1
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Zou Z, Peng Z, Bhusal D, Wije Munige S, Yang Z. MassLite: An integrated python platform for single cell mass spectrometry metabolomics data pretreatment with graphical user interface and advanced peak alignment method. Anal Chim Acta 2024; 1325:343124. [PMID: 39244309 DOI: 10.1016/j.aca.2024.343124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 08/07/2024] [Accepted: 08/18/2024] [Indexed: 09/09/2024]
Abstract
Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency.
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Affiliation(s)
- Zhu Zou
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA
| | - Zongkai Peng
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA
| | - Deepti Bhusal
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA
| | - Shakya Wije Munige
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA
| | - Zhibo Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
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2
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Wang Y, Wan L, Li Y, Qu Y, Qu L, Ma X, Yu Y, Wang X, Nie Z. Profiling of carbonyl metabolic fingerprints in urine of Graves' disease patients based on atmospheric ionization mass spectrometry. Talanta 2024; 277:126329. [PMID: 38815320 DOI: 10.1016/j.talanta.2024.126329] [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: 03/26/2024] [Revised: 05/22/2024] [Accepted: 05/25/2024] [Indexed: 06/01/2024]
Abstract
Graves' disease (GD) is considered among the organ autoimmune diseases and is somewhat linked to other autoimmune and secondary diseases. Commonly used detection methods rely on identifying characteristic clinical features and abnormal biochemical markers, but they have certain limitations and may be affected by patient medication. In this study, a desorption separation ionization (DSI) device coupled with a linear ion trap mass spectrometer is introduced for effective detection and screening of urine from GD patients. To enhance the sensitivity of MS analysis, derivatization reagent is utilized as a labeling method. The MS signal is used for metabolic profiling, through which differential metabolites and pathways are identified. Subsequently, processing the acquired spectra with a machine learning algorithm enables successful differentiation of GD patients and healthy individuals. This method is believed to provide versatile and powerful technical support for effective detection on the scene. Notably, this method offers the advantage of achieving early and rapid diagnosis of thyroid-related diseases.
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Affiliation(s)
- 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, 100049, China
| | - Li Wan
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 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
| | - 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, 100049, China
| | - Liangliang Qu
- School of Life Sciences, Nanchang University, Nanchang, 330031, China
| | - Xiaobing Ma
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
| | - Yang Yu
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoxia Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 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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3
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Chen J, Yu X, Qu Y, Wang X, Wang Y, Jia K, Du Q, Han J, Liu H, Zhang X, Wang X, Nie Z. High-Performance Metabolic Profiling of High-Risk Thyroid Nodules by ZrMOF Hybrids. ACS NANO 2024. [PMID: 39090798 DOI: 10.1021/acsnano.4c05700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Thyroid nodules (TNs) have emerged as the most prevalent endocrine disorder in China. Fine-needle aspiration (FNA) remains the standard diagnostic method for assessing TN malignancy, although a majority of FNA results indicate benign conditions. Balancing diagnostic accuracy while mitigating overdiagnosis in patients with benign nodules poses a significant clinical challenge. Precise, noninvasive, and high-throughput screening methods for high-risk TN diagnosis are highly desired but remain less explored. Developing such approaches can improve the accuracy of noninvasive methods like ultrasound imaging and reduce overdiagnosis of benign nodule patients caused by invasive procedures. Herein, we investigate the application of gold-doped zirconium-based metal-organic framework (ZrMOF/Au) nanostructures for metabolic profiling of thyroid diseases. This approach enables the efficient extraction of urine metabolite fingerprints with high throughput, low background noise, and reproducibility. Utilizing partial least-squares discriminant analysis and four machine learning models, including neural network (NN), random forest (RF), logistic regression (LR), and support vector machine (SVM), we achieved an enhanced diagnostic accuracy (98.6%) for discriminating thyroid cancer (TC) from low-risk TNs by using a diagnostic panel. Through the analysis of metabolic differences, potential pathway changes between benign nodule and malignancy are identified. This work explores the potential of rapid thyroid disease screening using the ZrMOF/Au-assisted LDI-MS platform, providing a potential method for noninvasive screening of thyroid malignant tumors. Integrating this approach with imaging technologies such as ultrasound can enhance the reliability of noninvasive diagnostic methods for malignant tumor screening, helping to prevent unnecessary invasive procedures and reducing the risk of overdiagnosis and overtreatment in patients with benign nodules.
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Affiliation(s)
- Junyu Chen
- Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Xi Yu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - 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
| | - Xiao 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
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan 250000, 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
| | - 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
| | - Qiuyao Du
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Jing Han
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, 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
| | - Xiaoyong Zhang
- Department of Chemistry, Nanchang University, 999 Xuefu Avenue, Nanchang 330031, China
| | - Xiaozhong Wang
- Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, 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
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Hachem M, Ahmmed MK, Nacir-Delord H. Phospholipidomics in Clinical Trials for Brain Disorders: Advancing our Understanding and Therapeutic Potentials. Mol Neurobiol 2024; 61:3272-3295. [PMID: 37981628 PMCID: PMC11087356 DOI: 10.1007/s12035-023-03793-y] [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: 05/19/2023] [Accepted: 10/31/2023] [Indexed: 11/21/2023]
Abstract
Phospholipidomics is a specialized branch of lipidomics that focuses on the characterization and quantification of phospholipids. By using sensitive analytical techniques, phospholipidomics enables researchers to better understand the metabolism and activities of phospholipids in brain disorders such as Alzheimer's and Parkinson's diseases. In the brain, identifying specific phospholipid biomarkers can offer valuable insights into the underlying molecular features and biochemistry of these diseases through a variety of sensitive analytical techniques. Phospholipidomics has emerged as a promising tool in clinical studies, with immense potential to advance our knowledge of neurological diseases and enhance diagnosis and treatment options for patients. In the present review paper, we discussed numerous applications of phospholipidomics tools in clinical studies, with a particular focus on the neurological field. By exploring phospholipids' functions in neurological diseases and the potential of phospholipidomics in clinical research, we provided valuable insights that could aid researchers and clinicians in harnessing the full prospective of this innovative practice and improve patient outcomes by providing more potent treatments for neurological diseases.
