A novel concentration and viability detection method for <Emphasis Type="Italic">Brettanomyces</Emphasis> 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.     

Direct concentration and viability measurement of yeast in corn mash using a novel imaging cytometry method

Worldwide awareness of fossil-fuel depletion and global warming has been increasing over the last 30 years. Numerous countries, including the USA and Brazil, have introduced large-scale industrial fermentation facilities for bioethanol, biobutanol, or biodiesel production. Most of these biofuel facilities perform fermentation using standard baker’s yeasts that ferment sugar present in corn mash, sugar cane, or other glucose media. In research and development in the biofuel industry, selection of yeast strains (for higher ethanol tolerance) and fermentation conditions (yeast concentration, temperature, pH, nutrients, etc.) can be studied to optimize fermentation performance. Yeast viability measurement is needed to identify higher ethanol-tolerant yeast strains, which may prolong the fermentation cycle and increase biofuel output. In addition, yeast concentration may be optimized to improve fermentation performance. Therefore, it is important to develop a simple method for concentration and viability measurement of fermenting yeast. In this work, we demonstrate an imaging cytometry method for concentration and viability measurements of yeast in corn mash directly from operating fermenters. It employs an automated cell counter, a dilution buffer, and staining solution from Nexcelom Bioscience to perform enumeration. The proposed method enables specific fluorescence detection of viable and nonviable yeasts, which can generate precise results for concentration and viability of yeast in corn mash. This method can provide an essential tool for research and development in the biofuel industry and may be incorporated into manufacturing to monitor yeast concentration and viability efficiently during the fermentation process.     

<Emphasis Type="Italic">Cachaça</Emphasis> yeast strains: alternative starters to produce beer and bioethanol

This work was performed to verify the potential of yeast strains isolated from cachaça distilleries for two specific biotechnological applications: beer and bioethanol production. In the beer production, the strains were tested for characteristics required in brewery practices, such as: capacity to ferment maltose and maltotriose, ability to grow at lowest temperatures, low H₂S production, and flocculation profile. Among the strains tested, two of them showed appropriate characteristics to produce two different beer styles: lager and ale. Moreover, both strains were tested for cachaça production and the results confirmed the capacity of these strains to improve the quality of cachaça. In the bioethanol production, the fermentation process was performed similarly to that used by bioethanol industries: recycling of yeast biomass in the fermentative process with sulfuric acid washings (pH 2.0). The production of ethanol, glycerol, organic acids, dry cell weight, carbohydrate consumption, and cellular viability were analyzed. One strain presented fermentative parameters similar to PE2, industrial/commercial strain, with equivalent ethanol yields and cellular viability during all fermentative cycles. This work demonstrates that cachaça distilleries seem to be an interesting environment to select new yeast strains to be used in biotechnology applications as beer and bioethanol production.     

Mechanisms of weak acid-induced stress tolerance in yeasts: Prospects for improved bioethanol production from lignocellulosic biomass

Weak acids are known to have a negative impact on yeast performance, restraining production efficiency during the production of bioethanol and other fermentative yeast-derived products. These acids, which might be hydrophilic or lipophilic exert negative effects on yeasts when they diffuse into the cell in their unionized state as a result of their pH being lower than the pka of yeast growth medium. Consequently, the unionized acids dissociate into their respective cations and anions, as intracellular pH is typically neutral. Further, proton accumulation tends to reduce intracellular pH. As a result, the anions destabilize the internal cell machinery, thus affecting cellular metabolism on various levels. Overcoming this acid-mediated stress in budding yeast would in part, harness the potential of using lignocellulosic biomass hydrolysate – which is typically acetic acid-rich – as a cheaper feedstock for large-scale bioethanol production. Since organic acids are key intermediates in ethanol fermentation, this review focuses on the prospects of bioethanol production from lignocellulosic biomass using weak acid-tolerant strains of yeasts derived by metabolic engineering.     

