Population analysis of a commercial Saccharomyces cerevisiae wine yeast in a batch culture by electric particle analysis, light diffraction and flow cytometry.

Data from electric particle analysis, light diffraction and flow cytometry analysis provide information on changes in cell morphology. Here, we report analyses of Saccharomyces cerevisiae populations growing in a batch culture using these techniques. The size distributions were determined by electric particle analysis and by light diffraction in order to compare their outcomes. Flow cytometry parameters forward (related to cell size) and side (related to cell granularity) scatter were also determined to complement this information. These distributions of yeast properties were analysed statistically and by a complexity index. The cell size of Saccharomyces at the lag phase was smaller than that at the beginning of the exponential phase, whereas during the stationary phase, the cell size converged with the values observed during the lag phase. These experimental techniques, when used together, allow us to distinguish among and characterize the cell size, cell granularity and the structure of the yeast population through the three growth phases. Flow cytometry patterns are better than light diffraction and electric particle analysis in showing the existence of subpopulations during the different phases, especially during the stationary phase. The use of a complexity index in this context helped to differentiate these phases and confirmed the yeast cell heterogeneity.

Skew-laplace and cell-size distribution in microbial axenic cultures: statistical assessment and biological interpretation.

We report a skew-Laplace statistical analysis of both flow cytometry scatters and cell size from microbial strains primarily grown in batch cultures, others in chemostat cultures and bacterial aquatic populations. Cytometry scatters best fit the skew-Laplace distribution while cell size as assessed by an electronic particle analyzer exhibited a moderate fitting. Unlike the cultures, the aquatic bacterial communities clearly do not fit to a skew-Laplace distribution. Due to its versatile nature, the skew-Laplace distribution approach offers an easy, efficient, and powerful tool for distribution of frequency analysis in tandem with the flow cytometric cell sorting.

Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis.

Predictive microbiology is the area of food microbiology that attempts to forecast the quantitative evolution of microbial populations over time. This is achieved to a great extent through models that include the mechanisms governing population dynamics. Traditionally, the models used in predictive microbiology are whole-system continuous models that describe population dynamics by means of equations applied to extensive or averaged variables of the whole system. Many existing models can be classified by specific criteria. We can distinguish between survival and growth models by seeing whether they tackle mortality or cell duplication. We can distinguish between empirical (phenomenological) models, which mathematically describe specific behaviour, and theoretical (mechanistic) models with a biological basis, which search for the underlying mechanisms driving already observed phenomena. We can also distinguish between primary, secondary and tertiary models, by examining their treatment of the effects of external factors and constraints on the microbial community. Recently, the use of spatially explicit Individual-based Models (IbMs) has spread through predictive microbiology, due to the current technological capacity of performing measurements on single individual cells and thanks to the consolidation of computational modelling. Spatially explicit IbMs are bottom-up approaches to microbial communities that build bridges between the description of micro-organisms at the cell level and macroscopic observations at the population level. They provide greater insight into the mesoscale phenomena that link unicellular and population levels. Every model is built in response to a particular question and with different aims. Even so, in this research we conducted a SWOT (Strength, Weaknesses, Opportunities and Threats) analysis of the different approaches (population continuous modelling and Individual-based Modelling), which we hope will be helpful for current and future researchers.

Analysis and IbM simulation of the stages in bacterial lag phase: basis for an updated definition.

The lag phase is the initial phase of a culture that precedes exponential growth and occurs when the conditions of the culture medium differ from the pre-inoculation conditions. It is usually defined by means of cell density because the number of individuals remains approximately constant or slowly increases, and it is quantified with the lag parameter lambda. The lag phase has been studied through mathematical modelling and by means of specific experiments. In recent years, Individual-based Modelling (IbM) has provided helpful insights into lag phase studies. In this paper, the definition of lag phase is thoroughly examined. Evolution of the total biomass and the total number of bacteria during lag phase is tackled separately. The lag phase lasts until the culture reaches a maximum growth rate both in biomass and cell density. Once in the exponential phase, both rates are constant over time and equal to each other. Both evolutions are split into an initial phase and a transition phase, according to their growth rates. A population-level mathematical model is presented to describe the transitional phase in cell density. INDividual DIScrete SIMulation (INDISIM) is used to check the outcomes of this analysis. Simulations allow the separate study of the evolution of cell density and total biomass in a batch culture, they provide a depiction of different observed cases in lag evolution at the individual-cell level, and are used to test the population-level model. The results show that the geometrical lag parameter lambda is not appropriate as a universal definition for the lag phase. Moreover, the lag phase cannot be characterized by a single parameter. For the studied cases, the lag phases of both the total biomass and the population are required to fully characterize the evolution of bacterial cultures. The results presented prove once more that the lag phase is a complex process that requires a more complete definition. This will be possible only after the phenomena governing the population dynamics at an individual level of description, and occurring during the lag and exponential growth phases, are well understood.

