The transcriptome of Dermacentor andersoni salivary glands at single cell resolution
Applications of transcriptomics have significantly advanced the identification of numerous tick salivary molecules involved in feeding and the transmission of tick-borne pathogens. In this study, we analyze the salivary gland transcriptome of Dermacentor andertsoni, the vector of Rickettsia rickettsii in the U.S., at single cell resolution. Overall design: Dermacentor andersoni adult ticks were collected in May 2023 from vegetation near Lake Como, Montana, USA. Ticks were sampled using the flagging method, where a white flannel cloth was swept over low-lying vegetation and bushes. After collection, ticks were transported to the laboratory and maintained in an environmental incubator at 21°C and 85% relative humidity, with a 12h/12h light/dark cycle. Ticks were removed from incubator and salivary glands were dissected. Salivary glands (SGs) were isolated and stored in fresh, ice-cold, nuclease-free HBSS buffer (Invitrogen, Carlsbad, CA, USA). To prepare a single-cell suspension, the salivary glands were dissociated following a mechanical and enzymatic protocol. The resulting cell suspension was filtered through a 40 µm Flowmi cell strainer and centrifuged at 600 rcf for five minutes to pellet the cells. The pellet was then resuspended in an appropriate volume of HBSS media to achieve the target cell concentration. Cell counting and viability were assessed using trypan blue staining and a Neubauer chamber for visualization. The single cell suspensions were loaded (7,000 cells) onto a Chromium Controller for GEM (Gel Bead-In-EMulsion) generation and barcoding using the Chromium Single Cell 3' Reagent Kits according to the manufacturer's protocol (10x Genomics). After cDNA synthesis, amplification, and library construction, the library was sequenced by the Illumina NovaSeq 6000 System. The raw sequencing data were demultiplexed and processed using the Cell Ranger Software Suite (10x Genomics). Raw sequencing data were aligned to the D. andersoni (GCF_023375885.1) reference genome and quantified using Cell Ranger tool (v7.0.1). Low-quality cells (4,405 cells) were excluded using a threshold of three median absolute deviations (MAD) from the median of the overall cell population for both unique molecular identifier (UMI) or feature counts and mitochondrial content. Putative doublets were identified and removed using scDblFinder R package (v 1.18.0), yielding 14,400 high-quality cells for downstream analyses. Cell clustering was performed following routine steps provided in the Seurat (v4.3) pipeline. Counts were natural log (log1p)-transformed, followed by scaling and dimensionality reduction (1:10), with visualization using PCA and UMAP. Cells were clustered using the shared nearest-neighbor approach based on the Louvain algorithm (resolution = 0.15), and the results were projected onto UMAP. Differential gene expression analysis (min.pct = 0.25, logfc.threshold = 0.25, q < 0.05) across cell clusters were conducted using the Wilcoxon rank-sum test via the 'FindMarkers' and 'FindAllMarkers' functions, respectively.