A novel high-throughput method for quantification of microbiota distribution at the single cell level using high dynamic range DNA barcoding

Abstract

Abstract In the bacteria-host interaction, the dynamics of microbiota contributes to the development of many diseases, e.g., infectious diseases, gastrointestinal cancers, autoimmune diseases, etc. The mechanisms behind the dynamics of microbiota remain unknown, and one of them is cell-cell interaction among the bacteria within microbiota. In order to investigate the mechanisms, a method to characterize the microbiota, including identification of species and the cell-number distribution of all species, is required. However, the current techniques measure the distribution of 16S rRNA copies, which is different from the cell-number distribution, since different bacterium has different 16S rRNA copies on its genome. Here we are developing a novel high-throughput bacterial counting/identification method by 16S rRNA sequencing and our developed cell barcoding. To validate this method, we measured a known ten bacterial community, and successfully identified their correct 16S rRNA sequences. This method identifies sequences independently of database and distinguishes single-base difference, so that it can identify unregistered 16S rRNA sequences as well. Based on these identified 16S rRNA sequences, we quantified the cell numbers of each bacterium. The results were consistent with the cell numbers measured under a microscope. Furthermore, we applied this method for studying the murine gut bacterial microbiota, and we found significant changes between the large intestine and the cecum. We also identified unregistered 16S rRNA sequences in both samples. This method offers a high-throughput experimental way that directly counts each bacterium in a bacterial community including unregistered bacteria at the cell level.