Coincidence between transcriptome analyses on different microarray platforms using a parametric framework

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Discrepancies among transcriptome studies are frequently reported, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks normalizes data against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using an in-house printed chip and GeneChip. The framework is based on a common statistical characteristic of microarray data, and each data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other methods

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19.

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100

NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

AbstractThe increasing availability of single-cell data revolutionizes the understanding of biological mechanisms at cellular resolution. For differential expression analysis in multi-subject single-cell data, negative binomial mixed models account for both subject-level and cell-level overdispersions, but are computationally demanding. Here, we propose an efficient NEgative Binomial mixed model Using a Large-sample Approximation (NEBULA). The speed gain is achieved by analytically solving high-dimensional integrals instead of using the Laplace approximation. We demonstrate that NEBULA is orders of magnitude faster than existing tools and controls false-positive errors in marker gene identification and co-expression analysis. Using NEBULA in Alzheimer’s disease cohort data sets, we found that the cell-level expression of APOE correlated with that of other genetic risk factors (including CLU, CST3, TREM2, C1q, and ITM2B) in a cell-type-specific pattern and an isoform-dependent manner in microglia. NEBULA opens up a new avenue for the broad application of mixed models to large-scale multi-subject single-cell data.</jats:p

Cardiac dopamine D1 receptor triggers ventricular arrhythmia in chronic heart failure

The pathophysiological role of dopamine D1 receptor (D1R) in chronic heart failure remains elusive. Here the authors show that D1R-expressing cardiomyocytes appear in chronic heart failure and play a pivotal role in triggering lethal ventricular arrhythmias

Activated β-catenin in Foxp3+ regulatory T cells links inflammatory environments to autoimmunity.

Foxp3+ regulatory T cells (Treg cells) are the central component of peripheral immune tolerance. Whereas a dysregulated Treg cytokine signature has been observed in autoimmune diseases, the regulatory mechanisms underlying pro- and anti-inflammatory cytokine production are elusive. Here, we identify an imbalance between the cytokines IFN-γ and IL-10 as a shared Treg signature present in patients with multiple sclerosis and under high-salt conditions. RNA-sequencing analysis on human Treg subpopulations revealed β-catenin as a key regulator of IFN-γ and IL-10 expression. The activated β-catenin signature was enriched in human IFN-γ+ Treg cells, as confirmed in vivo with Treg-specific β-catenin-stabilized mice exhibiting lethal autoimmunity with a dysfunctional Treg phenotype. Moreover, we identified prostaglandin E receptor 2 (PTGER2) as a regulator of IFN-γ and IL-10 production under a high-salt environment, with skewed activation of the β-catenin-SGK1-Foxo axis. Our findings reveal a novel PTGER2-β-catenin loop in Treg cells linking environmental high-salt conditions to autoimmunity