Simultaneous growth, survival and death: The trimodal behavior of Salmonella cells under osmotic stress giving rise to “Phoenix phenomenon”

Time-lapse microscopy methods were used to monitor growth, survival and death of Salmonella enterica serotype Agona individual cells on solid laboratory medium (tryptone soy agar) in the presence of various salt concentrations (0.5%, 3.5%, 4.5% and 5.7% NaCl). The results showed a highly heterogeneous behavior. As NaCl concentration increased, the distribution of the first division time was shifted to higher values and became wider. The mean first division time increased from 1.8 h at 0.5% NaCl to 5.48 h, 16.2 h, and 35.9 h at 3.5%, 4.5% and 5.7% NaCl, respectively. The concentration of NaCl in the growth medium also affected the ability of the cells to divide. The percentage of cells able to grow decreased from 88.9% at 0.5% NaCl to 66.5%, 32.8%, and 6.9% at 3.5%, 4.5% and 5.7% NaCl, respectively. In the latter case (5.7% NaCl), 74 cells out of 406 cells tested (18%) died with mean time to death 5.03 h and standard deviation 6.70 h. To investigate the effect of the behavior of individual cells on the dynamics of the whole population, simulation analysis was used. The simulation results showed that the simultaneous growth, survival and death of cells observed under osmotic stress can lead to a total population behavior known as the “Phoenix” phenomenon. The simulation findings were confirmed by validation experiments using both viable counts and time lapse microscopy. The results of the present study show the high heterogeneity of individual cell responses and the complexity in the behavior of microbial populations at conditions approaching the boundaries of growth.status: publishe

Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling

Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertainty. Conventional deterministic modeling approaches which provide point estimates of the pathogen's levels cannot quantify the uncertainty around the predictions. The objective of this study was to use Bayesian statistical modeling for describing uncertainty in predicted microbial thermal inactivation of Salmonella enterica Typhimurium DT104. A set of thermal inactivation data in broth with water activity adjusted to 0.75 at 9 different temperature conditions obtained from the ComBase database (www.combase.cc) was used. A log-linear microbial inactivation was used as a primary model while for secondary modeling, a linear relation between the logarithm of inactivation rate and temperature was assumed. For comparison, data were fitted with a two-step and a global Bayesian regression. Posterior distributions of model's parameters were used to predict Salmonella thermal inactivation. The combination of the joint posterior distributions of model's parameters allowed the prediction of cell density over time, total reduction time and inactivation rate as probability distributions at different time and temperature conditions. For example, for the time required to eliminate a Salmonella population of about 10⁷ CFU/ml at 65℃, the model predicted a time distribution with a median of 0.40 min and 5th and 95th percentiles of 0.24 and 0.60 min, respectively. The validation of the model showed that it can describe successfully uncertainty in predicted thermal inactivation with most observed data being within the 95% prediction intervals of the model. The global regression approach resulted in less uncertain predictions compared to the two-step regression. The developed model could be used to quantify uncertainty in thermal inactivation in risk-based processing design as well as in risk assessment studies

Additional file 8: of Image analysis driven single-cell analytics for systems microbiology

SalPhase time-lapse movie ground truth table. (XLSX 51 kb

Additional file 7: of Image analysis driven single-cell analytics for systems microbiology

Contains for each dataset the segmentation results of each method (.tif images) and corresponding parameterization files (.mat files). (ZIP 62299 kb