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Affiliation(s)
- Mayssa Hachem
- Department of Chemistry and Healthcare Engineering Innovation Center, Khalifa University of Sciences and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
| | - Mirja Kaizer Ahmmed
- Department of Fishing and Post-Harvest Technology, Chattogram Veterinary and Animal Sciences University, Chattogram, Bangladesh
- Riddet Institute, Massey University, Palmerston North, New Zealand
| | - Houda Nacir-Delord
- Department of Chemistry, Khalifa University of Sciences and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
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Luo Y, Ma S, Zhang J, Zhang Q, Zhang Y, Mao J, Yuan H, Ouyang G, Zhang S, Zhao W. Developing a novel strategy for fabricating matrix film to assess the distribution of potassium perfluorooctanic sulfonate by matrix-assisted laser desorption/ionization mass spectrometry imaging. Anal Chim Acta 2024; 1303:342528. [PMID: 38609267 DOI: 10.1016/j.aca.2024.342528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 03/03/2024] [Accepted: 03/24/2024] [Indexed: 04/14/2024]
Abstract
Matrix deposition plays a critical role in image quality of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). To improve the ionization efficiency and overcome the limitation of traditional matrix deposition methods in the face of difficult-to-sublimate or difficult-to-dissolve matrix, covalent organic frameworks (COFs) named COF-DhaTab was successfully synthesized and firstly used as matrix film. It was fabricated by imprinting of sieved COF-DhaTab powder on the surface of a double-sided adhesive tape. Outstanding reproducibility and uniformity of COF-DhaTab film were demonstrated by relative standard deviation (RSD) within 8.37% and 7.71% from dot-to-dot and plate-to-plate, respectively. With the introduction of double-sided adhesive tape, water contact angle (WCA) of COF-DhaTab film increased from 55° to 141°, resulting in significant suppression of analyte diffusion. Moreover, the intensity of potassium perfluorooctanic sulfonate (PFOS, C8F17SO3-, m/z 498.93) was 9.3 × 105, more than six hundred times higher than that using DHB matrix. This enhancement was attributed to the rough surface and multiple branches of the synthesized COF-DhaTab. To verify the ability of COF-DhaTab film as substrate, the spatial distribution of PFOS in zebrafish, rat liver and kidney tissues was explored. Superior imaging capability was displayed with high-spatial resolution and reliable location distribution. These results not only demonstrate the outstanding ability of COF-DhaTab as matrix for MALDI-MS and MALDI-MSI, but also provide a facile approach for fabrication of novel matrix films for MALDI-MSI.
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Affiliation(s)
- Yake Luo
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China
| | - Shanshan Ma
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China
| | - Jianxun Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; The Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China
| | - Qidong Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; The Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China
| | - Yanhao Zhang
- College of Ecology and Environment, Zhengzhou University, Zhengzhou, 450001, China.
| | - Jian Mao
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; The Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China
| | - Hang Yuan
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; College of Ecology and Environment, Zhengzhou University, Zhengzhou, 450001, China
| | - Gangfeng Ouyang
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Shusheng Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; College of Ecology and Environment, Zhengzhou University, Zhengzhou, 450001, China
| | - Wuduo Zhao
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; College of Ecology and Environment, Zhengzhou University, Zhengzhou, 450001, China.
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6
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Chen S, Pu K, Wang Y, Su Y, Qiu J, Wang X, Guo K, Hu J, Wei H, Wang H, Wei X, Chen Y, Lin W, Ni W, Lin Y, Chen J, Lai SKM, Ng KM. Hierarchical superstructure aerogels for in situ biofluid metabolomics. NANOSCALE 2024; 16:8607-8617. [PMID: 38602354 DOI: 10.1039/d3nr05895f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.
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Affiliation(s)
- Siyu Chen
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, China.
| | - Keyuan Pu
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, China.
| | - Yue Wang
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, China.
| | - Yang Su
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, China.
| | - Jiamin Qiu
- Department of Biology, Shantou University, Shantou, Guangdong, 515063, China
| | - Xin Wang
- The Cancer Hospital of Shantou University Medical College, Guangdong, 515031, China.
| | - Kunbin Guo
- The Cancer Hospital of Shantou University Medical College, Guangdong, 515031, China.
| | - Jun Hu
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, China.
| | - Huiwen Wei
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, China.
| | - Hongbiao Wang
- The Cancer Hospital of Shantou University Medical College, Guangdong, 515031, China.
| | - Xiaolong Wei
- The Cancer Hospital of Shantou University Medical College, Guangdong, 515031, China.
| | - Yuping Chen
- The Cancer Hospital of Shantou University Medical College, Guangdong, 515031, China.
| | - Wen Lin
- The Cancer Hospital of Shantou University Medical College, Guangdong, 515031, China.
| | - Wenxiu Ni
- Department of Medicinal Chemistry, Shantou University Medical College, Guangdong, 515041, China
- Chemistry and Chemical Engineering Guangdong Laboratory, Guangdong, 515063, China
| | - Yan Lin
- The Second Affiliated Hospital of Shantou University Medical College, Guangdong, 515041, China
| | - Jiayang Chen
- Instrumental Analysis & Testing Centre, Shantou University, Guangdong, 515063, China
| | - Samuel Kin-Man Lai
- Laboratory for Synthetic Chemistry and Chemical Biology Limited, Units 1503-1511, 15/F., Building 17 W, Hong Kong Science Park, New Territories, Hong Kong, China
| | - Kwan-Ming Ng
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, China.
- Chemistry and Chemical Engineering Guangdong Laboratory, Guangdong, 515063, China
- Laboratory for Synthetic Chemistry and Chemical Biology Limited, Units 1503-1511, 15/F., Building 17 W, Hong Kong Science Park, New Territories, Hong Kong, China
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Su H, Zhang H, Wu J, Huang L, Zhang M, Xu W, Cao J, Liu W, Liu N, Jiang H, Gu X, Qian K. Fast Label-Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305701. [PMID: 38348590 PMCID: PMC11022714 DOI: 10.1002/advs.202305701] [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: 08/15/2023] [Revised: 01/25/2024] [Indexed: 04/18/2024]
Abstract
Phenylketonuria (PKU) is the most common inherited metabolic disease in humans. Clinical screening of newborn heel blood samples for PKU is costly and time-consuming because it requires multiple procedures, like isotope labeling and derivatization, and PKU subtype identification requires an additional urine sample. Delayed diagnosis of PKU, or subtype identification can result in mental disability. Here, plasmonic silver nanoshells are used for laser desorption/ionization mass spectrometry (MS) detection of PKU with label-free assay by recognizing metabolic profile in dried blood spot (DBS) samples. A total of 1100 subjects are recruited and each DBS sample can be processed in seconds. This platform achieves PKU screening with a sensitivity of 0.985 and specificity of 0.995, which is comparable to existing clinical liquid chromatography MS (LC-MS) methods. This method can process 360 samples per hour, compared with the LC-MS method which processes only 30 samples per hour. Moreover, this assay enables precise identification of PKU subtypes without the need for a urine sample. It is demonstrated that this platform enables high-performance and fast, low-cost PKU screening and subtype identification. This approach might be suitable for the detection of other clinically relevant biomarkers in blood or other clinical samples.
