Lichtenstein, A. | Search bioRxiv

bioRxiv2025

A Plasma Membrane Vesicle Imaging-Based Platform for Studying Membrane Fusion

Membrane fusion is central to biological processes such as viral entry, fertilization and cell-to-cell fusion. Gaining a mechanistic understanding of fusion requires the ability to visualize and quantify the dynamic interaction between two membranes and their associated protein machineries at high temporal and spatial resolution. However, studying these processes in live cells remains challenging due to the complexity of the cellular environment. Here we demonstrate a versatile cell-free platform based on giant plasma membrane vesicles (GPMVs) that enables controlled, quantitative analysis of receptor binding and membrane fusion kinetics in a native membrane context. As proof of concept, we reconstitute the SARS-CoV-1 Spike-ACE2 interaction, capturing specific receptor engagement and accumulation at the membrane interface using confocal microscopy and micropipette aspiration. Fusion was induced by proteolytic activation and quantified using both high-resolution microscopy and high-throughput Imaging Flow Cytometry. The platform also reveals the influence of membrane composition on fusion efficiency, demonstrated by the impact of cholesterol depletion. This approach provides a broadly applicable system for dissecting membrane fusion and protein-protein interactions across membranes, with compatibility for biophysical, imaging and structural analysis. It offers new opportunities for mechanistic studies and inhibitor screening in a biologically relevant yet experimentally accessible context.

biophysics↗

bioRxiv2017

Learning of Active Binocular Vision in a Biomechanical Model of the Oculomotor System

We present a model for the autonomous learning of active binocular vision using a recently developed biome-chanical model of the human oculomotor system. The model is formulated in the Active Efficient Coding (AEC) framework, a recent generalization of classic efficient coding theories to active perception. The model simultaneously learns how to efficiently encode binocular images and how to generate accurate vergence eye movements that facilitate efficient encoding of the visual input. In order to resolve the redundancy problem arising from the actuation of the eyes through antagonistic muscle pairs, we consider the metabolic costs associated with eye movements. We show that the model successfully learns to trade off vergence accuracy against the associated metabolic costs, producing high fidelity vergence eye movements obeying Sherringtons law of reciprocal innervation.

neuroscience↗