Raman activated cell sorting and counting in continuous flow

McIlvenna, David (2015) Raman activated cell sorting and counting in continuous flow. PhD thesis, University of Glasgow.

Abstract

Bacteria play a key role in the natural maintenance of our ecosystem and are also used extensively in agriculture, environmental engineering and in the manufacturing of food products and medicines. However, it is estimated that up to 99% of all micro-organisms are currently unculturable. As a result, it is likely that a vast range of potentially useful phenotypes remain unknown.
Current techniques to investigate unculturable cells, such as metagenomics, lack the ability to link a specific piece of genetic information to an originating cell. As heterogeneity of phenotypes exists in populations of genetically identical bacteria, single cell studies are becoming more popular to characterize individual microorganisms.
In this thesis a continuous flow, Raman activated single cell sorting system has been developed for the first time. Single cell Raman spectroscopy provides the biochemical information of a cell, enabling the label-free and non-destructive characterisation of different cell types and physiological and phenotypic changes to living cells. The system was based on a novel microfluidic platform, where the effects of pressure fluctuations upon the cell detection region are minimised, thereby allowing mechanical switching. With this integrated platform, sorting carbon fixing Synechocystis sp. PCC6803 bacteria containing 12C and 13C isotopes at over 96% accuracy was successfully achieved.
Also presented in this thesis is a novel technique for Raman based cell counting in continuous flow. By characterising the likely errors resulting from weak Raman signals, the algorithm used allows accurate analysis of the proportions of known cell types in a mixture, at the fastest acquisition rates achievable on the Raman spectrometer used. The results, obtained in real-time, had an r2 correlation value of 0.996 to the independently measured input proportions. The combination of the algorithm with the novel microfluidic platform could allow real-time Raman based sample analysis and diagnostics to be realised.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: resonance Raman, carotenoid, single cell, bacteria, sorting, detection, counting, microfluidic, pressure divider, separation
• Subjects: Q Science > Q Science (General); Q Science > QR Microbiology
• Colleges/Schools: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Supervisor's Name: Yin, Dr. Huabing
• Date of Award: 2015
• Embargo Date: 30 September 2025
• Depositing User: Mr David McIlvenna
• Unique ID: glathesis:2015-6333
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 19 May 2015 12:30
• Last Modified: 25 Jul 2024 10:42
• Thesis DOI: 10.5525/gla.thesis.6333
• URI: https://theses.gla.ac.uk/id/eprint/6333