Development of automated imaging and spectroscopic cell sorting platforms for research into cancer and metabolic diseases

开发用于癌症和代谢疾病研究的自动成像和光谱细胞分选平台

• 批准号: 2606761
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2606761
• 关键词: Development; automated; imaging; spectroscopic; cell

Identifying and isolating rare target cells from a population is essential for diagnostics and fundamental research. Although fluorescence-based sorting techniques are commonly used, there are a vast diversity of applications, where fluorescence labelling is either not applicable or not desirable (e.g. when sorting cells for therapeutics).This project will develop a versatile microfluidic system that integrates with Raman spectroscopy for sorting living cells based on their intrinsic biomolecular and optical image profiles. It will build upon on our pioneering Raman-activated cell sorting (RACS) microfluidic platforms, with new developments in machine learning and opto-microfluidics. The technology will use single cell Raman spectra that are characteristic of the phenotype, metabolic activity and function of a cell.The outcome of this work will open a new avenue for 'deep mining' of untapped biological systems and facilitate the study of cellular metabolisms that are manifested in disease states. Within the project, depending on the student's interests, the focus could be on the study of cancer cells at different stages to discover potential metabolic biomarkers for early cancer diagnosis and screening for potential treatments.The project is highly interdisciplinary involving collaborations between engineers, cell biologists, and physical scientists. The student will gain a broad range of skills ranging from microfluidics, to imaging methods and analytical techniques, all in the context of studying the cell biology associated with different diseases.

从种群中识别和分离稀有靶细胞对于诊断和基础研究至关重要。虽然基于荧光的分选技术是常用的，但有大量的应用，其中荧光标记不适用或不希望(例如，当分选治疗细胞时)。这个项目将开发一个多功能的微流控系统，它与拉曼光谱相结合，根据活细胞的固有生物分子和光学图像特征来分选活细胞。它将建立在我们开创性的拉曼激活细胞分类(RACS)微流控平台的基础上，并在机器学习和光学微流控方面取得新的发展。这项技术将使用单细胞拉曼光谱，这是细胞表型、代谢活动和功能的特征。这项工作的结果将为深入挖掘尚未开发的生物系统开辟一条新的途径，并促进对疾病状态下细胞新陈代谢的研究。在这个项目中，根据学生的兴趣，重点可能是研究不同阶段的癌细胞，以发现潜在的代谢生物标记物，用于早期癌症诊断和筛选潜在的治疗方法。该项目是高度跨学科的，涉及工程师、细胞生物学家和物理学家之间的合作。学生将获得从微流体学到成像方法和分析技术的广泛技能，所有这些都是在学习与不同疾病相关的细胞生物学的背景下进行的。