This study introduces a novel approach for the sensitive and accurate detection of small molecule metabolites, employing metal-phenolic network (MPN) functionalized AuNPs as both adsorbent and matrix to enhance laser desorption/ionization mass spectrometry (LDI-MS) performance. The MPN comprising tannic acid (TA) and transition metal ions (Fe3+, Co2+, Ni2+, Cu2+, or Zn2+) was coated on the surface of AuNPs, forming metal-TA network-coated AuNPs (M-TA@AuNPs). The M-TA@AuNPs provided a tunable surface for specific interactions with analytes, displaying distinct enrichment efficacies for different amino acids, especially for Cu-TA@AuNPs exhibiting the highest affinity for histidine (His). Under the optimized condition, the proposed method enabled ultrasensitive detection of His, with good linearity (R-2 = 0.9627) in the low-concentration range (50 nM-1 mu M) and a limit of detection (LOD) as low as 0.9 nM. Furthermore, the method was successfully applied to detect His from human urine samples, showcasing its practical applications in clinical diagnostics, particularly in the realm of amino acid-based targeted metabolomics.

AbstractRenal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle‐enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884–0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821–0.915), and 0.925–0.932 for classifying subtypes of RCC (95% CI, 0.821–0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h-GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non-invasive diagnostic modality to decipher patients with h-GS PCa as early as possible. In this study, ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) to extract prostate fluid metabolic fingerprint (PSF-MF) is employed and combined with the clinical features of patients, such as prostate-specific antigen (PSA), to establish a multi-modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h-GS, surpassing the results of single-modal diagnosis using only PSF-MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h-GS PCa.

Accurate diagnosis and classification of kidney cancer are crucial for high-quality healthcare services. However, the current diagnostic platforms remain challenges in the rapid and accurate analysis of large-scale clinical biosamples. Herein, we fabricated a bifunctional smart nanoplatform based on tannic acid-modified gold nanoflowers (TA@AuNFs), integrating nanozyme catalysis for colorimetric sensing and self-assembled nanoarray-assisted LDI-MS analysis. The TA@AuNFs presented peroxidase (POD)- and glucose oxidase-like activity owing to the abundant galloyl residues on the surface of AuNFs. Combined with the colorimetric assay, the TA@AuNF-based sensing nanoplatform was used to directly detect glucose in serum for kidney tumor diagnosis. On the other hand, TA@AuNFs could self-assemble into closely packed and homogeneous two-dimensional (2D) nanoarrays at liquid-liquid interfaces by using Fe3+ as a mediator. The self-assembled TA@AuNFs (SA-TA@AuNFs) arrays were applied to assist the LDI-MS analysis of metabolites, exhibiting high ionization efficiency and excellent MS signal reproducibility. Based on the SA-TA@AuNF array-assisted LDI-MS platform, we successfully extracted metabolic fingerprints from urine samples, achieving early-stage diagnosis of kidney tumor, subtype classification, and discrimination of benign from malignant tumors. Taken together, our developed TA@AuNF-based bifunctional smart nanoplatform showed distinguished potential in clinical disease diagnosis, point-of-care testing, and biomarker discovery.

This study introduced metal ion-tannic acid network-coated gold nanoparticles (M-TA@AuNPs) as novel matrices for laser desorption/ionization mass spectrometry (LDI MS). M-TA@AuNPs as matrices could enhance the LDI MS performance for detection of metabolites at low molecular weight with low background noise, which overcame the limitations of organic matrices and bare AuNPs. Subsequently, the proposed method was applied in the analysis of complex biological samples including urine and cerebrospinal fluid, with high signal intensity (~10 5 ). Our work demonstrated the good potential of M-TA@AuNPs as LDI MS matrices in metabolic profiling and clinical diagnostics.