Yeasts in sustainable bioethanol production: A review

Bioethanol has been identified as the mostly used biofuel worldwide since it significantly contributes to the reduction of crude oil consumption and environmental pollution. It can be produced from various types of feedstocks such as sucrose, starch, lignocellulosic and algal biomass through fermentation process by microorganisms. Compared to other types of microoganisms, yeasts especially Saccharomyces cerevisiae is the common microbes employed in ethanol production due to its high ethanol productivity, high ethanol tolerance and ability of fermenting wide range of sugars. However, there are some challenges in yeast fermentation which inhibit ethanol production such as high temperature, high ethanol concentration and the ability to ferment pentose sugars. Various types of yeast strains have been used in fermentation for ethanol production including hybrid, recombinant and wild-type yeasts. Yeasts can directly ferment simple sugars into ethanol while other type of feedstocks must be converted to fermentable sugars before it can be fermented to ethanol. The common processes involves in ethanol production are pretreatment, hydrolysis and fermentation. Production of bioethanol during fermentation depends on several factors such as temperature, sugar concentration, pH, fermentation time, agitation rate, and inoculum size. The efficiency and productivity of ethanol can be enhanced by immobilizing the yeast cells. This review highlights the different types of yeast strains, fermentation process, factors affecting bioethanol production and immobilization of yeasts for better bioethanol production.     

Application of an automated colony counter for evaluation of the viability of a yeast culture

Application of an automated colony counter for evaluation of the viability of microbial cultures was investigated with yeast cultures as a model. Statistical comparison of the results of automated and visual (“manual”) colony counting is presented, as well as the results of the application of the bundled software to digital images obtained by light microscopy for determination of the cell concentration in suspensions. Automated counting is concluded to significantly accelerate the evaluation of culture viability by colony-forming capacity, provided that a certain requirements of sample preparation and analysis are observed.     

Determination of viable yeast cells by gravitational field-flow fractionation with fluorescence detection

Vinification processing is largely related to yeast performance and depends on the initial cell viability. To optimize the quality of wine fermentation, control of the yeast quality is mandatory. The present paper describes a new method using gravitational field flow fractionation (GrFFF) with fluorescence detection for the determination of yeast cell viability before the fermentation process. A GrFFF calibration procedure was developed using commercial yeast to prepare standards of viable cells and propidium iodide (PI) as fluorescent probe for nonviable cells. The suitability of the new method was tested with several commercial yeast strains with a g/L content ranging from 1 to 3. The validation of the method was performed by comparing GrFFF viability values with those obtained using Coulter counter and flow cytometry techniques.     

Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells

Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter (FACS). The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study, both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http://www.cs.tut.fi/sgn/csb/yeastdistrib/.     

Microbial interactions during sugar cane must fermentation for bioethanol production: does quorum sensing play a role?

Microbial interactions represent important modulatory role in the dynamics of biological processes. During bioethanol production from sugar cane must, the presence of lactic acid bacteria (LAB) and wild yeasts is inevitable as they originate from the raw material and industrial environment. Increasing the concentration of ethanol, organic acids, and other extracellular metabolites in the fermentation must are revealed as wise strategies for survival by certain microorganisms. Despite this, the co-existence of LAB and yeasts in the fermentation vat and production of compounds such as organic acids and other extracellular metabolites result in reduction in the final yield of the bioethanol production process. In addition to the competition for nutrients, reduction of cellular viability of yeast strain responsible for fermentation, flocculation, biofilm formation, and changes in cell morphology are listed as important factors for reductions in productivity. Although these consequences are scientifically well established, there is still a gap about the physiological and molecular mechanisms governing these interactions. This review aims to discuss the potential occurrence of quorum sensing mechanisms between bacteria (mainly LAB) and yeasts and to highlight how the understanding of such mechanisms can result in very relevant and useful tools to benefit the biofuels industry and other sectors of biotechnology in which bacteria and yeast may co-exist in fermentation processes.     

