scRNA-seq data in ovarian cancer
Description
Ovarian cancer (OC) is an aggressive gynecological tumour usually diagnosed at a late stage with widespread metastases and ascites. We depicted a single-cell landscape of OC ecosystem with five tumour-relevant sites: peripheral blood (PB), pelvic lymph node (PLN), primary tumour (Pri.OT), omentum metastasis (Met.Ome), and malignant ascites. Intercellular tissue dynamics of certain T cells, like GZMK+ effector memory T cell (Tem) and exhausted T cell (Tex), revealed potential roles of ascites as a pool for tumour-infiltrating T cells. Of the macrophages in tumours and ascites that exhibited distinct functional states, ascites-enriched macrophages were more of embryonic origin. For stromal cells in ascites, DES+ mesothelial cells account for the majority and help remodel its immune microenvironment. Further, we identified two endothelial subsets in primary tumours and one MAIT cluster in ascites predicting the responses to platinum-based chemotherapy of OC patients. Our study provides additional insights for ascites ecosystem and its connection with other tumour-relevant tissues, as well as potential markers for efficacy evaluation and therapies overcoming resistance in OC.
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We collected fourteen patients pathologically diagnosed with ovarian cancer. Fresh samples including primary ovarian tumour, omentum metastatic tumour, pelvic lymph node, malignant ascites and peripheral blood were obtained from these patients during surgery. Then, samples were processed into single-cell suspensions. Single-cell gene expression and immune repertoire measurements were conducted using the Chromium Single Cell V(D)J Reagent Kit (10x Genomics) following the manufacturer’s instructions. Completed libraries were sequenced on an Illumina NovaSeq6000 system. The gene expression matrices of the cells were generated with log normalization and linear regression using the NormalizeData and ScaleData functions of the Seurat package. For visualization, the dimensionality of each dataset was further reduced using uniform manifold approximation and projection (UMAP) with the Seurat function Run-UMAP. After main cell populations were identified by the first run clustering, we ran the Seurat pipeline for second time. The selection of resolution on the characteristics of each dataset, and the top n principal components (PCs from PCA) were used for identification of clusters.