bioRxivMicrobiologyPreprint
Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function
X. Jing, Y. Gong, Z. Diao, Y. Ma, Y. Meng, J. Chen, Y. Ren, Y. Li, W. Sun, J. Zhang, Y. Ji, Y. Liang, Z. Cong, S. Li, B. Ma, Z. Cui, L. Ma, J. Xu
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.
- assay: scRNA-seq
- assay: metabolomics
- research focus: cell type annotation
- research focus: metabolism & bioenergetics
- approach: deep learning
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