An autologous ex vivo model for exploring patient-specific responses to viro-immunotherapy in glioblastoma

Oncolytic virus (OV) clinical trials have demonstrated remarkable efficacy in subsets of patients with glioblastoma (GBM). However, the lack of tools to predict this response hinders the advancement of a more personalized application of OV therapy. In this study, we characterize an ex vivo co-culture system designed to examine the immune response to OV infection of patient-derived GBM neurospheres in the presence of autologous peripheral blood mononuclear cells (PBMCs). Co-culture conditions were optimized to retain viability and functionality of both tumor cells and PBMCs, effectively recapitulating the well-recognized immunosuppressive effects of GBM. Following OV infection, we observed elevated secretion of pro-inflammatory cytokines and chemokines, including interferon γ, tumor necrosis factor α, CXCL9, and CXCL10, and marked changes in immune cell activation markers. Importantly, OV treatment induced unique patient-specific immune responses. In summary, our co-culture platform presents an avenue for personalized screening of viro-immunotherapies in GBM, offering promise as a potential tool for future patient stratification in OV therapy.</p

Quantitative proteomics of small numbers of closely-related cells: Selection of the optimal method for a clinical setting

Mass spectrometry (MS)-based proteomics profiling has undoubtedly increased the knowledge about cellular processes and functions. However, its applicability for paucicellular sample analyses is currently limited. Although new approaches have been developed for single-cell studies, most of them have not (yet) been standardized and/or require highly specific (often home-built) devices, thereby limiting their broad implementation, particularly in non-specialized settings. To select an optimal MS-oriented proteomics approach applicable in translational research and clinical settings, we assessed 10 different sample preparation procedures in paucicellular samples of closely-related cell types. Particularly, five cell lysis protocols using different chemistries and mechanical forces were combined with two sample clean-up techniques (C18 filter- and SP3-based), followed by tandem mass tag (TMT)-based protein quantification. The evaluation was structured in three phases: first, cell lines from hematopoietic (THP-1) and non-hematopoietic (HT-29) origins were used to test the approaches showing the combination of a urea-based lysis buffer with the SP3 bead-based clean-up system as the best performer. Parameters such as reproducibility, accessibility, spatial distribution, ease of use, processing time and cost were considered. In the second phase, the performance of the method was tested on maturation-related cell populations: three different monocyte subsets from peripheral blood and, for the first time, macrophages/microglia (MAC) from glioblastoma samples, together with T cells from both tissues. The analysis of 50,000 cells down to only 2,500 cells revealed different protein expression profiles associated with the distinct cell populations. Accordingly, a closer relationship was observed between non-classical monocytes and MAC, with the latter showing the co-expression of M1 and M2 macrophage markers, although pro-tumoral and anti-inflammatory proteins were more represented. In the third phase, the results were validated by high-end spectral flow cytometry on paired monocyte/MAC samples to further determine the sensitivity of the MS approach selected. Finally, the feasibility of the method was proven in 194 additional samples corresponding to 38 different cell types, including cells from different tissue origins, cellular lineages, maturation stages and stimuli. In summary, we selected a reproducible, easy-to-implement sample preparation method for MS-based proteomic characterization of paucicellular samples, also applicable in the setting of functionally closely-related cell populations