High-grade serous ovarian cancer (HGSOC) is the most aggressive subtype of ovarian cancer, and its heterogeneity poses a challenge for the discovery of reliable diagnostic biomarkers, therapeutic targets, and predicting treatment response, particularly to immunotherapy. The current standard diagnostic and treatment options are inadequate, resulting in late diagnosis and poor prognosis. To improve our understanding of the immunophenotype of tumours, potentially enhancing diagnostic and treatment capabilities, the aim of the present study was to develop a stringent workflow for studying the immune microenvironment of HGSOC tumours. We utilized publicly available single-cell RNA sequencing data and literature to identify genes enriched in certain cell types of HGSOC tumours, followed by validation using immunofluorescent-based multiplex protein profiling. A 9-plex immunofluorescence workflow was developed using the Opal™ system, and quantitative image analysis was performed to evaluate the expression of PD-L1, CD8A, FoxP3, CD163, KRT7, PDGFRB, and CD79A in large tissue sections of ovarian cancer. Each of these markers are specific to different cell types, and by staining the multiplex marker panel together with new markers with little or no literature linked to HGSOC we can gain novel insights on the immune microenvironment of HGSOC. In this project, for a proof of concept, we focused on two proteins; GZMK and SLAMF7. The optimized multiplex panel developed as part of this project will be used to identify cell-type-specific markers that may play a crucial role in the immune microenvironment of HGSOC, which could lead to better immunophenotype stratification of patients and a more optimal immunotherapy response. Moreover, the panel could also be used to study markers of less well-known immune cell types, further improving our understanding of HGSOC. Overall, this project has the potential to significantly contribute to the development of reliable diagnostic biomarkers and therapeutic targets for HGSOC, ultimately improving patient outcomes.