Reticulocyte quantification by flow cytometry, image analysis, and manual counting.

Reticulocyte counting by flow cytometry with thiazole orange was compared to manual or automated counting of new methylene blue stained blood smears. Forty-nine samples were compared for manual counting from randomly chosen clinical samples. Two hundred and eighty-nine samples from bone marrow transplant patients were compared during the period before and through chemo-irradiation and engraftment. The slopes of correlation plots were less than 1 when flow cytometric data were the dependent variable, suggesting that thiazole orange is less sensitive than new methylene blue. In a third study, 407 samples from bone marrow transplant patients were compared after increasing the thiazole orange concentration. The reticulocyte fluorescence distribution was divided into four groups of the brightest (youngest) 40, 60, 80, and 100% of reticulocytes. The slopes from regression analysis were 0.25, 0.49, 0.78, and 1.14, respectively. This demonstrates that thiazole orange is more sensitive than new methylene blue because the window of analysis includes an increased fraction of mature reticulocytes. In addition, the precision of each assay as measured. The rank order of precision from high to low was flow cytometry > image analysis > manual counting.     

Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ

Measuring the concentration and viability of fungal cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of fungal cells require methods that provide easy, objective and reproducible high‐throughput calculations, especially for samples in complicated backgrounds. To answer this challenge, we explored and developed an easy‐to‐use fungal cell counting pipeline that combined the machine learning‐based ilastik tool with the freeware ImageJ, as well as a conventional photomicroscope. Briefly, learning from labels provided by the user, ilastik performs segmentation and classification automatically in batch processing mode and thus discriminates fungal cells from complex backgrounds. The files processed through ilastik can be recognized by ImageJ, which can compute the numeric results with the macro ‘Fungal Cell Counter’. Taking the yeast Cryptococccus deneoformans and the filamentous fungus Pestalotiopsis microspora as examples, we observed that the customizable software algorithm reduced inter‐operator errors significantly and achieved accurate and objective results, while manual counting with a haemocytometer exhibited some errors between repeats and required more time. In summary, a convenient, rapid, reproducible and extremely low‐cost method to count yeast cells and fungal spores is described here, which can be applied to multiple kinds of eucaryotic microorganisms in genetics, cell biology and industrial fermentation.     

Assessment of cell concentration and viability of isolated hepatocytes using flow cytometry

The assessment of cell concentration and viability of freshly isolated hepatocyte preparations has been traditionally performed using manual counting with a Neubauer counting chamber and staining for trypan blue exclusion. Despite the simple and rapid nature of this assessment, concerns about the accuracy of these methods exist. Simple flow cytometry techniques which determine cell concentration and viability are available yet surprisingly have not been extensively used or validated with isolated hepatocyte preparations. We therefore investigated the use of flow cytometry using TRUCOUNT Tubes and propidium iodide staining to measure cell concentration and viability of isolated rat hepatocytes in suspension. Analysis using TRUCOUNT Tubes provided more accurate and reproducible measurement of cell concentration than manual cell counting. Hepatocyte viability, assessed using propidium iodide, correlated more closely than did trypan blue exclusion with all indicators of hepatocyte integrity and function measured (lactate dehydrogenase leakage, cytochrome p450 content, cellular ATP concentration, ammonia and lactate removal, urea and albumin synthesis). We conclude that flow cytometry techniques can be used to measure cell concentration and viability of isolated hepatocyte preparations. The techniques are simple, rapid, and more accurate than manual cell counting and trypan blue staining and the results are not affected by protein-containing media.     

