Urine Metabolic Fingerprints Encode Subtypes of Kidney Diseases

Inorganic Matrices Assisted Laser Desorption/Ionization Mass Spectrometry for Metabolic Analysis in Bio-fluids

Metabolic analysis in bio-fluids interprets the end products in the bio-process, emerging as an irreplaceable disease diagnosis and monitoring platform. Laser desorption/ionization mass spectrometry (LDI MS) based metabolic analysis exhibits great potential for clinical applications in terms of high throughput, rapid signal readout, and minimal sample preparation. There are two essential elements to construct the LDI MS-based metabolic analysis: 1) well-designed nanomaterials as matrices; 2) machine learning algorithms for data analysis. This review highlights the development of various inorganic matrices to comprehend the advantages of LDI MS in metabolite detection and the recent diagnostic applications based on target metabolite detection and untargeted metabolic fingerprints in biological fluids.

Design of Multi-Shelled Hollow Cr2 O3 Spheres for Metabolic Fingerprinting

Schizophrenia (SZ) detection enables effective treatment to improve the clinical outcome, but objective and reliable SZ diagnostics are still limited. An ideal diagnosis of SZ suited for robust clinical screening must address detection throughput, low invasiveness, and diagnosis accuracy. Herein, we built a multi-shelled hollow Cr₂ O₃ spheres (MHCSs) assisted laser desorption/ionization mass spectrometry (LDI MS) platform for the direct metabolic profiling of biofluids towards SZ diagnostics. The MHCSs displayed strong light absorption for enhanced ionization and microscale surface roughness with stability for the effective LDI of metabolites. We profiled urine and serum metabolites (≈1 μL) with the enhanced LDI efficacy in seconds. We discriminated SZ patients (SZs) from healthy controls (HCs) with the highest area under the curve (AUC) value of 1.000 for the blind test. We identified four compounds with optimal diagnostic power as a simplified metabolite panel for SZ and demonstrated the metabolite quantification for clinic use. Our approach accelerates the growth of new platforms toward a precision diagnosis in the near future.

Plasmonic Sensors for Extracellular Vesicle Analysis: From Scientific Development to Translational Research

Extracellular vesicles (EVs), actively shed from a variety of neoplastic and host cells, are abundant in blood and carry molecular markers from parental cells. For these reasons, EVs have gained much interest as biomarkers of disease. Among a number of different analytical methods that have been developed, surface plasmon resonance (SPR) stands out as one of the ideal techniques given its sensitivity, robustness, and ability to miniaturize. In this Review, we compare different SPR platforms for EV analysis, including conventional SPR, nanoplasmonic sensors, surface-enhanced Raman spectroscopy, and plasmonic-enhanced fluorescence. We discuss different surface chemistries used to capture targeted EVs and molecularly profile their proteins and RNAs. We also highlight these plasmonic platforms' clinical applications, including cancers, neurodegenerative diseases, and cardiovascular diseases. Finally, we discuss the future perspective of plasmonic sensing for EVs and their potentials for commercialization and clinical translation.

FeOOH@Metal-Organic Framework Core-Satellite Nanocomposites for the Serum Metabolic Fingerprinting of Gynecological Cancers

High-throughput metabolic analysis is of significance in diagnostics, while tedious sample pretreatment has largely hindered its clinic application. Herein, we designed FeOOH@ZIF-8 composites with enhanced ionization efficiency and size-exclusion effect for laser desorption/ionization mass spectrometry (LDI-MS)-based metabolic diagnosis of gynecological cancers. The FeOOH@ZIF-8-assisted LDI-MS achieved rapid, sensitive, and selective metabolic fingerprints of the native serum without any enrichment or purification. Further analysis of extracted serum metabolic fingerprints successfully discriminated patients with gynecological cancers (GCs) from healthy controls and also differentiated three major subtypes of GCs. Given the low cost, high-throughput, and easy operation, our approach brings a new dimension to disease analysis and classification.