This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record.

There is a high demand for high‐throughput in vitro metabolic analysis in clinical practice. However, current metabolic analysis of biofluids requires tedious sample pretreatment due to sample complexity and low molecular abundance. Herein, a plasmonic chip with Au nanoparticles deposited on a dopamine‐bubble layer is constructed for clinical metabolic fingerprints. The structural parameters of the designed chip are optimized in terms of surface roughness and Au content. The established chip enables fast, sensitive, and selective detection of small metabolites in human serum without any enrichment or purification. On‐chip serum fingerprints further allow for differentiation between women diagnosed with cervical cancer and control subjects, as well as the therapeutic evaluation for potential clinical monitoring. This work boosts the application of laser desorption/ionization mass spectrometry in large‐scale clinical in vitro diagnosis.

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 Cr2O3 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.

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 Mn2O3/(Co,Mn)(Co,Mn)2O4 (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.

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This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record.

Metabolic analysis in biofluids interprets the end products in the bioprocess, emerging as an irreplaceable disease diagnosis and monitoring platform. Laser desorption/ionization mass spectrometry (LDI MS)‐based metabolic analysis holds 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.