Search

Abstract Glioblastoma (GBM) is the most common brain tumor and remains incurable. Primary GBM cultures are widely used tools for drug screening, but there is a lack of genomic and pharmacological characterization for these primary GBM cultures. Here, we collect 50 patient-derived glioma cell (PDGC) lines and characterize them by whole genome sequencing, RNA sequencing, and drug response screening. We identify three molecular subtypes among PDGCs: mesenchymal (MES), proneural (PN), and oxidative phosphorylation (OXPHOS). Drug response profiling reveals that PN subtype PDGCs are sensitive to tyrosine kinase inhibitors, whereas OXPHOS subtype PDGCs are sensitive to histone deacetylase inhibitors, oxidative phosphorylation inhibitors, and HMG-CoA reductase inhibitors. PN and OXPHOS subtype PDGCs stably form tumors in vivo upon intracranial transplantation into immunodeficient mice, whereas most MES subtype PDGCs fail to form tumors in vivo. In addition, PDGCs cultured by serum-free medium, especially long-passage PDGCs, carry MYC/MYCN amplification, which is rare in GBM patients. Our study provides a valuable resource for understanding primary glioma cell cultures and clinical translation and highlights the problems of serum-free PDGC culture systems that cannot be ignored.

Abstract Recent research in multi-principal element alloys (MPEAs) has increasingly focused on the role of short-range order (SRO) on material performance. However, the mechanisms of SRO formation and its precise control remain elusive, limiting the progress of SRO engineering. Here, leveraging advanced additive manufacturing techniques that produce samples with a wide range of cooling rates (up to 107 K s−1) and an enhanced semi-quantitative electron microscopy method, we characterize SRO in three CoCrNi-based face-centered-cubic (FCC) MPEAs. Surprisingly, irrespective of the processing and thermal treatment history, all samples exhibit similar levels of SRO. Atomistic simulations reveal that during solidification, prevalent local chemical order arises in the liquid-solid interface (solidification front) even under the extreme cooling rate of 1011 K s−1. This phenomenon stems from the swift atomic diffusion in the supercooled liquid, which matches or even surpasses the rate of solidification. Therefore, SRO is an inherent characteristic of most FCC MPEAs, insensitive to variations in cooling rates and even annealing treatments typically available in experiments.

Abstract Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

Dealloying usually relies on chemical dissolution of an alloy component. By contrast, the authors demonstrate an atomic-scale phase separation process that differs completely from the chemical dealloying mechanism and thus represents a significant departure from the well-known Hume-Rothery rules.