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Chen X, Yu W, Zhao Y, Ji Y, Qi Z, Guan Y, Wan J, Hao Y. Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting. Talanta 2024; 277:126328. [PMID: 38824860 DOI: 10.1016/j.talanta.2024.126328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 05/18/2024] [Accepted: 05/25/2024] [Indexed: 06/04/2024]
Abstract
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.
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Affiliation(s)
- Xiaonan Chen
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, PR China
| | - Wendi Yu
- Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Yinbing Zhao
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, PR China
| | - Yuxi Ji
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, PR China
| | - Ziheng Qi
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, PR China
| | - Yangtai Guan
- Department of Neurology, Punan Branch of Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200125, PR China.
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, PR China.
| | - Yong Hao
- Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
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2
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Wang D, Yao H, Ye J, Gao Y, Cong H, Yu B. Metal-Organic Frameworks (MOFs): Classification, Synthesis, Modification, and Biomedical Applications. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2404350. [PMID: 39149999 DOI: 10.1002/smll.202404350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 08/02/2024] [Indexed: 08/17/2024]
Abstract
Metal-organic frameworks (MOFs) are a new variety of solid crystalline porous functional materials. As an extension of inorganic porous materials, it has made important progress in preparation and application. MOFs are widely used in various fields such as gas adsorption storage, drug delivery, sensing, and biological imaging due to their high specific surface area, porosity, adjustable pore size, abundant active sites, and functional modification by introducing groups. In this paper, the types of MOFs are classified, and the synthesis methods and functional modification mechanisms of MOFs materials are summarized. Finally, the application prospects and challenges of metal-organic framework materials in the biomedical field are discussed, hoping to promote their application in multidisciplinary fields.
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Affiliation(s)
- Dayang Wang
- College of Chemistry and Chemical Engineering, College of Life Sciences, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao, 266071, China
| | - Huanchen Yao
- College of Chemistry and Chemical Engineering, College of Life Sciences, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao, 266071, China
| | - Jiashuo Ye
- College of Chemistry and Chemical Engineering, College of Life Sciences, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao, 266071, China
| | - Yan Gao
- College of Chemistry and Chemical Engineering, College of Life Sciences, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao, 266071, China
| | - Hailin Cong
- College of Chemistry and Chemical Engineering, College of Life Sciences, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao, 266071, China
- State Key Laboratory of Bio-Fibers and Eco-Textiles, Qingdao University, Qingdao, 266071, China
- School of Materials Science and Engineering, Shandong University of Technology, Zibo, 255000, China
| | - Bing Yu
- College of Chemistry and Chemical Engineering, College of Life Sciences, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao, 266071, China
- State Key Laboratory of Bio-Fibers and Eco-Textiles, Qingdao University, Qingdao, 266071, China
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3
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Zhang M, Shi F, Chen Y, Yang C, Zhang X, Deng C, Sun N. Straightforward Creation of Multishell Hollow Hybrids for an Integrated Metabolic Monitoring System in Disease Management. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2400941. [PMID: 38529737 DOI: 10.1002/smll.202400941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/08/2024] [Indexed: 03/27/2024]
Abstract
Multidimensional metabolic analysis has become a new trend in establishing efficient disease monitoring systems, as the constraints associated with relying solely on a single dimension in refined monitoring are increasingly pronounced. Here, coordination polymers are employed as derivative precursors to create multishell hollow hybrids, developing an integrated metabolic monitoring system. Briefly, metabolic fingerprints are extracted from hundreds of serum samples and urine samples, encompassing not only membranous nephropathy but also related diseases, using high-throughput mass spectrometry. With optimized algorithm and initial feature selection, the established combined panel demonstrates enhanced accuracy in both subtype differentiation (over 98.1%) and prognostic monitoring (over 95.6%), even during double blind test. This surpasses the serum biomarker panel (≈90.7% for subtyping, ≈89.7% for prognosis) and urine biomarker panel (≈94.4% for subtyping, ≈76.5% for prognosis). Moreover, after attempting to further refine the marker panel, the blind test maintains equal sensitivity, specificity, and accuracy, showcasing a comprehensive improvement over the single-fluid approach. This underscores the remarkable effectiveness and superiority of the integrated strategy in discriminating between MN and other groups. This work has the potential to significantly advance diagnostic medicine, leading to the establishment of more effective strategies for patient management.
