From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis.

Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1-100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.

Asymptomatic Hyperuricemia Promotes Recovery from Ischemic Organ Injury by Modulating the Phenotype of Macrophages.

Acute organ injury, such as acute kidney injury (AKI) and disease (AKD), are major causes of morbidity and mortality worldwide. Hyperuricemia (HU) is common in patients with impaired kidney function but the impact of asymptomatic HU on the different phases of AKI/AKD is incompletely understood. We hypothesized that asymptomatic HU would attenuate AKD because soluble, in contrast to crystalline, uric acid (sUA) can attenuate sterile inflammation. In vitro, 10 mg/dL sUA decreased reactive oxygen species and interleukin-6 production in macrophages, while enhancing fatty acid oxidation as compared with a physiological concentration of 5 mg/dL sUA or medium. In transgenic mice, asymptomatic HU of 7-10 mg/dL did not affect post-ischemic AKI/AKD but accelerated the recovery of kidney excretory function on day 14. Improved functional outcome was associated with better tubular integrity, less peritubular inflammation, and interstitial fibrosis. Mechanistic studies suggested that HU shifted macrophage polarization towards an anti-inflammatory M2-like phenotype characterized by expression of anti-oxidative and metabolic genes as compared with post-ischemic AKI-chronic kidney disease transition in mice without HU. Our data imply that asymptomatic HU acts as anti-oxidant on macrophages and tubular epithelial cells, which endorses the recovery of kidney function and structure upon AKI.

AUDACITY: A comprehensive approach for the detection and classification of Runs of Homozygosity in medical and population genomics.

Runs of Homozygosity (RoHs) are popular among geneticists as the footprint of demographic processes, evolutionary forces and inbreeding in shaping our genome, and are known to confer risk of Mendelian and complex diseases. Notwithstanding growing interest in their study, there is unmet need for reliable and rapid methods for genomic analyses in large data sets. AUDACITY is a tool integrating novel RoH detection algorithm and autozygosity prediction score for prioritization of mutation-surrounding regions. It processes data in VCF file format, and outperforms existing methods in identifying RoHs of any size. Simulations and analysis of real exomes/genomes show its potential to foster future RoH studies in medical and population genomics.