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Affiliation(s)
- Haiyang Su
- Henan Key Laboratory of Rare DiseasesEndocrinology and Metabolism CenterThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and TechnologyLuoyang471003P. R. China
- State Key Laboratory of Systems Medicine for CancerSchool of Biomedical EngineeringInstitute of Medical Robotics and Shanghai Academy of Experimental MedicineShanghai Jiao Tong UniversityShanghai200030P. R. China
| | - Huiwen Zhang
- Xinhua HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai200092P. R. China
| | - Jiao Wu
- State Key Laboratory of Systems Medicine for CancerSchool of Biomedical EngineeringInstitute of Medical Robotics and Shanghai Academy of Experimental MedicineShanghai Jiao Tong UniversityShanghai200030P. R. China
| | - Lin Huang
- Country Department of Clinical Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong UniversityShanghai200030P. R. China
| | - Mengji Zhang
- State Key Laboratory of Systems Medicine for CancerSchool of Biomedical EngineeringInstitute of Medical Robotics and Shanghai Academy of Experimental MedicineShanghai Jiao Tong UniversityShanghai200030P. R. China
| | - Wei Xu
- State Key Laboratory for Oncogenes and Related GenesDivision of CardiologyRenji Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghai200127P. R. China
| | - Jing Cao
- State Key Laboratory of Systems Medicine for CancerSchool of Biomedical EngineeringInstitute of Medical Robotics and Shanghai Academy of Experimental MedicineShanghai Jiao Tong UniversityShanghai200030P. R. China
| | - Wanshan Liu
- Xinhua HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai200092P. R. China
| | - Ning Liu
- School of Electronics Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai200240P. R. China
| | - Hongwei Jiang
- Henan Key Laboratory of Rare DiseasesEndocrinology and Metabolism CenterThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and TechnologyLuoyang471003P. R. China
| | - Xuefan Gu
- Xinhua HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai200092P. R. China
| | - Kun Qian
- State Key Laboratory of Systems Medicine for CancerSchool of Biomedical EngineeringInstitute of Medical Robotics and Shanghai Academy of Experimental MedicineShanghai Jiao Tong UniversityShanghai200030P. R. China
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Sun X, Yu Y, Qian K, Wang J, Huang L. Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis. SMALL METHODS 2024; 8:e2301317. [PMID: 38032130 DOI: 10.1002/smtd.202301317] [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: 09/27/2023] [Revised: 11/10/2023] [Indexed: 12/01/2023]
Abstract
Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.
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Affiliation(s)
- Xuming Sun
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, P. R. China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang Medical University, Xinxiang, 453003, P. R. China
- Xinxiang Key Laboratory of Neurobiosensor, Xinxiang Medical University, Xinxiang, 453003, P. R. China
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, P. R. China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang Medical University, Xinxiang, 453003, P. R. China
- Xinxiang Key Laboratory of Neurobiosensor, Xinxiang Medical University, Xinxiang, 453003, P. R. China
| | - Kun Qian
- School of Biomedical Engineering, Institute of Medical Robotics and Med X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jiayi Wang
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Lin Huang
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
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9
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Liang W, Yan W, Wang X, Yan X, Hu Q, Zhang W, Meng H, Yin L, He Q, Ma C. A single atom cobalt anchored MXene bifunctional platform for rapid, label-free and high-throughput biomarker analysis and tissue imaging. Biosens Bioelectron 2024; 246:115903. [PMID: 38048718 DOI: 10.1016/j.bios.2023.115903] [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: 08/20/2023] [Revised: 11/07/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023]
Abstract
Few of single-atom materials have been served as platform to analyze small molecules for surface assisted laser desorption/ionization mass spectrometry (SALDI-MS). Herein, a novel single Co atom-anchored MXene (Co-N-Ti3C2) is prepared to achieve enhanced SALDI-MS and mass spectrometry imaging (MSI) performance for the first time. The Co-N-Ti3C2 films were prepared by a simple in situ self-assembly strategy to generate an efficient SALDI-MS platform. Compared to typical inorganic/organic matrices, Co-N-Ti3C2 films exhibit superior performance in small molecules detection with ultra-high sensitivity (LOD at amol level), excellent repeatability (CV <4%), clean background and wide analyte coverage, enabling accurate quantitative analysis of various low-concentration metabolites from 1 μL biofluid in seconds. Its usage efficiently enhanced SALDI-MS detection of various small-molecule biomarkers such as amino acids, succinic acid, itaconic acid, arachidonic acid, citrulline, prostaglandin E2, creatinine, uric acid, glutamine, D-mannose, cholesterol and inositol in positive ion mode. The blood glucose level in humans was successfully determined from a linearity concentration range (0.25-10 mM). Notably, the Co-N-Ti3C2 assisted SALDI-MSI enables study the spatial distribution of small molecules covering the range central to metabolomics at a high resolution on a tissue section. Furthermore, Co-N-Ti3C2 platform revealed a specific peak profile that distinguishes osteoarthritis (OA) from rheumatoid arthritis (RA) tissue. Density functional theory theoretical investigation revealed that single Co atoms anchored on Ti3C2 could highly enhanced the ionization ability of metabolites, resulting in high-sensitivity and heterogeneous metabolome coverage.
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Affiliation(s)
- Weiqiang Liang
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, Shandong, China; Department of Bone and Joint Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014 Shandong province, China
| | - Weining Yan
- Department of Orthopedics, Trauma, and Reconstructive Surgery, Uniklinik RWTH Aachen, RWTH Aachen University, 52074 Aachen, Germany
| | - Xiao Wang
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, Shandong, China
| | - Xinfeng Yan
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014 Shandong province, China
| | - Qiongzheng Hu
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, Shandong, China
| | - Wenqiang Zhang
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014 Shandong province, China
| | - Hongzheng Meng
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014 Shandong province, China
| | - Luxu Yin
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014 Shandong province, China
| | - Qing He
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China; Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China; Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China.
| | - Chunxia Ma
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, Shandong, China.
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10
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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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11
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Wang Y, Xu X, Fang Y, Yang S, Wang Q, Liu W, Zhang J, Liang D, Zhai W, Qian K. Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis. ACS NANO 2024; 18:2409-2420. [PMID: 38190455 DOI: 10.1021/acsnano.3c10717] [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: 01/10/2024]
Abstract
Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.
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Affiliation(s)
- 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, People's Republic of 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, People's Republic of China
| | - Yuzheng Fang
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, 160 Pujian Road, Shanghai 200127, People's Republic of 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, People's Republic of China
| | - Qirui Wang
- Health Management Center, Renji Hospital of Medical School of Shanghai Jiao Tong University, Shanghai 200127, People's Republic of 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, People's Republic of China
| | - Juxiang Zhang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
| | - Dingyitai Liang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
| | - Wei Zhai
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, 160 Pujian Road, Shanghai 200127, People's Republic of 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, People's Republic of China
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12
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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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13
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Wang Y, Liu Y, Yang S, Yi J, Xu X, Zhang K, Liu B, Qian K. Host-Guest Self-Assembled Interfacial Nanoarrays for Precise Metabolic Profiling. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207190. [PMID: 36703514 DOI: 10.1002/smll.202207190] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Accurate and rapid metabolic profiling of cerebrospinal fluid (CSF) is urgently needed but remains challenging for clinical diagnosis of central nervous system diseases and biomarker discovery. Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) holds promise for metabolic analysis. Its low signal reproducibility, however, severely restricts acquisition of quantitative MS data in clinical practice. Herein, a multifunctional self-assembled AuNPs array (MSANA)-based LDI-MS platform for direct amino acids analysis and metabolic profiling in patient CSF samples is developed. MSANA featuring a highly ordered and closely packed two-dimensional nanostructure permits capture and direct analysis of aromatic amino acids by LDI-MS with high selectivity and micromolar sensitivity. Meanwhile, the MSANA-based LDI-MS platform exhibits excellent reproducibility (RSD < 10%), largely outperforming the direct matrix spotting approach widely used now (RSD < 44%). The platform is successfully used in metabolic profiling of CSF (1 µL) within minutes for discrimination of medulloblastoma patients from non-tumor controls. Taken together, the MSANA-based LDI-MS platform shows potential clinical values toward large-scale metabolic diagnostics and pathogenic mechanism study.
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Affiliation(s)
- Yuning Wang
- Department of Chemistry, Shanghai Stomatological Hospital and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200438, P. R. China
- 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, Shanghai, 200030, P. R. China
| | - Yu Liu
- Department of Neurosurgery, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200062, P. R. China
| | - Shouzhi Yang
- 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, Shanghai, 200030, P. R. China
| | - Jia Yi
- Department of Chemistry, Shanghai Stomatological Hospital and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200438, P. R. China
| | - Xiaoyu Xu
- 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, Shanghai, 200030, P. R. China
| | - Kun Zhang
- Shanghai Institute of Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, P. R. China
| | - Baohong Liu
- Department of Chemistry, Shanghai Stomatological Hospital and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200438, 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, Shanghai, 200030, P. R. China
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14
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Zhou Y, Li X, Zhao Y, Yang S, Huang L. Plasmonic alloys for quantitative determination and reaction monitoring of biothiols. J Mater Chem B 2023; 11:8639-8648. [PMID: 37491995 DOI: 10.1039/d3tb01076g] [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: 07/27/2023]
Abstract
Biothiols participate in numerous physiological and pathological processes in an organism. Quantitative determination and reaction monitoring of biothiols have important implications for evaluating human health. Herein, we synthesized plasmonic alloys as the matrix to assist the laser desorption and ionization (LDI) process of biothiols in mass spectrometry (MS). Plasmonic alloys were constructed with mesoporous structures for LDI enhancement and trimetallic (PdPtAu) compositions for noble metal-thiol hybridization, toward enhanced detection sensitivity and selectivity, respectively. Plasmonic alloys enabled direct detection of biothiols from complex biosamples without any enrichment or separation. We introduced internal standards into the quantitative MS system, achieving accurate quantitation of methionine directly from serum samples with a recovery rate of 103.19% ± 6.52%. Moreover, we established a rapid monitoring platform for the oxidation-reduction reaction of glutathione, consuming trace samples down to 200 nL with an interval of seconds. This work contributes to the development of molecular tools based on plasmonic materials for biothiol detection toward real-case applications.
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Affiliation(s)
- Yan Zhou
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China.
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Xvelian Li
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China.
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Yuewei Zhao
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China.
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Shouzhi Yang
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Lin Huang
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China.
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
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15
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Wu J, Liang C, Wang X, Huang Y, Liu W, Wang R, Cao J, Su X, Yin T, Wang X, Zhang Z, Shen L, Li D, Zou W, Wu J, Qiu L, Di W, Cao Y, Ji D, Qian K. Efficient Metabolic Fingerprinting of Follicular Fluid Encodes Ovarian Reserve and Fertility. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302023. [PMID: 37311196 PMCID: PMC10427401 DOI: 10.1002/advs.202302023] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/30/2023] [Indexed: 06/15/2023]
Abstract
Ovarian reserve (OR) and fertility are critical in women's healthcare. Clinical methods for encoding OR and fertility rely on the combination of tests, which cannot serve as a multi-functional platform with limited information from specific biofluids. Herein, metabolic fingerprinting of follicular fluid (MFFF) from follicles is performed, using particle-assisted laser desorption/ionization mass spectrometry (PALDI-MS) to encode OR and fertility. PALDI-MS allows efficient MFFF, showing fast speed (≈30 s), high sensitivity (≈60 fmol), and desirable reproducibility (coefficients of variation <15%). Further, machine learning of MFFF is applied to diagnose diminished OR (area under the curve of 0.929) and identify high-quality oocytes/embryos (p < 0.05) by a single PALDI-MS test. Meanwhile, metabolic biomarkers from MFFF are identified, which also determine oocyte/embryo quality (p < 0.05) from the sampling follicles toward fertility prediction in clinics. This approach offers a powerful platform in women's healthcare, not limited to OR and fertility.
