Flow Cytometry for Age Assessment of a Yeast Population and its Application in Beer Fermentations

Abstract

An expeditious method of yeast age estimation was developed based on selective bud scar staining (Alexa Fluor 488-labelled wheat-germ agglutinin) and subsequent fluorescence intensity measurement by flow cytometry. The calibration curve resulting from the cytometric determination of average bud scar fluorescence intensities vs. microscopically counted average bud scar numbers of the same cell populations showed a good correlation and allowed routine cell age estimation by flow cytometry. The developed method was applied for yeast age control in traditional batch and continuous beer fermentations. At the pitching rates used in industrial beer fermentations, our results support former findings by locating a gradient of increasing yeast age from the top to the bottom zone of the fermenter cone. The results also indicate that in continuous beer fermentation, the increasing bud scar fluorescence of immobilized cells could help to schedule the replacement of aged biomass, prior to loss of viability or deterioration of process performance and product quality.

Analysis of the effect of inoculum characteristics on the first stages of a growing yeast population in beer fermentations by means of an individual-based model

“…Our findings are in broad agreement with experimental research [3,5,20,22,24,25,30], and support the researchers' view that artificial selection of a population enriched with young or aged individuals influences yeast fermentation performance. The possibility of estimating the variation of a population, not only in number or biomass, but also in age, would lend favour to the proposition that the production capacity of each yeast cell depends on its cellular cycle state, its vitality and its ageing.…”

“…A reproducible flow cytometric determination of the average bud scar number per cell of a yeast population would be useful. In this line, some recent advances connected to beer fermentation technology have been reported [20]. Figure 10 presents histograms of the distribution of masses resulting from the same simulation.…”

“…In biotechnology, the power of this method lies both in the possibility of determining a wide range of cell parameters at the single cell level, and in the ability to obtain information about their distribution within cell populations, providing valuable information for bioprocess design and control [4]. For instance, this technique has been used for the identification of different daughter and parent subpopulations in asynchronously growing S. cerevisiae populations [22], for the physiological analysis of yeast cells during 'serial repitching' of low-malt beer fermentation [21], and for age assessment of a yeast population [20].…”

“…This facilitates removal of the lower portion of the crop, comprising a greater proportion of aged cells. Harvesting yeast may therefore select for a population with an imbalance of young and aged individuals, depending on the cropping mechanism employed [20,24,25,30].…”

MAPK feedback encodes a switch and timer for tunable stress adaptation in yeast

“…The cell pellets were then resuspended in 100 μl of PB + 100 U of benzonase (Sigma-Aldrich) and incubated at 37°C for 15 min to cleave free nucleotides. Pellets were againwashedin150 μl of PB, then incubated with wheat germ-agglutinin (0.5 mg/ml), a yeast bud scar stain (71) conjugated to Alexa Fluor 594 (Invitrogen, W11262), for 15 min, and rinsed three times in 150 μl of PB. The fixed and stained cells were stored in the dark at 4°C.…”