A single-cell RNA sequencing atlas of the COPD distal lung to predict cell-cell communication

In the lungs of chronic obstructive pulmonary disease (COPD) patients, numerous cell types interact in a structurally abnormal and inflammatory microenvironment. Assessing gene expression in individual cells by single-cell RNA sequencing (scRNA-seq) allows for a deeper understanding of cell types within a complex sample, like the COPD distal lung. ScRNA-seq data can be combined with cell-cell communication prediction tools to predict cellular interactions within a given sample; indeed, this approach was previously used to show evidence for increased endothelial CXCL signaling in COPD. To maximize the value of these tools, it is critical to have representation across cell lineages (e.g., epithelial, immune, endothelial, and mesenchymal). However, this can be challenging in diseases like COPD where inflammatory cells often dominate samples compared to less frequent cell types that are also perturbed in disease, such as the airway epithelium. To explore intercellular interactions in the COPD lung, we performed scRNA-seq on single cell suspensions from 6 COPD distal lung samples then combined these with previously published data to assemble a multisite COPD and control scRNA-seq dataset with increased representation of non-immune lineages. Distal lung tissue of 6 former smokers with COPD who underwent lung transplantation (3 male, 3 female, non-Hispanic white, mean age 58.5 years, mean pack-years of smoking 40.3) was finely minced then cryopreserved in 90%FBS and 10% DMSO until processed for sequencing. The first 3 samples and the last three samples were batched together. Aliquots from each lung sample were digested in an enzymatic cocktail (Miltenyi Multi Tissue Dissociation Kit 1) using a gentleMACS Octo Dissociator (Miltenyi, Inc.). Tissue homogenates were serially filtered through sterile gauze, then 100 μm, and 40-μm sterile cell strainers. Single-cell suspensions then were treated with ACK buffer to lyse blood cells and then underwent cell sorting using EPCAM magnetic bead based selection. The EPCAM enriched fraction was used as the input for the single cell libraries.