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16
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Semeniak D, Cruz DF, Chilkoti A, Mikkelsen MH. Plasmonic Fluorescence Enhancement in Diagnostics for Clinical Tests at Point-of-Care: A Review of Recent Technologies. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2107986. [PMID: 35332957 PMCID: PMC9986847 DOI: 10.1002/adma.202107986] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/26/2022] [Indexed: 05/31/2023]
Abstract
Fluorescence-based biosensors have widely been used in the life-sciences and biomedical applications due to their low limit of detection and a diverse selection of fluorophores that enable simultaneous measurements of multiple biomarkers. Recent research effort has been made to implement fluorescent biosensors into the exploding field of point-of-care testing (POCT), which uses cost-effective strategies for rapid and affordable diagnostic testing. However, fluorescence-based assays often suffer from their feeble signal at low analyte concentrations, which often requires sophisticated, costly, and bulky instrumentation to maintain high detection sensitivity. Metal- and metal oxide-based nanostructures offer a simple solution to increase the output signal from fluorescent biosensors due to the generation of high field enhancements close to a metal or metal oxide surface, which has been shown to improve the excitation rate, quantum yield, photostability, and radiation pattern of fluorophores. This article provides an overview of existing biosensors that employ various strategies for fluorescence enhancement via nanostructures and have demonstrated the potential for use as POCT. Biosensors using nanostructures such as planar substrates, freestanding nanoparticles, and metal-dielectric-metal nanocavities are discussed with an emphasis placed on technologies that have shown promise towards POCT applications without the need for centralized laboratories.
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Affiliation(s)
- Daria Semeniak
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Daniela F. Cruz
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Ashutosh Chilkoti
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Maiken H. Mikkelsen
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
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Zhang G, Ma C, He Q, Dong H, Cui L, Li L, Li L, Wang Y, Wang X. An efficient Pt@MXene platform for the analysis of small-molecule natural products. iScience 2023; 26:106622. [PMID: 37250310 PMCID: PMC10214401 DOI: 10.1016/j.isci.2023.106622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 02/01/2023] [Accepted: 03/31/2023] [Indexed: 05/31/2023] Open
Abstract
Small-molecule (m/z<500) natural products have rich biological activity and significant application value thus need to be effectively detected. Surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) has become a powerful detection tool for small-molecule analysis. However, more efficient substrates need to be developed to improve the efficiency of SALDI MS. Thus, platinum nanoparticle-decorated Ti3C2 MXene (Pt@MXene) was synthesized in this study as an ideal substrate for SALDI MS in positive ion mode and exhibited excellent performance for the high-throughput detection of small molecules. Compared with using MXene, GO, and CHCA matrix, a stronger signal peak intensity and wider molecular coverage was obtained using Pt@MXene in the detection of small-molecule natural products, with a lower background, excellent salt and protein tolerance, good repeatability, and high detection sensitivity. The Pt@MXene substrate was also successfully used to quantify target molecules in medicinal plants. The proposed method has potentially wide application.
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Affiliation(s)
- Guanhua Zhang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan 650500, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China
| | - Chunxia Ma
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China
| | - Qing He
- Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Hongjing Dong
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China
| | - Li Cui
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China
| | - Lili Li
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China
| | - Lingyu Li
- Key Laboratory of Food Processing Technology and Quality Control of Shandong Higher Education Institutes, College of Food Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China
| | - Yan Wang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan 650500, China
| | - Xiao Wang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan 650500, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China
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18
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Zou M, Yang S, Wang Y, Yang W, Lai C, Huang L, Chen J. Profiling aromatic constituents of Chimonanthus nitens Oliv. leaf granule using mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2023; 37:e9481. [PMID: 36721310 DOI: 10.1002/rcm.9481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/19/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
RATIONALE The chemical constituents of Chinese patent medicine are usually different from those of crude medicine because of specific preparation processes. Chimonanthus nitens Oliv. leaf granule is widely used for prevention against COVID-19 in China. However, no research has been reported on the chemical constituents of the granule and their variation during the preparation process. METHODS Fragmentation patterns of reference compounds were investigated using electrospray ionization mass spectrometry, and the new gas-phase reaction was demonstrated by electronic and steric effects and calculated chemistry. Then, a strategy based on new fragmentation patterns was used to profile aromatic constituents. In addition, based on untargeted metabolomics analytical workflow, a comparison was made on the chemical constituents of the leaf and granule. RESULTS New fragmentation patterns related to two competing reactions, ring-opening and ring-closing reactions for coumarin, have been proposed and investigated in depth. The newly established diagnostic ion at m/z 81.0331 worked strongly in the assignment of OH-7 and substituent at C-8 of coumarin. McLafferty rearrangement occurring in coumarin glycoside while sugar group locating at C-4 was first observed, and new diagnostic ions at m/z 147.0440, 119.0488, and 91.0543 were constructed. CONCLUSIONS Aromatic constituents of the granule were first profiled. A total of 114 aromatic compounds were identified; of these 85 compounds were identified first. Kaempferol-7-O-neohesperidoside and its homologues were mostly enriched in the granule. Considering their reported bioactivities, these analogues possibly contribute greatly to clinical efficacy. Our results provided a new fragmentation theory for coumarins and a new material basis for the quality control of the granule.
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Affiliation(s)
- Mailing Zou
- Chemical Engineering Deparment, School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, China
| | - Shanzheng Yang
- Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Yongping Wang
- Jiangxi Youmei Pharmaceutical Co., Ltd., Wuyuan, China
| | - Weiran Yang
- Chemical Engineering Deparment, School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, China
| | - Changjiangsheng Lai
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Luqi Huang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jinlong Chen
- Chemical Engineering Deparment, School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, China
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Su J, Cao J, Yang H, Xu W, Liu W, Wang R, Huang Y, Wu J, Gao X, Weng R, Pu J, Liu N, Gu Y, Qian K, Ni W. Diagnosis of Unruptured Intracranial Aneurysm by High-Performance Serum Metabolic Fingerprints. SMALL METHODS 2023; 7:e2201486. [PMID: 36634984 DOI: 10.1002/smtd.202201486] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.
