Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease (ILD) that results in the permanent scarring and damage of lung tissue. Currently, there is no known cause or viable treatment for this disease, and the majority of patients either receive a lung transplant or succumb to the disease within five years of diagnosis. This project centers around studying IPF through analyzing gene expression patterns in healthy vs. diseased lung tissue via spatial transcriptomics. Spatial transcriptomics is the study of individual RNA transcripts within cells on a spatial level. With the novel technology MERFISH, we can detect gene expression in a spatial context with single-cell resolution, allowing us to make inferences about certain patterns of gene expression that are solely driven by the pathology of the disease. A total of 120 cells were selected from 21 different lung samples - 6 healthy; 15 ILD. Within those lung samples, selected from 4 different tissue features - control, less fibrotic, more fibrotic, and cystic. We built an analysis pipeline in R to analyze cell type composition around these features at different distances from the center cell (0-75, 76-150, and 150-225 μm). Cell types were annotated at both a broad (less specific) and fine (more specific) level. Upon analyzing the relationship between the proportions of various cell types and distance from tissue features, we found that within the broad cell type annotation level, airway epithelium cells had a negative relationship with distance and were statistically significant through linear regression models. Within the fine cell type annotation level, ciliated/secretory cells displayed this same trend. The results above support our current understanding of cystic tissue in lung tissue, and is a foundation for understanding disease pathology as a whole.

The HLA, Human Leukocyte Antigens, are encoded by a polymorphic set of genes where even a single base change can impact the function of the body’s immune response to foreign antigens [1]. Although many methods exist to type these alleles using whole-genome sequencing (WGS), few can use RNA sequencing (RNA-seq) to show the functional expression of the alleles with its inconsistency in coverage, and none of these allow for novel allele discovery. We present an approach using partially ordered graphs to project sequenced data onto the known alleles allowing for accurate and efficient typing of the HLA genes with flexibility for discovering new alleles and tolerance for poor sequence quality. This graph-guided approach to assembling and typing the HLA genes from RNA-seq has applications throughout precision medicine, facilitating the prevention and treatment of autoimmune diseases where allele expression can change. It is also a necessary step for determining donors for organ transplants with the least likelihood of rejection. This novel approach of combining database matching with partially ordered graphs for assembling genetic sequences of RNA-seq data could be applied towards typing other alleles.