Flow cytometry can reliably capture gut microbial composition in healthy adults as well as dysbiosis dynamics in patients with aggressive B-cell non-Hodgkin lymphoma

Abstract

Modulation of commensal gut microbiota is increasingly recognized as a promising strategy to reduce mortality in patients with malignant diseases, but monitoring for dysbiosis is generally not routine clinical practice due to equipment, expertise and funding required for sequencing analysis. A low-threshold alternative is microbial diversity profiling by single-cell flow cytometry (FCM), which we compared to 16S rRNA sequencing in human fecal samples and employed to characterize longitudinal changes in the microbiome composition of patients with aggressive B-cell non-Hodgkin lymphoma undergoing chemoimmunotherapy. Diversity measures obtained from both methods were correlated and captured identical trends in microbial community structures, finding no difference in patients’ pretreatment alpha or beta diversity compared to healthy controls and a significant and progressive loss of alpha diversity during chemoimmunotherapy. Our results highlight the potential of FCM-based microbiome profiling as a reliable and accessible diagnostic tool that can provide novel insights into cancer therapy-associated dysbiosis dynamics.

Intestinal Microbes and Hematological Malignancies

“…The authors compared the phenotypic α-diversity metrics obtained from flow cytometry analysis to their taxonomic, 16S rRNA sequencing-based counterparts. The linear mixed effect model confirmed a statistically significant positive association between taxonomic and phenotypic α-diversity, there was no alterations of phenotypic microbial diversity in patients with non-Hodgkin’s lymphoma at the time of diagnosis, however, the α-diversity gradually decreased during chemoimmunotherapy [ 37 ].…”

Host–microbiota interaction during cancer progression from bulk to single‐cell level

“…Microbiome analysis by singlecell flow cytometry, newly developed recently, may be a trustworthy and easily available diagnostic tool that will give us new insights into dysbiosis associated with cancer therapy. 93 If these methods can be applied to explore the tumour-microbiome crosstalk in tumour tissues in the future, it will guide the therapy against tumours.…”

“…The use of advanced algorithms or models to seek accurate and effective interaction networks is a major motivation to explore the interplay between the host and the microbiome. Microbiome analysis by single‐cell flow cytometry, newly developed recently, may be a trustworthy and easily available diagnostic tool that will give us new insights into dysbiosis associated with cancer therapy 93 . If these methods can be applied to explore the tumour–microbiome crosstalk in tumour tissues in the future, it will guide the therapy against tumours.…”

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

“…Additional quantitative DNA staining of fixed bacterial communities correlated with their light scatter intensities, referred to as DNA profile, further allows the detailed characterization of microbial community structure and complexity and its alterations over time. Originally, the microbial community structure fingerprinting was applied primarily for environmental communities [27,28], but we and others have recently shown the application of microbial cytometric fingerprinting for clinically relevant samples [29][30][31]. Such applications are well suited for a rapid and cost-effective determination of, for example, the outgrowing of bacterial populations or prominent reduction of community diversity, both hallmarks of a compositional dysbiosis.…”

“…Such applications are well suited for a rapid and cost-effective determination of, for example, the outgrowing of bacterial populations or prominent reduction of community diversity, both hallmarks of a compositional dysbiosis. MFC fingerprinting has been shown to strongly correlate with the taxonomic diversity of the same sample [28][29][30][31]. Using machine learning approaches, the DNA profile allows prediction of the presence of certain bacteria in samples of low complexity (<100 OTUs/species) [32][33][34][35].…”