization microscopy, previous efforts have harnessed protein location density approaches, coupled with simulations, to extract membrane protein oligomeric states in cells, highlighting the value of such techniques. However, a comprehensive theoretical approach that can be universally applied across different proteins (e.g., membrane and cytosolic proteins) remains elusive. Here, we introduce the theoretical probability of neighbor density (PND) as a robust tool to discern protein oligomeric states in cellular environments. Utilizing our approach, the theoretical PND was validated against simulated data for both membrane and cytosolic proteins, consistently aligning with experimental baselines for membrane proteins. This congruence was maintained even when adjusting for protein concentrations or exploring proteins of various oligomeric states. The strength of our method lies not only in its precision but also in its adaptability, accommodating diverse cellular protein scenarios without compromising the accuracy. The development and validation of the theoretical PND facilitate accurate protein oligomeric state determination and bolster our understanding of protein-mediated cellular functions.

Quantitative live imaging reveals a direct interaction between CD44v6 and MET in membrane domains upon activation with both MET ligands, HGF and internalin B

Deregulation of the receptor tyrosine kinase MET/hepatocyte growth factor (HGF) pathway results in several pathological processes involved in tumor progression and metastasis. In a different context, MET can serve as an entry point for the bacterium Listeria monocytogenes, when activated by the internalin B (InlB) protein during infection of non-phagocytic cells. We have previously demonstrated that MET requires CD44v6 for its ligand-induced activation. However, the stoichiometry and the steps required for the formation of this complex, are still unknown. In this work, we studied the dynamics of the ligand-induced interaction of CD44v6 with MET at the plasma membrane. Using Förster resonance energy transfer-based fluorescence lifetime imaging microscopy in T-47D cells, we evidenced a direct interaction between MET and CD44v6 promoted by HGF and InlB in live cells. In the absence of MET, fluorescence correlation spectroscopy experiments further showed the dimerization of CD44v6 and the increase of its diffusion induced by HGF and InlB. In the presence of MET, stimulation of the cells by HGF or InlB significantly decreased the diffusion of CD44v6, in line with the formation of a ternary complex of MET with CD44v6 and HGF/InlB. Finally, similarly to HGF/InlB, disruption of liquid-ordered domains (Lo) by methyl-β-cyclodextrin increased CD44v6 mobility suggesting that these factors induce the exit of CD44v6 from the Lo domains. Our data led us to propose a model for MET activation, where CD44v6 dimerizes and diffuses rapidly out of Lo domains to form an oligomeric MET/ligand/CD44v6 complex that is instrumental for MET activation.

Absolute quantification of protein number and dynamics in single cells

Quantitative characterization of protein abundance and interactions in live cells is necessary to understand and predict cellular behavior. The accurate determination of copy number for individual proteins and heterologous complexes in individual cells is critical because small changes in protein dosage, often less than two-fold, can have strong phenotypic consequences. Here, we review the merits and pitfalls of different quantitative fluorescence imaging methods for single-cell determination of protein abundance, localization, interactions, and dynamics. In particular, we discuss how scanning number and brightness (sN&B) and its variation, Raster scanning image correlation spectroscopy (RICS), exploit stochastic noise in small measurement volumes to quantify protein abundance, stoichiometry, and dynamics with high accuracy.

CRISPR/Cas-Based Techniques for Live-Cell Imaging and Bioanalysis

CRISPR/Cas systems have found widespread applications in gene editing due to their high accuracy, high programmability, ease of use, and affordability. Benefiting from the cleavage properties (trans- or cis-) of Cas enzymes, the scope of CRISPR/Cas systems has expanded beyond gene editing and they have been utilized in various fields, particularly in live-cell imaging and bioanalysis. In this review, we summarize some fundamental working mechanisms and concepts of the CRISPR/Cas systems, describe the recent advances and design principles of CRISPR/Cas mediated techniques employed in live-cell imaging and bioanalysis, highlight the main applications in the imaging and biosensing of a wide range of molecular targets, and discuss the challenges and prospects of CRISPR/Cas systems in live-cell imaging and biosensing. By illustrating the imaging and bio-sensing processes, we hope this review will guide the best use of the CRISPR/Cas in imaging and quantifying biological and clinical elements and inspire new ideas for better tool design in live-cell imaging and bioanalysis.

