Automated quantification of budding Saccharomyces cerevisiae using a novel image cytometry method

Adaptation of Saccharomyces to High Glucose Concentrations and Its Impact on Growth Kinetics of Alcoholic Fermentations

Prior adaptation of Saccharomyces cerevisiae to the fermentation medium ensures its implantation and success in alcoholic fermentations. Fermentation kinetics can be characterized with mathematical models to objectively measure the success of adaptation and growth. The study aims at assessing and comparing two pre-culture procedures using, respectively, one or two adaptation steps, analyzing the impact of different initial glucose concentrations on the fermentation profiles of S. cerevisiae cultures, and assessing the performance of three predictive growth models (Buchanan's, modified Gompertz, and Baranyi and Roberts models) under varied initial glucose concentrations. We concluded that both protocols produced S. cerevisiae pre-cultures with similar viability and biomass increase, which suggests that short protocols may be more cost-effective. Furthermore, the study highlights the need of inoculating a high S. cerevisiae population to minimize the depletion of dissolved oxygen in the medium and to ensure that glucose is predominantly directed toward the ethanol formation at early fermentative steps. This study shows that the relationship between kinetic parameters is model-dependent, which hinders inter-study comparisons and stresses the need for standardized growth models. We advocate for the generalized use of confidence intervals of the kinetic parameters to facilitate objective inter-study comparisons.

Effects of Saccharomyces cerevisiae quorum sensing signal molecules on ethanol production in bioethanol fermentation process

In this study, the concentrations of Saccharomyces cerevisiae quorum sensing signal molecules (QSMs) were determined, not to mention the exploration of the effects of exogenous S. cerevisiae QSMs on the sole fermentation of S. cerevisiae and co-fermentation of S. cerevisiae and Lactobacillus plantarum. The results showed that the concentrations of three signal molecules (2-phenylethanol (2-PE), tyrosol and tryptophan) produced by S. cerevisiae increased with a higher bacteria density, which tends to become stable up to 118.02, 32.05 and 1.93 mg/L respectively when cultivating for 144 h. Among the three signaling molecules, only 2-PE promoted the ethanol production capacity of S. cerevisiae. The ethanol concentration of the sole fermentation of S. cerevisiae loaded with 120 mg/L 2-PE reached 3.2 g/L in 9 h, which was 58.7% higher than that of the group without 2-PE addition. Moreover, 2-PE reduced the negative impact of L. plantarum on S. cerevisiae. Within 12 h of the co-fermentation of L. plantarum and S. cerevisiae, the ethanol concentration in the co-fermentation group loaded with 2-PE reached 5.6 g/L, similar to that in the group fermenting with sole S. cerevisiae, and the yeast budding rate was also restored to 28.51%. qRT-PCR results of S. cerevisiae which was in sole fermentation with 2-PE addition for 9 h showed that the relative expression levels of ethanol dehydrogenase gene ADH1 in S. cerevisiae decreased by 25% and the relative expression levels of MLS1, CIT2, IDH1,CIT1 decreased by 26%, 30%, 22%,18%, respectively, meant that the glyoxylic and tricarboxylic acid cycles were greatly inhibited, which promotes the accumulation of ethanol. The results of this study provide basic data for using QSMs more than antibiotics in the the prevention of contamination during the industrialized bioethanol production.

High-Throughput Image Cytometry Detection Method for CAR-T Transduction, Cell Proliferation, and Cytotoxicity Assays

Chimeric antigen receptor (CAR)-T cell therapy has drawn much attention due to its recent clinical success in B-cell malignancies. In general, the CAR-T cell discovery process consists of CAR identification, T-cell activation, transduction, and expansion, as well as assessment of CAR-T cytotoxicity. The current evaluation methods for the CAR-T discovery process can be time-consuming, low-throughput and requires the preparation of multiple sacrificial samples in order to produce kinetic data. In this study, we employed the use of a plate-based image cytometer to monitor anti-CAIX (carbonic anhydrase IX) G36 CAR-T generation and assess its cytotoxic potency of direct and selective killing against CAIX⁺ SKRC-59 human renal cell carcinoma cells. The transduction efficiency and cytotoxicity results were analyzed using image cytometry and compared directly to flow cytometry and Chromium 51 (⁵¹ Cr) release assays, showing that image cytometry was comparable against these conventional methods. Image cytometry method streamlines the assays required during the CAR-T cell discovery process by analyzing a plate of T cells from CAR-T generation to in vitro functional assays with minimum disruption. The proposed method can reduce assay time and uses less cell samples by imaging and analyze the same plate over time without the need to sacrifice any cells. The ability to monitor kinetic data can allow additional insights into the behavior and interaction between CAR-T and target tumor cells. © 2020 International Society for Advancement of Cytometry.

Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

BACKGROUND

The morphology of yeast cells changes during budding, depending on the growth rate and cultivation conditions. A photo-optical microscope was adapted and used to observe such morphological changes of individual cells directly in the cell suspension. In order to obtain statistically representative samples of the population without the influence of sampling, in situ microscopy (ISM) was applied in the different phases of a Saccharomyces cerevisiae batch cultivation. The real-time measurement was performed by coupling a photo-optical probe to an automated image analysis based on a neural network approach.

RESULTS

Automatic cell recognition and classification of budding and non-budding cells was conducted successfully. Deviations between automated and manual counting were considerably low. A differentiation of growth activity across all process stages of a batch cultivation in complex media became feasible. An increased homogeneity among the population during the growth phase was well observable. At growth retardation, the portion of smaller cells increased due to a reduced bud formation. The maturation state of the cells was monitored by determining the budding index as a ratio between the number of cells, which were detected with buds and the total number of cells. A linear correlation between the budding index as monitored with ISM and the growth rate was found.

CONCLUSION

It is shown that ISM is a meaningful analytical tool, as the budding index can provide valuable information about the growth activity of a yeast cell, e.g. in seed breeding or during any other cultivation process. The determination of the single-cell size and shape distributions provided information on the morphological heterogeneity among the populations. The ability to track changes in cell morphology directly on line enables new perspectives for monitoring and control, both in process development and on a production scale.

A novel concentration and viability detection method for Brettanomyces using the Cellometer image cytometry

Brettanomyces spp. can present unique cell morphologies comprised of excessive pseudohyphae and budding, leading to difficulties in enumerating cells. The current cell counting methods include manual counting of methylene blue-stained yeasts or measuring optical densities using a spectrophotometer. However, manual counting can be time-consuming and has high operator-dependent variations due to subjectivity. Optical density measurement can also introduce uncertainties where instead of individual cells counted, an average of a cell population is measured. In contrast, by utilizing the fluorescence capability of an image cytometer to detect acridine orange and propidium iodide viability dyes, individual cell nuclei can be counted directly in the pseudohyphae chains, which can improve the accuracy and efficiency of cell counting, as well as eliminating the subjectivity from manual counting. In this work, two experiments were performed to demonstrate the capability of Cellometer image cytometer to monitor Brettanomyces concentrations, viabilities, and budding/pseudohyphae percentages. First, a yeast propagation experiment was conducted to optimize software counting parameters for monitoring the growth of Brettanomyces clausenii, Brettanomyces bruxellensis, and Brettanomyces lambicus, which showed increasing cell concentrations, and varying pseudohyphae percentages. The pseudohyphae formed during propagation were counted either as multiple nuclei or a single multi-nuclei organism, where the results of counting the yeast as a single multi-nuclei organism were directly compared to manual counting. Second, a yeast fermentation experiment was conducted to demonstrate that the proposed image cytometric analysis method can monitor the growth pattern of B. lambicus and B. clausenii during beer fermentation. The results from both experiments displayed different growth patterns, viability, and budding/pseudohyphae percentages for each Brettanomyces species. The proposed Cellometer image cytometry method can improve efficiency and eliminate operator-dependent variations of cell counting compared with the traditional methods, which can potentially improve the quality of beverage products employing Brettanomyces yeasts.

Dynamic changes in brewing yeast cells in culture revealed by statistical analyses of yeast morphological data

The vitality of brewing yeasts has been used to monitor their physiological state during fermentation. To investigate the fermentation process, we used the image processing software, CalMorph, which generates morphological data on yeast mother cells and bud shape, nuclear shape and location, and actin distribution. We found that 248 parameters changed significantly during fermentation. Successive use of principal component analysis (PCA) revealed several important features of yeast, providing insight into the dynamic changes in the yeast population. First, PCA indicated that much of the observed variability in the experiment was summarized in just two components: a change with a peak and a change over time. Second, PCA indicated the independent and important morphological features responsible for dynamic changes: budding ratio, nucleus position, neck position, and actin organization. Thus, the large amount of data provided by imaging analysis can be used to monitor the fermentation processes involved in beer and bioethanol production.