Single-Cell Transcriptomic Analysis to Identify Drivers of Pulmonary Fibrosis

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PROJECT SUMMARY This proposal for a K08 Award is motivated by two overarching goals: 1) to support the scientific and professional development of the candidate, Dr. Paul Reyfman, towards achieving his career goal of succeeding as a physician scientist and independent investigator with expertise in systems biology approaches applied to the study of chronic lung disease, and 2) to investigate whether single-cell transcriptomic profiling can provide novel insights into the pathobiology of pulmonary fibrosis. Pulmonary fibrosis is a deadly and progressive condition for which diagnostic approaches remain imprecise and effective therapies are lacking. Together with his mentors, Drs. Scott Budinger and Luís Amaral, the candidate has developed a comprehensive training plan that will ensure Dr. Reyfman acquires new knowledge and proficiencies in developing hypotheses, designing and completing experiments, analyzing data, and communicating findings to the scientific community. As an essential component of this training plan, the candidate will employ systems biology approaches to developing tools for analysis of single-cell transcriptomic datasets generated from patients with pulmonary fibrosis. Single-cell transcriptomic profiling is increasingly used to investigate the pathobiology of disease in humans and as a basis for developing novel biomarkers of disease. Developing and validating tools for analyzing the rapidly growing quantity of single-cell transcriptomic data is as much of a challenge as refining techniques for generating data from healthy and diseased tissues. In particular, it is not known whether analysis of single-cell RNA sequencing (scRNA-Seq) and single-nucleus RNA sequencing (snRNA-Seq) data can be used to quantify accurately the cellular composition of the lung and to identify gene expression differences between health and pulmonary fibrosis. Our preliminary data suggest that scRNA-Seq of lung identifies profibrotic gene expression in patients with pulmonary fibrosis that is heterogeneous between individuals. We also found that scRNA-Seq undersampled certain constituent lung cellular populations. Accordingly, we designed this proposal to test the hypothesis that single-cell transcriptomic analysis of lung samples from patients with pulmonary fibrosis can be used to identify disease endotypes. In Specific Aim 1, the candidate will determine whether snRNA-Seq enables quantification of the cellular composition of the lung during pulmonary fibrosis. In Specific Aim 2, the candidate will develop tools for using scRNA-Seq performed on specimens obtained from patients with SSc-ILD and normal controls to gain novel insights into disease pathobiology. Over the course of this award, the candidate will gain new skills including in generating snRNA-Seq from cryopreserved lung tissue, in lung stereology, in RNA in situ hybridization, in analysis of complementary genomic datasets, and in incorporation of clinical phenotypic information into genomic analyses. Accomplishing the proposed work will provide a rigorous training program for Dr. Reyfman and will provide insights that could lead to improved therapies for patients with pulmonary fibrosis.