INTRODUCTION AND OBJECTIVES: Ovarian cancer (OC) is the major cause of death related to gynecological tumors, with high-grade serous ovarian cancer (HGSOC) being the most common subtype of this disease accounting for the majority of deaths. The elevated mortality rates of HGSOC have been reported to be related to the interconnected signaling networks, cellular composition, and spatio-temporal localization of cells within the tumor microenvironment (TME). Therefore, the aim of this project is to characterize the tumor microenvironment, integrating bulk and single-cell transcriptomics of HGSOC and investigate the impact of different subpopulations in patients’ outcome. MATERIAL AND METHODS: Single-cell RNA sequencing data (scRNA-seq) from public datasets were analyzed using the Seurat R package (version 3.2.0), in which quality control, normalization, clustering, differential gene expression analysis, and the annotation of the resulting clusters into cell types were performed. Also, a thorough review of gene markers was made to support defining the populations. For a comprehensive assessment of the TME subpopulations' roles in a larger cohort, we used CIBERSORTx, a deconvolution tool named by the authors an "in silico cytometry", which employs machine-learning to calculate the abundance of cell types in bulk RNA-seq data based on a reference signature matrix, in this case, derived from is the scRNA-seq. Subsequently, survival analyses using Survival (version 3.2-3) and Survminer (version 0.4.7) R packages were performed to assess the prognostic values of each cell type. RESULTS AND CONCLUSION: In total, the scRNA-seq data resulted in 26,786 cells from 5 patients, and the approach revealed a heterogeneous TME in HGSOC divided into 24 clusters with distinct profiles within malignant, immune, and stromal major populations relevant to HGSOC. The cell types identified in the analysis comprised not only commonly observed subpopulations related to cancer, e.g. T CD4 and T CD8, but also more rare ones, e.g. pericytes and adipogenic fibroblasts. Finally, in silico cytometry with bulk RNA-Seq data of 454 patients from The Cancer Genome Atlas and International Cancer Genome Consortium databases revealed that cancer-associated fibroblasts, endothelial cells, and malignant cells were the most abundant subpopulations in HGSOC patients. When analyzing the hazard ratios, T CD8 was displayed as a meaningfully important subpopulation regarding the impact on a good prognosis, which corroborates with the high lymphocyte density as a common indicator of good prognosis at different stages of disease in many malignancies, including HGSOC. Our approach to explore the HGSOC TME will provide insights into how intratumoral content besides cancer cells can operate as a prevalent factor in the patient's prognosis, as well as offer potential diagnostic biomarkers and therapeutic targets in future clinical practice.