Recovery of strain-resolved genomes from human microbiome through an integration framework of single-cell genomics and metagenomics

Abstract

Background Obtaining high-quality (HQ) reference genomes from microbial communities is crucial for understanding the phylogeny and function of uncultured microbes in complex microbial ecosystems. Despite improvements in bioinformatic approaches to generate curated metagenome-assembled genomes (MAGs), existing metagenome binners obtain population consensus genomes but they are nowhere comparable to genomes sequenced from isolates in terms of strain level resolution. Here, we present a framework for the integration of single-cell genomics and metagenomics, referred to as single-cell (sc) metagenomics, to reconstruct strain-resolved genomes from microbial communities at once. Results Our sc-metagenomics integration framework, termed SMAGLinker, uses single-cell amplified genomes (SAGs) generated using microfluidic technology as binning guides and integrates them with metagenome-assembled genomes (MAGs) to recover improved draft genomes. We compared sc-metagenomics with the metagenomics-alone approach using conventional metagenome binners. The sc-metagenomics approach showed precise contig binning and higher recovery rates (>97%) of rRNA and plasmids than conventional metagenomics in genome reconstruction from the cell mock community. In human microbiota samples, sc-metagenomics recovered the largest number of genomes with a total of 103 gut microbial genomes (21 HQ, with 65 showing >90% completeness) and 45 skin microbial genomes (10 HQ, with 40 showing >90% completeness), respectively. Conventional metagenomics recovered one Staphylococcus hominis genome, whereas sc-metagenomics recovered two S. hominis genomes from identical skin microbiota sample. Single-cell sequencing revealed that these S. hominis genomes were derived from two distinct strains harboring specifically different plasmids. We found that all conventional S. hominis MAGs had a substantial lack or excess of genome sequences and contamination from other Staphylococcus species (S. epidermidis). Conclusions SMAGLinker enabled us to obtain strain-resolved genomes in the mock community and human microbiota samples by assigning metagenomic sequences correctly and covering both highly conserved genes such as rRNA genes and unique extrachromosomal elements, including plasmids. SMAGLinker will provide HQ genomes that are difficult to obtain using metagenomics alone and will facilitate the understanding of microbial ecosystems by elucidating detailed metabolic pathways and horizontal gene transfer networks. SMAGLinker is available at https://github.com/kojiari/smaglinker.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

“…On the bioinformatics front, new software tools such as SMAGLinker, Strainberry, and STrain Resolution ON assembly Graphs (STRONG) allow for obtaining strain-resolved genomes in microbiota samples for both short-read and long-read sequencing data. Metagenomic sequences are assigned to several bins and merged for taxonomic identification [ 124 , 125 , 126 ]. For RNA viruses, the RNA-dependent RNA polymerase (RdRp) can be used as a baseline core motif for species identification [ 127 , 128 ].…”

Toward hypothesis-driven, personalized microbiome screening

“…This has been and is currently addressed through mechanistic studies including via in vitro and ex vivo models ( Shah et al., 2016 ), interventions ( Cani et al., 2021 ), and animal models ( Kostic et al., 2013 ). Current microbiome studies predominantly focus on reconstructing microbial genomes from sequence data into either metagenomically assembled genomes (MAGs) and/or by single amplified genomes (SAGs) ( Arikawa et al., 2021 ). While MAGs allow insights into the abundance of different taxa, SAGs provide essential information regarding uncultivated and low-abundance strains.…”

One Cell at a Time: Advances in Single-Cell Methods and Instrumentation for Discovery in Aquatic Microbiology

“…The thermostable mutant phi29 polymerase in the MDA step has been shown to improve amplification performance ( Stepanauskas et al, 2017 ). Additionally, interpretation of SCG results can be facilitated by complementing with available metagenomics data ( Mende et al, 2016 ; Arikawa et al, 2021 ). Hosokawa et al (2017) demonstrated that microfluidics-based cell separation and MDA improved both completeness and decreased contamination rate for SAGs of soil bacteria.…”