A high-throughput imaging platform to characterize extracellular pH in organotypic three-dimensional in vitro models of liver cancer.

Given the extraordinary nature of tumor metabolism in hepatocellular carcinoma and its impact on oncologic treatment response, this study introduces a novel high-throughput extracellular pH (pHe ) mapping platform using magnetic resonance spectroscopic imaging in a three-dimensional (3D) in vitro model of liver cancer. pHe mapping was performed using biosensor imaging of redundant deviation in shifts (BIRDS) on 9.4 T and 11.7 T MR scanners for validation purposes. 3D cultures of four liver cancer (HepG2, Huh7, SNU475, VX2) and one hepatocyte (THLE2) cell line were simultaneously analyzed (a) without treatment, (b) supplemented with 4.5 g/L d-glucose, and (c) treated with anti-glycolytic 3-bromopyruvate (6.25, 25, 50, 75, and 100 µM). The MR results were correlated with immunohistochemistry (GLUT-1, LAMP-2) and luminescence-based viability assays. Statistics included the unpaired t-test and ANOVA test. High-throughput pHe imaging with BIRDS for in vitro 3D liver cancer models proved feasible. Compared with non-tumorous hepatocytes (pHe = 7.1 ± 0.1), acidic pHe was revealed in liver cancer (VX2, pHe = 6.7 ± 0.1; HuH7, pHe = 6.8 ± 0.1; HepG2, pHe = 6.9 ± 0.1; SNU475, pHe = 6.9 ± 0.1), in agreement with GLUT-1 upregulation. Glucose addition significantly further decreased pHe in hyperglycolytic cell lines (VX2, HepG2, and Huh7, by 0.28, 0.06, and 0.11, respectively, all p < 0.001), whereas 3-bromopyruvate normalized tumor pHe in a dose-dependent manner without affecting viability. In summary, this study introduces a non-invasive pHe imaging platform for high-yield screening using a translational 3D liver cancer model, which may help reveal and target mechanisms of therapy resistance and inform personalized treatment of patients with hepatocellular carcinoma.

Science to Practice: Molecular-targeted Drug Delivery in Combination with Radiofrequency Ablation of Liver Cancer: A Magic Bullet?

In an effort to improve the technical success rates and clinical outcomes of radiofrequency (RF) ablation, Yan et al validated the use of a tumor-penetrating peptide and thermosensitive doxorubicin (DOX)-loaded nanoparticles in combination with RF ablation in a hepatocellular carcinoma mouse model. By achieving higher chemotherapeutic drug concentrations in target lesions, fewer toxic effects, and improved survival end points in an animal tumor model, the authors conclude that superior tumor treatment with RF ablation is possible when combined with molecular-targeted drug delivery systems.

A multilevel gamma-clustering layout algorithm for visualization of biological networks.

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ -clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs.