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Yang C, Zhou D, Yu H, Chen Y, Lin H, Wu H, Deng C. Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease. Anal Chem 2024; 96:10841-10850. [PMID: 38889297 DOI: 10.1021/acs.analchem.4c02369] [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: 06/20/2024]
Abstract
Nonalcoholic fatty liver disease (NAFLD), along with its progressive forms nonalcoholic steatohepatitis (NASH) and NASH fibrosis, has emerged as a global health crisis. However, the absence of robust screening and risk evaluation tools contributes to the underdiagnosis of NAFLD. Herein, we reported a multichannel nanogenerator-assisted laser desorption/ionization mass spectrometry (LDI-MS) platform for early screening and risk evaluation of NAFLD. Specifically, titanium oxide nanosheets (TiNS) and covalent-organic framework nanosheets (COFNS) were employed as nanogenerators with excellent optical properties and exhibited efficient desorption/ionization during the LDI-MS process. Only ∼0.025 μL of serum without pretreatments and separation, serum metabolic fingerprints (SMFs) can be extracted within seconds. Notably, integrated SMFs from TiNS and COFNS significantly improved diagnostic performance and achieved the area under the curve (AUC) values of 1.000 with 100% sensitivity and 100% specificity for the validation sets of global diagnosis, early diagnosis, high-risk NASH, and NASH fibrosis evaluation. Additionally, four biomarker panels were identified, and their diagnostic AUC values were more than 0.944. Ultimately, key metabolic pathways indicating the change from simple NAFLD to high-risk NASH and NASH fibrosis were uncovered. This work provided a noninvasive and high-throughput screening and risk evaluation strategy for NAFLD healthcare management, thus contributing to the precise treatment of the NALFD.
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Affiliation(s)
- Chenjie Yang
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Da Zhou
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Hailong Yu
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Yijie Chen
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Hairu Lin
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Hao Wu
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chunhui Deng
- Department of Chemistry, Fudan University, Shanghai 200433, China
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Department of Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
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2
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Qu Y, Chen M, Wang Y, Qu L, Wang R, Liu H, Wang L, Nie Z. Rapid screening of infertility-associated gynecological conditions via ambient glow discharge mass spectrometry utilizing urine metabolic fingerprints. Talanta 2024; 274:125969. [PMID: 38608629 DOI: 10.1016/j.talanta.2024.125969] [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: 12/04/2023] [Revised: 02/29/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024]
Abstract
Infertility presents a widespread challenge for many families worldwide, often arising from various gynecological diseases (GDs) that hinder successful pregnancies. Current diagnostic methods for GDs have disadvantages such as low efficiency, high cost, misdiagnose, invasive injury and etc. This paper introduces a rapid, non-invasive, efficient, and straightforward analytical method that utilizes desorption, separation, and ionization mass spectrometry (DSI-MS) platform in conjunction with machine learning (ML) to detect urine metabolite fingerprints in patients with different GDs. We analyzed 257 samples from patients diagnosed with polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), endometriosis (EMS), recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), and 87 samples from healthy control (HC) individuals. We identified metabolite differences and dysregulated pathways through dimensionality reduction methods, with the result of the discovery of 7 potential biomarkers for GDs diagnosis. The ML method effectively distinguished subtle differences in urine metabolite fingerprints. We anticipate that this innovative approach will offer a patient-friendly, rapid screening, and differentiation method for infertility-related GDs patients.
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Affiliation(s)
- Yijiao Qu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Ming Chen
- Centre of Reproductive Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518000, China; Department of Gynecology and Obstetrics, Guangxi University of Chinese Medicine, Nanning, 530200, China
| | - Yiran Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Liangliang Qu
- School of Life Sciences, Nanchang University, Nanchang, 330031, China
| | - Ruiyue Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Huihui Liu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Liping Wang
- Centre of Reproductive Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518000, China.
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China.
