Metabolomic differentiation of tumor core versus edge in glioma.

OBJECTIVE: Gliomas exhibit high intratumor and interpatient heterogeneity. Recently, it has been shown that the microenvironment and phenotype differ significantly between the glioma core (inner) and edge (infiltrating) regions. This proof-of-concept study differentiates metabolic signatures associated with these regions, with the potential for prognosis and targeted therapy that could improve surgical outcomes. METHODS: Paired glioma core and infiltrating edge samples were obtained from 27 patients after craniotomy. Liquid-liquid metabolite extraction was performed on the samples and metabolomic data were obtained via 2D liquid chromatography-mass spectrometry/mass spectrometry. To gauge the potential of metabolomics to identify clinically relevant predictors of survival from tumor core versus edge tissues, a boosted generalized linear machine learning model was used to predict metabolomic profiles associated with O6-methylguanine DNA methyltransferase (MGMT) promoter methylation. RESULTS: A panel of 66 (of 168) metabolites was found to significantly differ between glioma core and edge regions (p ≤ 0.05). Top metabolites with significantly different relative abundances included DL-alanine, creatine, cystathionine, nicotinamide, and D-pantothenic acid. Significant metabolic pathways identified by quantitative enrichment analysis included glycerophospholipid metabolism; butanoate metabolism; cysteine and methionine metabolism; glycine, serine, alanine, and threonine metabolism; purine metabolism; nicotinate and nicotinamide metabolism; and pantothenate and coenzyme A biosynthesis. The machine learning model using 4 key metabolites each within core and edge tissue specimens predicted MGMT promoter methylation status, with AUROCEdge = 0.960 and AUROCCore = 0.941. Top metabolites associated with MGMT status in the core samples included hydroxyhexanoycarnitine, spermine, succinic anhydride, and pantothenic acid, and in the edge samples metabolites included 5-cytidine monophosphate, pantothenic acid, itaconic acid, and uridine. CONCLUSIONS: Key metabolic differences are identified between core and edge tissue in glioma and, furthermore, demonstrate the potential for machine learning to provide insight into potential prognostic and therapeutic targets.

Acoustofluidic-mediated molecular delivery to human T cells with a three-dimensional-printed flow chamber.

Cell-based therapies have garnered significant interest to treat cancer and other diseases. Acoustofluidic technologies are in development to improve cell therapy manufacturing by facilitating rapid molecular delivery across the plasma membrane via ultrasound and microbubbles (MBs). In this study, a three-dimensional (3D) printed acoustofluidic device was used to deliver a fluorescent molecule, calcein, to human T cells. Intracellular delivery of calcein was assessed after varying parameters such as MB face charge, MB concentration, flow channel geometry, ultrasound pressure, and delivery time point after ultrasound treatment. MBs with a cationic surface charge caused statistically significant increases in calcein delivery during acoustofluidic treatment compared to MBs with a neutral surface charge (p < 0.001). Calcein delivery was significantly higher with a concentric spiral channel geometry compared to a rectilinear channel geometry (p < 0.001). Additionally, calcein delivery was significantly enhanced at increased ultrasound pressures of 5.1 MPa compared to lower ultrasound pressures between 0-3.8 MPa (p < 0.001). These results demonstrate that a 3D-printed acoustofluidic device can significantly enhance intracellular delivery of biomolecules to T cells, which may be a viable approach to advance cell-based therapies.