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Cell Papers on Liver Proteogenomics, Single-Cell RNA Vector Fields, Microbial Metabolite Dynamics

A team from Ghent University and elsewhere presents a spatial proteogenomic analysis of mouse and human livers based on single-nucleus sequencing, single-cell CITE-seq, spatial transcriptomics, spatial proteomics, and other approaches. With data generated for humans and mice classified as obese, along with their leaner counterparts, the researchers identified and mapped out cell types within the livers. Their atlas highlights evolutionarily conserved cells in the liver, including a group of lipid-associated macrophages that appeared to have altered localization in livers of leaner and heavier representatives. Moreover, "[b]y screening broadly, we identify the best surface markers for the isolation and localization of hepatic [macrophages] and their respective niche cells," the authors write. "Moving forward, applying … relatively cheap antibody panels to large patient cohorts or multiple transgenic mouse models should enable any perturbations disturbing liver homeostasis to be efficiently identified."

Researchers at the Whitehead Institute for Biomedical Research and other centers describe a computational approach called dynamo, which they used to retrace RNA velocity and single-cell transcriptomic "vector fields," and regulatory features from time-resolved single-cell RNA sequence data. "This framework represents a notable advance from the metaphor of epigenetic landscape to a quantitative and predictive theory of the time evolution of single-cell transcriptomics, applicable to many biological systems and at genome-wide scale," the team explains. By applying dynamo to time-resolved scRNA-seq data from differentiating human hematopoietic stem and progenitor cells, for example, the authors got a look at dynamic mechanisms involved in hematopoiesis. "Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions," they write, adding that "in silico perturbations predict cell fate diversions induced by gene perturbations."

Finally, researchers at the University of Chicago, Northwestern University, and the University of Illinois at Urbana-Champaign characterize genetically related metabolite shifts in microbial communities. Focusing on the denitrification process, the team sequenced and phenotyped dozens of diverse bacterial isolates, teasing out ties between bacterial genes and metabolite dynamics within microbial communities. From these and other findings, the authors argue that "the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism."

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