Single-cell RNA-seq analysis profiling characterizes differences in cell composition and physiology between normal tissue, treatment-naive, and cisplatin-treated ovarian cancer

BackgroundIntense efforts have focused on identifying heterogeneity of the cellular composition in ovarian cancer. However, tissue composition and physiological conditions of cancer cells in cisplatin-sensitive ovarian cancer remains largely unknown. Moreover, comparisons of different cellular states in normal tissue, in treatment naive ovarian cancer, and in cisplatin-treated tissue after adjuvant therapy of cisplatin-sensitive ovarian cancer at the single-cell level might offer clues for ovarian cancer treatment and prevention of cisplatin-resistance formation. MethodsSingle-cell transcriptome sequencing of a cisplatin-treated ovarian cancer was performed. Data sets of non-tumorous ovarian tissues and treatment-naive ovarian cancer were downloaded from the European Genome-phenome Archive (accession number EGAS00001004987). Quality control, batch effect correction, integration, and clustering analysis of the integrated single-cell transcriptome data was done. Cell subsets were annotated based on surface marker phenotypes. Finally, the proportions of subclusters, the immune cell population, and the potential biological processes among different cellular states were compared. ResultsSixteen distinct cell subsets were identified from the integrated single-cell transcriptome sequencing data of a pool of all tissues. The composition of the three different tissue types was characterized. The proportion of fibroblasts in cisplatin-treated ovarian tumor was remarkably lower than in treatment-naive ovarian tumor (1.33% vs. 13.53%, p < 0.05). Moreover, each subjects sample had differing relative proportions of the identified cell types. In primary untreated ovarian cancer, the prevalent immune cells were B cells and myeloid-related immunosuppressive M2 macrophages. However, there were less B cells and myeloid-related immunosuppressive M2 macrophages after cisplatin-treatment, while significantly more T cells were found. The physiological cellular state in primary untreated ovarian tumors was associated with dysfunctional gene expression and modulation of cellular homeostasis, while cells from cisplatin-treated tumor showed more activation of immune and inflammatory genes as compared to healthy ovarian tissue. ConclusionOur molecular gene expression analysis allowed for the separation and identification of differences in normal ovarian tissues, treatment-naive, and cisplatin-sensitive ovarian cancer cell populations at single-cell resolution. We identified different cell type composition and discriminative marker expression concerning specific cell subsets and identified differences among their physiological cell states. This knowledge may open new possibilities for elucidating important pathogenetic features and therapeutic strategies for treating ovarian cancer.