Phenotypic characterization by mass cytometry of the microenvironment in ovarian cancer and impact of tumor dissociation methods
Shamundeeswari Anandan (University of Bergen and Bjerknes Centre for Climate Research, Haukeland University Hospital)
Liv Cecilie V. Thomsen (Haukeland University Hospital, University of Bergen)
Stein Erik Gullaksen (University of Bergen and Bjerknes Centre for Climate Research)
Tamim Abdelaal (Delft Bioinformatics Lab, TU Delft - Pattern Recognition and Bioinformatics)
Katrin Kleinmanns (University of Bergen and Bjerknes Centre for Climate Research)
Jørn Skavland (University of Bergen and Bjerknes Centre for Climate Research)
Geir Bredholt (University of Bergen and Bjerknes Centre for Climate Research)
Bjørn Tore Gjertsen (University of Bergen and Bjerknes Centre for Climate Research, Haukeland University Hospital)
Emmet McCormack (University of Bergen and Bjerknes Centre for Climate Research)
Line Bjørge (Haukeland University Hospital, University of Bergen and Bjerknes Centre for Climate Research)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Improved molecular dissection of the tumor microenvironment (TME) holds promise for treating high-grade serous ovarian cancer (HGSOC), a gynecological malignancy with high mortality. Reliable disease-related biomarkers are scarce, but single-cell mapping of the TME could identify patient-specific prognostic differences. To avoid technical variation effects, however, tissue dissociation effects on single cells must be considered. We present a novel Cytometry by Time-of-Flight antibody panel for single-cell suspensions to identify individual TME profiles of HGSOC patients and evaluate the effects of dissociation methods on results. The panel was developed utilizing cell lines, healthy donor blood, and stem cells and was applied to HGSOC tissues dissociated by six methods. Data were analyzed using Cytobank and X-shift and illustrated by t-distributed stochastic neighbor embedding plots, heatmaps, and stacked bar and error plots. The panel distinguishes the main cellular subsets and subpopulations, enabling characterization of individual TME profiles. The dissociation method affected some immune (n = 1), stromal (n = 2), and tumor (n = 3) subsets, while functional marker expressions remained comparable. In conclusion, the panel can identify subsets of the HGSOC TME and can be used for in-depth profiling. This panel represents a promising profiling tool for HGSOC when tissue handling is considered.