Rational probe design for efficient rRNA depletion and improved metatranscriptomic analysis of human microbiomes

The microbiota that colonize the human gut and other tissues are dynamic, varying both in composition and functional state between individuals and over time. Gene expression measurements can provide insights into microbiome composition and function. However, efficient and unbiased removal of microbial ribosomal RNA (rRNA) presents a barrier to acquiring metatranscriptomic data. Here we describe a probe set that achieves efficient enzymatic rRNA removal of complex human-associated microbial communities. We demonstrate that the custom probe set can be further refined through an iterative design process to efficiently deplete rRNA from a range of human microbiome samples. Using synthetic nucleic acid spike-ins, we show that the rRNA depletion process does not introduce substantial quantitative error in gene expression profiles. Successful rRNA depletion allows for efficient characterization of taxonomic and functional profiles, including during the development of the human gut microbiome. The pan-human microbiome enzymatic rRNA depletion probes described here provide a powerful tool for studying the transcriptional dynamics and function of the human microbiome.

Automated summary

The microbiota in the human gut and other tissues are dynamic and vary in composition and function over time and between individuals. Gene expression measurements can provide insights into the microbiome, but removing microbial ribosomal RNA (rRNA) efficiently and unbiasedly is challenging. In this study, a probe set is described that effectively removes rRNA from complex human-associated microbial communities. Through an iterative design process, the probe set can be optimized to deplete rRNA from various human microbiome samples. The successful rRNA depletion allows for efficient characterization of taxonomic and functional profiles, including during the development of the human gut microbiome. The enzymatic rRNA depletion probes described here provide a powerful tool for studying the transcriptional dynamics and function of the human microbiome.