Multi User High-Content Confocal Fluorescence Microscope

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.

We propose to add a state-of-the-art confocal fluorescence high-content microscope (HCM) to the multi-user core facility at the University of Bath. Quantitative image analysis is a cornerstone of modern biological sciences, and a purpose-built HCM will enable researchers across diverse fields to acquire thousands of images quickly and reproducibly, which will streamline research, save hundreds of person-hours, reduce observer bias, and improve statistical power. Recent advances in computer vision tools for automated image analysis yield extremely high-dimensional datasets containing a wealth of information at the levels of populations, individual cells, and subcellular structures, such as cell morphology, protein quantity and localisation, cell and subcellular dynamics and motility, and molecular interactions. In addition to imaging multiwell plates in minutes, the confocal capability of the proposed HCM will allow researchers to extend their studies to 3D structures, such as organoids and zebrafish larvae, and to sub-micron resolution. The instrument will also be equipped with climate control, including O2, for live cell and organism studies. The integration of a new HCM into the core facility, including extensive training for facility staff, will ensure equipment support and maintenance. This proposal also outlines a programme of training and events to support life sciences researchers in the computational and quantitative methods needed to take advantage of HCM and large, complex datasets and to recruit quantitative researchers to tackle biological questions. HCM at Bath will therefore foster interdisciplinary collaborations across the university to drive innovation in biological sciences, computer science, and mathematics research.