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3
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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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4
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Liu Y, Wang Y, Wan X, Huang H, Shen J, Wu B, Zhu L, Wu B, Liu W, Huang L, Qian K, Ma J. Ferric particle-assisted LDI-MS platform for metabolic fingerprinting of diabetic retinopathy. Clin Chem Lab Med 2024; 62:988-998. [PMID: 38018477 DOI: 10.1515/cclm-2023-0775] [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: 07/23/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023]
Abstract
OBJECTIVES To explore the metabolic fingerprints of diabetic retinopathy (DR) in individuals with type 2 diabetes using a newly-developed laser desorption/ionization mass spectrometry (LDI-MS) platform assisted by ferric particles. METHODS Metabolic fingerprinting was performed using a ferric particle-assisted LDI-MS platform. A nested population-based case-control study was performed on 216 DR cases and 216 control individuals with type 2 diabetes. RESULTS DR cases and control individuals with type 2 diabetes were comparable for a list of clinical factors. The newly-developed LDI-MS platform allowed us to draw the blueprint of plasma metabolic fingerprints from participants with and without DR. The neural network afforded diagnostic performance with an average area under curve value of 0.928 for discovery cohort and 0.905 for validation cohort (95 % confidence interval: 0.902-0.954 and 0.845-0.965, respectively). Tandem MS and Fourier transform ion cyclotron resonance MS with ultrahigh resolution identified seven specific metabolites that were significantly associated with DR in fully adjusted models. Of these metabolites, dihydrobiopterin, phosphoserine, N-arachidonoylglycine, and 3-methylhistamine levels in plasma were first reported to show the associations. CONCLUSIONS This work advances the design of metabolic analysis for DR and holds the potential to promise as an efficient tool for clinical management of DR.
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Affiliation(s)
- Yu Liu
- Department of Endocrinology and Metabolism, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Yihan Wang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiaotong University, Shanghai, P.R. China
| | - Xu Wan
- Department of Pharmacy, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Hongtao Huang
- School of Biomedical Engineering, Institute of Medical Robotics and Med X Research Institute, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Jie Shen
- Department of Ophthalmology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Bin Wu
- Department of Pharmacy, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Lina Zhu
- Department of Ophthalmology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Beirui Wu
- Department of Nursing, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Wei Liu
- Department of Endocrinology and Metabolism, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Lin Huang
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Kun Qian
- School of Biomedical Engineering, Institute of Medical Robotics and Med X Research Institute, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Jing Ma
- Department of Endocrinology and Metabolism, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
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5
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Jia K, Wang Y, Jiang L, Lai M, Liu W, Wang L, Liu H, Cao X, Li Y, Nie Z. Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO 3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2024; 16:12302-12309. [PMID: 38414269 DOI: 10.1021/acsami.3c19007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.
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Affiliation(s)
- Ke Jia
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Chemistry and Chemical Engineering, Jiangxi Province Engineering Research Center of Ecological Chemical Industry, Jiujiang University, Jiujiang, Jiangxi 332005, China
| | - Lixia Jiang
- Gannan Medical University, Ganzhou 341000, China
| | - Mi Lai
- Gannan Medical University, Ganzhou 341000, China
| | - Wenlan Liu
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Liping Wang
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Huihui Liu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohua Cao
- School of Chemistry and Chemical Engineering, Jiangxi Province Engineering Research Center of Ecological Chemical Industry, Jiujiang University, Jiujiang, Jiangxi 332005, China
| | - Yuze Li
- State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering, College of Chemistry and Chemical Engineering, Ningxia University, Yinchuan 750021, China
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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6
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Shan L, Qiao Y, Ma L, Zhang X, Chen C, Xu X, Li D, Qiu S, Xue X, Yu Y, Guo Y, Qian K, Wang J. AuNPs/CNC Nanocomposite with A "Dual Dispersion" Effect for LDI-TOF MS Analysis of Intact Proteins in NSCLC Serum Exosomes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307360. [PMID: 38224220 DOI: 10.1002/advs.202307360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/07/2023] [Indexed: 01/16/2024]
Abstract
Detecting exosomal markers using laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS) is a novel approach for examining liquid biopsies of non-small cell lung cancer (NSCLC) samples. However, LDI-TOF MS is limited by low sensitivity and poor reproducibility when analyzing intact proteins directly. In this report, gold nanoparticles/cellulose nanocrystals (AuNPs/CNC) is introduced as the matrix for direct analysis of intact proteins in NSCLC serum exosomes. AuNPs/CNC with "dual dispersion" effects dispersed and stabilized AuNPs and improved ion inhibition effects caused by protein aggregation. These features increased the signal-to-noise ratio of [M+H]+ peaks by two orders of magnitude and lowered the detection limit of intact proteins to 0.01 mg mL-1. The coefficient of variation with or without AuNPs/CNC is measured as 10.2% and 32.5%, respectively. The excellent reproducibility yielded a linear relationship (y = 15.41x - 7.983, R2 = 0.989) over the protein concentration range of 0.01 to 20 mg mL-1. Finally, AuNPs/CNC-assisted LDI-TOF MS provides clinically relevant fingerprint information of exosomal proteins in NSCLC serum, and characteristic proteins S100 calcium-binding protein A10, Urokinase plasminogen activator surface receptor, Plasma protease C1 inhibitor, Tyrosine-protein kinase Fgr and Mannose-binding lectin associated serine protease 2 represented excellent predictive biomarkers of NSCLC risk.
