A study on identification of bacteria in environmental samples using single-cell Raman spectroscopy: feasibility and reference libraries

Simulating a reference medium for determining bacterial growth in hospital wastewater for Raman spectroscopic investigation

Wastewater is a very complex and diverse medium, which despite low nutrient density still harbors bacteria. Especially the wastewater from hospitals contains a high germ load. However, wastewater is also very variable and differs not only from day to day, but also from house to house. Since wastewater is always changing and medium has an impact on Raman spectra of bacteria, it is necessary to find a surrogate material in which bacteria can be cultured to mimic a real hospital wastewater sample. In this study, we investigate two different artificial wastewaters for their abilities as a good alternative to real wastewater from the Jena University Hospital and to serve as a reference material for bacterial cultivation with subsequent Raman measurement. Each of the artificial wastewater on its own was not suitable to be used as a reference medium. Only the combination of the two simulated wastewaters achieved satisfactory results in the Raman spectroscopic identification of bacteria from real wastewater. These results could be used later in new experiments as a reference dataset to identify bacteria from real hospital wastewater samples.

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification

Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a novel method that uses artificial intelligence to analyze biological Raman spectra and identify the microbes at a single-cell level. The combination of a framework of convolutional neural network (ConvNet) and Raman spectroscopy allows the extraction of the Raman spectral features of a single microbial cell and then categorizes cells according to their spectral features. As the proof of concept, we measured Raman spectra of 14 microbial species at a single-cell level and constructed an optimal ConvNet model using the Raman data. The average accuracy of classification by ConvNet is 95.64 ± 5.46%. Meanwhile, we introduced an occlusion-based Raman spectra feature extraction to visualize the weights of Raman features for distinguishing different species.

Evaluation of the impact of buffered peptone water composition on the discrimination between Salmonella enterica and Escherichia coli by Raman spectroscopy

The detection of Salmonella spp. in food samples is regulated by the ISO 6579:2002 standard, which requires that precise procedures are followed to ensure the reliability of the detection process. This standard requires buffered peptone water as a rich medium for the enrichment of bacteria. However, the effects of different brands of buffered peptone water on the identification of microorganisms by Raman spectroscopy are unknown. In this regard, our study evaluated the discrimination between two bacterial species, Salmonella enterica and Escherichia coli, inoculated and analyzed with six of the most commonly used buffered peptone water brands. The results showed that bacterial cells behaved differently according to the brand used in terms of biomass production and the spectral fingerprint. The identification accuracy of the analyzed strains was between 85% and 100% depending on the given brand. Several batches of two brands were studied to evaluate the classification rates between the analyzed bacterial species. The chemical analysis performed on these brands showed that the nutrient content was slightly different and probably explained the observed effects. On the basis of these results, Raman spectroscopy operators are encouraged to select an adequate culture medium and continue its use throughout the identification process to guarantee optimal recognition of the microorganism of interest.

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

Infectious diseases caused by bacteria still pose major diagnostic challenges in spite of the availability of various molecular approaches. Irrespective of the type of infection, rapid identification of the causative pathogen with a high degree of sensitivity and specificity is essential for initiating appropriate treatment. While existing methods like PCR possess high sensitivity, they are incapable of identifying the viability status of the pathogen and those which can, like culturing, are inherently slow. To overcome these limitations, we developed a diagnostic platform based on Raman microspectroscopy, capable of detecting biochemical signatures from a single bacterium for identification as well as viability assessment. The study also establishes a decontamination protocol for handling live pathogenic bacteria which does not affect identification and viability testing, showing applicability in the analysis of sputum samples containing pathogenic mycobacterial strains. The minimal sample processing along with multivariate analysis of spectroscopic signatures provides an interface for automatic classification, allowing the prediction of unknown samples by mapping signatures onto available datasets. Also, the novelty of the current work is the demonstration of simultaneous identification and viability assessment at a single bacterial level for pathogenic bacteria. Graphical abstract.

Perspective on Improving Environmental Monitoring of Biothreats

For more than a decade, the United States has performed environmental monitoring by collecting and analyzing air samples for a handful of biological threat agents (BTAs) in order to detect a possible biological attack. This effort has faced numerous technical challenges including timeliness, sampling efficiency, sensitivity, specificity, and robustness. The cost of city-wide environmental monitoring using conventional technology has also been a challenge. A large group of scientists with expertise in bioterrorism defense met to assess the objectives and current efficacy of environmental monitoring and to identify operational and technological changes that could enhance its efficacy and cost-effectiveness, thus enhancing its value. The highest priority operational change that was identified was to abandon the current concept of city-wide environmental monitoring because the operational costs were too high and its value was compromised by low detection sensitivity and other environmental factors. Instead, it was suggested that the focus should primarily be on indoor monitoring and secondarily on special-event monitoring because objectives are tractable and these operational settings are aligned with likelihood and risk assessments. The highest priority technological change identified was the development of a reagent-less, real-time sensor that can identify a potential airborne release and trigger secondary tests of greater sensitivity and specificity for occasional samples of interest. This technological change could be transformative with the potential to greatly reduce operational costs and thereby create the opportunity to expand the scope and effectiveness of environmental monitoring.