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Affiliation(s)
- Man Zhang
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Fangying Shi
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yijie Chen
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Chenyu Yang
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Xiangmin Zhang
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Chunhui Deng
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
- School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, 330031, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
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4
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Chen X, Wang Y, Pei C, Li R, Shu W, Qi Z, Zhao Y, Wang Y, Lin Y, Zhao L, Peng D, Wan J. Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312755. [PMID: 38692290 DOI: 10.1002/adma.202312755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/31/2024] [Indexed: 05/03/2024]
Abstract
Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.
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Affiliation(s)
- Xiaonan Chen
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Yun Wang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Congcong Pei
- School of Chemistry, Zhengzhou University, Zhengzhou, 450001, P. R. China
- Center of Advanced Analysis and Gene Sequencing, Zhengzhou University, Zhengzhou, 450001, P. R. China
| | - Rongxin Li
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Weikang Shu
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Ziheng Qi
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Yinbing Zhao
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Yanhui Wang
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Yingying Lin
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Liang Zhao
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Daihui Peng
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
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5
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Zhao M, Li Q, Zhao Y, Zhou H, Yan Y, Kong RM, Tan Q, Kong W, Qu F. Dual-Aptamer Recognition of DNA Logic Gate Sensor-Based Specific Exosomal Proteins for Ovarian Cancer Diagnosis. ACS Sens 2024; 9:2540-2549. [PMID: 38635557 DOI: 10.1021/acssensors.4c00270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Clinical diagnosis of ovarian cancer lacks high accuracy due to the weak selection of specific biomarkers along with the circumstance biomarkers localization. Clustering analysis of proteins transported on exosomes enables a more precise screening of effective biomarkers. Herein, through bioinformatics analysis of ovarian cancer and exosome proteomes, two coexpressed proteins, EpCAM and CD24, specifically enriched, were identified, together with the development of an as-derived dual-aptamer targeted exosome-based strategy for ovarian cancer screening. In brief, a DNA ternary polymer with aptamers targeting EpCAM and CD24 was designed to present a logic gate reaction upon recognizing ovarian cancer exosomes, triggering a rolling circle amplification chemiluminescent signal. A dynamic detection range of 6 orders of magnitude was achieved by quantifying exosomes. Moreover, for clinical samples, this strategy could accurately differentiate exosomes from healthy persons, other cancer patients, and ovarian cancer patients, enabling promising in situ detection. By accurately selecting biomarkers and constructing a dual-targeted exosomal protein detection strategy, the limitation of insufficient specificity of traditional protein markers was circumvented. This work contributed to the development of exosome-based prognosis monitoring in ovarian cancer through the identification of disease-specific exosome protein markers.
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Affiliation(s)
- Mingzhu Zhao
- Key Laboratory of Life-Organic Analysis of Shandong Province, School of Chemistry and Chemical Engineering, Qufu Normal University, Qufu 273165, Shandong, China
| | - Qin Li
- Department of Pathology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- School of Molecular Medicine, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, Zhejiang, China
| | - Yan Zhao
- Key Laboratory of Life-Organic Analysis of Shandong Province, School of Chemistry and Chemical Engineering, Qufu Normal University, Qufu 273165, Shandong, China
| | - Hanlin Zhou
- Key Laboratory of Life-Organic Analysis of Shandong Province, School of Chemistry and Chemical Engineering, Qufu Normal University, Qufu 273165, Shandong, China
| | - Yuntian Yan
- Key Laboratory of Life-Organic Analysis of Shandong Province, School of Chemistry and Chemical Engineering, Qufu Normal University, Qufu 273165, Shandong, China
| | - Rong-Mei Kong
- Key Laboratory of Life-Organic Analysis of Shandong Province, School of Chemistry and Chemical Engineering, Qufu Normal University, Qufu 273165, Shandong, China
| | - Qingqing Tan
- Department of Pathology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- School of Molecular Medicine, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, Zhejiang, China
| | - Weiheng Kong
- Key Laboratory of Life-Organic Analysis of Shandong Province, School of Chemistry and Chemical Engineering, Qufu Normal University, Qufu 273165, Shandong, China
| | - Fengli Qu
- Department of Pathology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- School of Molecular Medicine, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, Zhejiang, China
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6
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Zhang J, Teng F, Hu B, Liu W, Huang Y, Wu J, Wang Y, Su H, Yang S, Zhang L, Guo L, Lei Z, Yan M, Xu X, Wang R, Bao Q, Dong Q, Long J, Qian K. Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2311431. [PMID: 38241281 DOI: 10.1002/adma.202311431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/11/2024] [Indexed: 01/21/2024]
Abstract
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.
