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.

Previous recombination rate estimation studies in rhesus macaques have been mostly restricted to a singular approach (e.g., using microsatellite loci). Here, we employ a bilateral method in estimating recombination rates—pedigree-based and linkage-disequilibrium-based—from whole-genome data of rhesus macaques to estimate CO and NCO recombination events and to compare contemporary and historical rates of recombination.

Heat shock factors (HSFs) are transcriptional regulators that play a crucial role in the cellular response to environmental stress, particularly heat stress. Understanding the evolution of HSFs can provide insights into the adaptation of organisms to their changing environments. This project explored the evolution of HSFs within tetrapods, a group of animals that includes amphibians, reptiles, turtles, and mammals. Through an analysis of the available genomic data and subsequent genomic methodologies, HSFs have undergone significant changes throughout tetrapod evolution, as evidenced by loss events observed in protein sequences of the species under examination. Moreover, several conserved and divergent regions within HSF proteins were identified, which may reflect functional differences between HSFs in different tetrapod lineages. Our findings suggest that the evolution of HSFs has contributed to the adaptation of tetrapods to their diverse environments and that further research on the functional and regulatory differences between HSFs may provide a better understanding of how organisms cope with stress in heat-stressed environments.