Near-Infrared Blinking Carbon Dots Designed for Quantitative Nanoscopy

Blinking carbon dots (CDs) have attracted attention as a probe for single molecule localization microscopy (SMLM), yet quantitative analysis is limited because of inept blinking and low signal-to-noise ratio (SNR). Here we report the design and synthesis of near-infrared (NIR) blinking CDs with a maximum emission of around 750 nm by weaving a nitrogen-doped aromatic backbone with surplus carboxyl groups on the surface. The NIR-CDs allow conjugation to monovalent antibody fragments for labeling and imaging of cellular receptors as well as afford increases of 52% in SNR and 33% in localization precision over visible CDs. Analysis of fluorescent bursts allows for accurate counting of cellular receptors at the nanoscale resolution. Using NIR-CDs-based SMLM, we demonstrate oligomerization and internalization of programmed cell death-ligand 1 by a small molecule inhibitor for checkpoint blockade. Our NIR-CDs can become a generally applicable probe for quantitative nanoscopy in chemistry and biology.

mEos4b Photoconversion Efficiency Depends on Laser Illumination Conditions Used in PALM

Green-to-red photoconvertible fluorescent proteins (PCFPs) are widely employed as markers in photoactivated localization microscopy (PALM). However, their highly complex photophysical behavior complicates their usage. The fact that only a limited fraction of a PCFP ensemble can form the photoconverted state upon near-UV light illumination, termed photoconversion efficiency (PCE), lowers the achievable spatial resolution in PALM and creates undercounting errors in quantitative counting applications. Here, we show that the PCE of mEos4b is not a fixed property of this PCFP but strongly depends on illumination conditions. Attempts to reduce long-lived blinking in red mEos4b by application of 488 nm light lead to a reduction of the PCE. Furthermore, the PCE of mEos4b strongly depends on the applied 405 nm power density. A refined photophysical model of mEos4b accounts for the observed effects, involving nonlinear green-state photobleaching upon violet light illumination favored by photon absorption by a putative radical dark state.

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.

Small Peptide-Protein Interaction Pair for Genetically Encoded, Fixation Compatible Peptide-PAINT

Super-resolution microscopy via PAINT has been widely adopted in life sciences to interrogate the nanoscale architecture of many cellular structures. However, obtaining quantitative information in fixed cellular samples remains challenging because control of labeling stoichiometry is hampered in current approaches due to click-chemistry and additional targeting probes. To overcome these challenges, we have identified a small, PDZ-based, peptide-protein interaction pair that is genetically encodable and compatible with super-resolution imaging upon cellular fixation without additional labeling. Stoichiometric labeling control by genetic incorporation of this probe into the cellular vimentin network and mitochondria resulted in super-resolved 3D reconstructions with high specificity and spatial resolution. Further characterization reveals that this peptide-protein interaction is compatible with quantitative PAINT and that its binding kinetics remains unaltered upon fixation. Finally, by fusion of our probe to nanobodies against conventional expression markers, we show that this approach provides a versatile addition to the super-resolution toolbox.

Automated Analysis of Fluorescence Kinetics in Single-Molecule Localization Microscopy Data Reveals Protein Stoichiometry

Understanding the function of protein complexes requires information on their molecular organization, specifically, their oligomerization level. Optical super-resolution microscopy can localize single protein complexes in cells with high precision, however, the quantification of their oligomerization level, remains a challenge. Here, we present a Quantitative Algorithm for Fluorescent Kinetics Analysis (QAFKA), that serves as a fully automated workflow for quantitative analysis of single-molecule localization microscopy (SMLM) data by extracting fluorophore "blinking" events. QAFKA includes an automated localization algorithm, the extraction of emission features per localization cluster, and a deep neural network-based estimator that reports the ratios of cluster types within the population. We demonstrate molecular quantification of protein monomers and dimers on simulated and experimental SMLM data. We further demonstrate that QAFKA accurately reports quantitative information on the monomer/dimer equilibrium of membrane receptors in single immobilized cells, opening the door to single-cell single-protein analysis.