Primary supervisor: Assoc. Prof. Christopher Bolch
Co-supervisor: Dr Lennart Bach (IMAS Ecology and biodiversity, Salamanca)
Additional supervisors: Dr Terry Pinfold (CSL Flow Cytometry, Menzies Institute, Hobart)
Brief project description:
Full spectrum flow cytometry is a powerful approach to characterise particles, especially those that contain naturally fluorescent light-harvesting pigments such as phytoplankton. The most recently developed analytical instruments have vastly improved spectral range and resolution, and are now capable of distinguishing subtle changes in cell fluorescence intensity and across the full spectrum of photosynthetic pigments. Recent preliminary work by the sjpervisors and others show that these instruments can distinguish differences in related species, different life-cycle stages, and also changes in physiological status, such as response to changed light/temperature and the onset of nutrient limitation. The Menzies institute recently acquired a new full-spectrum – the Cytek Aurora, to complement the hi-resolution the MoFlo Astrios cell sorting flow cytometer. Used together, these instruments may provide capacity for in-situ comparison and analysis of the physiological state of phytoplankton. However, to develop this approach, it is necessary to understand natural variation of induced fluorescence signatures, and how these signatures change in response to common environmental factors and stresses experienced by cells.
This project aims to systematically examine how spectral signatures vary between different phytoplankton species at species genus and class level. The broader objective of the work is to build a library of fluorescent spectrum profiles that can be used for automated classification of phytoplankton cells and physiological status from natural samples.
Skills students will develop during this research project:
The student will learn the following skills during this project. Flow cytometry; Algal and microbial culture methods; DNA sequencing and bacterial phylogenetics; NGS-based microbial community profiling; community and NGS-data analysis