Characterization of Heterogeneity and Dynamics of Lysis of Single Bacillus subtilis Cells upon Prophage Induction During Spore Germination, Outgrowth, and Vegetative Growth Using Raman Tweezers and Live-Cell Phase-Contrast Microscopy

Rapid and accurate identification of marine bacteria spores at a single-cell resolution by laser tweezers Raman spectroscopy and deep learning

Marine bacteria have been considered as important participants in revealing various carbon/sulfur/nitrogen cycles of marine ecosystem. Thus, how to accurately identify rare marine bacteria without a culture process is significant and valuable. In this work, we constructed a single-cell Raman spectra dataset from five living bacteria spores and utilized convolutional neural network to rapidly, accurately, nondestructively identify bacteria spores. The optimal CNN architecture can provide a prediction accuracy of five bacteria spore as high as 94.93% ± 1.78%. To evaluate the classification weight of extracted spectra features, we proposed a novel algorithm by occluding fingerprint Raman bands. Based on the relative classification weight arranged from large to small, four Raman bands located at 1518, 1397, 1666, and 1017 cm-1 mostly contribute to producing such high prediction accuracy. It can be foreseen that, LTRS combined with CNN approach have great potential for identifying marine bacteria, which cannot be cultured under normal condition.

Accurate identification of living Bacillus spores using laser tweezers Raman spectroscopy and deep learning

Accurately, rapidly, and noninvasively identifying Bacillus spores can greatly contribute to controlling a plenty of infectious diseases. Laser tweezers Raman spectroscopy (LTRS) has confirmed to be a powerful tool for studying Bacillus spores at a single cell level. In this study, we constructed a single-cell Raman spectra dataset of living Bacillus spores and utilized deep learning approach to accurately, nondestructively identify Bacillus spores. The trained convolutional neural network (CNN) could efficiently extract tiny Raman spectra features of five spore species, and provide a prediction accuracy of specie identification as high as 100 %. Moreover, the spectral feature differences in three Raman bands at 660, 826, and 1017 cm^-1 were confirmed to mostly contribute to producing such high prediction accuracy. In addition, optimal CNN model was employed to monitor and identify sporulation process at different metabolic phases in one growth cycle. The obtained average prediction accuracy of metabolic phase identification was approximately 88 %. It can be foreseen that, LTRS combined with CNN approach have great potential for accurately identifying spore species and metabolic phases at a single cell level, and can be gradually extended to perform identification for many unculturable bacteria growing in soil, water, and food.

Label-Free Raman Microspectroscopy for Identifying Prokaryotic Virocells

Raman microspectroscopy has been used to thoroughly assess growth dynamics and heterogeneity of prokaryotic cells, yet little is known about how the chemistry of individual cells changes during infection with virulent viruses, resulting in so-called virocells. Here, we investigate biochemical changes of bacterial and archaeal cells of three different species in laboratory cultures before and after addition of their respective viruses using single-cell Raman microspectroscopy. By applying multivariate statistics, we identified significant differences in the spectra of single cells with/without addition of virulent dsRNA phage (phi6) for Pseudomonas syringae. A general ratio of wavenumbers that contributed the greatest differences in the recorded spectra was defined as an indicator for virocells. Based on reference spectra, this difference is likely attributable to an increase in nucleic acid versus protein ratio of virocells. This method also proved successful for identification of Bacillus subtilis cells infected with the double-stranded DNA (dsDNA) phage phi29, displaying a decrease in respective ratio, but failed for archaeal virocells (Methanosarcina mazei with the dsDNA methanosarcina spherical virus) due to autofluorescence. Multivariate and univariate analyses suggest that Raman spectral data of infected cells can also be used to explore the complex biology behind viral infections of bacteria. Using this method, we confirmed the previously described two-stage infection of P. syringae's phi6 and that infection of B. subtilis with phi29 results in a stress response within single cells. We conclude that Raman microspectroscopy is a promising tool for chemical identification of Gram-positive and Gram-negative virocells undergoing infection with virulent DNA or RNA viruses. IMPORTANCE Viruses are highly diverse biological entities shaping many ecosystems across Earth. However, understanding the infection of individual microbial cells and the related biochemical changes remains limited. Using Raman microspectroscopy in conjunction with univariate and multivariate statistics, we established a marker for identification of infected Gram-positive and Gram-negative bacteria. This nondestructive, label-free analytical method at single-cell resolution paves the way for future studies geared towards analyzing virus-host systems of prokaryotes to further understand the complex chemistry and function of virocells.

Characterization and Genome Study of Novel Lytic Bacteriophages against Prevailing Saprophytic Bacterial Microflora of Minimally Processed Plant-Based Food Products

The food industry is still searching for novel solutions to effectively ensure the microbiological safety of food, especially fresh and minimally processed food products. Nowadays, the use of bacteriophages as potential biological control agents in microbiological food safety and preservation is a promising strategy. The aim of the study was the isolation and comprehensive characterization of novel bacteriophages with lytic activity against saprophytic bacterial microflora of minimally processed plant-based food products, such as mixed leaf salads. From 43 phages isolated from municipal sewage, four phages, namely Enterobacter phage KKP 3263, Citrobacter phage KKP 3664, Enterobacter phage KKP 3262, and Serratia phage KKP 3264 have lytic activity against Enterobacter ludwigii KKP 3083, Citrobacter freundii KKP 3655, Enterobacter cloacae KKP 3082, and Serratia fonticola KKP 3084 bacterial strains, respectively. Transmission electron microscopy (TEM) and whole-genome sequencing (WGS) identified Enterobacter phage KKP 3263 as an Autographiviridae, and Citrobacter phage KKP 3664, Enterobacter phage KKP 3262, and Serratia phage KKP 3264 as members of the Myoviridae family. Genome sequencing revealed that these phages have linear double-stranded DNA (dsDNA) with sizes of 39,418 bp (KKP 3263), 61,608 bp (KKP 3664), 84,075 bp (KKP 3262), and 148,182 bp (KKP 3264). No antibiotic resistance genes, virulence factors, integrase, recombinase, or repressors, which are the main markers of lysogenic viruses, were annotated in phage genomes. Serratia phage KKP 3264 showed the greatest growth inhibition of Serratia fonticola KKP 3084 strain. The use of MOI 1.0 caused an almost 5-fold decrease in the value of the specific growth rate coefficient. The phages retained their lytic activity in a wide range of temperatures (from -20 °C to 50 °C) and active acidity values (pH from 4 to 11). All phages retained at least 70% of lytic activity at 60 °C. At 80 °C, no lytic activity against tested bacterial strains was observed. Serratia phage KKP 3264 was the most resistant to chemical factors, by maintaining high lytic activity across a broader range of pH from 3 to 11. The results indicated that these phages could be a potential biological control agent against saprophytic bacterial microflora of minimally processed plant-based food products.