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

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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.