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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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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:e2312755. [PMID: 38692290 DOI: 10.1002/adma.202312755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [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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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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Zhou LL, Guan Q, Dong YB. Covalent Organic Frameworks: Opportunities for Rational Materials Design in Cancer Therapy. Angew Chem Int Ed Engl 2024; 63:e202314763. [PMID: 37983842 DOI: 10.1002/anie.202314763] [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: 10/02/2023] [Revised: 11/18/2023] [Accepted: 11/20/2023] [Indexed: 11/22/2023]
Abstract
Nanomedicines are extensively used in cancer therapy. Covalent organic frameworks (COFs) are crystalline organic porous materials with several benefits for cancer therapy, including porosity, design flexibility, functionalizability, and biocompatibility. This review examines the use of COFs in cancer therapy from the perspective of reticular chemistry and function-oriented materials design. First, the modification sites and functionalization methods of COFs are discussed, followed by their potential as multifunctional nanoplatforms for tumor targeting, imaging, and therapy by integrating functional components. Finally, some challenges in the clinical translation of COFs are presented with the hope of promoting the development of COF-based anticancer nanomedicines and bringing COFs closer to clinical trials.
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Affiliation(s)
- Le-Le Zhou
- College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Normal University, Jinan, 250014, China
| | - Qun Guan
- College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Normal University, Jinan, 250014, China
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau Taipa, Macau SAR, 999078, China
| | - Yu-Bin Dong
- College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Normal University, Jinan, 250014, China
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Li S, Ding H, Qi Z, Yang J, Huang J, Huang L, Zhang M, Tang Y, Shen N, Qian K, Guo Q, Wan J. Serum Metabolic Fingerprints Characterize Systemic Lupus Erythematosus. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304610. [PMID: 37953381 PMCID: PMC10787061 DOI: 10.1002/advs.202304610] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/01/2023] [Indexed: 11/14/2023]
Abstract
Metabolic fingerprints in serum characterize diverse diseases for diagnostics and biomarker discovery. The identification of systemic lupus erythematosus (SLE) by serum metabolic fingerprints (SMFs) will facilitate precision medicine in SLE in an early and designed manner. Here, a discovery cohort of 731 individuals including 357 SLE patients and 374 healthy controls (HCs), and a validation cohort of 184 individuals (SLE/HC, 91/93) are constructed. Each SMF is directly recorded by nano-assisted laser desorption/ionization mass spectrometry (LDI MS) within 1 minute using 1 µL of native serum, which contains 908 mass to charge features. Sparse learning of SMFs achieves the SLE identification with sensitivity/specificity and area-under-the-curve (AUC) up to 86.0%/92.0% and 0.950 for the discovery cohort. For the independent validation cohort, it exhibits no performance loss by affording the sensitivity/specificity and AUC of 89.0%/100.0% and 0.992. Notably, a metabolic biomarker panel is screened out from the SMFs, demonstrating the unique metabolic pattern of SLE patients different from both HCs and rheumatoid arthritis patients. In conclusion, SMFs characterize SLE by revealing its unique metabolic pattern. Different regulation of small molecule metabolites contributes to the precise diagnosis of autoimmune disease and further exploration of the pathogenic mechanisms.
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Affiliation(s)
- Shunxiang Li
- School of Biomedical Engineering, and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, and Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Huihua Ding
- Department of Rheumatology, and Shanghai Institute of Rheumatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Ziheng Qi
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Jing Yang
- School of Biomedical Engineering, and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, and Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Jingyi Huang
- School of Biomedical Engineering, and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Lin Huang
- Shanghai Institute of Thoracic Tumors, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Mengji Zhang
- School of Biomedical Engineering, and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, and Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Yuanjia Tang
- Department of Rheumatology, and Shanghai Institute of Rheumatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Nan Shen
- Department of Rheumatology, and Shanghai Institute of Rheumatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Kun Qian
- School of Biomedical Engineering, and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, and Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Qiang Guo
- Department of Rheumatology, and Shanghai Institute of Rheumatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
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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: 0] [Impact Index Per Article: 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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Ouyang D, Wang C, Zhong C, Lin J, Xu G, Wang G, Lin Z. Organic metal chalcogenide-assisted metabolic molecular diagnosis of central precocious puberty. Chem Sci 2023; 15:278-284. [PMID: 38131069 PMCID: PMC10732007 DOI: 10.1039/d3sc05633c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 11/25/2023] [Indexed: 12/23/2023] Open
Abstract
Metabolic analysis in biofluids based on laser desorption/ionization mass spectrometry (LDI-MS), featuring rapidity, simplicity, small sample volume and high throughput, is expected to be a powerful diagnostic tool. Nevertheless, the signals of most metabolic biomarkers obtained by matrix-assisted LDI-MS are too limited to achieve a highly accurate diagnosis due to serious background interference. To address this issue, nanomaterials have been frequently adopted in LDI-MS as substrates. However, the "trial and error" approach still dominates the development of new substrates. Therefore, rational design of novel LDI-MS substrates showing high desorption/ionization efficiency and no background interference is extremely desired. Herein, four few-layered organic metal chalcogenides (OMCs) were precisely designed and for the first time investigated as substrates in LDI-MS, which allowed a favorable internal energy and charge transfer by changing the functional groups of organic ligands and metal nodes. As a result, the optimized OMC-assisted platform satisfyingly enhanced the mass signal by ≈10 000 fold in detecting typical metabolites and successfully detected different saccharides. In addition, a high accuracy diagnosis of central precocious puberty (CPP) with potential biomarkers of 12 metabolites was realized. This work is not only expected to provide a universal detection tool for large-scale clinical diagnosis, but also provides an idea for the design and selection of LDI-MS substrates.
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Affiliation(s)
- Dan Ouyang
- Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University Fuzhou Fujian 350108 China
| | - Chuanzhe Wang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences (CAS) Fuzhou Fujian 350002 China
| | - Chao Zhong
- Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University Fuzhou Fujian 350108 China
| | - Juan Lin
- Department of Cardiology, Fujian Provincial Governmental Hospital Fuzhou 350003 China
| | - Gang Xu
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences (CAS) Fuzhou Fujian 350002 China
| | - Guane Wang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences (CAS) Fuzhou Fujian 350002 China
| | - Zian Lin
- Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University Fuzhou Fujian 350108 China
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