Top-Down Rational Engineering of Heteroatom-Doped Graphene Quantum Dots for Laser Desorption/Ionization Mass Spectrometry Detection and Imaging of Small Biomolecules

Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

Molecularly Imprinted Heterostructure-Assisted Laser Desorption Ionization Mass Spectrometry Analysis and Imaging of Quinolones

Quinolone residues resulting from body metabolism and waste discharge pose a significant threat to the ecological environment and to human health. Therefore, it is essential to monitor quinolone residues in the environment. Herein, an efficient and sensitive matrix-assisted laser desorption/ionization mass spectrometry (MALDI/MS) method was devised by using a novel molecularly imprinted heterojunction (MIP-TNs@GCNs) as the matrix. Molecularly imprinted titanium dioxide nanosheets (MIP-TNs) and graphene-like carbon nitrides (GCNs) were associated at the heterojunction interface, allowing for the specific, rapid, and high-throughput ionization of quinolones. The mechanism of MIP-TNs@GCNs was clarified using their adsorption properties and laser desorption/ionization capability. The prepared oxygen-vacancy-rich MIP-TNs@GCNs heterojunction exhibited higher light absorption and ionization efficiencies than TNs and GCNs. The good linearity (in the quinolone concentration range of 0.5-50 pg/μL, R2 > 0.99), low limit of detection (0.1 pg/μL), good reproducibility (n = 8, relative standard deviation [RSD] < 15%), and high salt and protein resistance for quinolones in groundwater samples were achieved using the established MIP-TNs@GCNs-MALDI/MS method. Moreover, the spatial distributions of endogenous compounds (e.g., amino acids, organic acids, and flavonoids) and xenobiotic quinolones from Rhizoma Phragmitis and Rhizoma Nelumbinis were visualized using the MIP-TNs@GCNs film as the MALDI/MS imaging matrix. Because of its superior advantages, the MIP-TNs@GCNs-MALDI/MS method is promising for the analysis and imaging of quinolones and small molecules.

Host-Guest Self-Assembled Interfacial Nanoarrays for Precise Metabolic Profiling

Accurate and rapid metabolic profiling of cerebrospinal fluid (CSF) is urgently needed but remains challenging for clinical diagnosis of central nervous system diseases and biomarker discovery. Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) holds promise for metabolic analysis. Its low signal reproducibility, however, severely restricts acquisition of quantitative MS data in clinical practice. Herein, a multifunctional self-assembled AuNPs array (MSANA)-based LDI-MS platform for direct amino acids analysis and metabolic profiling in patient CSF samples is developed. MSANA featuring a highly ordered and closely packed two-dimensional nanostructure permits capture and direct analysis of aromatic amino acids by LDI-MS with high selectivity and micromolar sensitivity. Meanwhile, the MSANA-based LDI-MS platform exhibits excellent reproducibility (RSD < 10%), largely outperforming the direct matrix spotting approach widely used now (RSD < 44%). The platform is successfully used in metabolic profiling of CSF (1 µL) within minutes for discrimination of medulloblastoma patients from non-tumor controls. Taken together, the MSANA-based LDI-MS platform shows potential clinical values toward large-scale metabolic diagnostics and pathogenic mechanism study.

Boron nitride quantum dots-enhanced laser desorption/ionization mass spectrometry analysis and imaging of bisphenol A

Bisphenol A (BPA) displays harmful effects on the human health, including potent endocrine activity and potential impact on the development of cancer. Analysis BPA residues in water and plastic products attracted considerable attention in the past decades. However, dominantly used conventional analysis techniques are unable to directly and non-destructively identify the correct species of BPA in plastic products. Hence, this study demonstrates the effective utilisation of boron nitride quantum dots (BNQDs) as an inorganic matrix in matrix-assisted laser desorption/ionization mass spectrometry analysis and imaging (MALDI-MS & MSI) for BPA. The presence of abundant hydroxyl and amino groups on the BNQDs' surface is favourable for the formation of hydrogen bonds with BPA, and increases their ionization and chemoselectivity. Intriguingly, the BNQDs matrix offers a distinct signal for phenolic hazardous molecules featuring different hydroxyl groups. The method was applied to detect BPA at nanomolar level in environmental water, and also allowed non-destructive and in situ mapping of BPA in plastics and pacifiers. This research provides a novel strategy for adapting nanomaterials as inorganic matrices for analysis of small molecular pollutants in environmentally relevant samples using MALDI-MS & MSI.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.