Resolving Oral Bacteria Interactions with a High-Throughput Low-Cost Single-Cell Transcriptomics Approach

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1 Abstract 2 Interactions among the microbiota within human microbiome are what define the community function during 3 healthy homeostasis and changes in these relationships can result in increased virulence leading to disease. 4 Bacteria from different species in the oral cavity make up the diverse plaque biofilms and are well known to 5 recognize and bind each other when grown together or artificially mixed (aggregation). Many studies rely on this 6 model to determine how cell-cell interactions affect each member. However, a major limitation to understanding 7 how direct binding between microbes may regulate expression within a simple dual bacteria:bacteria interaction 8 is the fact that there are mixed cell states within the co-culture. In reality, there is not a controlled 1:1 relationship, 9 and not all cells within the culture are directly bound to another cell. Many cells will remain free but still influenced 10 by soluble metabolites and signals in the shared media environment. When bulk transcriptomics are applied to 11 measure the gene expression of such mixed cultures, the average expression of all the cell states together 12 generates noise that interferes with the true signal of interest – what is occurring in specific biological states, 13 such as cells that are attached to each other. This results in a high probability that a true positive will be 14 missed and the inability to confidently attribute observed changes to the cells of interest. Ultimately this problem 15 represents a universal issue in all microbiology that has largely been ignored to date mainly due to the 16 technological limitations of physically capturing only the cells of interest as well as the biological limitation of low 17 bacterial mRNA content. Here we propose to leverage a recent breakthrough in single-cell transcriptomics 18 (scRNAseq) that has been developed for prokaryotes and successfully applied to monocultures and artificially 19 mixed non-interacting bacteria (MPI Kuchina et al., Science 2021), designated MicroSPLiT (microbial split-pool 20 ligation transcriptomics). MicroSPLiT was achieved through overcoming major challenges specific to bacteria, 21 such as their low mRNA content, diversity in cell size, and cell wall architecture. This technique is designed to 22 be high throughput, profiling tens of thousands of cells in a single experiment, low-cost, and importantly, an 23 approach achievable by any lab with only basic laboratory equipment. The goal of this study is to leverage and 24 expand upon this very recent, highly innovative breakthrough to: 1) Overcome a major universal problem in 25 microbiology by developing a comprehensive approach to determine true cell-cell binding interactions at the 26 single-cell level, and 2) apply this technique to gain insight into several important interactions between oral 27 microbial species including known periopathogens and those between recently discovered ultrasmall, reduced 28 genome parasitic oral bacteria and their bacterial host. Overall, the outcomes of this project are expected to 29 directly advance our understanding of the regulation of genes between physically interacting microbiota and 30 develop a protocol for the research community to be able to utilize this new widely accessible approach. 31