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Nature Papers Examine Single-Cell, Multi-Omic SARS-CoV-2 Response; Flatfish Sequences; More

A single-cell, multi-omics analysis of the human immune response to SARS-CoV-2 infection is reported in Nature Medicine this week, providing new insights into COVID-19 pathogenesis. In the study, an international team led by the Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research COVID-19 BioResource Collaboration performed a combined single-cell transcriptome, cell-surface protein, and lymphocyte antigen receptor repertoire analysis of peripheral blood from 130 COVID-19 patients with varying disease severity. They uncover evidence of a coordinated immune response that contributes to COVID-19 progression, as well the different cellular components involved that could potentially be targeted for therapeutic intervention.

By sequencing the genomes of different flatfish species, a team led by scientists from the Chinese Academy of Sciences has uncovered new details about the origin of the flatfish's unique body plan. Flatfish have evolved a specialized morphology that includes a flattened body with both eyes on the upper side to enable binocular vision, but the evolutionary and genetic origins of this body plan are unclear. The researchers analyzed the genomes of 11 flatfish species and discover a polyphyletic origin for the animals, with real flatfish of the suborder Pleuronectoidei and flatfish-like Psettodoidei suborder evolving independently from different ancestors. The findings, the authors write in Nature Genetics, substantially clarify the "long-standing controversies over the phylogeny of flatfishes, while the genes highlighted in this study lay a blueprint for future functional characterization of the molecular mechanisms underlying the unusual body plan of flatfishes."

An online tool for integrating large, diverse, and continually arriving single-cell multi-omic datasets is described in Nature Biotechnology. High-throughput single-cell sequencing technologies have enabled the profiling of multiple molecular modalities, including gene expression, chromatin accessibility, and DNA methylation. Existing methods to integrate single-cell data, however, are not designed for multiple modalities or do not scale for massive datasets. To address this, a team led by University of Michigan scientists developed an algorithm called online iNMF — short for integrative non-negative matrix factorization — that can scale to arbitrarily large numbers of cells using fixed memory, iteratively incorporate new datasets as they are generated, and allow multiple users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. The researchers demonstrate online iNMF by integrating more than 1 million cells on a standard laptop, integrating large single-cell RNA sequencing and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex.