Raman Microscopic Analysis of Dry-Surface Biofilms on Clinically Relevant Materials

Salmonella dry surface biofilm: morphology, single-cell landscape, and sanitization

In this study, Salmonella Typhimurium dry surface biofilm (DSB) formation was investigated in comparison with wet surface biofilm (WSB) development. Confocal laser scanning microscopic analysis revealed a prominent green cell signal during WSB formation, whereas a red signal predominated during DSB formation. Electron microscopy was also used to compare the features of DSB and WSB. Overall, WSB was unevenly scattered over the surface, whereas DSB was evenly dispersed. In contrast to WSB cells, which have a distinct plasma membrane and outer membrane layer, DSB cells are contained in large capsules and compressed. Next, microbiome single-cell transcriptomics was used to investigate the functional heterogeneity of the Salmonella DSB microbiome, with nine clusters successfully identified. Although over 60% of the dried cells were metabolically inactive, the rest of the Salmonella cells still demonstrated specific antioxidative and virulence capabilities, suggesting a possible concern for low-moisture food (LMF) safety. Finally, because sanitization in LMF industries must be conducted without water, a list of 39 flavonoids was tested for their combined effect with 70% isopropyl alcohol (IPA) against DSB, and morin induced the greatest reduction in the green:red ratio from 3.67 to 0.43. Significantly higher reductions of Salmonella viability in DSB were achieved by 10-, 100-, 1,000-, and 10,000-µg/mL morin (1.69 ± 0.25, 3.21 ± 0.23, 4.32 ± 0.24, and 5.18 ± 0.16 log CFU/sample reductions) than 70% IPA alone (1.55 ± 0.20 log CFU/sample reduction) (P < 0.05), indicating the potential to be formulated as a dry sanitizer for the LMF industry.IMPORTANCEDSB growth of foodborne pathogens in LMF processing environments is associated with food safety, financial loss, and compromised consumer trust. This work is the first comprehensive examination of the characteristics of Salmonella DSB while exploring its underlying survival mechanisms. Furthermore, morin dissolved in 70% IPA was proposed as an efficient dry sanitizer against DSB to provide insights into biofilm control during LMF processing.

Biofilms, mobile genetic elements and the persistence of pathogens on environmental surfaces in healthcare and food processing environments

Biofilms are the natural state for bacterial and fungal species. To achieve surface hygiene in commercial facilities, the presence of biofilms must be adequately considered. However, standard disinfectant and sanitizer efficacy tests required by the US-EPA and the European Committee for Standardization (CEN) do not currently consider the role of environmental biofilms. This selective review will discuss what biofilms are and why they are important. We will also cover where they are commonly found in healthcare and food processing facilities and explore how current antimicrobial test methods required for product registration do not test for the presence of biofilms. Additionally, we will explore how a lack of efficacy against biofilms may play a role in the development of antimicrobial resistance in healthcare facilities due to the exchange of mobile genetic elements that occur readily in a biofilm matrix.

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%-88%. The six remaining species were correctly predicted by only 0%-49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.

Selective Optical Imaging for Detection of Bacterial Biofilms in Tissues

SIGNIFICANCE

The development of an imaging technique to accurately identify biofilm regions on tissues and in wounds is crucial for the implementation of precise surface-based treatments, leading to better patient outcomes and reduced chances of infection.

AIM

The goal of this study was to develop an imaging technique that relies on selective trypan blue (TB) staining of dead cells, necrotic tissues, and bacterial biofilms, to identify biofilm regions on tissues and wounds.

APPROACH

The study explored combinations of ambient multi-colored LED lights to obtain maximum differentiation between stained biofilm regions and the underlying chicken tissue or glass substrate during image acquisition. The TB imaging results were then visually and statistically compared to fluorescence images using a shape similarity measure.

RESULTS

The comparisons between the proposed TB staining method and the fluorescence standard used to detect biofilms on tissues and glass substrates showed up to 97 percent similarity, suggesting that the TB staining method is a promising technique for identifying biofilm regions.

CONCLUSIONS

The TB staining method demonstrates significant potential as an effective imaging technique for the identification of fluorescing and non-fluorescing biofilms on tissues and in wounds. This approach could lead to improved precision in surface-based treatments and better patient outcomes.

