Label-free Raman microspectroscopy for identifying virocells
Raman microspectroscopy has been thoroughly used to assess growth dynamics and heterogeneity of prokaryotic cells. Yet, little is known about how the chemistry of individual cells changes during infection with lytic 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 and cells after addition of lytic 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 vs. protein ratio of virocells. This method proved also successful for identification of Bacillus subtilis cells infected with phi29 displaying a decrease in respective ratio but failed for archaeal virocells (Methanosarcina mazei with 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. syringaes phi6 and that infection of B. subtilis by 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 lytic DNA or RNA viruses. ImportanceViruses are highly diverse biological entities shaping many ecosystems across Earth. Yet, understanding the infection of individual microbial cells and the related biochemical changes remains limited. Using Raman microspectroscopy in conjunction with univariate and unsupervised machine learning approaches, we established a marker for identification of infected Gram-positive and Gram-negative bacteria. This non-destructive, label-free analytical method at single-cell resolution paves the way for future studies geared towards analyzing complex biological changes of virus-infected cells in pure culture and natural ecosystems.