Multi User High-Content Confocal Fluorescence Microscope

我们正在为高含量荧光显微镜寻求资助，这是巴斯大学光学显微镜设施的关键设备，将提高数据质量并节省宝贵的研究时间。使用高含量显微镜，我们将对生命从发育到疾病的动态过程有新的认识。该提案中的项目涵盖了巴斯大学生物科学的不同研究领域，包括干细胞生物学、衰老和神经变性、伤口愈合和组织再生、葡萄糖代谢、抗生素耐药性和植物科学。高含量荧光显微镜可以在几分钟内从数百个样品中捕获数万张图像，只需点击鼠标。这最大限度地提高了效率，也产生了比手动获取更可靠的成像数据。此外，高含量成像使研究人员能够在一次实验中测量更大范围的药物剂量、孵育时间或其他条件，并在每种条件下包括更多的重复，从而提高统计能力。自动化还减少了观察者偏差的可能性，例如对人群中罕见事件的更多了解，这可能导致高估或低估治疗效果。提出的高含量显微镜将具有从纳米尺度（如纳米粒子生物传感器、细胞内囊泡和微生物）到毫米尺度（如模型组织和模型生物）对物体成像的能力。它还将能够捕捉延时视频，以跟踪细胞和蛋白质随时间的运动和行为。为了利用高含量显微镜和自动图像分析产生的丰富数据，我们将利用计算机视觉和机器学习的最新进展。图像分析软件使用算法和人工智能来识别和分类物体，每次实验为研究人员提供数千到数百万个单个细胞的数百个测量值。巴斯和其他地方正在开发尖端的数学工具，以深入研究单细胞和其他图像数据集的复杂性。重要的是，该提案包括一个在计算和数学方法方面为生物科学研究人员提供培训和支持的项目，并且包括旨在将定量科学家和生命科学家聚集在一起进行跨学科合作的活动。

We are seeking funding for a high-content fluorescence microscope - a critical piece of equipment for the University of Bath's light microscopy facility that will improve the quality of data and save valuable research time. Using high-content microscopy, we will gain new insights into the dynamic processes of life from development to disease. The projects in this proposal span the diverse research areas in biological sciences at Bath, including stem cell biology, ageing and neurodegeneration, wound healing and tissue regeneration, glucose metabolism, antibiotic resistance, and plant science. A high-content fluorescence microscope can capture tens of thousands of images from hundreds of samples in minutes with the click of a mouse. This maximises efficiency and also produces more reliable imaging data than is possible to acquire manually. Furthermore, high-content imaging allows researchers to measure a broader range of drug doses, incubation times or other conditions in a single experiment, and to include more replicates per condition which improves statistical power. Automation also reduces the likelihood of observer bias, such as being more aware of rare events in a population, which can lead to over- or underestimating treatment effects. The proposed high-content microscope will have the capability to image objects from the nanometre scale, such as nanoparticle biosensors, intracellular vesicles, and microorganisms, to the millimetre scale, such as model tissues and model organism. It will also be able to capture time-lapse videos to track the motion and behaviour of cells and proteins over time.To make use of the wealth of data produced by high-content microscopy and automated image analysis, we will take advantage of recent advances in computer vision and machine learning. Image analysis software uses algorithms and artificial intelligence to identify and classify objects, providing researchers with hundreds of measurements for thousands to millions of individual cells per experiment. Cutting-edge mathematical tools are being developed at Bath and elsewhere to delve into the complexity of single cell and other image datasets. Importantly, this proposal includes a programme of training and support for biological sciences researchers in computational and mathematical methods, and includes events designed to bring quantitative and life scientists for together interdisciplinary collaborations.