Flow cytometry protocol for cell death analysis in glioblastoma organoids: A technical note

Tumor organoid models have emerged as a promising tool in cancer research. By preserving intra- and intertumoral heterogeneity and structural integrity they provide a physiologically relevant platform for drug-response studies. However, valid methodological approaches for cell death analyses applying flow cytometry, particularly in complex, large organoids, are lacking. Using glioblastoma organoids (GBOs), we developed a flow cytometry protocol to quantify cell death as an important readout in cancer research. Human GBOs were generated out of tumor material from six patients. Temozolomide (TMZ) and lomustine (CCNU) were used as cytotoxic agents commonly employed in glioblastoma therapy. After treatment for 144 and 288 hours, single cell suspensions from densely-packed GBOs were generated through a combined approach of enzymatic and mechanical dissociation. Cells were permeabilized with Triton X and subsequently stained with propidium iodide (PI). PI staining labels fragmented nuclear DNA, yielding a hypodiploid sub-G1 peak in flow cytometry that markes cell death. After treatment for 288 hours with physiologically-relevant concentrations of TMZ and CCNU cell death rates reached up to 63% in our GBO model. Across three GBO populations, the impact of CCNU at the given concentration was more pronounced compared to that observed with TMZ and the cell death rates of treatment for 288 hours surpassed that of the 144-hour treatment. Both biological and technical replicates showed low variability. Hoechst 33258 staining on the same samples confirmed trends in cell death rates obtained from PI-based analysis. We further validated the treatment-induced effect using a plate-based lactate dehydrogenase release assay and measurements of GBO diameter. Our single-stain flow-cytometry protocol scales to large, dense organoids and provides a practical balance of performance, hands-on time, cost, specificity, and throughput. This protocol could support development and evaluation of subtype-specific therapeutic strategies in translational cancer research.