Automated flow cytometry for monitoring CHO cell cultures

Flow cytometry has been used to accurately monitor cell events that indicate the spatio-temporal state of a bioreactor culture. The introduction of process analytical technology (PAT) has led to process improvements using real-time or semi real-time monitoring systems. Integration of flow cytometry into an automated scheme for improved process monitoring can benefit PAT in bioreactor-based biopharmaceutical productions by establishing optimum process conditions and better quality protocols. Herein, we provide detailed protocols for establishing an automated flow cytometry system that can be used to investigate and monitor cell growth, viability, cell size, and cell cycle data. A method is described for the use of such a system primarily focused on CHO cell culture, although it is foreseen the information gathered from automated flow cytometry can be applied to a variety of cell lines to address both PAT requirements and gain further understanding of complex biological systems.     

Conventional and nonconventional strategies for controlling bacterial contamination in fuel ethanol fermentations

Ethanol bio-production in Brazil has some unique characteristics that inevitably lead to bacterial contamination, which results in the production of organic acids and biofilms and flocculation that impair the fermentation yield by affecting yeast viability and diverting sugars to metabolites other than ethanol. The ethanol-producing units commonly give an acid treatment to the cells after each fermentative cycle to decrease the bacterial number, which is not always effective. An alternative strategy must be employed to avoid bacterial multiplication but must be compatible with economic, health and environmental aspects. This review analyzes the issue of bacterial contamination in sugarcane-based fuel ethanol fermentation, and the potential strategies that may be utilized to control bacterial growth besides acid treatment and antibiotics. We have emphasized the efficiency and suitability of chemical products other than acids and those derived from natural sources in industrial conditions. In addition, we have also presented bacteriocins, bacteriophages, and beneficial bacteria as non-conventional antimicrobial agents to mitigate bacterial contamination in the bioethanol industry.     

Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method

Computer-based image analysis and pattern recognition methodswere used to construct a system able automatically to identify,count and measure selected groups of phytoplankton. An imageanalysis algorithm was employed to isolate and measure objectsfrom digitized images of a phytoplankton sample. The measurementsobtained were used to identify selected groups of phytoplanktonby a combination of artificial neural networks and simple rule-basedprocedures. The system was trained and tested using samplesof lake water covering an annual growth cycle from Lough Neaghin Northern Ireland. Total volume estimates were obtained forthe four major phytoplankton species, using both the automatedsystem and a manual counting method. Estimates of total cellvolume obtained from the automated system were within 10% ofthose derived by manual analysis of the same cells. The automatedsystem produced total cell volume estimates close to those obtainedfrom manual analysis of different aliquots of the same watersample. Variation between successive counts of the same watersample was higher with the automated system than with the manualcounting method. Limitations and possible improvements to thetechnology are discussed.     

Stochastic Petri Net extension of a yeast cell cycle model

This paper presents the definition, solution and validation of a stochastic model of the budding yeast cell cycle, based on Stochastic Petri Nets (SPN). A specific family of SPNs is selected for building a stochastic version of a well-established deterministic model. We describe the procedure followed in defining the SPN model from the deterministic ODE model, a procedure that can be largely automated. The validation of the SPN model is conducted with respect to both the results provided by the deterministic one and the experimental results available from literature. The SPN model catches the behavior of the wild type budding yeast cells and a variety of mutants. We show that the stochastic model matches some characteristics of budding yeast cells that cannot be found with the deterministic model. The SPN model fine-tunes the simulation results, enriching the breadth and the quality of its outcome.     

Stress Tolerance Variations in Saccharomyces cerevisiae Strains from Diverse Ecological Sources and Geographical Locations

The budding yeast Saccharomyces cerevisiae is a platform organism for bioethanol production from various feedstocks and robust strains are desirable for efficient fermentation because yeast cells inevitably encounter stressors during the process. Recently, diverse S. cerevisiae lineages were identified, which provided novel resources for understanding stress tolerance variations and related shaping factors in the yeast. This study characterized the tolerance of diverse S. cerevisiae strains to the stressors of high ethanol concentrations, temperature shocks, and osmotic stress. The results showed that the isolates from human-associated environments overall presented a higher level of stress tolerance compared with those from forests spared anthropogenic influences. Statistical analyses indicated that the variations of stress tolerance were significantly correlated with both ecological sources and geographical locations of the strains. This study provides guidelines for selection of robust S. cerevisiae strains for bioethanol production from nature.     

