Quantification of Gene Expression in Airway Epithelial Cells from Models of Primary Ciliary Dyskinesia — 106p — Reesa Wilcox, Casey McKenzie, Hannah Leppert

Primary ciliary dyskinesia (PCD) is a rare autosomal recessive syndrome that affects 1 in 16,000 live births, causing defects in motile cilia that line various organ systems^1,2. Individuals suffering from this disease may experience chronic respiratory infections, decline of lung function, infertility, and hydrocephalus due to dyskinetic motile cilia. Defects in ciliary motility in mice lacking in protein complexes SPEF2 (bgh), CFAP221 (nm1054), and CFAP54 (Cfap54^gt/gt) have been shown to each have a phenotype of PCD^3,4,5. We investigated gene expression of trachea epithelial cells affected by dyskinetic motile cilia by completing a single- cell RNA- sequencing (scRNA-seq) approach, resulting in identification of differentially expressed genes (DEGs) within early- and late- deuterosomal and ciliated cells. To validate this data, analysis of RNA expression within these cells types was completed by imaging slides stained with RNAscope probes. Images were pre-processed by applying a mask to the region of interest (ROI), excluding non-epithelial cells. Quantification was performed using multiple software packages by identifying objects in DAPI, GFP, and Texas Red channels containing the nuclei, gene of interest (GOI), and cell-specific marker (CellID), respectively. After segmentation of nuclei, the region was expanded to approximate cell boundaries. Number and intensity of RNA expression in GOI and CellID were identified as objects and measured within the cell boundary. Cells containing two or more objects in these channels were counted, minimizing instances of false positives. Data readouts were aggregated and analyzed for statistical significance with R studio. This quantitative validation was completed first with the Thermo- Fischer High Content Screening (HCS) program, revealing statistical significance of up- and down- regulation of the DEGs in our models compared to wild type. In this study, we seek to further validate these findings and elucidate correlation to the scRNA-seq data by utilizing multiple softwares to quantify the RNAscope images.

Sanford Research
Dr. Lance Lee