Background As a dual-function metabolite, succinate has emerged in cell function and plays a key signaling role in linking mitochondrial function to other cellular functions. Succinate accumulation in the cytoplasm is commonly associated with hypoxia in the microenvironment and immune cell activation. Extracellular succinate released into the microenvironment is considered an inflammatory alarm that can be sensed by its membrane receptor SUCNR1, which boosts proinflammatory responses and acts akin to classical hormones and cytokines. Succinate plays an important role in the development of inflammatory diseases. Whether succinate facilitates the progression of endometriosis (EMs), characterized by chronic inflammation and peritoneal adhesion, is worth exploring. Objective We mimicked the ectopic milieu in vitro and in vivo to evaluate the main source and potential role of succinate in endometriosis. We assessed the molecular and functional effects of succinate on macrophages and peritoneal mesothelial cells in peritoneal cavity. The effect of succinate/SUCNR1 signaling on ectopic endometrial stromal cells (ESCs) was further explored in this study. Methods In this study, we used targeted organic acid metabolomics analysis and in vitro assays to assess the potential accumulation of succinate in the peritoneal fluid of EMs patients. We examined its correlation with disease severity, Visual Analogue Scale, and the Endometriosis Fertility Index. Flow cytometry, enzyme linked immunosorbent assay, western blot assay, quantitative real-time PCR, and other molecular biology techniques were used to explore the potential mechanisms. Results By mimicking the ectopic milieu, we constructed an in vitro co-culture system and found that M1 polarized macrophages and that the peritoneal mesothelial cell line (HMrSV5) mainly released succinate into their microenvironment and activated the succinate receptor (SUCNR1) signal, which further polarized the macrophages and significantly enhanced the invasive survival of ESCs, and the adhesion to the peritoneum. We further investigated the pathological effects of extracellular succinate in vivo using a xenograft mouse models of endometriosis. Conclusions Succinate-SUCNR1 signaling facilitates the creation of inflammatory cells and plays a vital role in EMs progression and peritoneal adhesion. Our work on the molecular mechanisms underlying succinate accumulation and function will help elucidate the phenotypic mysteries of pain and infertility in EMs.

You have accessJournal of UrologyProstate Cancer: Detection & Screening IV (MP49)1 May 2024MP49-07 DEFINING HIGH GLEASON SCORE PROSTATE CANCER BY PROSTATE FLUID METABOLIC FINGERPRINT BASED MULTI-MODAL DIAGNOSIS Zehong Peng, Yuning Wang, Xinrui Wu, Xingxing Du, Cong Hu, Qi Chen, Yang Ge, Yanhao Dong, Kun Qian, Liang Dong, and Wei Xue Zehong PengZehong Peng , Yuning WangYuning Wang , Xinrui WuXinrui Wu , Xingxing DuXingxing Du , Cong HuCong Hu , Qi ChenQi Chen , Yang GeYang Ge , Yanhao DongYanhao Dong , Kun QianKun Qian , Liang DongLiang Dong , and Wei XueWei Xue View All Author Informationhttps://doi.org/10.1097/01.JU.0001008696.31772.28.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer with high Gleason score remains a key challenge in healthcare due to its rapid progression and poor prognosis. This study aims to make rapid and early diagnosis of high Gleason score prostate cancer by using the metabolic fingerprint of prostate fluid. METHODS: we enrolled 108 patients and then classified the patients into two groups: PCa (n=45) and BPH (n=63).We then developed a ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) detection platform to identify the metabolomics in prostate fluid. By employing various machine learning algorithms, we combined the metabolomics with prostate-specific antigen (PSA) to establish a multi-modal recognition approach for prostate cancer with high Gleason score(n=11). Additionally, we selected key metabolites from the metabolomics and combined them with PSA to enhance the efficiency of diagnosis. Simultaneously, we discovered several metabolic pathways associated with the progression of prostate cancer. RESULTS: We successfully constructed the metabolic fingerprint of prostate fluid and developed a multi-modal identification system. This system can accurately diagnose prostate cancer with high Gleason score , with an area under the curve (AUC) of 0.83 in the training group and 0.87 in the test group. Additionally, from the prostate fluid metabolic fingerprint (PSF-MF), we identified six metabolites as biomarkers and their associated metabolic pathways. The panel composed of these metabolites also demonstrated good clinical predictive ability for prostate cancer with high Gleason score, with an AUC of 0.89 in the training group and 0.87 in the test group. CONCLUSIONS: In this study, we were able to achieve a non-invasive and rapid diagnosis of prostate cancer with high Gleason score by developing a multi-modal recognition using the metabolic fingerprint of prostate fluid. Download PPT Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e785 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Zehong Peng More articles by this author Yuning Wang More articles by this author Xinrui Wu More articles by this author Xingxing Du More articles by this author Cong Hu More articles by this author Qi Chen More articles by this author Yang Ge More articles by this author Yanhao Dong More articles by this author Kun Qian More articles by this author Liang Dong More articles by this author Wei Xue More articles by this author Expand All Advertisement PDF downloadLoading ...

