Identifying Reversible Molecular Networks in Human Pulmonary Fibrosis Using Single Nuclear Transcriptomics and Systems Biology
Abstract
Pulmonary fibrosis (PF) describes a chronic lung disease in which lung tissue becomes scarred over time in response to microinjuries leading to progressive shortness of breath and ultimately to death within 3-5 years. In most cases, the cause for this condition is not known. In some rare cases, we know what causes pulmonary fibrosis, namely genetic or autoimmune disorders, or exposure to environmental toxins, chemical warfare, or radiation. Idiopathic pulmonary fibrosis (IPF) is the most common pulmonary fibrosis of unknown cause that affects approximately 120,000 patients in the US. Like many other diseases, IPF is caused by abnormal changes in the basic units of all known living organisms, the cells. Under the microscope, in IPF lungs, we can detect cells forming scar tissue, and that cells of the surface of the pulmonary vesicle perish and are replaced by cells of the respiratory tract. The presence of acute or active disease along with progressive disease (mature fibrotic scar) and non-diseased lung are a disease characteristic specific to IPF. To understand how IPF progresses and how to stop and cure the disease, one needs to understand the processes on a cellular level. We hypothesized that studying how cells behave and function in well-characterized, differentially affected regions within the IPF lung would allow us to investigate cell-type-specific regulatory networks associated with disease progression and to discover novel, more specific, targets for future drugs as treatment for IPF. Using an innovative technology, single nuclei sequencing, it is possible to investigate how individual cells in a piece of tissue function and how they behave abnormally. We will study what instructs a cell on how to behave and how cells communication with each other in the IPF lung. Knowing the abnormal behavior patterns of cells and abnormal cellular communication signals in IPF lungs, we can use computational methods to predict targets for novel therapeutics within the cells to reverse these abnormalities. This will lead to identification of new cell-specific, disease stage-specific biomarkers and drug targets.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 19, 2019
- Source ID
- W81XWH1910131
Entities
People
- Jonas Schupp
Organizations
- United States Army
- Yale University