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Affiliation(s)
- Juxiang Zhang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Fei Teng
- Department of Gastrointestinal Surgery, Minhang Hospital, Fudan University, Shanghai, 201199, China
- Key Laboratory of Whole-Period Monitoring and Precise Intervention of Digestive Cancer, Shanghai Municipal Health Commission, Minhang Hospital, Fudan University, Shanghai, 201199, China
| | - Beiyuan Hu
- Department of Pancreatic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Wanshan Liu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yida Huang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Jiao Wu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yuning Wang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Haiyang Su
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Shouzhi Yang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Lumin Zhang
- Key Laboratory of Whole-Period Monitoring and Precise Intervention of Digestive Cancer, Shanghai Municipal Health Commission, Minhang Hospital, Fudan University, Shanghai, 201199, China
| | - Lingchuan Guo
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China
| | - Zhe Lei
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China
| | - Meng Yan
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China
| | - Xiaoyu Xu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Ruimin Wang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Qingui Bao
- Fosun Diagnostics (Shanghai) Co., Ltd, Shanghai, 200435, China
| | - Qiongzhu Dong
- Key Laboratory of Whole-Period Monitoring and Precise Intervention of Digestive Cancer, Shanghai Municipal Health Commission, Minhang Hospital, Fudan University, Shanghai, 201199, China
| | - Jiang Long
- Department of Pancreatic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Kun Qian
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
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7
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Li Z, Peng W, Zhou J, Shui S, Liu Y, Li T, Zhan X, Chen Y, Lan F, Ying B, Wu Y. Multidimensional Interactive Cascading Nanochips for Detection of Multiple Liver Diseases via Precise Metabolite Profiling. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312799. [PMID: 38263756 DOI: 10.1002/adma.202312799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/11/2024] [Indexed: 01/25/2024]
Abstract
It is challenging to detect and differentiate multiple diseases with high complexity/similarity from the same organ. Metabolic analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is a promising platform for disease diagnosis, while the enhanced property of its core nanomatrix materials has plenty of room for improvement. Herein, a multidimensional interactive cascade nanochip composed of iron oxide nanoparticles (FeNPs)/MXene/gold nanoparticles (AuNPs), IMG, is reported for serum metabolic profiling to achieve high-throughput detection of multiple liver diseases. MXene serves as a multi-binding site and an electron-hole source for ionization during NMALDI-MS analysis. Introduction of AuNPs with surface plasmon resonance (SPR) properties facilitates surface charge accumulation and rapid energy conversion. FeNPs are integrated into the MXene/Au nanocomposite to sharply reduce the thermal conductivity of the nanochip with negligible heat loss for strong thermally-driven desorption, and construct a multi-interaction proton transport pathway with MXene and AuNPs for strong ionization. Analysis of these enhanced serum fingerprint signals detected from the IMG nanochip through a neural network model results in differentiation of multiple liver diseases via a single pass and revelation of potential metabolic biomarkers. The promising method can rapidly and accurately screen various liver diseases, thus allowing timely treatment of liver diseases.
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Affiliation(s)
- Zhiyu Li
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Weili Peng
- Machine Intelligence Lab, College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Juan Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Shaoxuan Shui
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Yicheng Liu
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Tan Li
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Xiaohui Zhan
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Yuanyuan Chen
- Machine Intelligence Lab, College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Fang Lan
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Yao Wu
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
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8
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Yang C, Yu H, Li W, Lin H, Wu H, Deng C. High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry. Anal Chem 2024; 96:6264-6274. [PMID: 38600676 DOI: 10.1021/acs.analchem.3c05527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.
