Transcriptomic characterization of lung cancer using single-cell RNA-sequencing. Jinhong Kim and Paola A. Marignani Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia Canada Background: Lung cancer is the leading cause of cancer-related deaths globally. To improve survivorship, a better understanding of the molecular heterogeneity of lung cancer is required in order to design unique precision medicines to treat an individual’s cancer. We applied a single-cell RNA sequencing (scRNA-seq) approach to profile differential gene expression to identify unique biomarkers and key genes associated with the heterogeneity of lung carcinogenesis. Methods: We constructed 3’-end cDNA libraries from lung cancer cell lines (A549, H460, H1299 and Calu3) using the Fluidigm C1 Single-cell Auto Prep Station and sequenced the libraries using illumina Nextseq. t-SNE and heat map clustering analyses were performed on normalized gene expression values. Fold change values of DEGs were validated using a qRT-PCR. Gene expression maps and PPIN were created using cell line- or cluster-specifically expressed genes. Results: Over 82.9 million 3’-end cDNA sequence reads from 1,441 single cells of the four cell lines were successfully aligned to 24,424 genes on human reference genome. t-SNE clustering analysis classified the single cells into four distinct groups that were composed of single cells more than two cell lines. Heat-map clustering analysis revealed that a total of 1,970 genes were group-specifically expressed. Following a comparison of normalized gene expression values, we detected a total of 2,735 DEGs showing ≥|2| fold change difference among four clustered groups. Inflammatory chemokines were highly expressed in groups containing single cells from A549, H460 and Calu3 cell lines compared with a group dominantly containing H1299 single cells. Through PPIN analysis, we found proteins associated with cell migration may play a role in chemokine-mediated carcinogenesis. Conclusion: We successfully applied scRNA-seq to characterize lung cancer single-cell transcriptomes. The antigens we detected by clustering analyses are key genes associated with the heterogeneity of lung cancer. Our goal is to improve lung cancer survivorship and quality life for lung cancer patients through scRNA-seq guided precision medicines.