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Affiliation(s)
- Liang Shan
- Department of Clinical Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Yongxia Qiao
- School of Public Health, Shanghai Jiao Tong University School of Medicine, No. 227, South Chongqing Road, Shanghai, 200025, P. R. China
| | - Lifang Ma
- Department of Clinical Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Xiao Zhang
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Changqiang Chen
- Department of Clinical Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Xin Xu
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Dan Li
- Department of Clinical Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Shiyu Qiu
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Xiangfei Xue
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Yongchun Yu
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
| | - Yinlong Guo
- National Center for Organic Mass Spectrometry in Shanghai, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, No. 345, Lingling Road, Shanghai, 200032, P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, No. 1954, Huashan Road, Shanghai, 200030, P. R. China
| | - Jiayi Wang
- Department of Clinical Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, No. 241, West Huaihai Road, Shanghai, 200030, P. R. China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, No. 227, South Chongqing Road, Shanghai, 200025, P. R. China
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7
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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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8
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Peng Z, Wang Y, Wu X, Yang S, Du X, Xu X, Hu C, Liu W, Zhu Y, Dong B, Pan J, Bao Q, Qian K, Dong L, Xue W. Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint-Based Multi-Modal Recognition. SMALL METHODS 2024:e2301684. [PMID: 38258603 DOI: 10.1002/smtd.202301684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/09/2024] [Indexed: 01/24/2024]
Abstract
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.
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Affiliation(s)
- Zehong Peng
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Yuning Wang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Xinrui Wu
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Shouzhi Yang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Xinxing Du
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Xiaoyu Xu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Cong Hu
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Wanshan Liu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Yinjie Zhu
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Baijun Dong
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Jiahua Pan
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Qingui Bao
- Fosun Diagnostics (Shanghai) Co., Ltd., No. 830, Chengyin Road, Baoshan, Shanghai, 200435, P. R. China
| | - Kun Qian
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Liang Dong
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Wei Xue
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
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9
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Huang Y, Yang H, Li J, Wang F, Liu W, Liu Y, Wang R, Duan L, Wu J, Gao Z, Cao J, Bian F, Zhang J, Zhao F, Yang S, Cao S, Yang A, Wang X, Geng M, Hao A, Li J, Cao J, Li C, Zhang Z, Zhang N, Huang Y, Zhang Y, Qian K, Zhou F. Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study. SMALL METHODS 2024; 8:e2301046. [PMID: 37803160 DOI: 10.1002/smtd.202301046] [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: 08/09/2023] [Revised: 09/22/2023] [Indexed: 10/08/2023]
Abstract
Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.
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Affiliation(s)
- Yida Huang
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
- 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
- Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Haijun Yang
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Junkuo Li
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Fuqiang Wang
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Wanshan Liu
- 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
- Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Yiwen Liu
- The First Affiliated Hospital, Henan Key Laboratory of Cancer Epigenetics, Henan University of Science and Technology, Luoyang, 471003, P. R. China
| | - Ruimin Wang
- 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
- Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Lijuan Duan
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Jiao Wu
- 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
- Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Zhaowei Gao
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Jing Cao
- 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
- Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Fang Bian
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Juxiang 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
- Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Fang Zhao
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, 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
- Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Shasha Cao
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Aihua Yang
- Department of Laboratory Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, 200433, P. R. China
| | - Xueliang Wang
- Shanghai Center for Clinical Laboratory, Shanghai Academy of Experimental Medicine, Shanghai, 200126, P. R. China
| | - Mingfei Geng
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Anlin Hao
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Jian Li
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Jianwei Cao
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Chaowei Li
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Zheyuan Zhang
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Ning Zhang
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Yanlin Huang
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
| | - Yaowen Zhang
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, 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
- Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Fuyou Zhou
- Anyang Tumor Hospital, Anyang Tumor Hospital affiliated to Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang, 455001, P. R. China
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10
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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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