Skew-Laplace distribution in Gram-negative bacterial axenic cultures: new insights into intrinsic cellular heterogeneity.

The application of flow cytometry and skew-Laplace statistical analysis to assess cellular heterogeneity in Gram-negative axenic cultures is reported. In particular, fit to the log-skew-Laplace distribution for cellular side scatter or 'granulosity' is reported, and a number of theoretical and applied issues are considered in relation to the biological significance of this fit.

Flow cytometry discrimination between bacteria and clay-humic acid particles during growth-linked biodegradation of phenanthrene by Pseudomonas aeruginosa 19SJ.

Abstract Methods that quickly assess microbial density and aggregation in soil and sediments are needed in environmental microbiology. We report a flow cytometry method that uses the green and orange emission of the fluorochrome SYTO-13 to discriminate between bacteria and clay-humic acid particles. This approach distinguishes single or clustered bacteria, and clusters of bacteria and abiotic particles during the growth of the biosurfactant-producing strain Pseudomonas aeruginosa 19SJ on solid phenanthrene in the presence of humic acid-clay complexes.

Flow cytometric detection and quantification of heterotrophic nanoflagellates in enriched seawater and cultures.

A flow cytometric protocol to detect and enumerate heterotrophic nanoflagellates (HNF) in enriched waters is reported. At present, the cytometric protocols that allow accurate quantification of bacterioplankton cannot be used to quantify protozoa for the following reasons: i) the background produced by the bacterial acquisitions does not allow the discrimination of protozoa at low abundance, ii) since the final protozoan fluorescence is much higher than the bacterioplankton fluorescence (more than 35 fold) the protozoa acquisitions lie outside the range. With an increase in the fluorescence threshold and a reduction of the fluorescence detector voltage, low fluorescence particles (bacteria) are beneath the detection limits and only higher fluorescence particles (most of them heterotrophic nanoflagellates) are detected. The main limitation for the application of the cytometric protocol developed is that a ratio of bacteria/HNF below 1000 is needed. At higher ratios, the background of larger cells of bacterioplankton makes it difficult to discriminate protozoa. The proposed protocol has been validated by epifluorescence microscopy analyzing both a mixed community and two single species of HFN: Rhynchomonas nasuta and Jakoba libera. Taking into account the required bacteria/HNF ratio cited above, the results provide evidence that the flow cytometric protocol reported here is valid for counting mixed communities of HNF in enriched seawater and in experimental micro or mesocosms. In the case of single species of HNF previous knowledge of the biological characteristics of the protist and how they can affect the effectiveness of the flow cytometric count is necessary.

Cytometric monitoring of growth, sporogenesis and spore cell sorting in Paenibacillus polymyxa (formerly Bacillus polymyxa).

AIMS: Formation of bacterial endospores is a basic process in Gram-positive bacteria and has implications for health, industry and the environment. Flow cytometry offers a practical alternative for the rapid detection, enumeration and characterization of bacterial endospores. METHODS AND RESULTS: Paenibacillus polymyxa was chosen for this study because its spores cause sporangium deformation and have thick walls with a star-shaped section. Sporulating populations were analysed with a particle analyser and a flow cytometer after labelling with propidium iodide and Syto-13. Flow cytometric detection of single spores was confirmed by optical and scanning electron microscopy after cell sorting. Four cell sub-populations were cytometrically detected in P. polymyxa cultures grown in liquid sporulation medium. Two sub-populations consisted of vegetative cells differing in both morphology and viability; the other two sub-populations consisted of spores differing in their viability. CONCLUSIONS: This work has shown that flow cytometry is a simple and fast method (less than 15 minutes for sample preparation and analysis) for the study of the sporulation in P. polymyxa. The use of this technique allowed both detection and quantification of sporulation inside a culture, and distinguished cells that differed in viability despite being morphologically identical under microscopic observation. SIGNIFICANCE AND IMPACT OF THE STUDY: Flow cytometry has been proved to be a valuable tool for the analysis of sporulation in P. polymyxa cultures, with the unique capacity of distinguishing between endospores and vegetative cells, and between live and dead cells, in the same analysis. An important percentage of non-viable endospores has been found in aged cultures using this method.