bioRxiv · 10.1101/2023.11.27.568714
Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function
Abstract
Although microbiome-wide association studies (MWAS) have uncovered many marker organisms for an ecosystem trait, mechanisms of most microbiota-mediated processes remain elusive, due to challenges in validating the markers in situ metabolic activities and tracing such activities to individual genomes. Here we introduced a phylogeny-metabolism dual-directed single-cell genomics approach called Fluorescence-In-Situ-Hybridization-guided Single-Cell Raman-activated Sorting and Sequencing (FISH-scRACS-Seq). It directly localizes individual cells from target taxon via a FISH probe for marker organism, profiles their in situ metabolic functions via single-cell Raman spectra, sorts cells of target taxonomy and target metabolism, and produces indexed, high-coverage and precisely-one-cell genomes. From cyclohexane-contaminated seawater, cells representing the MWAS-derived marker taxon of {gamma}-Proteobacteria and that are actively degrading cyclohexane in situ were directly identified via FISH and Raman respectively, then sorted and sequenced for one-cell full genomes. In such a Pseudoalteromonas fuliginea cell, we discovered a three-component cytochrome P450 system that can convert cyclohexane to cyclohexanol in vitro, representing a previously unknown group of cyclohexane-degrading enzymes and organisms. By culture-independently unveiling enzymes, pathways, genomes and their in situ functions specifically for those single-cells with ecological relevance, FISH-scRACS-Seq is a rational and generally applicable approach for dissecting and mining microbiota functions. TeaserFISH-scRACS-Seq is a new strategy to dissect microbiota functional mechanism at single-cell resolution.
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Jing, X., Gong, Y., Diao, Z., Ma, Y., Meng, Y., Chen, J., Ren, Y., Li, Y., Sun, W., Zhang, J., Ji, Y., Liang, Y., Cong, Z., Li, S., Ma, B., Cui, Z., Ma, L., Xu, J.. 2023-11-27. Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function. https://doi.org/10.1101/2023.11.27.568714
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