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Affiliation(s)
- Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
| | - Jing Cao
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
| | - Wei Xu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Wanshan Liu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Ruimin Wang
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Yida Huang
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jiao Wu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Xinjie Gao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
| | - Ruiyuan Weng
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
| | - Jun Pu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Ning Liu
- School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, P. R. China
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Cao J, Xiao Y, Zhang M, Huang L, Wang Y, Liu W, Wang X, Wu J, Huang Y, Wang R, Zhou L, Li L, Zhang Y, Ren L, Qian K, Wang J. Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2206349. [PMID: 36470664 DOI: 10.1002/smll.202206349] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/17/2022] [Indexed: 06/17/2023]
Abstract
Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.
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Affiliation(s)
- Jing Cao
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Yan Xiao
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
| | - Mengji Zhang
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Lin Huang
- Country Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Ying Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
| | - Wanshan Liu
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Xinming Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
| | - Jiao Wu
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Yida Huang
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Ruimin Wang
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Li Zhou
- Beijing health biotech co. Ltd, Beijing, 100193, P. R. China
| | - Lin Li
- Beijing health biotech co. Ltd, Beijing, 100193, P. R. China
| | - Yong Zhang
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Lili Ren
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Jianwei Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
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21
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Waris M, Koçak E, Gonulalan EM, Demirezer LO, Kır S, Nemutlu E. Metabolomics analysis insight into medicinal plant science. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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22
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Fournelle F, Lauzon N, Yang E, Chaurand P. Metal-Assisted Laser Desorption Ionization Imaging Mass Spectrometry for Biological and Forensic Applications. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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23
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Wang L, Zhang M, Pan X, Zhao M, Huang L, Hu X, Wang X, Qiao L, Guo Q, Xu W, Qian W, Xue T, Ye X, Li M, Su H, Kuang Y, Lu X, Ye X, Qian K, Lou J. Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203786. [PMID: 36257825 PMCID: PMC9731719 DOI: 10.1002/advs.202203786] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/21/2022] [Indexed: 05/16/2023]
Abstract
Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.
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Affiliation(s)
- Lin Wang
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
- Department of Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Mengji Zhang
- State Key Laboratory for Oncogenes and Related GenesSchool of Biomedical EngineeringInstitute of Medical Robotics and Med‐X Research InstituteShanghai Jiao Tong UniversityShanghai200030P. R. China
- State Key Laboratory for Oncogenes and Related GenesDivision of CardiologyRenji HospitalShanghai Jiao Tong University School of MedicineShanghai200127P. R. China
| | - Xufeng Pan
- Department of Thoracic SurgeryShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Mingna Zhao
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
- Department of Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Lin Huang
- Department of Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Xiaomeng Hu
- Department of Laboratory MedicineThe Third Hospital of Hebei Medical UniversityShijiazhuang050051P. R. China
| | - Xueqing Wang
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
| | - Lihua Qiao
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
| | - Qiaomei Guo
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
| | - Wanxing Xu
- School of MedicineJiangsu UniversityZhenjiang212013P. R. China
| | - Wenli Qian
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
| | - Tingjia Xue
- Department of RadiologyShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Xiaodan Ye
- Department of RadiologyShanghai Institute of Medical ImagingZhongshan HospitalFudan UniversityShanghai200032P. R. China
| | - Ming Li
- Department of Laboratory DiagnosticsThe First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhui230001P. R. China
| | - Haixiang Su
- Gansu Academic Institute for Medical ResearchGansu Cancer HospitalLanzhouGansu730050P. R. China
| | - Yinglan Kuang
- Department of A. I. ResearchJoint Research Center of Liquid Biopsy in Guangdong, Hong Kong, and MacaoZhuhaiGuangdong519000P. R. China
| | - Xing Lu
- Department of A. I. ResearchJoint Research Center of Liquid Biopsy in Guangdong, Hong Kong, and MacaoZhuhaiGuangdong519000P. R. China
| | - Xin Ye
- Department of Product DevelopmentJoint Research Center of Liquid Biopsy in Guangdong, Hong Kong, and MacaoZhuhaiGuangdong519000P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related GenesSchool of Biomedical EngineeringInstitute of Medical Robotics and Med‐X Research InstituteShanghai Jiao Tong UniversityShanghai200030P. R. China
- State Key Laboratory for Oncogenes and Related GenesDivision of CardiologyRenji HospitalShanghai Jiao Tong University School of MedicineShanghai200127P. R. China
| | - Jiatao Lou
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
- Department of Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
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24
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Shi F, Huang C, Ren Y, Deng C, Sun N, Shen X. Multiscale Element-Doped Nanowire Array-Coupled Machine Learning Reveals Metabolic Fingerprints of Nonreversible Liver Diseases. Anal Chem 2022; 94:16204-16212. [DOI: 10.1021/acs.analchem.2c03743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Fangying Shi
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Department of Chemistry, Institue of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai 200032, China
| | - Chuwen Huang
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Department of Chemistry, Institue of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai 200032, China
| | - Yuan Ren
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Department of Chemistry, Institue of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai 200032, China
| | - Chunhui Deng
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Department of Chemistry, Institue of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai 200032, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Department of Chemistry, Institue of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai 200032, China
| | - Xizhong Shen
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Department of Chemistry, Institue of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai 200032, China
