Browsing by Subject "multiplexed imaging"

The advancement of high-throughput imaging technologies has revolutionized the study of the tumor microenvironment (TME), including high-grade serous ovarian carcinoma (HGSOC), a cancer type characterized by genetic instability and high intra-tumor heterogeneity. HGSOC is often diagnosed at advanced stages and has a high relapse rate following initial treatment, presenting significant clinical challenges. Understanding the dynamic and complex tumor microenvironment in HGSOC is crucial for developing effective therapeutic strategies, as it includes various interacting cells and structures. Currently most methods are focusing on deciphering the TME on a single cell level, but the volume of the data poses a challenge in large scale studies. This thesis focuses on developing a comprehensive pipeline for accurate detection and phenotyping of immune cells within the TME using tissue cyclic immunofluorescence imaging. The proposed pipeline integrates Napari, an advanced visualization tool, and several existing computational methods to handle large-scale imaging datasets efficiently. The primary aim is to create Napari plugins for fast browsing and detailed visualization of these datasets, enabling precise cell phenotyping and quality control. Handling large images was resolved through the implementation of Zarr and Dask methodologies, enabling efficient data management. Key image processing methodologies include the use of the StarDist algorithm for cell segmentation, preprocessing steps for fluorescence intensity normaliza tion, and the Tribus tool for semi-automated cell type classification. In total, we annotated 976,082 single cells on three HGSOC samples originating from pre- or post-neoadjuvant chemotherapy tumor sections. The accurate annotation of immune sub-populations was enhanced by visual evaluation steps, addressing the limitations of the discussed methods. Accurately annotating dense tissue areas is crucial for describing the cellular composition of samples, particularly tumor-infiltrating immune populations. The results indicate that the proposed pipeline not only enhances the understanding of the TME in HGSOC but also provides a robust framework for future studies involving large-scale imaging data.