Multi User High-Content Confocal Fluorescence Microscope

我们正在为一台高含量荧光显微镜寻求资金，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，我们将获得对生命从发育到疾病的动态过程的新见解。该提案中的项目涵盖巴斯生物科学的不同研究领域，包括干细胞生物学、衰老和神经退行性变、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。一台高含量的荧光显微镜只需点击鼠标，就可以在几分钟内从数百个样本中捕获数万张图像。这最大限度地提高了效率，并产生了比手动获取的更可靠的成像数据。此外，高内容成像允许研究人员在单个实验中测量更广泛的药物剂量、孵化时间或其他条件，并在每个条件下包括更多重复，这提高了统计能力。自动化还减少了观察者偏见的可能性，例如更多地意识到人群中的罕见事件，这可能导致高估或低估治疗效果。拟议的高含量显微镜将有能力成像从纳米级的物体，如纳米颗粒生物传感器、细胞内小泡和微生物，到毫米级的物体，如模型组织和模型生物体。它还将能够捕捉延时视频，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对象，为研究人员提供每个实验数千到数百万个单个细胞的数百个测量数据。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，这项建议包括对生物科学研究人员进行计算和数学方法方面的培训和支持的计划，并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.