Abstract
Microalgae are emerging as a next-generation biotechnological production system in the pharmaceutical, biofuel, and food domain. The economization of microalgal biorefineries remains a main target, where culture contamination and prokaryotic upsurge are main bottlenecks to impair culture stability, reproducibility, and consequently productivity. Automated online flow cytometry (FCM) is gaining momentum as bioprocess optimization tool, as it allows for spatial and temporal landscaping, real-time investigations of rapid microbial processes, and the assessment of intrinsic cell features. So far, automated online FCM has not been applied to microalgal ecosystems but poses a powerful technology for improving the feasibility of microalgal feedstock production through in situ, real-time, high-temporal resolution monitoring. The study lays the foundations for an application of automated online FCM implying far-reaching applications to impel and facilitate the implementation of innovations targeting at microalgal bioprocesses optimization. It shows that emissions collected on the FL1/FL3 fluorescent channels, harnessing nucleic acid staining and chlorophyll autofluorescence, enable a simultaneous assessment (quantitative and diversity-related) of prokaryotes and industrially relevant phototrophic Chlorella vulgaris in mixed ecosystems of different complexity over a broad concentration range (2.2–1,002.4 cells ⋅μL–1). Automated online FCM combined with data analysis relying on phenotypic fingerprinting poses a powerful tool for quantitative and diversity-related population dynamics monitoring. Quantitative data assessment showed that prokaryotic growth phases in engineered and natural ecosystems were characterized by different growth speeds and distinct peaks. Diversity-related population monitoring based on phenotypic fingerprinting indicated that prokaryotic upsurge in mixed cultures was governed by the dominance of single prokaryotic species. Automated online FCM is a powerful tool for microalgal bioprocess optimization owing to its adaptability to myriad phenotypic assays and its compatibility with various cultivation systems. This allows advancing bioprocesses associated with both microalgal biomass and compound production. Hence, automated online FCM poses a viable tool with applications across multiple domains within the biobased sector relying on single cell–based value chains.
Highlights
Cellular agriculture and along with it renewable biobased materials relying on single-cell biorefineries as, for instance, those associated with yeasts, bacteria, and microalgae, are gaining momentum
No shift was observed in microalgal nucleic acid or chlorophyll content throughout the cocultures assessed, which was indicated by a 100% coverage within the gate established on the FL1/FL3 fluorescent channels
Gates for assessing prokaryotic populations were initially adopted from Prest et al (2013), who proposed a discrimination of prokaryotic regions characterized by low (LNA) and high nucleic acid (HNA) content
Summary
Cellular agriculture and along with it renewable biobased materials relying on single-cell biorefineries as, for instance, those associated with yeasts, bacteria, and microalgae, are gaining momentum. Microalgae have attracted attention as a sustainable means of a next-generation biotechnological production system for the food, feed, pharmaceutical, nutraceutical, and biofuels sector. They are of emerging interest owing the sustainable notion of their connected value chains. Flow cytometry (FCM) poses a viable technology for improving the feasibility of the bioprocesses associated with microalgal biorefineries. The development of automated tools adjunctive to FCM that enable online and inline culture monitoring further perpetuates the application of the technology for single-cell bioprocess management. Automated online FCM enables spatial and temporal landscaping, as well as investigations of rapid processes on a quantitative and phenotype-related base harnessing intrinsic cell features in situ, at real-time, and at high-temporal resolution
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