Pathogenic Autoimmunity in Atherosclerosis Evolves From Initially Protective Apolipoprotein B100–Reactive CD4+ T-Regulatory Cells

TL;DR: A review of the role of the immune system in atherosclerosis by discussing findings from preclinical research and clinical trials is provided in this article , where the authors identify important challenges that need to be addressed to advance the field and for successful clinical translation.

TL;DR: It is demonstrated that FOXP3(+) Tregs inhibit atherosclerosis by modulating lipoprotein metabolism by mediating the altered lipid phenotype.

TL;DR: In this article , the authors analyzed various typical phenomena and outcomes of cellular pro-inflammatory stress in atherosclerosis, as well as the role of endothelial dysfunction in local and systemic manifestations of low-grade inflammation.

TL;DR: In this article, the authors discuss how single-cell technologies, such as singlecell mass cytometry, singlecell RNA sequencing and cellular indexing of transcriptomes and epitopes by sequencing, are ideal for mapping the cellular and molecular composition of human atherosclerotic plaques and how these data can aid in the discovery of new precise immunotherapies.

TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.

TL;DR: The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer.

TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.

TL;DR: A droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample is described and sequence variation in the transcriptome data is used to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.