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.