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Abstract
This work presents an automated pipeline designed for the inference of the subcellular and population-level mechanome in red blood cells (RBCs). The system utilizes high-resolution spatial (65 nm/px) and medium-resolution temporal (30 Hz) video microscopy to capture the dynamics of cell membrane thermal fluctuations (flickering).
From a mathematical and data-driven perspective, the computational framework integrates deep learning models (YOLO/ResNet) for cell localization. Membrane quantification is performed by searching for the maximum intensity gradient within the polar-transformed bounding box of the selected cell, achieving subpixel contour tracking. This architecture allows for modeling membrane fluctuations as stochastic processes, from which fundamental biophysical properties constituting the mechanome—such as viscoelasticity, mechanical power, and entropy production—are derived.
The pipeline demonstrates real-time performance levels, having processed a total population of approximately 5,000 individual cells. The method was tested by evaluating the effect of quercetin on membrane stiffness, yielding results consistent with established literature and demonstrating high sensitivity for detecting molecular modulations and subcellular mechanical heterogeneities. This advancement provides a robust statistical and computational framework for large-scale analysis in cellular mechanobiology.
Bibliography
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