Imaging G protein-coupled receptor islands

Plasmonic Imaging of Dynamic Interactions between Membrane Receptor Clusters beyond the Diffraction Limit in Live Cells

As a general mechanism, ligand-induced receptor clustering on cell membrane plays determinative roles in pattern recognition and transmembrane signaling. Nevertheless, probing the dynamic characteristics for the complicated interactions between receptor clusters remains difficult because of the lack of strategy for receptor cluster labeling and long-term monitoring in live cells. Herein, we proposed a data-mining-integrated plasmon coupling microscopy to study the dynamic cluster-cluster interactions on cell surface. The receptor clusters were activated and labeled with multivalent plasmonic nanoprobes, which enables the real-time monitoring of individual receptor clusters and the measurement of cluster-cluster interactions from the analysis of plasmonic coupling for the nanoprobe pairs beyond the diffraction limit. Using this method, we found that the protease-activated receptor 1 (PAR1) clusters would experience an initial contact and then form a weakly bound cluster-cluster complex, followed by cluster fusion to generate large-sized signaling complexes. The underlying state transitions for the cluster-cluster fusion process were uncovered using a data-mining technique named the K-means-based hidden Markov model with the scattering intensity of coupled nanoprobe pairs as observations. All of the findings from single-particle analysis and bulk measurements suggested that the allosteric inhibitors could suppress the dynamic transitions from the weakly bound cluster-cluster complexes to fused signaling complexes, leading to the subsequent downregulation of intracellular calcium signaling pathways. We believe that this strategy is promising for imaging and monitoring receptor clustering as well as protein phase separation on the cell surface in various biological and physiological processes.

Clusters of proteins in biomembranes: insights into the roles of interaction potential shapes and of protein diversity

It has recently been proposed that proteins embedded in lipidic biomembranes can spontaneously self-organize into stable small clusters, or membrane nanodomains, due to the competition between short-range attractive and longer-range repulsive forces between proteins, specific to these systems. In this paper, we carry on our investigation, by Monte Carlo simulations, of different aspects of cluster phases of proteins in biomembranes. First, we compare different long-range potentials (including notably three-body terms) to demonstrate that the existence of cluster phases should be quite generic. Furthermore, a real membrane contains hundreds of different protein species that are far from being randomly distributed in these nanodomains. We take this protein diversity into account by modulating protein-protein interaction potentials both at short and longer range. We confirm theoretical predictions in terms of biological cluster specialization by deciphering how clusters recruit only a few protein species. In this respect, we highlight that cluster phases can turn out to be an advantage at the biological level, for example by enhancing the cell response to external stimuli.

What do diffusion measurements tell us about membrane compartmentalisation? Emergence of the role of interprotein interactions

The techniques of diffusion analysis based on optical microscopy approaches have revealed a great diversity of the dynamic organisation of cell membranes. For a long period, two frameworks have dominated the way of representing the membrane structure: the membrane skeleton fences and the lipid raft models. Progresses in the methods of data analysis have shed light on the features and consequently the possible origin of membrane domains: Inter-protein interactions play a role in confinement. Innovative developments pushing forward the spatiotemporal resolution limits are currently emerging, which are likely to provide in the future a detailed understanding of the intimate functional dynamic organisation of the cell membrane.

Thermodynamics of nanocluster phases: a unifying theory

We propose a unifying, analytical theory accounting for the self-organization of colloidal systems in nanocluster or microcluster phases. We predict the distribution of cluster sizes with respect to interaction parameters and colloid concentration. In particular, we anticipate a proportionality regime where the mean cluster size grows proportionally to the concentration, as observed in several experiments. We emphasize the interest in a predictive theory in soft matter, nanotechnologies, and biophysics.