A combined ultrahigh plex whole slide staining and imaging system for a multiuser advanced imaging facility

用于多用户高级成像设施的组合式超高重全玻片染色和成像系统

• 批准号: MR/X012107/1
• 负责人: Gareth Miles
• 金额: $ 45.78万
• 依托单位: University of Leicester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX012107%2F1
• 关键词: combined; ultrahigh; plex; whole; slide

Pathology is a clinical discipline, whereby consultant doctors can look under a microscope at different pieces of human tissue which have been removed from patients during surgery, and help make an accurate diagnosis of the disease. Typically, this is achieved by looking at the structure and architecture of the tissue, or looking for individual proteins to see if there is more or less of those proteins. This allows us to delineate where these proteins are expressed, e.g. at the membrane, in the cytoplasm, or in the nucleus of a cell. These technologies and techniques are used by basic, translational and clinical researchers alike. More recently however, there has been a shift in the way that we can visualise different proteins under a microscope in a piece of tissue. Moving away from detecting single proteins, we can now look at multiple proteins of interest at once using a process called multiplexed immunofluorescence (mIF). Current mainstream technology allows for the combined detection up to 7-9 proteins with relative ease, in a high throughput setting. Any more than this, and technical difficulties start to occur.The ability to detect multiple proteins in the same piece of tissue, allows the researcher to look for novel protein expression patterns in different pathologies, for example: a) are multiple different proteins expressed in the same cells? b) Does this expression change depending on where in the tissue the cells are located? c) What cells are present and where in the tissue? Moreover, in a piece of human tissue, there are multiple different cell types present, these could be for example, cancer cells, and the patient's own individual immune cells all nestled together and communicating with each other. Having the ability to decipher what cell types express what proteins and where, the abundance of different cell types present and under what disease states can help to inform us about potential markers of drug resistance/sensitivity for specific diseases, and infer the mechanisms by which disease progresses. mIF can now be achieved evaluating over 100 markers in the same piece of tissue using ultrahigh-plex imaging , giving unprecedented insight into how the spatial geography of tissue, different cell types and disease states are interconnected. What's more, this process is fast, and capable of analysing every single cell on a microscope slide, compared to alternative methods which typically only allow for the high throughput evaluation of specific regions of interest (ROI) within a given piece of tissue. This means the way researchers analyse data is completely unbiased. The ability to evaluate such high level data in an unbiased way would greatly enhance our ability to progress biomarker and drug discovery, and validate any identified biomarkers or druggable targets in different disease pathologies. More specifically, the technology will allow researchers to decipher the potential roles that specific immune cells have in disease progression, and drug resistance in multiple disease indications, for example: cancer, nephropathy, and asthma.To evaluate such complex data, advanced image analysis software, driven by artificial intelligence (AI) is required to robustly analyse ultrahigh-plex images in a reproducible and timely manner. Software to identify every cell in the tissue, and analyse protein expression rapidly and accurately in each cell is now commercially available. By installing one of these ultrahigh-plex systems and the associated required software infrastructure at the Leicester Advanced Imaging Facility, we aim to promote world-class translational research, and biomarker and drug discovery, making it accessible to academia and industrial partners.

病理学是一门临床学科，顾问医生可以在显微镜下观察手术期间从患者身上切除的不同人体组织，并帮助对疾病做出准确的诊断。通常情况下，这是通过观察组织的结构和架构来实现的，或者寻找单个蛋白质以查看这些蛋白质的数量是否更多或更少。这使我们能够描绘这些蛋白质在哪里表达，例如在细胞膜，细胞质或细胞核中。这些技术和技巧被基础、转化和临床研究人员所使用。然而，最近，我们可以在显微镜下观察一块组织中不同蛋白质的方式发生了变化。从检测单个蛋白质开始，我们现在可以使用一种称为多重免疫荧光（mIF）的方法同时观察多个感兴趣的蛋白质。目前的主流技术允许在高通量环境中相对容易地组合检测多达7-9种蛋白质。在同一块组织中检测多种蛋白质的能力，使研究人员能够在不同的病理中寻找新的蛋白质表达模式，例如：a）在相同的细胞中表达多种不同的蛋白质吗？B）这种表达是否随细胞在组织中的位置而变化？c）组织中存在哪些细胞以及在组织中的位置？此外，在一块人体组织中，存在多种不同的细胞类型，例如，癌细胞和患者自己的免疫细胞都依偎在一起并相互交流。有能力破译什么细胞类型表达什么蛋白质，在哪里，不同细胞类型的丰度存在，以及在什么疾病状态下，可以帮助我们了解特定疾病的耐药性/敏感性的潜在标志物，并推断疾病进展的机制。mIF现在可以使用超高倍成像在同一块组织中评估100多个标志物，从而前所未有地深入了解组织的空间地理学，不同的细胞类型和疾病状态是如何相互联系的。更重要的是，这个过程是快速的，并且能够分析显微镜载玻片上的每一个细胞，相比之下，替代方法通常只允许对给定组织内的特定感兴趣区域（ROI）进行高通量评估。这意味着研究人员分析数据的方式是完全公正的。以无偏见的方式评估这种高水平数据的能力将大大增强我们进行生物标志物和药物发现的能力，并验证不同疾病病理中任何鉴定的生物标志物或可药用靶标。更具体地说，该技术将使研究人员能够破译特定免疫细胞在疾病进展中的潜在作用，以及多种疾病适应症中的耐药性，例如：癌症，肾病和哮喘。为了评估这些复杂的数据，需要由人工智能（AI）驱动的先进图像分析软件，以可重现和及时的方式稳健地分析超高倍图像。用于识别组织中每个细胞并快速准确地分析每个细胞中蛋白质表达的软件现在已经上市。通过在莱斯特先进成像设施安装这些超高复杂系统和相关的必要软件基础设施之一，我们的目标是促进世界一流的转化研究，生物标志物和药物发现，使其可供学术界和工业合作伙伴使用。