An image-based AI tool to identify stiffness- or age-related mechanotransduction abnormalities in vascular smooth muscle cells

点击翻译按钮获取中文摘要

In this project we will build an artificial intelligence (AI)-based image analysis tool called "MechanoAI-tool" trained to identify changes in vascular smooth muscle cell (SMC) shape and structure in response to changes in the stiffness of the surface the cells are plated on.Being able to easily identify these changes in SMC shape is important because vascular SMCs are the cell types that make up the walls of blood vessels and control blood flow to our organs by contracting or relaxing. This contraction or relaxation depends on a number of signals which includes the mechanical factors acting on the cell such as stiffness or stretch. However, as we age, our vessels become stiffer and the way vascular SMCs respond changes, which together can impact blood flow to vital organs.Measuring how vascular SMCs respond to stiffness changes is a good indicator of ageing. However, many methods for studying stiffness responses require complex genetic techniques and expensive microscopes which act as barriers to entry for many research groups.We think that image analysis of vascular SMC responses under simple phase contrast microscopy, which is cheap and widely available, will provide an accessible, and robust way to test whether any given SMCs respond appropriately to stiffness changes or not. However, interpreting the changes on a simple phase contrast microscope is not easy and we will therefore develop an AI-based image analysis tool to identify normal vs abnormal responses quickly and reliably.These findings and the Mechano-AI-tool that we develop could be used to predict biological properties of vascular SMCs by any research group world-wide and have applications in SMC biology, ageing and vascular disease research.