control versus allergic rhinitis basal cells in response to Der p1

Basal cells are the epithelial progenitor cells of the airways and play a critical role in tissue homeostasis and repair. In a healthy upper airway epithelium, the basal cells are usually shielded from environmental triggers as they are positioned in close contact with the basement membrane. In allergic rhinitis, it is known that the epithelial barrier is dysfunctional and it is believed that in this situation basal cells become more accessible to airborne particles, however, it is not known whether basal cells can sense these triggers directly. In addition, previous research has shown that control basal cells phenotypically and functionally differ from allergic rhinitis (AR) basal cells. In this study, we aimed to further compare phenotypical differences between sorted primary control and house dust mite (HDM)-allergic AR basal cells, before and after culture, using bulk RNA sequencing. Moreover, we wanted to evaluate the basal cell transcriptional response to HDM-derived Der p1 to study their potential as an environmental sensor. Basal cell transcriptomics were analyzed from 6 control and 6 AR patients. For each patient sample, conditions included fresh basal cells (i.e., immediately after sorting), two weeks culture, and two weeks culture followed by 6 hours stimulation with 1 µg/mL Der p1 (22A02, Citeq Biologicals). Cultured cells were grown for one week in PneumaCult Ex Plus medium (05040, StemCell Technologies) followed by one week in BEGM (CC-3170, Lonza). Cells were lysed using RLT lysis buffer (79216, Qiagen) and RNA was extracted using the RNeasy Mini Kit (74104, Qiagen). Sample purity and RNA concentration was evaluated using a NanoDrop OneC UV-Vis Spectrophotometer (Thermo Fischer Scientific, Waltham, Massachusetts, USA), and quality control was performed using Bioanalyzer (Agilent). Library prep was done using the QuantSeq 3’ mRNA-Seq Library Prep Kit FWD (196, Lexogen), followed by sequencing on the NextSeq 2000 device (Illumina, San Diego, California, USA). Quality control of the raw sequencing data was performed using FastQC. Within Galaxy, Cutadapt was used to remove the poly-A tail, mitochondrial RNA and ribosomal RNA. Trimmomatic was used to filter low quality read. Mapping to the hg38 human genome was performed using HISAT. Principal component analysis (PCA) was performed to visualize clustering between controls and AR samples under the respective conditions based on the transcriptome data. Differentially expressed genes (DEGs) were identified using the DESeq2 package in R. Multiple DEG comparisons were carried out, including 1) main effect control versus AR, and 2) control culture versus AR Der p1. DEGs were selected with adjusted p-value < 0.05 and |log2 fold change (log2 FC)| > 0.5. DEGs were visualized using volcano plot and heatmap created with the R packages ‘ggplot2’ and ‘ComplexHeatmap’, respectively. Gene set enrichment analysis (GSEA) of the DEGs was performed using the Molecular Signatures Database (MSigDB)27,28 and ‘ClusterProfiler’ package in R, evaluating overrepresentation of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and cell type signature gene sets.