Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning.

Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

Plasmon Coupling in DNA-Assembled Silver Nanoclusters.

Quantum-size metal clusters with multiple delocalized electrons could support collective plasmon excitation, and thus, theoretically, coupling of plasmons in the few-atom limit might exist between assembled metal clusters, while currently few experimental observations about this phenomenon have been reported. Here we examined the optical absorption of DNA-templated Ag nanoclusters (DNA-AgNCs) assembled through DNA hybridization and found their absorption peaks were sensitive to the assembled distances, which share common characteristics with classical plasmon coupling. Dipolar charge distribution, multiple transition contributed optical absorption, and strongly enhanced electric field simulated by time-dependent density functional theory (TDDFT) indicated the origin of the absorption of individual DNA-AgNCs is a plasmon. The consistency of the peak-shifting trend between experimental and simulation results for assembled DNA-AgNCs suggested the possible presence of plasmon coupling. Our data imply the possibility for quantum-size structures to support plasmon coupling and also show that DNA-AgNCs possess the potential to be promising materials for construction of plasmon-coupling devices with ultrasmall size, site-specific and stoichiometric binding abilities, and biocompatibility.

Precise Deposition of Polydopamine on Cancer Cell Membrane as Artificial Receptor for Targeted Drug Delivery.

Compared with conventional chemotherapy and radiotherapy, targeted molecular therapy, e.g., antibody-drug conjugates or aptamer-drug conjugates, can specifically identify overexpressed natural receptors on the cancer cell, perform targeted release of anticancer drugs, and achieve targeted killing of tumor cells. However, many natural receptors are also expressed on non-cancer cells, thereby diverting the targeting molecules to healthy cells. By generating artificial cell surface receptors specific to diseased cells, aptamer-drug conjugates can identify these artificial receptors, improve therapeutic efficacy, and decrease the minimum effective dosage. In this study, we use high K+ and high H2O2 of the tumor microenvironment (TME) to produce polydopamine only on living cancer cell membrane. Owing to the significant reactivity of polydopamine with amino groups, e.g., the amino group of proteins, polydopamine can deposit on tumor cells and act as "artificial receptors" for targeted delivery of anticancer drugs with amino groups, in other words, amino-containing drugs and protein drugs.

Circular Bispecific Aptamer-Mediated Artificial Intercellular Recognition for Targeted T Cell Immunotherapy.

Adoptive T cell immunotherapy, such as chimeric antigen receptor (CAR) T cell therapy, has proven to be highly efficient in the treatment of hematologic malignancies. However, it is challenged by complicated ex vivo engineering, systemic side effects, and low expression of tumor-specific antigen, especially in solid tumors. In this paper, we present a "recognition-then-activation" strategy, which first assists naïve T cells to recognize and adhere to cancer cells and then activates the accumulated T cell in situ to specifically kill cancer cells. In this way, we could unleash the antitumor power of the T cell without complicated and time-consuming cell engineering. To this end, circular bispecific aptamers (cb-aptamers), a class of chemically cyclized aptamers with improved stability and molecular recognition ability which can simultaneously bind to two different types of cells, were first constructed to form artificial intercellular recognition between naïve T cells and tumor cells. After T cell accumulation in the tumor mediated by cb-aptamers, T cells in the tumor site were subsequently activated in situvia commercial CD3/CD28 T cell activator beads to induce tumor-specific killing. Furthermore, by simply choosing different anticancer aptamers, the application of this "recognition-then-activation" strategy can be expanded for targeted treatment of various types of cancer. This may represent a simple T cell immunotherapy that is useful for the treatment of multiple cancers.