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Affiliation(s)
- Chenjie Yang
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Hailong Yu
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Weihong Li
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Hairu Lin
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Hao Wu
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chunhui Deng
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- School of Chemistry and Chemical Engineering, Nanchang University, Nanchang 330031, China
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9
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Deng Y, Zhou T, Hu K, Peng Y, Jia X, Yang J, Li G. An electrochemical biosensor designed with entropy-driven autocatalytic DNA circuits for sensitive detection of ovarian cancer-derived exosomes. Biosens Bioelectron 2024; 250:116060. [PMID: 38278121 DOI: 10.1016/j.bios.2024.116060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/13/2024] [Accepted: 01/22/2024] [Indexed: 01/28/2024]
Abstract
Intelligent artificial DNA circuits have emerged as a promising approach for modulating signaling pathways and signal transduction through rational design, which may contribute to comprehensively realizing biomolecular sensing of organisms. In this work, we have fabricated an electrochemical biosensor for the sensitive and accurate detection of ovarian cancer-derived exosomes by constructing an entropy-driven autocatalytic DNA circuit (EADC). Specifically, the robust EADC is prepared by the self-assembly of well-designed DNA probes, and upon stimulation of the presence of ovarian cancer cells-derived exosomes, numerous inputs can be produced to feedback and accelerate the reaction. The catalytic abilities of the generated input sequences play a pivotal role in EADC and dramatically enhance the signal amplification capability. Through the combination of the autocatalytic circuit and circular cleavage reactions, significantly changed electrochemical signals can be recorded for sensitive analysis of the exosomes with a remarkably low detection limit of 30 particles/μL. Moreover, the proposed enzyme-free biosensor shows exceptional performance in distinguishing patient samples from healthy samples, which exhibits promising prospects for the clinical diagnosis of ovarian cancer.
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Affiliation(s)
- Ying Deng
- State Key Laboratory of Analytical Chemistry for Life Science, School of Life Sciences, Nanjing University, Nanjing, 210023, PR China
| | - Tianci Zhou
- State Key Laboratory of Analytical Chemistry for Life Science, School of Life Sciences, Nanjing University, Nanjing, 210023, PR China
| | - Kai Hu
- Department of Gynecology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, 210004, PR China
| | - Ying Peng
- School of Biology and Engineering (School of Modern Industry for Health and Medicine), Guizhou Medical University, Guiyang, 550025, PR China
| | - Xuemei Jia
- Department of Gynecology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, 210004, PR China.
| | - Jie Yang
- State Key Laboratory of Analytical Chemistry for Life Science, School of Life Sciences, Nanjing University, Nanjing, 210023, PR China
| | - Genxi Li
- State Key Laboratory of Analytical Chemistry for Life Science, School of Life Sciences, Nanjing University, Nanjing, 210023, PR China; Center for Molecular Recognition and Biosensing, School of Life Sciences, Shanghai University, Shanghai, 200444, PR China.
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10
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Liu W, Ma J, Zhang J, Cao J, Hu X, Huang Y, Wang R, Wu J, Di W, Qian K, Yin X. Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis. EMBO Mol Med 2024; 16:988-1003. [PMID: 38355748 PMCID: PMC11018850 DOI: 10.1038/s44321-024-00033-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/16/2024] Open
Abstract
Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.
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Affiliation(s)
- Wanshan Liu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Jinglan Ma
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China
| | - Juxiang Zhang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Jing Cao
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Xiaoxiao Hu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China
| | - Yida Huang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Ruimin Wang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Jiao Wu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Wen Di
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China.
| | - Kun Qian
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China.
- School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China.
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
| | - Xia Yin
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai, 200127, P. R. China.
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11
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Shi F, Ning L, Sun N, Yao Q, Deng C. Multiscale Structured Trimetal Oxide Heterojunctions for Urinary Metabolic Phenotype-Dependent Screening of Early and Small Hepatocellular Carcinoma. SMALL METHODS 2024:e2301634. [PMID: 38517273 DOI: 10.1002/smtd.202301634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/31/2024] [Indexed: 03/23/2024]
Abstract
Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well-defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface-driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high-quality urinary metabolic fingerprints using high-throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high-risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision-recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small-tumor, alpha-fetoprotein-negative HCC (93.3%-97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting-edge matrices-based metabolic phenotyping in practical clinical diagnostics.
