Abstract
Our understanding of the role of human microbiome in health and disease has been growing rapidly in recent years. Amplicon sequencing of highly conserved 16S ribosomal RNA (rRNA) regions has long been the standard technique used to assess patient microbial diversity, however there are limitations to this method. 16S rRNA amplicon sequencing only captures prokaryotic diversity and misses eukaryotic and viral components of the microbiome. While an additional amplicon sequencing of the internal transcribed spacer 1 (ITS1) region can capture fungal diversity, there is no known parallel technique for viral detection. Furthermore, these rRNA amplicon methods are generally only genus-specific. To obtain species-level differentiation, multiple variable regions of the rRNA need to be assessed in repeated experiments. Additionally, important strain information is not detected. Strain-to-strain variation is responsible for pathogenicity, toxins, virulence factors, epitopes, and antibiotic resistance characteristics. Whole genome sequencing (WGS) can determine strain-to strain variation and has more applications beyond microbial community identification. Metagenomic homolog discovery from uncharacterized organisms is enabling pathway design for production of molecules of interest. Using WGS, biologists are prospecting for natural enzymatic solutions to their challenges. Miniaturizing WGS significantly lowers the cost to sequence and discover, as well as reduces process cycle time. As researchers continue to deepen our understanding of human microbiome, and as biologists explore the metagenomic space, our tools and analyses need to scale accordingly. A workflow utilizing the Labcyte Echo 525 Liquid Handler can provide the solution to cost-effectively scale WGS of microbiomes to meet the demands of this new era of microbiome research. In this study, we demonstrate that the Echo 525 Liquid Handler can be used to miniaturize WGS of microbial communities. Raw data can be processed, analyzed, and ready for interpretation within the hour, by utilizing CosmosID’s best-in-class microbiome bioinformatics platform. We see accurate representation of microbiome samples to their references.