Monitoring Rhodotorula glutinis CCMI 145 Stress Physiological Response during Fed-Batch Fermentations Using Multi-Parameter Flow Cytometry
Teresa Lopes da Silva*, Daniela Feijão and Alberto Reis
Laboratório Nacional de Energia e Geologia (LNEG), Unidade de Bioenergia, Estrada do Paço do Lumiar, 22, Edifício F1649-038 Lisboa, Portugal
- *Corresponding Author:
- Dr. Teresa Lopes da Silva
Laboratório Nacional de Energia e Geologia (LNEG)
Unidade de Bioenergia Estrada do Paço do Lumiar, 22
Edifício F1649-038 Lisboa, Portugal
E-mail: [email protected]
Received Date: December 10, 2010; Accepted Date: January 22, 2011; Published Date: January 24, 2011
Citation: da Silva TL, Feijão D, Reis A (2011) Monitoring Rhodotorula glutinis CCMI 145 Stress Physiological Response during Fed-Batch Fermentations Using Multi-Parameter Flow Cytometry. J Microbial Biochem Technol 3: 006-012. doi: 10.4172/1948-5948.1000042
Copyright: © 2011 da Silva TL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Multi-parameter flow cytometry was used to monitor R. glutinis stress response during a fed-batch fermentation, through cell viability, lipid content and intrinsic light scatter. During the yeast fermentation, the proportion of cells with permeabilized membrane (dead cells) increased when nutrients and/or oxygen became limiting. Yeast cells showed a higher injury level when grown under other nutrient limitation than under oxygen limiting conditions, as the dead cells reduced their internal content and size in the former situation, suggesting drastic cells lysis.
The maximum yeast lipid content was 8% (w/w) at t=38.3 h. Such low lipid content was attributed to oxygen limitation, which highlights the importance of the oxygen transfer rate when producing lipids from aerobic yeast cultures.
Changes in Forward and Side scatter light signals were detected during the yeast growth, which can provide a useful and fast way to identify the yeast growth phase.
The multi-parameter approach here reported represents a better control system based at the individual cell level that can be used for optimization of yeast bioprocess performance, and may also be used for quick screening of yeast strains for single cell oil production.