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Abstract Objectives Interleukin 33 (IL-33) is a crucial inflammatory factor that functions as an alarm signal in endometriosis (EMs). Epithelial-mesenchymal transition (EMT), a process related to inflammatory signals, intracellular reactive oxygen species (ROS) production, and lipid peroxidation, have been proposed as potential mechanisms that contribute to the development and progression of EMs. IL-33 is highly upregulated in the ectopic milieu. Moreover, ectopic endometrial cells constitutively express interleukin-33 receptor ST2 (IL-33R). However, the role of IL-33/ST2 in the EMT of EMs remains largely unknown. In this study, we aimed to mechanistically determine the role of IL-33/ST2 in EMs-associated fibrosis. Materials and methods We established a non-lethal oxidative stress model to explore the conditions that trigger IL-33 induction. We performed α-smooth muscle actin (α-SMA) protein detection, cell counting kit-8 (CCK-8) assays, and scratch assays to analyze the impact of IL-33 on primary endometrial stromal cells (ESCs) proliferation and invasion. Clinical samples from patients with or without EMs were subjected to immunohistochemical (IHC) and and immunofluorescence(IF) staining to assess the clinical relevance of IL-33 receptor ST2 and EMT-related proteins. Furthermore, we used the ectopic human endometrial epithelial cell line 12Z and normal human epithelial cell line EEC to evaluate the effects of IL-33 on Wnt/β-catenin signaling. The effect of IL-33 on EMT-associated fibrosis was validated in vivo by intraperitoneal injections of IL-33 and antiST2. Results We observed that ectopic milieu, characterized by ROS, TGF-β1, and high level of estrogen, triggers the secretion of IL-33 from ectopic ESCs. Ectopic endometrial lesions exhibited higher level of fibrotic characteristics and ST2 expression than that in the normal endometrium. Exogenous recombinant human (rhIL-33) enhanced ESC migration and survival. Similarly, 12Z cells displayed a higher degree of EMT characteristics with elevated expression of CCN4 and Fra-1, downstream target genes of the WNT/β-catenin pathway, than that observed in EECs. Conversely, blocking IL-33 with neutralizing antibodies, knocking down ST2 or β-catenin with siRNA, and β-catenin dephosphorylation abolished its effects on EMT promotion. In vivo validation demonstrated that IL-33 significantly promotes EMs-related fibrosis through the activation of Wnt/β-catenin signaling. Conclusion Our data strongly support the vital role of the IL-33/ST2 pathway in EMs-associated fibrosis and emphasize the importance of the EMT in the pathophysiology of fibrosis. Targeting the IL-33/ST2/Wnt/β-catenin axis may hold promise as a feasible therapeutic approach for controlling fibrosis in EMs.

Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.