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25
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Metal-organic framework nanofilm enhances serum metabolic profiles for diagnosis and subtype of cardiovascular disease. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.107992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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26
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Jiang L, Kong KV, He S, Yong K. Plasmonic Biosensing with Nano‐Engineered Van der Waals Interfaces. Chempluschem 2022; 87:e202200221. [DOI: 10.1002/cplu.202200221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/27/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Li Jiang
- School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Singapore
- State Key Laboratory of Modern Optical Instrumentation Centre for Optical and Electromagnetics Research JORCEP (Sino-Swedish Joint Research Center of Photonics) Zhejiang University Hangzhou 310058 P. R. China
- CINTRA CNRS/NTU/THALES, UMI 3288 Research Techno Plaza 50 Nanyang Drive Border X Block 637553 Singapore Singapore
| | - Kien Voon Kong
- Department of Chemistry National Taiwan University Taipei City Taiwan 10617
| | - Sailing He
- State Key Laboratory of Modern Optical Instrumentation Centre for Optical and Electromagnetics Research JORCEP (Sino-Swedish Joint Research Center of Photonics) Zhejiang University Hangzhou 310058 P. R. China
| | - Ken‐Tye Yong
- School of Biomedical Engineering The University of Sydney Sydney New South Wales 2006 Australia
- The University of Sydney Nano Institute The University of Sydney Sydney New South Wales 2006 Australia
- The Biophotonics and MechanoBioengineering Lab The University of Sydney Sydney New South Wales 2006 Australia
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27
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Li D, Yi J, Han G, Qiao L. MALDI-TOF Mass Spectrometry in Clinical Analysis and Research. ACS MEASUREMENT SCIENCE AU 2022; 2:385-404. [PMID: 36785658 PMCID: PMC9885950 DOI: 10.1021/acsmeasuresciau.2c00019] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/15/2022] [Accepted: 07/15/2022] [Indexed: 05/04/2023]
Abstract
In the decade after being awarded the Nobel Prize in Chemistry in 2002, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used as an analytical chemistry tool for the detection of large and small molecules (e.g., polymers, proteins, peptides, nucleic acids, amino acids, lipids, etc.) and for clinical analysis and research (e.g., pathogen identification, genetic disorders screening, cancer diagnosis, etc.). In view of the fast development of MALDI-TOF MS in clinical usage, this review systematically summarizes the most important applications of MALDI-TOF MS in clinical analysis and research by analyzing MALDI TOF MS-related reviews collected in the Web of Science database. On the basis of the analysis of keyword co-occurrence of over 2000 review articles, four themes consisting of "pathogen identification", "disease diagnosis", "nucleic acids analysis", and "small molecules analysis" were found. For each theme, the review further outlined their application implications, analytical methods, and systems as well as limitations that need to be addressed. Overall, the review summarizes and elaborates on the clinical applications of MALDI-TOF MS, providing a comprehensive picture for researchers embarking on MALDI TOF MS-related clinical analysis and research.
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Hu X, Wang Z, Chen H, Zhao A, Sun N, Deng C. Diagnosing, Typing, and Staging of Renal Cell Carcinoma by Designer Matrix-Based Urinary Metabolic Analysis. Anal Chem 2022; 94:14846-14853. [PMID: 36260912 DOI: 10.1021/acs.analchem.2c01563] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Molecular diagnosing, typing, and staging have been considered to be the ideal alternatives of imaging-based detection methods in clinics. Designer matrix-based analytical tools, with high speed, throughout, efficiency and low/noninvasiveness, have attracted much attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on highly porous metal oxides derived from available metal-organic frameworks (MOFs), which elaborately inherit the morphology and porosity of MOFs and newly incorporate laser adsorption capacity of metal oxides. Through optimized conditions, direct high-quality fingerprinting spectra in 0.5 μL of urine are acquired. Using these fingerprinting spectra, we can discriminate the renal cell carcinoma (RCC) from healthy controls with higher than 0.99 of area under the curve (AUC) values (R2Y(cum) = 0.744, Q2 (cum) = 0.880), as well, from patients with other tumors (R2Y(cum) = 0.748, Q2(cum) = 0.871). We also realize the typing of three RCC subtypes, including clear cell RCC, chromophobe RCC (R2Y(cum) = 0.620, Q2(cum) = 0.656), and the staging of RCC (R2Y(cum) = 0.755, Q2(cum) = 0.857). Moreover, the tumor sizes (threshold value is 3 cm) can be remarkably recognized by this advanced metabolic analysis tool (R2Y(cum) = 0.710, Q2(cum) = 0.787). Our work brings a bright prospect for designer matrix-based analytical tools in disease diagnosis, typing and staging.
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Affiliation(s)
- Xufang Hu
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - Zongping Wang
- Department of Urology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310000, China
| | - Haolin Chen
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - An Zhao
- Experimental Research Center, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310000, China.,Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - Chunhui Deng
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
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29
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Chen H, Huang C, Wu Y, Sun N, Deng C. Exosome Metabolic Patterns on Aptamer-Coupled Polymorphic Carbon for Precise Detection of Early Gastric Cancer. ACS NANO 2022; 16:12952-12963. [PMID: 35946596 DOI: 10.1021/acsnano.2c05355] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Gastric cancer (GC) presents high mortality worldwide because of delayed diagnosis. Currently, exosome-based liquid biopsy has been applied in diagnosis and monitoring of diseases including cancers, whereas disease detection based on exosomes at the metabolic level is rarely reported. Herein, the specific aptamer-coupled Au-decorated polymorphic carbon (CoMPC@Au-Apt) is constructed for the capture of urinary exosomes from early GC patients and healthy controls (HCs) and the subsequent exosome metabolic pattern profiling without extra elution process. Combining with machine learning algorithm on all exosome metabolic patterns, the early GC patients are excellently discriminated from HCs, with an accuracy of 100% for both the discovery set and blind test. Ulteriorly, three key metabolic features with clear identities are determined as a biomarker panel, obtaining a more than 90% diagnostic accuracy for early GC in the discovery set and validation set. Moreover, the change law of the key metabolic features along with GC development is revealed through making a comparison among HCs and GC at early stage and advanced stage, manifesting their monitoring ability toward GC. This work illustrates the high specificity of exosomes and the great prospective of exosome metabolic analysis in disease diagnosis and monitoring, which will promote exosome-driven precision medicine toward practical clinical application.
