CHIC—an automated approach for the detection of dynamic variations in complex microbial communities

Abstract

Altering environmental conditions change structures of microbial communities. These effects have an impact on the single-cell level and can be sensitively detected using community flow cytometry. However, although highly accurate, microbial monitoring campaigns are still rarely performed applying this technique. One reason is the limited access to pattern analysis approaches for the evaluation of microbial cytometric data. In this article, a new analyzing tool, Cytometric Histogram Image Comparison (CHIC), is presented, which realizes trend interpretation of variations in microbial community structures (i) without any previous definition of gates, by working (ii) person independent, and (iii) with low computational demand. Various factors influencing a sensitive determination of changes in community structures were tested. The sensitivity of this technique was found to discriminate down to 0.5% internal variation. The final protocol was exemplarily applied to a complex microbial community dataset, and correlations to experimental variation were successfully shown.