Date of Award
Medical Doctor (MD)
Idiopathic pulmonary fibrosis is progressive, fatal lung disease with unclear mechanistic etiology and a dearth of treatment options. Transcriptional profiling has served a valuable tool in understanding the underlying perturbations in the lung tissues of patients and disease model systems, however whole tissue profiling obscures both the contribution of individual cells types in the diseased tissue as well as the contribution of non-fibrotic tissue surrounding the diseased tissue. The averaging effect confounds the ability to extract a strong disease signal and understand the cell-of-origin. Single-cell techniques have recently emerged that allow profiling of the transcriptomes of individual cells. In this work, we employ two state-of-the-art single cell RNA-seq techniques to IPF-relevant disease systems to understand cell specific contributions. In one set of experiments, we extracted and dissociated lung tissue from Tgfβ1 induced, as well as bleomycin injured mice systems. Single cells were isolated into individual wells using the Fluidigm C1 Auto Prep Array IFC system and single cell libraries were generated and sequenced. We observed the upregulation of fibroblast specific genes in cells with epithelial cell markers reinforcing theories of epidermal to mesenchymal transition. In another set of experiments, we used a high-throughput, droplet-based system to study the knockdown of FENDRR, a novel long-non coding RNA (lncRNA) implicated in lung fibrosis in normal human lung fibroblasts (NHLFs). Here we observed cell-specific upregulation of genes associated with fibrosis and quiescence, as well as a stochastic effects demonstrating cell-cycling that would have otherwise been indiscernible without single-cell methods. In this work, we also address the significant challenges in creating robust single cell libraries using both human and mouse tissue. These challenges, shortcomings, and future opportunities for single-cell sequencing are highlighted.
Munivar, Azim, "Insights On Fibrotic Diseases Using Single-Cell Analysis Methods" (2017). Yale Medicine Thesis Digital Library. 2155.