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Affiliation(s)
- Fangying Shi
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Liuxin Ning
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Department of Gastroenterology and Hepatology, Shanghai Geriatric Medical Center, Shanghai, 201104, China
- Shanghai Institute of Liver Diseases, Shanghai, 200032, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Qunyan Yao
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Department of Gastroenterology and Hepatology, Shanghai Geriatric Medical Center, Shanghai, 201104, China
- Shanghai Institute of Liver Diseases, Shanghai, 200032, China
| | - Chunhui Deng
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
- School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, 330031, China
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12
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Su H, Song Y, Yang S, Zhang Z, Shen Y, Yu L, Chen S, Gao L, Chen C, Hou D, Wei X, Ma X, Huang P, Sun D, Zhou J, Qian K. Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations. ACS CENTRAL SCIENCE 2024; 10:331-343. [PMID: 38435520 PMCID: PMC10906255 DOI: 10.1021/acscentsci.3c01201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/11/2023] [Accepted: 12/27/2023] [Indexed: 03/05/2024]
Abstract
Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 μL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques.
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Affiliation(s)
- Haiyang Su
- State
Key Laboratory of Systems Medicine for Cancer, School of Biomedical
Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
| | - Yuanlin Song
- Department
of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research
Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China
- Shanghai
Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai 200032, P. R. China
- Center
of Emergency and Critical Medicine, Jinshan
Hospital of Fudan University, Shanghai 201508, P. R. China
| | - Shouzhi Yang
- State
Key Laboratory of Systems Medicine for Cancer, School of Biomedical
Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
| | - Ziyue Zhang
- State
Key Laboratory of Systems Medicine for Cancer, School of Biomedical
Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
| | - Yao Shen
- Department
of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Fudan University, Shanghai 201399, P. R. China
| | - Lan Yu
- Clinical
Medical Research Center, Inner Mongolia
People’s Hospital, Hohhot 010017, Inner Mongolia, P. R. China
- Inner
Mongolia Key Laboratory of Gene Regulation of The Metabolic Disease, Inner Mongolia People’s Hospital, Hohhot 010017, Inner Mongolia, P.
R. China
- Inner
Mongolia Academy of Medical Sciences, Inner
Mongolia People’s Hospital, Hohhot 010017, Inner
Mongolia, P. R. China
| | - Shujing Chen
- Department
of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research
Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China
- Shanghai
Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai 200032, P. R. China
| | - Lei Gao
- Department
of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research
Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China
- Shanghai
Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai 200032, P. R. China
| | - Cuicui Chen
- Department
of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research
Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China
- Shanghai
Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai 200032, P. R. China
| | - Dongni Hou
- Department
of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research
Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China
- Shanghai
Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai 200032, P. R. China
| | - Xinping Wei
- Shanghai
Minhang District Gumei Community Health Center affiliated with Fudan
University, Shanghai 201102, P. R. China
| | - Xuedong Ma
- Shanghai
Minhang District Gumei Community Health Center affiliated with Fudan
University, Shanghai 201102, P. R. China
| | - Pengyu Huang
- Shanghai
Minhang District Gumei Community Health Center affiliated with Fudan
University, Shanghai 201102, P. R. China
| | - Dejun Sun
- Inner
Mongolia Key Laboratory of Gene Regulation of The Metabolic Disease, Inner Mongolia People’s Hospital, Hohhot 010017, Inner Mongolia, P.
R. China
- Department
of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot 010017, P. R. China
| | - Jian Zhou
- Department
of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research
Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China
- Shanghai
Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai 200032, P. R. China
- Center
of Emergency and Critical Medicine, Jinshan
Hospital of Fudan University, Shanghai 201508, P. R. China
| | - Kun Qian
- State
Key Laboratory of Systems Medicine for Cancer, School of Biomedical
Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
- Shanghai
Key Laboratory of Gynecologic Oncology, Renji Hospital, School of
Medicine, Shanghai Jiao Tong University, Shanghai 200127, P. R. China
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13
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Gao H, Chen J, Huang Y, Zhao R. Advances in targeted tracking and detection of soluble amyloid-β aggregates as a biomarker of Alzheimer's disease. Talanta 2024; 268:125311. [PMID: 37857110 DOI: 10.1016/j.talanta.2023.125311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/09/2023] [Accepted: 10/13/2023] [Indexed: 10/21/2023]
Abstract
Misfolding and aggregation of amyloid-β (Aβ) peptides are key hallmarks of Alzheimer's disease (AD). With accumulating evidence suggesting that different Aβ species have varied neurotoxicity and implications in AD development, the discovery of affinity ligands and analytical approaches to selective distinguish, detect, and monitor Aβ becomes an active research area. Remarkable advances have been achieved, which not only promote our understanding of the biophysical chemistry of the protein aggregation during neurodegeneration, but also provide promising tools for early detection of the disease. In view of this, we summarize the recent progress in selective and sensitive approaches for tracking and detection of Aβ species. Specific attentions are given to soluble Aβ oligomers, due to their crucial roles in AD development and occurrence at early stages. The design principle, performance of targeting units, and their cooperative effects with signal reporters for Aβ analysis are discussed. The applications of the novel targeting probes and sensing systems for dynamic monitoring oligomerization, measuring Aβ in biosamples and in vivo imaging in brain are summarized. Finally, the perspective and challenges are discussed regarding the future development of Aβ-targeting analytical tools to explore the unknown field to contribute to the early diagnosis and treatment of AD.
