In Situ Characterization and Deep Profiling of Engineered Multispecific Nanoparticle Metabolite Coronas for Precise Serum Diagnostics

Heyuhan Zhang, Yunqiang Zhang, Yun Wu, Yiwen Lin, Jingxin Ding, Nianrong Sun, Chunhui Deng
Analytical chemistry · 2026 · vol. 98(1) , pp. 673–684 · doi:10.1021/acs.analchem.5c05667 · PMID:41451882
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The paper develops a high-throughput platform that uses engineered magnetic multifunctional nanoparticles to form metabolite biomolecular coronas in serum and then profiles these coronas directly by integrating in situ characterization with MALDI mass spectrometry in a short workflow. Across engineered nanoparticle surface functionalizations, the authors report five distinct metabolite corona composition patterns, including temporal evolution trajectories, and demonstrate that the method can acquire interference-resistant metabolite corona fingerprints on pristine nanoparticle surfaces under protein-coexisting conditions without additional isolation. Using a panel of engineered nanoparticles, the platform profiles 192 clinical serum samples and applies XGBoost with SHAP explainability to decode metabolite corona fingerprints for early detection and biomarker panel screening, reporting AUCs of 0.96–0.99 for endometriosis-associated ovarian cancer. The authors explicitly frame the work as a platform for scalable metabolite corona research and diagnostics, but the summary of study performance is limited to the described assay and dataset rather than broad external validation. This paper is centrally about endometriosis-related diagnosis — it specifically targets endometriosis-associated ovarian cancer using metabolite corona fingerprints for early detection.

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Abstract

Upon exposure to biofluids, engineered nanoparticles (NPs) spontaneously form reproducible biomolecular coronas via selective diverse biomolecule adsorption. The corona characterization of metabolites poses greater analytical challenges than proteins due to their inherent molecular complexity, hindering research on this integral biomolecular corona component. Here we report a high-throughput platform integrating a series of magnetic multifunctional NPs (MFNs) with matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) technology for engineered formation and in situ characterization of the metabolite corona. Operating through a 2-3 min streamlined workflow (excluding incubation time), this integrated platform delivers direct interference-resistant metabolite corona fingerprints (MCFs) acquisition on pristine NP surfaces under protein-coexisting conditions without additional isolation procedures. We revealed five distinct composition patterns of metabolite coronas associated with surface functionalization strategies and characterized their temporal evolution trajectories. A panel of MFNs was further engineered for this platform, achieving high-coverage MCFs acquisition in an unbiased manner across 192 clinical serum samples. Integration of Xgboost-SHapley Additive exPlanations (XGB-SHAP) algorithms for MCFs decoding achieved early detection, biomarker panel screening, and risk factor interpretability of endometriosis-associated ovarian cancer (EAOC) with area under the curves (AUCs) of 0.96-0.99, developing a noninvasive diagnostic tool for early malignant transformation of endometriosis. Our work not only provides a reproducible framework for scalable metabolite corona research but also extends its precision medicine applications.
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In Situ Characterization and Deep Profiling of Engineered Multispecific Nanoparticle Metabolite Coronas for Precise Serum DiagnosticsClick to copy article linkArticle link copied! - Heyuhan ZhangHeyuhan ZhangDepartment of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, ChinaMore by Heyuhan Zhang - Yunqiang ZhangYunqiang ZhangObstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai 200433, ChinaMore by Yunqiang Zhang - Yun WuYun WuDepartment of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, ChinaMore by Yun Wu - Yiwen LinYiwen LinDepartment of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, ChinaMore by Yiwen Lin - Jingxin Ding*Jingxin Ding*Email: [email protected]Obstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai 200433, ChinaMore by Jingxin Ding - Nianrong Sun*Nianrong Sun*Email: [email protected]Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, ChinaMore by Nianrong Sun - Chunhui Deng*Chunhui Deng*Email: [email protected]Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 201399, ChinaMore by Chunhui Deng Abstract Upon exposure to biofluids, engineered nanoparticles (NPs) spontaneously form reproducible biomolecular coronas via selective diverse biomolecule adsorption. The corona characterization of metabolites poses greater analytical challenges than proteins due to their inherent molecular complexity, hindering research on this integral biomolecular corona component. Here we report a high-throughput platform integrating a series of magnetic multifunctional NPs (MFNs) with matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) technology for engineered formation and in situ characterization of the metabolite corona. Operating through a 2–3 min streamlined workflow (excluding incubation time), this integrated platform delivers direct interference-resistant metabolite corona fingerprints (MCFs) acquisition on pristine NP surfaces under protein-coexisting conditions without additional isolation procedures. We revealed five distinct composition patterns of metabolite coronas associated with surface functionalization strategies and characterized their temporal evolution trajectories. A panel of MFNs was further engineered for this platform, achieving high-coverage MCFs acquisition in an unbiased manner across 192 clinical serum samples. Integration of Xgboost-SHapley Additive exPlanations (XGB-SHAP) algorithms for MCFs decoding achieved early detection, biomarker panel screening, and risk factor interpretability of endometriosis-associated ovarian cancer (EAOC) with area under the curves (AUCs) of 0.96–0.99, developing a noninvasive diagnostic tool for early malignant transformation of endometriosis. Our work not only provides a reproducible framework for scalable metabolite corona research but also extends its precision medicine applications. Cited By This article has not yet been cited by other publications. Article Views Altmetric Citations Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days. Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts. The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

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endometriosis

MeSH descriptors

Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles Nanoparticles

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[{'doi': '10.13039/501100012166', 'name': 'National Key Research and Development Program of China', 'awards': ['2024YFA1307503']}, {'doi': '10.13039/501100012166', 'name': 'National Key Research and Development Program of China', 'awards': ['2024YFC3405402']}, {'doi': '10.13039/501100001809', 'name': 'National Natural Science Foundation of China', 'awards': ['22574028']}]

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