Antibody-induced crosslinking and cholesterol-sensitive, anomalous diffusion of nicotinic acetylcholine receptors

Abstract

Short title: Antibody-acetylcholine receptor diffusion and myastheniaThe abbreviations used are: BTX, α-bungarotoxin; mAb, monoclonal antibody; nAChR, nicotinic acetylcholine receptor; CDx, methyl-β-cyclodextrin; CDx-Chol, cholesterol-methyl-β-cyclodextrin complex; MSD, mean-squared displacement; SPT, single-particle tracking. Abstract Synaptic strength depends on the number of cell-surface neurotransmitter receptors in dynamic equilibrium with intracellular pools. Dysregulation of this homeostatic balance occurs e.g. in myasthenia gravis, an autoimmune disease characterized by a decrease in the number of postsynaptic nicotinic acetylcholine receptors (nAChRs). Monoclonal antibody mAb35 mimics this effect. Here we use STORM nanoscopy to characterize the individual and ensemble dynamics of mAb35-crosslinked receptors in the clonal cell line CHO-K1/A5, which robustly expresses adult muscle-type nAChRs. Antibody labeling of live cells results in 80% receptor immobilization. The remaining mobile fraction exhibits a heterogeneous combination of Brownian and anomalous diffusion. Single-molecule trajectories exhibit a two-state switching behavior between free Brownian walks and anticorrelated walks within confinement areas. The latter act as permeable fences (~34 nm radius, ~400 ms lifetime). Dynamic clustering, trapping and immobilization also occur in larger nanocluster zones (120-180 nm radius) with longer lifetimes (11 ± 1 s), in a strongly cholesterol-sensitive manner. Cholesterol depletion increases the size and average duration of the clustering phenomenon; cholesterol enrichment has the opposite effect. The disclosed high proportion of mAb35-crosslinked immobile receptors, together with their anomalous, cholesterol-sensitive diffusion and clustering, provides new insights into the antibody-enhanced antigenic modulation that leads to physiopathological internalization and degradation of receptors in myasthenia.A preliminary version of this work has appeared in the biorXiv repository: https://www.biorxiv.org/content/10.1101/744664v1. The study was not pre-registered.

A deep learning-based approach to model anomalous diffusion of membrane proteins: The case of the nicotinic acetylcholine receptor

“…The great majority of the nAChR trajectories appear to conform to the two-state physical model (Figure 3). Previous work from our laboratory showed that free-diffusing individual nAChR trajectories were interrupted by confinement sojourns of a few hundred millisecond duration (Mosqueira et al, 2018;Mosqueira et al, 2020). Turning-angle analysis (Burov et al, 2013;Sadegh et al, 2017) and escape-time analysis (Weigel et al, 2011) led us to conclude that the hindrance to motion experienced by diffusing molecules in the confined state was due to physical impediments, and the steepness of the turning-angle curves indicated high density of these physical obstacles, in agreement with an obstructed diffusion case (Weigel et al, 2012;Burov et al, 2013) (reviewed in (Krapf, 2015)).…”

“…The STORM study confirmed the heterogeneity of diffusional regimes, from the predominant subdiffusive to the infrequent superdiffusive motion. STORM nanoscopy was subsequently applied to characterize the diffusion of the receptor tagged with the monoclonal antibody mAb35 (Mosqueira et al, 2020). The motional behavior of the nAChRs changed, albeit subtly, suggesting that multivalent antibody-induced crosslinking of the receptor contributes to temporarily hinder its Brownian displacement and confines it in nanoscale domains of ~34 nm radius.…”

“…This network returns the state vector for each trajectory, which provides a flexible solution to extract information about the segments. In previous studies from our laboratory (Mosqueira et al, 2018;Mosqueira et al, 2020) this information was extracted using recurrent analysis and other analytical tools introduced by Krapf and his group to study molecular trajectories (Weigel et al, 2011).…”

“…Customarily, dSTORM is intended for fixed samples. We have optimized image acquisition protocols using this technique on live cells for short times (Mosqueira et al, 2018;Mosqueira et al, 2020). The acquired trajectories with short durations (usually < 50 steps) pose inherent challenges such as sensitivity to the localization error of methods such as mean-squared displacement (MSD).…”

“…We introduce a set of modifications to the neural networks proposed by these authors and by Volpe and coworkers (Bo et al, 2019) to characterize the diffusion properties at the single-molecule level, and we additionally introduce a two-state model which combines Brownian and obstructed diffusion based on the switching diffusion concept (Grebenkov, 2019). Previous results from our laboratory analyzed using classical methods such as mean squared displacement analysis (MSD) suggested that this model could explain the anomalous diffusion behavior of some of the nAChR trajectories (Mosqueira et al, 2018;Mosqueira et al, 2020). To the best of our knowledge, the analysis of the nAChR dynamics utilizing neural networks presents an original use of machine learning,…”