Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification

Single-cell phenotyping of bacteria combined with deep sequencing for improved contextualization of microbiome analyses

Great effort was made to characterize the bacterial communities inhabiting the human body as a factor in disease, resulting in the realization that a wide spectrum of diseases is associated with an altered composition of the microbiome. However, the identification of disease-relevant bacteria has been hindered by the high cross-sectional diversity of individual microbiomes, and in most cases, it remains unclear whether the observed alterations are cause or consequence of disease. Hence, innovative analysis approaches are required that enable inquiries of the microbiome beyond mere taxonomic cataloging. This review highlights the utility of microbiota flow cytometry, a single-cell analysis platform to directly interrogate cellular interactions, cell conditions, and crosstalk with the host's immune system within the microbiome to take into consideration the role of microbes as critical interaction partners of the host and the spectrum of microbiome alterations, beyond compositional changes. In conjunction with advanced sequencing approaches it could reveal the genetic potential of target bacteria and advance our understanding of taxonomic diversity and gene usage in the context of the microenvironment. Single-cell bacterial phenotyping has the potential to change our perspective on the human microbiome and empower microbiome research for the development of microbiome-based therapy approaches and personalized medicine.

Starvation responses impact interaction dynamics of human gut bacteria Bacteroides thetaiotaomicron and Roseburia intestinalis

Bacterial growth often alters the environment, which in turn can impact interspecies interactions among bacteria. Here, we used an in vitro batch system containing mucin beads to emulate the dynamic host environment and to study its impact on the interactions between two abundant and prevalent human gut bacteria, the primary fermenter Bacteroides thetaiotaomicron and the butyrate producer Roseburia intestinalis. By combining machine learning and flow cytometry, we found that the number of viable B. thetaiotaomicron cells decreases with glucose consumption due to acid production, while R. intestinalis survives post-glucose depletion by entering a slow growth mode. Both species attach to mucin beads, but only viable cell counts of B. thetaiotaomicron increase significantly. The number of viable co-culture cells varies significantly over time compared to those of monocultures. A combination of targeted metabolomics and RNA-seq showed that the slow growth mode of R. intestinalis represents a diauxic shift towards acetate and lactate consumption, whereas B. thetaiotaomicron survives glucose depletion and low pH by foraging on mucin sugars. In addition, most of the mucin monosaccharides we tested inhibited the growth of R. intestinalis but not B. thetaiotaomicron. We encoded these causal relationships in a kinetic model, which reproduced the observed dynamics. In summary, we explored how R. intestinalis and B. thetaiotaomicron respond to nutrient scarcity and how this affects their dynamics. We highlight the importance of understanding bacterial metabolic strategies to effectively modulate microbial dynamics in changing conditions.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Exploration of microbiome diversity of stacked fermented grains by flow cytometry and cell sorting

Sauce-flavor baijiu is one of the twelve flavor types of Chinese distilled fermented product. Microbial composition plays a key role in the stacked fermentation of Baijiu, which uses grains as raw materials and produces flavor compounds, however, the active microbial community and its relationship remain unclear. Here, we investigated the total and active microbial communities of stacked fermented grains of sauce-flavored Baijiu using flow cytometry and high-throughput sequencing technology, respectively. By using traditional high-throughput sequencing technology, a total of 24 bacterial and 14 fungal genera were identified as the core microbiota, the core bacteria were Lactobacillus (0.08–39.05%), Acetobacter (0.25–81.92%), Weissella (0.03–29.61%), etc. The core fungi were Issatchenkia (23.11–98.21%), Monascus (0.02–26.36%), Pichia (0.33–37.56%), etc. In contrast, using flow cytometry combined with high-throughput sequencing, the active dominant bacterial genera after cell sorting were found to be Herbaspirillum, Chitinophaga, Ralstonia, Phenylobacterium, Mucilaginibacter, and Bradyrhizobium, etc., whereas the active dominant fungal genera detected were Aspergillus, Pichia, Exophiala, Candelabrochaete, Italiomyces, and Papiliotrema, etc. These results indicate that although the abundance of Acetobacter, Monascus, and Issatchenkia was high after stacked fermentation, they may have little biological activity. Flow cytometry and cell sorting techniques have been used in the study of beer and wine, but exploring the microbiome in such a complex environment as Chinese baijiu has not been reported. The results also reveal that flow cytometry and cell sorting are convenient methods for rapidly monitoring complex microbial flora and can assist in exploring complex environmental samples.

Technical versus biological variability in a synthetic human gut community

Synthetic communities grown in well-controlled conditions are an important tool to decipher the mechanisms driving community dynamics. However, replicate time series of synthetic human gut communities in chemostats are rare, and it is thus still an open question to what extent stochasticity impacts gut community dynamics. Here, we address this question with a synthetic human gut bacterial community using an automated fermentation system that allows for a larger number of biological replicates. We collected six biological replicates for a community initially consisting of five common gut bacterial species that fill different metabolic niches. After an initial 12 hours in batch mode, we switched to chemostat mode and observed the community to stabilize after 2-3 days. Community profiling with 16S rRNA resulted in high variability across replicate vessels and high technical variability, while the variability across replicates was significantly lower for flow cytometric data. Both techniques agree on the decrease in the abundance of Bacteroides thetaiotaomicron, accompanied by an initial increase in Blautia hydrogenotrophica. These changes occurred together with reproducible metabolic shifts, namely a fast depletion of glucose and trehalose concentration in batch followed by a decrease in formic acid and pyruvic acid concentrations within the first 12 hours after the switch to chemostat mode. In conclusion, the observed variability in the synthetic bacterial human gut community, as assessed with 16S rRNA gene sequencing, is largely due to technical variability. The low variability seen in HPLC and flow cytometry data suggests a highly deterministic system.