The monitoring of metabolites in biofluids provides critical clues for disease diagnosis and evaluation. Yet, the quantitative detection of metabolites remains challenging for surface-enhanced Raman spectroscopy (SERS) due to poor reproducibility in preparation and manipulation of SERS nanop-robes. Herein, we develop an activity-based, slippery liquid-infused porous surface SERS (abSLIPSERS) sensor for facile quantification of metabolites with unmodified naked metal nanoparticles (NPs) by integrating biocatalysis-boronate oxidation cascades with SLIPS-driven self-concentration and delivering. Upon mixing the target metabolite with a specific oxidase, a H2O2-sensitive phenylboronate probe, and the naked Au NPs, H2O2 produced from the biocatalytic reaction oxidizes the phenylboronate probe to phenol, resulting in a ratiometric SERS response. Meanwhile, the SLIPS enables the complete enrichment of molecules and NPs within an evaporating liquid droplet, delivering the probes to the SERS-active sites for Raman amplification. Compared with conventional SERS biosensors, abSLIPSERS avoids multistep synthesis and biofunctionalization of nanoprobes, which significantly simplifies the detection workflow and improves the reproducibility. The abSLIPSERS sensor also shows tunable dynamic range beyond 4 orders of magnitude and allows quantifying any other metabolites with specific enzymes. We demonstrate abSLIPSERS sensing of lactate, glucose, and choline in human serum for exploring energy metabolism in lung cancer. This study opens up a new opportunity for future point-of-care testing of circulating metabolites by SERS and will help to facilitate the translation of SERS bioanalysis to clinical settings.

Oligomerization of cellular membrane receptors plays crucial roles in activating intracellular downstream signaling cascades for controlling cellular behaviors in physiological and pathological processes. However, the reversible and controllable regulation of receptors in a user-defined manner remains challenging. Herein, we developed a versatile DNA nanorobot (nR) with installed aptamers and hairpin structures to reversibly and controllably regulate cell migration. This was achieved by dimerization and de-dimerization of mesenchymal-epithelial transition (Met) receptors through DNA strand displacement reactions. The functionalized DNA nR not only plays similar roles as hepatocyte growth factor (HGF) in inducing cell migration but also allows a downgrade to the original state of cell migration. The advanced DNA nanomachines can be flexibly designed to target other receptors for manipulating cellular behaviors and thus represent a powerful tool for the future of biological and medical engineering.

Enzymes are highly efficient catalysts employed in the biomedical field. However, their use is limited by poor stability and high cost. To address this, there is an imperative need for research on nanozymes. In the last decade, the introduction of nanozymes has led to exponential growth in research on nanozyme‐based biosensors. The rapid and precise detection of clinical biomarkers is crucial for various disease diagnostics. In recent years, researchers have developed different catalytic active nanomaterials and combined them with colorimetric, fluorescent, and electrochemical methods to detect clinical biomarkers. This review critically overviews the progress, future prospects, and challenges of nanozyme‐based clinical biomarker assays in the past few years.

Body fluids contain valuable information about human body and are the most significant evidence in forensic investigation at crime scenes. Their characterizations can provide investigative leads and contributes to case proof. Identifying different kinds of body fluids is critical but challenging. Traditional forensic methods rely on enzymatic assays as presumptive tests, which are destructive and invasive to samples, as well as time-consuming and complex in procedures, resulting in their inability to meet the demand of rapid detection. Here, a body fluids (including serum, plasma, tear, and urine) integrated identification platform through machine learning of metabolic fingerprinting (MF) was developed, using nanoparticle enhanced laser desorption/ionization mass spectrometry (NPELDI MS). The direct MFs of multiplex body fluids were acquired with fast speed (10 s per sample) and high reproducibility (coefficient of variation, CV < 15%) at sample volume of 5 nL, coexisting with minimum sample invasiveness. Further, two strategies (four classifications and dichotomy) were conducted to identify diverse body fluids through machine learning with the area under the cures (AUCs) of 0.969 for four classifications and 0.973 to 1.000 for dichotomy, respectively. Finally, a biomarker panel was established, and it improved the performance of body fluids identification with AUC of 1.000. Our work provides a frontier reference for differentiating body fluids through metabolomics and innovative perspectives for forensic application.

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.