Clinical Diagnostics of Bacterial Infections and Their Resistance to Antibiotics-Current State and Whole Genome Sequencing Implementation Perspectives

Antimicrobial resistance (AMR), defined as the ability of microorganisms to withstand antimicrobial treatment, is responsible for millions of deaths annually. The rapid spread of AMR across continents warrants systematic changes in healthcare routines and protocols. One of the fundamental issues with AMR spread is the lack of rapid diagnostic tools for pathogen identification and AMR detection. Resistance profile identification often depends on pathogen culturing and thus may last up to several days. This contributes to the misuse of antibiotics for viral infection, the use of inappropriate antibiotics, the overuse of broad-spectrum antibiotics, or delayed infection treatment. Current DNA sequencing technologies offer the potential to develop rapid infection and AMR diagnostic tools that can provide information in a few hours rather than days. However, these techniques commonly require advanced bioinformatics knowledge and, at present, are not suited for routine lab use. In this review, we give an overview of the AMR burden on healthcare, describe current pathogen identification and AMR screening methods, and provide perspectives on how DNA sequencing may be used for rapid diagnostics. Additionally, we discuss the common steps used for DNA data analysis, currently available pipelines, and tools for analysis. Direct, culture-independent sequencing has the potential to complement current culture-based methods in routine clinical settings. However, there is a need for a minimum set of standards in terms of evaluating the results generated. Additionally, we discuss the use of machine learning algorithms regarding pathogen phenotype detection (resistance/susceptibility to an antibiotic).

Formation and emergent dynamics of spatially organized microbial systems

Spatial organization is the norm rather than the exception in the microbial world. While the study of microbial physiology has been dominated by studies in well-mixed cultures, there is now increasing interest in understanding the role of spatial organization in microbial physiology, coexistence and evolution. Where studied, spatial organization has been shown to influence all three of these aspects. In this mini review and perspective article, we emphasize that the dynamics within spatially organized microbial systems (SOMS) are governed by feedbacks between local physico-chemical conditions, cell physiology and movement, and evolution. These feedbacks can give rise to emergent dynamics, which need to be studied through a combination of spatio-temporal measurements and mathematical models. We highlight the initial formation of SOMS and their emergent dynamics as two open areas of investigation for future studies. These studies will benefit from the development of model systems that can mimic natural ones in terms of species composition and spatial structure.

Understanding Raman Spectral Based Classifications with Convolutional Neural Networks Using Practical Examples of Fungal Spores and Carotenoid-Pigmented Microorganisms

Numerous publications showing that robust prediction models for microorganisms based on Raman micro-spectroscopy in combination with chemometric methods are feasible, often with very precise predictions. Advances in machine learning and easier accessibility to software make it increasingly easy for users to generate predictive models from complex data. However, the question regarding why those predictions are so accurate receives much less attention. In our work, we use Raman spectroscopic data of fungal spores and carotenoid-containing microorganisms to show that it is often not the position of the peaks or the subtle differences in the band ratios of the spectra, due to small differences in the chemical composition of the organisms, that allow accurate classification. Rather, it can be characteristic effects on the baselines of Raman spectra in biochemically similar microorganisms that can be enhanced by certain data pretreatment methods or even neutral-looking spectral regions can be of great importance for a convolutional neural network. Using a method called Gradient-weighted Class Activation Mapping, we attempt to peer into the black box of convolutional neural networks in microbiological applications and show which Raman spectral regions are responsible for accurate classification.

Dry surface biofilms in the food processing industry: An overview on surface characteristics, adhesion and biofilm formation, detection of biofilms, and dry sanitization methods

Bacterial biofilm formation in low moisture food processing (LMF) plants is related to matters of food safety, production efficiency, economic loss, and reduced consumer trust. Dry surfaces may appear dry to the naked eye, however, it is common to find a coverage of thin liquid films and microdroplets, known as microscopic surface wetness (MSW). The MSW may favor dry surface biofilm (DSB) formation. DSB formation is similar in other industries, it occurs through the processes of adhesion, production of extracellular polymeric substances, development of microcolonies and maturation, it is mediated by a quorum sensing (QS) system and is followed by dispersal, leading to disaggregation. Species that survive on dry surfaces develop tolerance to different stresses. DSB are recalcitrant and contribute to higher resistance to sanitation, becoming potential sources of contamination, related to the spoilage of processed products and foodborne disease outbreaks. In LMF industries, sanitization is performed using physical methods without the presence of water. Although alternative dry sanitizing methods can be efficiently used, additional studies are still required to develop and assess the effect of emerging technologies, and to propose possible combinations with traditional methods to enhance their effects on the sanitization process. Overall, more information about the different technologies can help to find the most appropriate method/s, contributing to the development of new sanitization protocols. Thus, this review aimed to identify the main characteristics and challenges of biofilm management in low moisture food industries, and summarizes the mechanisms of action of different dry sanitizing methods (alcohol, hot air, UV-C light, pulsed light, gaseous ozone, and cold plasma) and their effects on microbial metabolism.