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Nature Papers on NEAT-Seq, Gut Microbiome-UTI Links, Approach to Combine Single-Cell Multi-Omics Data

A novel method for the quantification of nuclear proteins along with chromatin accessibility and gene expression in single cells is presented by a Stanford University team in Nature Methods this week. Multimodal single-cell technologies have enabled the characterization of cell states and the identification of gene regulatory programs across various cell types. For instance, pairing assay for transposase-accessible chromatin with high-throughput sequencing — or ATAC-seq — and RNA sequencing in single cells have allowed association of epigenetic status with transcriptional output, enabling identification of putative target genes of regulatory elements. However, quantification of nuclear gene regulatory proteins along with chromatin accessibility profiling and RNA-seq has not been achieved. To address this gap, the researchers developed NEAT-seq, short for sequencing of nuclear protein epitope abundance, chromatin accessibility, and the transcriptome in single cells. They use the approach to profile CD4 memory T cells and illustrate its use for interrogating the relationship between master transcription factor abundance, chromatin accessibility, and gene expression.

The results of a year-long multi-omics project investigating links between the gut microbiome and recurrent urinary tract infections (rUTIs) are published in this week's Nature Microbiology, pointing to a role for the gut-bladder axis in rUTI susceptibility. The study, led by scientists from the Broad Institute, involved metagenomic and transcriptomic analysis of blood, urine, and fecal samples from women with and without a history of rUTIs. The researchers found that the gut microbiome of individuals with a history of rUTIs was significantly depleted in microbial richness and butyrate-producing bacteria compared with controls, although the Escherichia coli gut and bladder populations were comparable between the cohorts. Transcriptional analysis of peripheral blood mononuclear cells, meanwhile, revealed expression profiles indicative of differential systemic immunity between the two groups of women. The findings, the study's authors write, indicates that susceptibility to rUTI is in part mediated through a syndrome involving the gut-bladder axis, comprising a dysbiotic gut microbiome with reduced butyrate production and apparent alterations of systemic immunity.

A computational framework for simultaneously integrating unpaired single-cell multi-omics data and inferring regulatory interactions is described in Nature Biotechnology this week. Advances in single-cell sequencing have enabled the probing of regulatory maps through multiple omics layers, such as chromatin accessibility, DNA methylation, and the transcriptome. While simultaneous assays have recently emerged, different omics are usually measured independently and produce unpaired data, requiring effective and efficient in silico multi-omics integration. To that end, a pair of scientists from Peking University created GLUE, or graph-linked unified embedding, to bridge the gaps between various omics-specific feature spaces in a biologically intuitive manner. They benchmark GLUE to show that it is more accurate, robust, and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. The researchers also demonstrate GLUE in different tasks including triple-omics integration, integrative regulatory inference, and multi-omics human cell atlas construction over millions of cells. GLUE, which is publicly available online, also features a modular design that allows it to be flexibly extended and enhanced for new analysis tasks, they write.

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