SPATIAL SEQUENCING METHODS TO MAP THE HOST-MICROBIOME INTERFACE
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Human-associated microbiomes influence health and disease and are spatially structured, with composition varying across anatomical locations. Yet the current standard in microbiome science relies on assays that lack spatial context and often measure host and microbial signals separately, limiting our ability to capture interactions where they occur. Spatial transcriptomics offers a potential solution by simultaneously measuring host and microbial RNA within intact tissue architecture. However, conventional methods are engineered to capture polyadenylated molecules, effectively excluding bacterial transcripts that predominantly lack poly(A) tails.In this dissertation, I present methods that overcome this limitation by combining enzymatic in situ polyadenylation with high-resolution spatial transcriptomics platforms, capturing the total transcriptome and building detailed maps of host gene expression and microbial abundance. Application of this approach to a mouse model of intestinal neoplasia revealed that microbiome composition varies as a function of anatomical location. Spatial profiling uncovered frequent short-range inter-microbial interactions, local shaping of microbial niches by host gene expression programs, and tumor-associated alterations in the spatial architecture of the host-microbiome interface. To demonstrate broader applicability, I extended high-resolution total RNA spatial sequencing to viral myocarditis and spermatogenesis in mice. In viral-induced myocarditis, the method localized viral transcripts within the inflamed heart, profiled viral mutations across individual infection foci, and associated viral diversity with tissue remodeling patterns. In the testis, spatial total RNA profiling recovered non-polyadenylated transcripts with spermatogenesis stage-specific expression signatures and revealed distinct spatial patterning between spliced and unspliced isoforms of the same gene, providing insight into post-transcriptional regulation during development. Together, this work establishes that spatial context is essential for understanding host-microbiome interactions and demonstrates that RNA serves as an effective proxy for capturing molecular interactions at tissue interfaces. The methods presented here are compatible with commercial spatial transcriptomics platforms and provide a framework for utilizing total transcriptome readouts to understand host-microbe interactions in steady-state physiology, infection, and development.