A microscopy-based approach for determining growth probability and lag time of individual bacterial cells.

The development of relevant predictive models for single-cell lag time and growth probability near growth limits is of critical importance for predicting pathogen behavior in foods. The classical methods for data acquisition in this field are based on turbidity measurements of culture media in microplate wells inoculated with approximately one bacterial cell per well. Yet, these methods are labour intensive and would benefit from higher throughput. In this study, we developed a quantitative experimental method using automated microscopy to determine the single-cell growth probability and lag time. The developed method consists of the use of direct cell observation with phase-contrast microscopy equipped with a 100× objective and a high-resolution device camera. The method is not a time-lapse method but is based on the observation of high numbers of colonies for a given time. Automation of image acquisition and image analysis was used to reach a high throughput. The single-cell growth probabilities and lag times of four strains of Listeria monocytogenes were determined at 4 °C. The microscopic method was shown to be a promising method for the determination of individual lag times and growth probability at the single-cell level.

Modeling carbon dioxide effect in a controlled atmosphere and its interactions with temperature and pH on the growth of L. monocytogenes and P. fluorescens.

The effect of carbon dioxide, temperature, and pH on growth of Listeria monocytogenes and Pseudomonas fluorescens was studied, following a protocol to monitor microbial growth under a constant gas composition. In this way, the CO2 dissolution didn't modify the partial pressures in the gas phase. Growth curves were acquired at different temperatures (8, 12, 22 and 37 °C), pH (5.5 and 7) and CO2 concentration in the gas phase (0, 20, 40, 60, 80, 100% of the atmospheric pressure, and over 1 bar). These three factors greatly influenced the growth rate of L. monocytogenes and P. fluorescens, and significant interactions have been observed between the carbon dioxide and the temperature effects. Results showed no significant effect of the CO2 concentration at 37 °C, which may be attributed to low CO2 solubility at high temperature. An inhibitory effect of CO2 appeared at lower temperatures (8 and 12 °C). Regardless of the temperature, the gaseous CO2 is sparingly soluble at acid pH. However, the CO2 inhibition was not significantly different between pH 5.5 and pH 7. Considering the pKa of the carbonic acid, these results showed the dissolved carbon under HCO3- form didn't affect the bacterial inhibition. Finally, a global model was proposed to estimate the growth rate vs. CO2 concentration in the aqueous phase. This dissolved concentration is calculated according to the physical equations related to the CO2 equilibriums, involving temperature and pH interactions. This developed model is a new tool available to manage the food safety of MAP.

Comparison of individual-based modeling and population approaches for prediction of foodborne pathogens growth.

Individual-based modeling (IBM) approach combined with the microenvironment modeling of vacuum-packed cold-smoked salmon was more effective to describe the variability of the growth of a few Listeria monocytogenes cells contaminating irradiated salmon slices than the traditional population models. The IBM approach was particularly relevant to predict the absence of growth in 25% (5 among 20) of artificially contaminated cold-smoked salmon samples stored at 8 °C. These results confirmed similar observations obtained with smear soft cheese (Ferrier et al., 2013). These two different food models were used to compare the IBM/microscale and population/macroscale modeling approaches in more global exposure and risk assessment frameworks taking into account the variability and/or the uncertainty of the factors influencing the growth of L. monocytogenes. We observed that the traditional population models significantly overestimate exposure and risk estimates in comparison to IBM approach when contamination of foods occurs with a low number of cells (<100 per serving). Moreover, the exposure estimates obtained with the population model were characterized by a great uncertainty. The overestimation was mainly linked to the ability of IBM to predict no growth situations rather than the consideration of microscale environment. On the other hand, when the aim of quantitative risk assessment studies is only to assess the relative impact of changes in control measures affecting the growth of foodborne bacteria, the two modeling approach gave similar results and the simplest population approach was suitable.

Combining individual-based modeling and food microenvironment descriptions to predict the growth of Listeria monocytogenes on smear soft cheese.

An individual-based modeling (IBM) approach was developed to describe the behavior of a few Listeria monocytogenes cells contaminating smear soft cheese surface. The IBM approach consisted of assessing the stochastic individual behaviors of cells on cheese surfaces and knowing the characteristics of their surrounding microenvironments. We used a microelectrode for pH measurements and micro-osmolality to assess the water activity of cheese microsamples. These measurements revealed a high variability of microscale pH compared to that of macroscale pH. A model describing the increase in pH from approximately 5.0 to more than 7.0 during ripening was developed. The spatial variability of the cheese surface characterized by an increasing pH with radius and higher pH on crests compared to that of hollows on cheese rind was also modeled. The microscale water activity ranged from approximately 0.96 to 0.98 and was stable during ripening. The spatial variability on cheese surfaces was low compared to between-cheese variability. Models describing the microscale variability of cheese characteristics were combined with the IBM approach to simulate the stochastic growth of L. monocytogenes on cheese, and these simulations were compared to bacterial counts obtained from irradiated cheeses artificially contaminated at different ripening stages. The simulated variability of L. monocytogenes counts with the IBM/microenvironmental approach was consistent with the observed one. Contrasting situations corresponding to no growth or highly contaminated foods could be deduced from these models. Moreover, the IBM approach was more effective than the traditional population/macroenvironmental approach to describe the actual bacterial behavior variability.