Novel tannic acid functionalized gold nanoflowers (TA@AuNFs) based laser desorption and ionization mass spectrometry (LDI-MS) has been introduced for the analysis of small molecule metabolites. TA@AuNFs were synthesized using a one-pot green method and were demonstrated as an excellent LDI-MS matrix in metabolic analysis. Subsequently, our proposed method was utilized in metabolic profiling of complex biological samples such as serum, urine, cerebrospinal fluid, and prostatic fluid, with high signal intensity $(\sim10^{5})$. This research shows promising prospects in the analysis of clinical metabolites and disease diagnosis.

Background:Endometriosis (EMs) is a chronic inflammatory condition that is highly heterogeneous. Current clinical staging fails to accurately predict drug responses and prognosis. In this study, we aimed to reveal the heterogeneity of ectopic lesions and investigate the possible underlying mechanisms using transcriptomic data and clinical information.Methods:The EMs microarray dataset GSE141549 was obtained from the Gene Expression Omnibus database. Unsupervised hierarchical clustering was performed to identify EMs subtypes, which was followed by the functional enrichment analysis and estimation of immune infiltrates. Subtype-associated gene signatures were identified and further validated in other independent datasets, including GSE25628, E-MTAB-694, and GSE23339. Additionally, tissue microarrays (TMAs) were generated from premenopausal patients with EMs to investigate the potential clinical implications of the two identified subtypes.Results:The unsupervised clustering analysis revealed that ectopic EMs lesions can be classified into two distinct subtypes: stroma-enriched (S1) and immune-enriched (S2). The functional analysis revealed that S1 correlated with fibroblast activation and extracellular matrix remodeling in the ectopic milieu, whereas S2 was characterized by the upregulation of immune pathways and a higher positive correlation with the immunotherapy response. Moreover, we identified a subtype signature composed of FHL1 and SORBS1, and constructed a subtype diagnostic model. Based on the cohort data from the TMAs, we found that S2 was strongly associated with the failure of/intolerance to hormone therapy.Conclusions:This study identified two distinct subtypes that are varyingly associated with hormone resistance, stroma-immunity, and molecular features, thereby highlighting the importance of this stromal-immune heterogeneity in identifying EMs subtypes and providing novel insights into future personalized hormone-free therapy in EMs.

EDITORIAL article Front. Bioeng. Biotechnol., 04 September 2023Sec. Nanobiotechnology Volume 11 - 2023 | https://doi.org/10.3389/fbioe.2023.1281462

Point-of-care testing (POCT), as one part of in vitro diagnostic (IVD), has been gradually used in early diagnosis and personalized treatment in the clinics. Accurate and sensitive detection of clinically related biomarkers is valuable, especially using the methods with simple, rapid, high throughput, miniaturiza-tion, and low cost. Nowadays, various biomolecules have been regarded as biomarkers for diseases diagnosis, including proteins, nucleic acids, metabolites, circulating tumor cells (CTCs) and cancer-derived exosomes, etc. Owing to the extremely low abundance of most of the biomarkers, it is hard to detect them directly. Therefore, combining with the current advanced techniques, we summarize the signal amplified biosensors for biomarkers detection based on electrochemistry, fluorescence, surface-enhanced Raman scattering (SERS), and mass spectrometry (MS), and focus on their analytical perfor-mances, applications, and further challenges in the clinical diagnosis and the potential perspective.(c) 2022 Elsevier B.V. All rights reserved.

Glucose is a source of energy for daily activities of the human body and is regarded as a clinical biomarker, due to the abnormal glucose level in the blood leading to many endocrine metabolic diseases. Thus, it is indispensable to develop simple, accurate, and sensitive methods for glucose detection. However, the current methods mainly depend on natural enzymes, which are unstable, hard to prepare, and expensive, limiting the extensive applications in clinics. Herein, we propose a dual-mode Cu2O nanoparticles (NPs) based biosensor for glucose analysis based on colorimetric assay and laser desorption/ionization mass spectrometry (LDI MS). Cu2O NPs exhibited excellent peroxidase-like activity and served as a matrix for LDI MS analysis, achieving visual and accurate quantitative analysis of glucose in serum. Our proposed method possesses promising application values in clinical disease diagnostics and monitoring.