bioRxivCell Biology
Detection of Fluorescent Protein Mechanical Switching in Cellulo
T. C. Shoyer, K. L. Collins, T. R. Ham, A. T. Blanchard, J. N. Malavade, J. L. West, B. D. Hoffman
Abstract
ABSTRACT/SUMMARYThe ability of cells to sense and respond to mechanical forces is critical in many physiological and pathological processes. However, the mechanisms by which forces affect protein function inside cells remain unclear. Motivated by in vitro demonstrations of fluorescent proteins (FPs) undergoing reversible mechanical switching of fluorescence, we investigated if force-sensitive changes in FP function could be visualized in cells. Guided by a computational model of FP mechanical switching, we develop a formalism for its detection in Forster resonance energy transfer (FRET)-based biosensors and demonstrate its occurrence in cellulo in a synthetic actin-crosslinker and the mechanical linker protein vinculin. We find that in cellulo mechanical switching is reversible and altered by manipulation of cellular force generation as well as force-sensitive bond dynamics of the biosensor. Together, this work describes a new framework for assessing FP mechanical stability and provides a means of probing force-sensitive protein function inside cells. MOTIVATIONThe ability of cells to sense mechanical forces is critical in developmental, physiological, and pathological processes. Cells sense mechanical cues via force-induced alterations in protein structure and function, but elucidation of the molecular mechanisms is hindered by the lack of approaches to directly probe the effect of forces on protein structure and function inside cells. Motivated by in vitro observations of reversible fluorescent protein mechanical switching, we developed an approach for detecting fluorescent protein mechanical switching in cellulo. This enables the visualization of force-sensitive protein function inside living cells.
- organism: human
- assay: fluorescence / live-cell microscopy
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
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