Lüchtefeld, I. | Search bioRxiv

bioRxiv2025

Quantifying Intracellular Mechanosensitive Response upon Spatially Defined Mechano-Chemical Triggering

The mechanotransduction process relies on the interaction of mechanical and biochemical cues, transmitting cellular forces to intracellular organelles to activate biochemical pathways and elicit responses. This involves mechanoresponsive components like actin filaments, microtubules (MTs), and the lamin meshwork. Fluidic force microscopy (FluidFM), a force-controlled micropipette allows for the manipulation of intact cells mechanically and chemically, providing a novel approach to study mechanotransmission in cells in situ. FluidFM combined with fluorescence lifetime imaging microscopy (FLIM), enables high-resolution mapping of intracellular tension dynamics. Here, we used cells with varying nuclear lamina compositions to explore the laminas role in initiating mechanoresponse to external cues. We found that A-type and B-type lamins trigger nuclear mechanoresponse distinctly, with A-type lamins contributing to nuclear elasticity, whereas B-type lamins influence viscous response. Moreover, MTs underwent mechanical adaptation and assisted in releasing the tension in lamin A/C knockout (KO) cells, contrasting with healthy cells where MTs aid to preserve the tension locally rather than transferring it. This research provides insights into the dynamic mechanoresponse of cellular components and supports targeted therapies for mechanical stress-related diseases.

biophysics↗

bioRxiv2025

High-Resolution Time-Lapse Imaging of Droplet-Cell Dynamics via Optimal Transport and Contrastive Learning

Single-cell analysis is essential for uncovering heterogeneous biological functions that arise from intricate cellular interaction. Microfluidic droplet arrays enable precise dynamic data collection through cell encapsulation in picoliter volumes. The time-lapse imaging of these arrays can reveal functional kinetics and cellular fates, but accurate tracking of cell identities across time frames remains challenging when droplets move significantly. Specifically, existing machine learning methods often depend on labeled data or require neighboring cells as reference; without them, these methods struggle to track identical objects across long distances with complex movements. To address these limitations, we developed a pipeline combining visual object detection, feature extraction via contrastive learning, and optimal transport-based object matching, which minimizes reliance on labeled training data. Our approach was validated across various experimental conditions and was able to track thousands of water-in-oil microfluidic droplets over large distances and long (> 30 min) time-separated frames. We achieved high precision in previously untraceable scenarios, tracking small, medium and large movements (corresponding to ~126, ~800 and ~10,000 {micro}m, respectively) with a success rate of correctly tracked droplets of > 90% for average movements within 212 object diameters, and > 60% for average movements of > 100 object diameters. This workflow lays the foundation for high-resolution, dynamic analysis of droplets and cells in both spatial and temporal dimensions without relying on visual labeling, allowing high-accuracy tracking in samples, where the uniqueness of the sample makes repeating experiments infeasible.

bioinformatics↗