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Affiliation(s)
- Han Gao
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Analytical Chemistry for Living Biosystems, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jian Chen
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Analytical Chemistry for Living Biosystems, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yanyan Huang
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Analytical Chemistry for Living Biosystems, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Rui Zhao
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Analytical Chemistry for Living Biosystems, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
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14
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Wang Y, Li R, Shu W, Chen X, Lin Y, Wan J. Designed Nanomaterials-Assisted Proteomics and Metabolomics Analysis for In Vitro Diagnosis. SMALL METHODS 2024; 8:e2301192. [PMID: 37922520 DOI: 10.1002/smtd.202301192] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/12/2023] [Indexed: 11/05/2023]
Abstract
In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle-fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD.
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Affiliation(s)
- Yanhui Wang
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Rongxin Li
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Weikang Shu
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Xiaonan Chen
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Yingying Lin
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
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15
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Pei C, Su R, Lu S, Chen X, Ding Y, Li R, Shu W, Zeng Y, Lin Y, Xu L, Mi Y, Wan J. Hollow multishelled heterostructures with enhanced performance for laser desorption/ionization mass spectrometry based metabolic diagnosis. J Mater Chem B 2023; 11:8206-8215. [PMID: 37554072 DOI: 10.1039/d3tb00766a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
High-performance metabolic diagnosis-based laser desorption/ionization mass spectrometry (LDI-MS) improves the precision diagnosis of diseases and subsequent treatment. Inorganic matrices are promising for the detection of metabolites by LDI-MS, while the structure and component impacts of the matrices on the LDI process are still under investigation. Here, we designed a multiple-shelled ZnMn2O4/(Co, Mn)(Co, Mn)2O4 (ZMO/CMO) as the matrix from calcined MOF-on-MOF for detecting metabolites in LDI-MS and clarified the synergistic impacts of multiple-shells and the heterostructure on LDI efficiency. The ZMO/CMO heterostructure allowed 3-5 fold signal enhancement compared with ZMO and CMO with the same morphology. Furthermore, the ZMO/CMO heterostructure with a triple-shelled hollow structure displayed a 3-fold signal enhancement compared to its nanoparticle counterpart. Taken together, the triple-shelled hollow ZMO/CMO exhibits 102-fold signal enhancement compared to the commercial matrix products (e.g., DHB and DHAP), allowing for sensitive metabolic profiling in bio-detection. We directly extracted metabolic patterns by the optimized triple-shelled hollow ZMO/CMO particle-assisted LDI-MS within 1 s using 100 nL of serum and used machine learning as the readout to distinguish hepatocellular carcinoma from healthy controls with the area under the curve value of 0.984. Our approach guides us in matrix design for LDI-MS metabolic analysis and drives the development of a nanomaterial-based LDI-MS platform toward precision diagnosis.
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Affiliation(s)
- Congcong Pei
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
| | - Rui Su
- Tianjin Second People's Hospital, Tianjin Medical University, Tianjin 300192, China.
- Tianjin Institute of Hepatology, Tianjin 300192, China
| | - Songting Lu
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
| | - Xiaonan Chen
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
| | - Yajie Ding
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
| | - Rongxin Li
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
| | - Weikang Shu
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
| | - Yu Zeng
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
| | - Yingying Lin
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
| | - Liang Xu
- Tianjin Second People's Hospital, Tianjin Medical University, Tianjin 300192, China.
| | - Yuqiang Mi
- Tianjin Second People's Hospital, Tianjin Medical University, Tianjin 300192, China.
