Interactive analysis of single-cell data using flexible workflows with SCTK2

A specialized population of monocyte-derived tracheal macrophages promote airway epithelial regeneration through a CCR2-dependent mechanism

Macrophages are critical for maintenance and repair of mucosal tissues. While functionally distinct subtypes of macrophage are known to have important roles in injury response and repair in the lungs, little is known about macrophages in the proximal conducting airways. Single-cell RNA sequencing and flow cytometry demonstrated murine tracheal macrophages are largely monocyte-derived and are phenotypically distinct from lung macrophages at homeostasis. Following sterile airway injury, monocyte-derived macrophages are recruited to the trachea and activate a pro-regenerative phenotype associated with wound healing. Animals lacking the chemokine receptor CCR2 have reduced numbers of circulating monocytes and tracheal macrophages, deficient pro-regenerative macrophage activation and defective epithelial repair. Together, these studies indicate that recruitment and activation of monocyte-derived tracheal macrophages is CCR2-dependent and is required for normal airway epithelial regeneration.

Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis

The burgeoning amount of single-cell data has been accompanied by revolutionary changes to computational methods to map, quantify, and analyze the outputs of these cutting-edge technologies. Many are still unable to reap the benefits of these advancements due to the lack of bioinformatics expertise. To address this issue, we present Ursa, an automated single-cell multiomics R package containing 6 automated single-cell omics and spatial transcriptomics workflows. Ursa allows scientists to carry out post-quantification single or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics, and immunomics at the single-cell level. It serves as a 1-stop analytic solution by providing users with outcomes to quality control assessments, multidimensional analyses such as dimension reduction and clustering, and extended analyses such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims bridge the gap between those with bioinformatics expertise and those without by providing an easy-to-use bioinformatics package for scientists in hoping to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.