<title>Abstract</title>To improve our understanding of the environmental microbiome, we developed a single-cell genome sequencing platform, named SAG-gel, which utilizes gel beads for single-cell isolation, cell lysis, and whole genome amplification (WGA) for sequencing. SAG-gel enables serial, parallel and independent reactions of &gt; 100,000 single cells in a single tube, delivering high-quality genome recovery with storable randomized single-cell genome libraries. From soil and marine environmental sources, we acquired 734 partial genomes that are recapitulated in 231 species, 35% of which were assigned as high-to-medium qualities. We found that each genome to be almost unique and 98.7% of them were newly identified, implying the complex genetic diversities across 44 phyla. The various metabolic capabilities including virulence factors and biosynthetic gene clusters were found across the lineages at single-cell resolution. This technology will accelerate the accumulation of reference genomes of uncharacterized environmental microbes and provide us new insights for their roles.

BACKGROUND: The gut microbiota can have dramatic effects on host metabolism; however, current genomic strategies for uncultured bacteria have several limitations that hinder their ability to identify responders to metabolic changes in the microbiota. In this study, we describe a novel single-cell genomic sequencing technique that can identify metabolic responders at the species level without the need for reference genomes, and apply this method to identify bacterial responders to an inulin-based diet in the mouse gut microbiota. RESULTS: Inulin-feeding changed the mouse fecal microbiome composition to increase Bacteroides spp., resulting in the production of abundant succinate in the mouse intestine. Using our massively parallel single-cell genome sequencing technique, named SAG-gel platform, we obtained 346 single-amplified genomes (SAGs) from mouse gut microbes before and after dietary inulin supplementation. After quality control, the SAGs were classified as 267 bacteria, spanning 2 phyla, 4 classes, 7 orders, and 14 families, and 31 different strains of SAGs were graded as high- and medium-quality draft genomes. From these, we have successfully obtained the genomes of the dominant inulin-responders, Bacteroides spp., and identified their polysaccharide utilization loci and their specific metabolic pathways for succinate production. CONCLUSIONS: Our single-cell genomics approach generated a massive amount of SAGs, enabling a functional analysis of uncultured bacteria in the intestinal microbiome. This enabled us to estimate metabolic lineages involved in the bacterial fermentation of dietary fiber and metabolic outcomes such as short-chain fatty acid production in the intestinal environment based on the fibers ingested. The technique allows the in-depth isolation and characterization of uncultured bacteria with specific functions in the microbiota and could be exploited to improve human and animal health. Video abstract.

Spatial transcriptomics is useful for understanding the molecular organization of a tissue and providing insights into cellular function in a morphological context. In order to obtain reproducible results in spatial transcriptomics, we have to maintain tissue morphology and RNA molecule stability during the image acquisition and biomolecule collection processes. Here, we developed a tissue processing method for robust and reproducible RNA-seq from tissue microdissection samples. In this method, we suppressed RNA degradation in fresh-frozen tissue specimens by dehydration fixation and effectively collected a small amount of RNA molecules from microdissection samples by magnetic beads. We demonstrated the spatial transcriptome analysis of the mouse liver and brain in serial microdissection samples (100 μm in a diameter and 10 μm in thickness) produced by a microdissection punching system. Using our method, we could prevent RNA degradation at room temperature and effectively produce a sequencing library with Smart-seq2. This resulted in reproducible sequence read mapping in exon regions and the detection of more than 2000 genes compared to non-fixed samples in the RNA-seq analysis. Our method would be applied to various transcriptome analyses, providing the information for region specific gene expression in tissue specimens.