ABSTRACT
The ETS transcription factor ERG is a master regulator of endothelial gene specificity and highly enriched in the capillary, vein, and arterial endothelial cells. ERG expression is critical for endothelial barrier function, permeability, and vascular inflammation. A dysfunctional vascular endothelial ERG has been shown to impair lung capillary homeostasis, contributing to pulmonary fibrosis as previously observed in IPF lungs. Our preliminary observations indicate that lymphatic endothelial cells (LEC) in the human IPF lung also lack ERG. To understand the role of ERG in pulmonary LECs, we developed LEC-specific inducible Erg-CKO and Erg-GFP-CKO conditional knockout (CKO) mice under Prox1 promoter. Whole lung microarray analysis, flow cytometry, and qPCR confirmed an inflammatory and pro-lymphvasculogenic predisposition in Erg-CKO lung. FITC-Dextran tracing analysis showed an increased pulmonary interstitial lymphatic fluid transport from the lung to the axial lymph node. Single-cell transcriptomics confirmed that genes associated with cell junction integrity were downregulated in Erg-CKO pre-collector and collector LECs. Integrating Single-cell transcriptomics and CellChatDB helped identify LEC specific communication pathways contributing to pulmonary inflammation, trans-endothelial migration, inflammation, and Endo-MT in Erg-CKO lung. Our findings suggest that downregulation of lymphatic Erg crucially affects LEC function, LEC permeability, pulmonary LEC communication pathways and lymphatic transcriptomics.
ABSTRACT
Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.
ABSTRACT
Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, various technical artifacts can be present in scRNA-seq data and should be assessed before performing downstream analyses. While several tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Here, to streamline the process of generating and visualizing QC metrics for scRNA-seq data, we built the SCTK-QC pipeline within the singleCellTK R package. The SCTK-QC workflow can import data from several single-cell platforms and preprocessing tools and includes steps for empty droplet detection, generation of standard QC metrics, prediction of doublets, and estimation of ambient RNA. It can run on the command line, within the R console, on the cloud platform or with an interactive graphical user interface. Overall, the SCTK-QC pipeline streamlines and standardizes the process of performing QC for scRNA-seq data.
