Abstract
The aim of the research was to design an advanced analytical tool for the precise characterization of microbial aggregates from biofilms formed on food-processing surfaces. The approach combined imaging flow cytometry with a machine learning-based interpretation protocol. Biofilm samples were collected from three diagnostic points of the food-processing lines at two independent time points. The samples were investigated for the complexity of microbial aggregates and cellular metabolic activity. Thus, aggregates and singlets of biofilm-associated microbes were simultaneously examined for the percentages of active, mid-active, and nonactive (dead) cells to evaluate the physiology of the microbial cells forming the biofilm structures. The tested diagnostic points demonstrated significant differences in the complexity of microbial aggregates. The significant percentages of the bacterial aggregates were associated with the dominance of active microbial cells, e.g., 75.3% revealed for a mushroom crate. This confirmed the protective role of cellular aggregates for the survival of active microbial cells. Moreover, the approach enabled discriminating small and large aggregates of microbial cells. The developed tool provided more detailed characteristics of bacterial aggregates within a biofilm structure combined with high-throughput screening potential. The designed methodology showed the prospect of facilitating the detection of invasive biofilm forms in the food industry environment.
Highlights
Instrumental methods, assessing the complexity of microbial populations, with particular emphasis on differences in the physiological state of individual cells, constitute a significant breakthrough in microbiological analyses [1]
The analysis of samples, constituting fragments of biofilms formed on the surfaces of food-processing technological lines, has focused on developing a protocol for interpreting the results obtained with the use of an Amnis® FlowSight® imaging flow cytometer
The interpretation protocol was based on the machine learning (ML) module, a feature of Amnis® FlowSight® instrument IDEAS® software
Summary
Instrumental methods, assessing the complexity of microbial populations, with particular emphasis on differences in the physiological state of individual cells, constitute a significant breakthrough in microbiological analyses [1] These methods enrich the identification procedures used so far, with the assessment of the activity of all identified species or even individual representatives of these species. Food-processing environments can be colonized by a wide variety of bacterial species, which can contribute to the formation of biofilm structures. This process is based on interactions within microbial communities to constitute a complex and dynamic network shaping biofilm architecture responsible for specific functions [7,8,9,10]. Multiple studies have demonstrated that multispecies biofilms are less sensitive to antimicrobial agents than their monospecies counterparts [11,12,13,14,15,16,17,18]
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