Soluble Signals to Improve Endothelial Integrity in the Lung

Disorders in pulmonary vascular integrity are a prominent feature in many lung diseases, including acute respiratory distress syndrome (ARDS), capillary leak syndrome, and COVID19. Paracrine signals are enriched in the lung and are critically important in regulating the homeostasis of the functional pulmonary microvasculature. Here, we employed single-cell RNA-sequencing (scRNAseq) to study ligand and receptor interactions in the native human lung microvascular niche, and identified soluble factors that are critical in endothelial integrity. The scRNAseq data reveals a total of 47 cell populations consisting of five vascular endothelial subtypes in human lungs, including general capillary EC, aerocyte capillary EC (EC aCap), arterial EC, pulmonary venous EC, and systemic venous EC. Using EC aCap as a signal receiving core (Receptors) and the putative adjacent cell types (alveolar fibroblast, ATI, ATII, pericyte, plasma cell, etc.) in the EC aCap niche as senders (Ligands), we identified that SLIT2-ROBO4, ANGPT1-TIE1, ADM-RAMP2, VEGFD-KDR, and BMP5-BMPR2 are the top specific and abundant pairs in the niche. Immunostaining and ELISA assays confirmed their spatial information and secretion level. Furthermore, upon treatment with these ligands, real-time resistance recorded using an electric cell-substrate impedance sensing (ECIS) system revealed that VEGFD, ANGPT1 (angiopoietin 1), and ADM (adrenomedullin) could markedly increase the electrical resistance of human lung microvascular, arterial, and venous endothelial cells, suggesting their strong impact on the endothelial barrier function. Deciphering the cell-cell soluble signals that improve endothelial integrity in human lungs lays the foundation for uncovering the pathogenesis of pulmonary vascular disorders and the development of ex vivo functional lung vasculature.

Interactive Web-Based Lung Cell Atlases Lower Barriers to Transcriptomic Data Sharing, Mining and Dissemination

Rationale: Recent advancements in sequencing technologies have led to a substantial increase in the scale and resolution of transcriptomic data. Despite this progress, accessibility to this data, particularly among those who are coming from non-computational backgrounds is limited. To facilitate improved access and exploration of our single-cell RNA sequencing data, we generated several data sharing, mining and dissemination portals to accompany our idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), and lung endothelial cells (Lung EC) cell atlases. Descriptions and links of each website can be found here: https://medicine.yale.edu/lab/kaminski/research/atlas/. Methods: Each interactive data mining website is coded in the R language using the Shiny package and is hosted by Shinyapps.io. Percell expression data for each website is stored on a MySQL database hosted by Amazon Web Services (AWS). Time-associated website engagement statistics and gene query information is collected for each website using a combination of Google Analytics and a gene search table stored on our MySQL database. User exploration of available data is facilitated through several easy-touse visualization tools available on each website. Results: Website usage statistics since the publication of each website shows that 9,772 unique users from 56 countries and five continents have accessed at least one of the three websites. At the time of writing, 300,748 total queries have been made for 15,627 unique genes across the websites. The top five searched genes for the IPF Cell Atlas are CD14, ACE2, ACTA2, IL11 and MUC5B while for the COPD Cell Atlas they are FAM13A, MIRLET7BHG, HHIP, ISM1 and DDT. Finally, the top searched genes for the Lung Endothelial Cell Atlas are BMPR2, PECAM1, EDNRB, APLNR and PROX1. Of note, interaction with the IPF Cell Atlas increased dramatically at the start of the COVID-19 pandemic, with queries for the ACE2 gene, the putative binding receptor for the SARS-CoV-2 virus, increasing substantially at the pandemic's onset in the United States. Conclusions: Usage statistics, gene query information and feedback from users, both within academia and industry, have shown broad engagement with our websites by individuals across computational and non-computational backgrounds. We envision widespread adoption of web-based portals similar to ours will facilitate novel discoveries within these complex datasets and new scientific collaborations.

Comparative scRNA-seq Analysis of Peripheral Blood Mononuclear Cells from Patients with COVID-19 and Idiopathic Pulmonary Fibrosis Demonstrates Dissimilar Cell Shifts and Gene Expression Profiles

Rationale: Similar to other human coronaviruses like MERS and SARS, severe manifestations of COVID-19 are associated with acute lung injury and sustained pulmonary dysfunction. A recent single-cell study of lung tissue from severe COVID-19 and idiopathic pulmonary fibrosis (IPF) patients suggested these diseases share common pro-fibrotic molecular pathways. To determine whether similar changes can be detected in the blood, we compared single-cell RNA-seq profiles of peripheral blood mononuclear cells (PBMCs) from patients with IPF or COVID-19, using influenza and healthy individuals as controls. Methods: 25 IPF, 18 COVID-19, and 13 healthy control PBMC samples were sequenced in our lab using 10X Genomics 5' single-cell technology. This data was processed using CellRanger and integrated with publicly available datasets of Covid-19, influenza, and healthy PBMC samples, yielding ∼300,000 single cells. Severe COVID-19 patients were treated in the ICU and succumbed to the disease, while severe IPF had transplant-free survival of fewer than three years. Downstream analysis was performed with the R package Seurat. The Louvain clustering algorithm generated 28 distinct cell clusters. Wilcoxon rank-sum test was used to determine significant cell type proportion differences and differentially expressed genes (DEGs). Significantly enriched pathways were found using EnrichR and Gene Set Enrichment Analysis (GSEA). Results: We report significantly increased platelets as a proportion of total cells in patients with severe COVID-19 (p = 0.0047) and severe IPF (p = 0.05) compared to healthy patients. Stable IPF and severe COVID-19 shared similar cell proportions of platelets (p=0.15) and monocytes (p=0.42). Across most cell types, COVID-19 and influenza patients had gene expression changes consistent with type I interferon activation while IPF patients exhibited changes in ribosomal upregulation and pro-fibrotic pathways relative to healthy controls. Using a composite pro-fibrotic score of TGFB1 targets and effectors, hierarchical clustering markedly differentiates between IPF and controls versus COVID-19 and influenza, perhaps distinctly highlighting mechanisms of disease. Within monocytes, we did not observe a significant pro-fibrotic phenotype (SPP1, MMP9, CHI3L1, PLA2G7) in samples of patients with any disease;hierarchical clustering of these genes again segregated IPF and controls from COVID-19 and influenza. Conclusions: Pro-fibrotic gene expression patterns could not be seen in PBMCs from patients with acute severe COVID-19 infection. More studies are needed in distinct COVID-19 patient populations, such as those with prolonged respiratory failure or with sustained respiratory dysfunction after recovery.