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Science Papers on Multiplexed Single-Cell RNA-Seq Approach, Immune System Variation, Protein Biomarkers for Cardiovascular Outcomes

A team led by researchers from the University of California, San Francisco report in this week's Science on the use of a multiplexed single-cell RNA sequencing method uncover cell type-specific molecular and genetic associations to the autoimmune disease systemic lupus erythematosus (SLE). Bulk transcriptomic profiling has implicated increased type 1 interferon signaling, dysregulated lymphocyte activation, and failure of apoptotic clearance as hallmarks of SLE, while other research has shown that many of the genes involved in these processes are proximal to the roughly 100 loci associated with SLE. Despite such progress, a comprehensive census of circulating immune cells in the condition remains incomplete and annotating the cell types and contexts that mediate genetic associations remains challenging. In the new study, the UCSF-led group used their sequencing technique to profile more than 1.2 million peripheral blood mononuclear cells from 162 SLE patients and 99 controls. They find that SLE patients exhibited elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells. The scientists also find that cell type-specific expression features predicted case-control status and stratified patients into two molecular subtypes. "We integrated dense genotyping data to map cell type-specific cis-expression quantitative trait loci and to link SLE-associated variants to cell type-specific expression," they write.

By sequencing peripheral blood mononuclear cells (PBMCs) from nearly 1,000 healthy individuals, a group led by Garvan Institute of Medical Research investigators has uncovered new details about the molecular drivers of immune system variation. In the study, which appears in Science this week, the researchers performed single-cell RNA sequencing on 1,267,758 peripheral blood mononuclear cells from 982 healthy individuals. For 14 cell types, they identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTLs, with most showing cell type-specific effects on gene expression. They further show how eQLTs have dynamic allelic effects in B cells that are transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to interindividual variation in immune function. The scientists also identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. "Our results demonstrate how segregating genetic variation influences the expression of genes that encode proteins involved in critical immune regulatory and signaling pathways in a cell type-specific manner," they conclude. "Understanding the genetic underpinnings of immune system regulation will have broad implications in the treatment of autoimmune diseases and infections, transplantation, and cancers."

Combining proteomics and machine learning, a team led by scientists from SomaLogic and the University of California, San Francisco, have identified a panel of protein biomarkers that could serve as a surrogate for cardiovascular outcomes. A key hurdle for drug development is the lack of surrogates for cardiovascular risk in clinical trials. To address this, the researchers measured about 5,000 proteins in 32,130 archived plasma samples from 22,849 participants in nine clinical studies. As reported in Science Translational Medicine this week, they applied machine learning to these data and derived a 27-protein model for predicting the four-year likelihood of myocardial infarction, stroke, heart failure, or death. The proteins represented 10 mechanistic pathways, and 12 of them were associated with at least one cardiovascular disease-related trait. The team independently validated their model across more than 11,000 participants from multiple large clinical trials, finding that it is sensitive to both adverse and beneficial changes in outcome. The model, the scientists write, has the potential to be a surrogate endpoint for cardiovascular risk in Phase II trials and, potentially, for accelerated drug approval of breakthrough products. "In medical practice, it ideally would also be used as a test for individualized and cost-effective cardioprotective drug allocation and for monitoring of responses," they write.

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