Targeted Cell Sorting Combined With Single Cell Genomics Captures Low Abundant Microbial Dark Matter With Higher Sensitivity Than Metagenomics
Overview
Affiliations
Rare members of environmental microbial communities are often overlooked and unexplored, primarily due to the lack of techniques capable of acquiring their genomes. Chloroflexi belong to one of the most understudied phyla, even though many of its members are ubiquitous in the environment and some play important roles in biochemical cycles or biotechnological applications. We here used a targeted cell-sorting approach, which enables the selection of specific taxa by fluorescent labeling and is compatible with subsequent single-cell genomics, to enrich for rare Chloroflexi species from a wastewater-treatment plant and obtain their genomes. The combined workflow was able to retrieve a substantially higher number of novel Chloroflexi draft genomes with much greater phylogenetical diversity when compared to a metagenomics approach from the same sample. The method offers an opportunity to access genetic information from rare biosphere members which would have otherwise stayed hidden as microbial dark matter and can therefore serve as an essential complement to cultivation-based, metagenomics, and microbial community-focused research approaches.
Meta-omics assisted microbial gene and strain resources mining in contaminant environment.
Huang Y, Hu H, Zhang T, Wang W, Liu W, Tang H Eng Life Sci. 2024; 24(5):2300207.
PMID: 38708415 PMC: 11065330. DOI: 10.1002/elsc.202300207.
Cantin L, Dunning Hotopp J, Foster J Front Microbiol. 2024; 15:1352378.
PMID: 38426058 PMC: 10902005. DOI: 10.3389/fmicb.2024.1352378.
Host DNA depletion methods and genome-centric metagenomics of bovine hindmilk microbiome.
da Silva Duarte V, Porcellato D mSphere. 2023; 9(1):e0047023.
PMID: 38054728 PMC: 10826364. DOI: 10.1128/msphere.00470-23.
A comprehensive overview of the Chloroflexota community in wastewater treatment plants worldwide.
Petriglieri F, Kondrotaite Z, Singleton C, Nierychlo M, Dueholm M, Nielsen P mSystems. 2023; 8(6):e0066723.
PMID: 37992299 PMC: 10746286. DOI: 10.1128/msystems.00667-23.
Lo H, Wink K, Nitz H, Kastner M, Belder D, Muller J mSystems. 2023; 8(6):e0099823.
PMID: 37982643 PMC: 10734494. DOI: 10.1128/msystems.00998-23.