Predicting local tissue mechanics using immunohistochemistry

Abstract

Local tissue stiffness provides an important signal to which cells respond in vivo. However, assessing tissue mechanics is currently challenging and requires sophisticated technology. We here developed a model quantitatively predicting nervous tissue stiffness heterogeneities at cellular resolution based on cell density, myelin and GFAP fluorescence intensities. These histological parameters were identified by a correlation analysis of atomic force microscopy-based elasticity maps of spinal cord sections and immunohistochemical stainings. Our model provides a simple tool to estimate local stiffness distributions in nervous tissue, and it can easily be expanded to other tissue types, thus paving the way for studies of the role of mechanical signals in development and pathology.