- Tianjin Institute of Hepatology, Tianjin 300192, China
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
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16
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Yang C, Pan Y, Yu H, Hu X, Li X, Deng C. Hollow Crystallization COF Capsuled MOF Hybrids Depict Serum Metabolic Profiling for Precise Early Diagnosis and Risk Stratification of Acute Coronary Syndrome. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302109. [PMID: 37340584 PMCID: PMC10460873 DOI: 10.1002/advs.202302109] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Indexed: 06/22/2023]
Abstract
Acute coronary syndrome (ACS), comprising unstable angina (UA) and acute myocardial infarction (AMI), is the leading cause of death worldwide. Currently, lacking effective strategies for classifying ACS hinders the prognosis improvement of ACS patients. Disclosing the nature of metabolic disorders holds the potential to reflect disease progress and high-throughput mass spectrometry-based metabolic analysis is a promising tool for large-scale screening. Herein, a hollow crystallization COF capsuled MOF hybrids (UiO-66@HCOF) assisted serum metabolic analysis is developed for the early diagnosis and risk stratification of ACS. UiO-66@HCOF exhibits unrivaled chemical and structural stability as well as endowing satisfying desorption/ionization efficiency in the detection of metabolites. Paired with machine learning algorithms, early diagnosis of ACS is achieved with the area under the curve (AUC) value of 0.945 for validation sets. Besides, a comprehensive ACS risk stratification method is established, and the AUC value for the discrimination of ACS from healthy controls, and AMI from UA are 0.890, and 0.928. Moreover, the AUC value of the subtyping of AMI is 0.964. Finally, the potential biomarkers exhibit high sensitivity and specificity. This study makes metabolic molecular diagnosis a reality and provided new insight into the progress of ACS.
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Affiliation(s)
- Chenjie Yang
- Department of ChemistryFudan UniversityShanghai200433China
| | - Yilong Pan
- Department of CardiologyShengjing Hospital of China Medical UniversityNO.36 Sanhao Street, Heping DistrictShenyang110004China
| | - Hailong Yu
- Department of ChemistryFudan UniversityShanghai200433China
| | - Xufang Hu
- School of Chemical Science and TechnologyYunnan UniversityNo. 2 North Cuihu RoadKunming650091P. R. China
| | - Xiaodong Li
- Department of CardiologyShengjing Hospital of China Medical UniversityNO.36 Sanhao Street, Heping DistrictShenyang110004China
| | - Chunhui Deng
- Department of ChemistryFudan UniversityShanghai200433China
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17
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Shi F, Qi Y, Jiang S, Sun N, Deng C. Hollow Core-Shell Metal Oxide Heterojunctions for the Urinary Metabolic Fingerprint-Based Noninvasive Diagnostic Strategy. Anal Chem 2023; 95:7312-7319. [PMID: 37121232 DOI: 10.1021/acs.analchem.3c00369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Urine is a preferred object for noninvasive diagnostic strategies. Urinary metabolic analysis is speculatively regarded as an ideal tool for screening diseases closely related to the genitourinary system in view of the intimate relationship between metabolomics and phenotype. Herein, we propose a urinary metabolic fingerprint-based noninvasive diagnostic strategy by designing hollow core-shell metal oxide heterojunctions (denoted as MOHs). With outstanding light absorption and electron-hole separation ability, MOHs aid in the extraction of high-performance urine metabolic fingerprints. Coupled with optimized machine learning algorithms, we establish a metabolic marker panel for accurate diagnosis of prostate cancer (PCa), which is the most common malignant tumor of the male genitourinary system, achieving accuracies of 84.72 and 83.33% in the discovery and validation sets, respectively. Furthermore, metabolite variations and related pathway analyses confirm the credibility and change correlation of key metabolic features in PCa. This work tends to advance the noninvasive diagnostic strategy toward clinical realities.
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Affiliation(s)
- Fangying Shi
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Yu Qi
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Shuai Jiang
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Department of Urology, Zhongshan Hospital Wusong Branch, Fudan University, Shanghai 200940, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chunhui Deng
- Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China
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