Yeast selection for fuel ethanol production in Brazil

Brazil is one of the largest ethanol biofuel producers and exporters in the world and its production has increased steadily during the last three decades. The increasing efficiency of Brazilian ethanol plants has been evident due to the many technological contributions. As far as yeast is concerned, few publications are available regarding the industrial fermentation processes in Brazil. The present paper reports on a yeast selection program performed during the last 12 years aimed at selecting Saccharomyces cerevisiae strains suitable for fermentation of sugar cane substrates (cane juice and molasses) with cell recycle, as it is conducted in Brazilian bioethanol plants. As a result, some evidence is presented showing the positive impact of selected yeast strains in increasing ethanol yield and reducing production costs, due to their higher fermentation performance (high ethanol yield, reduced glycerol and foam formation, maintenance of high viability during recycling and very high implantation capability into industrial fermenters). Results also suggest that the great yeast biodiversity found in distillery environments could be an important source of strains. This is because during yeast cell recycling, selective pressure (an adaptive evolution) is imposed on cells, leading to strains with higher tolerance to the stressful conditions of the industrial fermentation.     

Improved Flow Cytometric Analysis of the Budding Yeast Cell Cycle

The budding yeast, Saccharomyces cerevisiae has been a remarkably useful model system for the study of eukaryotic cell cycle regulation. Flow cytometric analysis of DNA content in budding yeast has become a standard tool for the analysis of cell cycle progression. However, popular protocols utilizing the DNA binding dye, propidium iodide, suffer from a number of drawbacks that confound accurate analysis by flow cytometry. Here we show the utility of the DNA binding dye, SYTOX Green, in the cell cycle analysis of yeast. Samples analyzed using SYTOX Green exhibited better coefficients of variation, improved linearity between DNA content and fluorescence, and decreased peak drift associated with changes in dye concentration, growth conditions or cell size.

Key Words:

Flow cytometry, Cell cycle, Saccharomyces cerevisiae, SYTOX Green, Propidium iodide     

Genotypic and phenotypic characterization of industrial autochthonous Saccharomyces cerevisiae for the selection of well-adapted bioethanol-producing strains

In northwestern Argentina, sugarcane-derived industrial fermentation is being extensively used for bioethanol production, where highly adaptive native strains compete with the baker's yeast Saccharomyces cerevisiae traditionally used as starter culture. Yeast populations of 10 distilleries from Tucumán (Argentina) were genotypic and phenotypic characterized to select well-adapted bioethanol-producing autochthonous strains to be used as starter cultures for the industrial production of bioethanol fuel. From the 192 isolates, 69.8% were identified as S. cerevisiae, 25.5% as non-Saccharomyces, and 4.7% as Saccharomyces sp. wild yeasts. The majority of S. cerevisiae isolates (68.5%) were non-flocculating yeasts, while the flocculating strains were all obtained from the only continuous fermentation process included in the study. Simple Sequence Repeat analysis revealed a high genetic diversity among S. cerevisiae genotypes, where all of them were very different from the original baker's strain used as starter. Among these, 38 strains multi-tolerant to stress by ethanol (8%), temperature (42.5 °C) and pH (2.0) were obtained. No major differences were found among these strains in terms of ethanol production and residual sugars in batch fermentation experiments with cell recycling. However, only 10 autochthonous strains maintained their viability (more than 80%) throughout five consecutive cycles of sugarcane-based fermentations. In summary, 10 autochthonous isolates were found to be superior to baker's yeast used as starter culture (S. cerevisiae Calsa) in terms of optimal technological, physiological and ecological properties. The knowledge generated on the indigenous yeast populations in industrial fermentation processes of bioethanol-producing distilleries allowed the selection of well-adapted bioethanol-producing strains.