Highly multiplexed imaging of microbial diversity

Next generation sequencing technologies have greatly accelerated discoveries and understanding of phylogenetic diversity in natural microbial communities. However, the spatial structure of these communities is difficult to measure, primarily due to spectral overlap associated with fluorescence imaging. We have previously developed High Phylogenetic Resolution Fluorescence In-situ Hybridization (HiPR-FISH), a highly multiplexed FISH method that can overcome the multiplexity limitations of fluorescence imaging, and enable studies of the spatial organization of microbial communities at their full diversity. In this talk, we will present results that demonstrate the multiplexity capability of HiPR-FISH on five synthetic communities of E. coli encoded as different 10-bit binary words, with 63 binary words per community. We will also discuss identification errors associated with spectra classification using basic machine learning algorithms. Finally, we will show preliminary results from application of HiPR-FISH on microbial communities from a few model organisms.