Research Overview by Dr. Katie Golden, MD: Quantitative Microbiome Profiling Links Gut Community Variation to Microbial Load

As our understanding of microbiome-associated disease evolves, the scientific community must be rigorous in questioning their investigative techniques in order to best understand and characterize the connection between gut microbiota and disease development.  Our current understanding of the microbiome often comes from sequencing-based analysis of fecal samples, which characterizes flora based on relative abundance in these samples. In this study, Vandeputte and coworkers challenge this approach, recognizing that it neglects the potential contribution of microbial abundance as a key factor in host health.

To address this discrepancy, investigators detail an approach using both traditional sequencing-based analysis, as well as flow cytometry to quantify microbial loads in fecal samples. The resulting data highlights a diversity in individual microbiome variation that had previously been under appreciated. Not only did this approach detect up to tenfold variation in cell counts between individuals, but also large fluctuations in daily microbial counts in the same individual when studied over the course of a week. They found that quantified cell counts correlated with microbial load variation, thus supporting its relevance in microbiome analysis.

Researchers then studied the implications of this new technique on data analyses. In the analysis of healthy individual fecal samples, they compared a ranked order of microbial genera by relative and absolute abundance. They found significant changes in rank order position for multiple types of microbial genera using these two different approaches, which in turn had important implications for data analysis. To give an example, they no longer detected an independent contribution of age to microbiome variation. In comparing the relative versus quantitative approach, they found both increased detection of true associations, as well decreased false positive correlations.

In the final stage of the study, investigators applied this investigative technique to stool samples from patients with Crohn’s disease. They found that these patients had cell counts that were three times lower than healthy controls. Furthermore, while relative abundances showed increased levels of Bacteroides in Crohn’s disease, quantitative analysis reveals this can better be described as a decrease in abundance of Prevotella species. These findings suggest the microbiome variations seen in Crohn’s disease are better characterized by decreased microbiota cell counts, rather than aberrant bacterial proliferation. This quantitative approach adds an important dimension to microbiome analysis, to better characterize variations in microbial populations both between individuals and over time.