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

一种基于图像的人工智能工具，用于识别血管平滑肌细胞中与硬度或年龄相关的机械转导异常

• 批准号: BB/Y513994/1
• 负责人: Sanjay Sinha
• 金额: $ 32.87万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY513994%2F1
• 关键词: image; based; AI; tool; identify

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.

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.这种收缩或放松取决于许多信号，这些信号包括作用在细胞上的机械因素，例如刚度或拉伸。但是，随着年龄的增长，我们的血管变得更硬，血管SMC的反应变化变化，这会影响血液流向重要的器官。衡量血管SMC对刚度变化的反应如何良好的衰老指标。但是，许多研究僵硬反应的方法都需要复杂的遗传技术和昂贵的显微镜，这些方法是许多研究小组进入的障碍。我们认为，在简单相比的显微镜下，对血管SMC响应的图像分析（便宜且广泛可用），可访问且可访问的方式可访问，并且可以很好地测试任何给定的SMC对刚度是否适当地响应僵硬的变化。但是，解释简单阶段对比显微镜上的变化并不容易，因此我们将开发一种基于AI的图像分析工具，以迅速而可靠地识别正常反应与异常反应。这些发现以及我们开发的机械型工具可用于预测任何研究小组的血管SMC生物学特性，该小组在世界各地的任何研究小组中都应用了SMC生物学研究。