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Science Papers Present Method for Single-Cell Microbial Sequencing, More

A high-throughput method for single-cell microbial sequencing with strain resolution and its application to the human gut microbiome is reported in Science this week. The gut microbiome is a complex ecosystem specific to each individual and composed of hundreds of microbial species, and characterizing these microbial communities with single-cell resolution has been a long-standing goal of microbiology. To that end, a Harvard University team developed Microbe-seq, an approach that uses microfluidic devices to encapsulate individual microbes into droplets, after which their DNA is liberated, amplified, barcoded, and sequenced. They use Microbe-seq to sequence 21,914 microbial single-amplified genomes from a single human donor, which they co-assemble into 76 species-level genomes, many from species that are difficult to culture. The investigators use these strain-resolved genomes to reconstruct the horizontal gene transfer (HGT) network of the individual's microbiome, observing HGT between 92 species pairs. They also identify a significant host-phage association between crAssphage, the most abundant bacteriophage known in the human gut microbiome and a particular strain of Bacteroides vulgatus. "Our methodology is general and immediately applicable to other complex microbial communities, such as the microbiomes in the soil and ocean," the study's authors write. "Applying our method to a broader human population and integrating Microbe-seq with other techniques, including functional screening, sorting, and long-read sequencing, could significantly enhance the understanding of the gut microbiome and its interaction with human health."

A machine learning-based approach for discovering cis-regulatory elements (CREs) and their relations with target genes from single-cell multi-omics data is presented in this week's Science Advances. The emergence of single-cell multi-omics data allows for the examination of transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the CRE-to-gene relationships at a single-cell level remains a major challenge. To address this, scientists from Wuhan University and the University of California, Irvine, developed DIRECT-NET — short for discover cis-regulatory elements and construct TF regulatory network — which can identify high-confidence functional CREs, link them with their target genes, and build multimodal transcription factor-to-CRE-to-gene regulatory networks using single-cell transcriptomic and epigenomic data. "By extensively evaluating and characterizing DIRECT-NET's predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy for inferring CRE-to-gene relationships in comparison to existing methods," the method's developers write. "DIRECT-NET is also capable of revealing cell subpopulation-specific and dynamic regulatory linkages." The method, they add, provides an effective way in detecting functional CREs and building gene regulatory networks, "addressing an urgent need in dissecting cell heterogeneity and regulatory mechanisms from single-cell data."