Cell type markers indicate distinct contributions of decidual stromal cells and natural killer cells in preeclampsia.

In brief: Preeclampsia is a common serious disorder that can occur during pregnancy. This study uses integrative analysis of preeclampsia transcriptomes and single-cell transcriptomes to predict cell type-specific contributions to preeclampsia. Abstract: Preeclampsia is a devastating pregnancy disorder and a major cause of maternal and perinatal mortality. By combining previous transcriptomic results on preeclampsia with single-cell sequencing data, we here predict distinct and partly unanticipated contributions of decidual stromal cells and uterine natural killer cells in early- and late-onset preeclampsia.

Dirichlet process mixture models for single-cell RNA-seq clustering.

Clustering of cells based on gene expression is one of the major steps in single-cell RNA-sequencing (scRNA-seq) data analysis. One key challenge in cluster analysis is the unknown number of clusters and, for this issue, there is still no comprehensive solution. To enhance the process of defining meaningful cluster resolution, we compare Bayesian latent Dirichlet allocation (LDA) method to its non-parametric counterpart, hierarchical Dirichlet process (HDP) in the context of clustering scRNA-seq data. A potential main advantage of HDP is that it does not require the number of clusters as an input parameter from the user. While LDA has been used in single-cell data analysis, it has not been compared in detail with HDP. Here, we compare the cell clustering performance of LDA and HDP using four scRNA-seq datasets (immune cells, kidney, pancreas and decidua/placenta), with a specific focus on cluster numbers. Using both intrinsic (DB-index) and extrinsic (ARI) cluster quality measures, we show that the performance of LDA and HDP is dataset dependent. We describe a case where HDP produced a more appropriate clustering compared to the best performer from a series of LDA clusterings with different numbers of clusters. However, we also observed cases where the best performing LDA cluster numbers appropriately capture the main biological features while HDP tended to inflate the number of clusters. Overall, our study highlights the importance of carefully assessing the number of clusters when analyzing scRNA-seq data.

Computational strategies for single-cell multi-omics integration.

Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.

Ribosome-Targeting Antibiotics Impair T Cell Effector Function and Ameliorate Autoimmunity by Blocking Mitochondrial Protein Synthesis.

While antibiotics are intended to specifically target bacteria, most are known to affect host cell physiology. In addition, some antibiotic classes are reported as immunosuppressive for reasons that remain unclear. Here, we show that Linezolid, a ribosomal-targeting antibiotic (RAbo), effectively blocked the course of a T cell-mediated autoimmune disease. Linezolid and other RAbos were strong inhibitors of T helper-17 cell effector function in vitro, showing that this effect was independent of their antibiotic activity. Perturbing mitochondrial translation in differentiating T cells, either with RAbos or through the inhibition of mitochondrial elongation factor G1 (mEF-G1) progressively compromised the integrity of the electron transport chain. Ultimately, this led to deficient oxidative phosphorylation, diminishing nicotinamide adenine dinucleotide concentrations and impairing cytokine production in differentiating T cells. In accordance, mice lacking mEF-G1 in T cells were protected from experimental autoimmune encephalomyelitis, demonstrating that this pathway is crucial in maintaining T cell function and pathogenicity.