Single-cell metabolome profiling for phenotyping parasitic diseases in phytoplankton - Vallet, Marine; Kaftan, Filip; Buaya, Anthony; Thines, Marco; Guillou, Laure; Svatoš, Aleš; Pohnert, Georg (Frontiers in Analytical Science, 2023)
Bloom-forming phytoplankton are key players in aquatic ecosystems, fixing carbon dioxide and forming the base of the marine food web. Diverse stresses, such as nutrient depletion, temperature increase, and pathogen emergence can influence the health and dynamics of algal populations. While population responses to these stressors are well-documented in the aquatic ecosystems, little is known about the individual cellular adaptations. These are however the key to an in-depth physiological understanding of microbiome dynamics in the plankton. Finding solutions to disease control in aquaculture also depends on knowledge of infection dynamics and physiology in algae. Single-cell metabolomics can give insight into infection processes by providing a snapshot of small molecules within a biological system. We used a single-cell metabolome profiling workflow to track metabolic changes of diatoms and dinoflagellates subjected to parasite infection caused by the oomycete Lagenisma coscinodisci and the alveolate Parvilucifera spp.. We accurately classified the healthy phenotype of bloom-forming phytoplankton, including the diatoms Coscinodiscus granii and Coscinodiscus radiatus, and the toxic dinoflagellate Alexandrium minutum. We discriminated the infection of the toxic dinoflagellate A. minutum with the alveolate parasitoids Parvilucifera infectans and P. rostrata down to the single-cell resolution. Strain and species-specific responses of the diatom hosts Coscinodiscus spp. infected with the oomycete pathogen Lagenisma coscinodisci could be recognized. LC-HRMS and fragmentation pattern analysis enabled the structure elucidation of metabolic predictors of infection (guanine, xanthine, DMSP, and pheophorbide). The purine salvage pathway and DMSP lysis could be assigned as regulated processes during host invasion. The findings establish single-cell metabolome profiling with LDI-HRMS coupled with classification analysis as a reliable diagnostic tool to track metabolic changes in algae.