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Affiliation(s)
- Haolin Chen
- Department of Chemistry, Metabolism and Integrative Biology (IMIB), Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Chuwen Huang
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yonglei Wu
- Department of Chemistry, Metabolism and Integrative Biology (IMIB), Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chunhui Deng
- Department of Chemistry, Metabolism and Integrative Biology (IMIB), Zhongshan Hospital, Fudan University, Shanghai 200433, China
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30
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Hu X, Zhang Y, Deng C, Sun N, Wu H. Metabolic Molecular Diagnosis of Inflammatory Bowel Disease by Synergistical Promotion of Layered Titania Nanosheets with Graphitized Carbon. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:261-271. [PMID: 36939785 PMCID: PMC9590550 DOI: 10.1007/s43657-022-00055-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/12/2022] [Accepted: 04/15/2022] [Indexed: 02/07/2023]
Abstract
Due to inefficient diagnostic methods, inflammatory bowel disease (IBD) normally progresses into severe complications including cancer. Highly efficient extraction and identification of metabolic fingerprints are of significance for disease surveillance. In this work, we synthesized a layered titania nanosheet doped with graphitized carbon (2D-GC-mTNS) through a simple one-step assembly process for assisting laser desorption ionization mass spectrometry (LDI-MS) for metabolite analysis. Based on the synergistic effect of graphitized carbon and mesoporous titania, 2D-GC-mTNS exhibits good extraction ability including high sensitivity (< 1 fmol µL-1) and great repeatability toward metabolites. A total of 996 fingerprint spectra were collected from hundreds of native urine samples (including IBD patients and healthy controls), each of which contained 1220 m/z metabolite features. Diagnostic model was further established for precise discrimination of patients from healthy controls, with high area under the curve value of 0.972 and 0.981 toward discovery cohort and validation cohort, respectively. The 2D-GC-mTNS promotes LDI-MS to be close to clinical application, with rapid speed, minimum sample consumption and free of sample pretreatment. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00055-0.
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Affiliation(s)
- Xufang Hu
- grid.8547.e0000 0001 0125 2443Department of Chemistry, Institute of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai, 200433 China
| | - Yang Zhang
- grid.8547.e0000 0001 0125 2443Department of Chemistry, Institute of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai, 200433 China
| | - Chunhui Deng
- grid.8547.e0000 0001 0125 2443Department of Chemistry, Institute of Metabolism & Integrate Biology (IMIB), Fudan University, Shanghai, 200433 China
- grid.8547.e0000 0001 0125 2443Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032 China
| | - Nianrong Sun
- grid.8547.e0000 0001 0125 2443Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032 China
| | - Hao Wu
- grid.8547.e0000 0001 0125 2443Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032 China
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31
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Gao X, Lin L, Hu A, Zhao H, Kang L, Wang X, Yuan C, Yang P, Shen H. Shotgun lipidomics combined targeted MRM reveals sphingolipid signatures of coronary artery disease. Talanta 2022; 245:123475. [DOI: 10.1016/j.talanta.2022.123475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/07/2022] [Accepted: 04/09/2022] [Indexed: 11/16/2022]
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32
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Shi F, Zhou J, Wu Y, Hu X, Xie Q, Deng C, Sun N. In Vitro Diagnostic Examination and Prognosis Surveillance by Hierarchical Heterojunction-Assisted Metabolic Analysis. Anal Chem 2022; 94:10497-10505. [PMID: 35839420 DOI: 10.1021/acs.analchem.2c01784] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
High-throughput metabolic analysis based on laser desorption/ionization mass spectrometry exhibits broad prospects in the field of large-scale precise medicine, for which the assisted ionization ability of the matrix becomes a determining step. In this work, the gold-decorated hierarchical metal oxide heterojunctions (dubbed Au/HMOHs) are proposed as a matrix for extracting urine metabolic fingerprints (UMFs) of primary nephrotic syndrome (PNS). The hierarchical heterojunctions are simply derived from metal-organic framework (MOF)-on-MOF hybrids, and the native built-in electric field from heterojunctions plus the extra Au decoration provides remarkable ionization efficiency, attaining high-quality UMFs. These UMFs are employed to realize precise diagnosis, subtype classification, and effective prognosis evaluation of PNS by appropriate machine learning, all with 100% accurate ratios. Moreover, a high-confidence marker panel for PNS diagnosis is constructed. Interestingly, all panel metabolite markers present obviously uniform downregulation in PNS compared to healthy controls, shedding light on mechanism exploration and pathway analysis. This work drives the application of metabolomics toward precision medicine.
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Affiliation(s)
- Fangying Shi
- Department of Chemistry, Institute of Metabolism & Integrate Biology (IMIB), Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Jie Zhou
- Division of Nephrology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yonglei Wu
- Department of Chemistry, Institute of Metabolism & Integrate Biology (IMIB), Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Xufang Hu
- School of Chemical Science and Technology, Yunnan University, Kunming 650091, China
| | - Qionghong Xie
- Division of Nephrology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Chunhui Deng
- Department of Chemistry, Institute of Metabolism & Integrate Biology (IMIB), Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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33
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Li Y, Jiang L, Wang Z, Wang Y, Cao X, Meng L, Fan J, Xiong C, Nie Z. Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry. Anal Chem 2022; 94:9894-9902. [PMID: 35762528 DOI: 10.1021/acs.analchem.2c01890] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with N,N-dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Additionally, the DMED derivatization increases detection sensitivity and distinguishes carboxyl, aldehyde, and ketone groups in untreated urine samples. Carbonyl metabolic fingerprints of urine from 41 BC patients and 41 controls were portrayed and 9 potential biomarkers were identified. The mechanisms of the regulations of these biomarkers have been tentatively discussed. A logistic regression (LR) machine learning algorithm was applied to discriminate BC from controls, and an accuracy of 85% was achieved. We believe that the method proposed here may pave the way toward the point-of-care diagnosis of BC in a patient-friendly manner.
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Affiliation(s)
- Yuze Li
- 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
| | - Lixia Jiang
- Department of Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi 341000, China
| | - Zhenpeng Wang
- National Center for Mass Spectrometry in Beijing, Beijing 100190, 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 100049, China
| | - Xiaohua Cao
- College of Chemical Engineering, Jiujiang University, Jiujiang, Jiangxi 332005, China
| | - Lingwei Meng
- 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
| | - Jinghan Fan
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Caiqiao Xiong
- 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
| | - 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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34
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Zhang Y, Huang Y, Chen R, Chen S, Lü X. The interaction mechanism of nickel ions with L929 cells based on integrative analysis of proteomics and metabolomics data. Regen Biomater 2022; 9:rbac040. [PMID: 35812349 PMCID: PMC9258689 DOI: 10.1093/rb/rbac040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/18/2022] [Accepted: 05/28/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
The aim of this paper was to study the toxicity mechanism of nickel ions (Ni2+) on L929 cells by combining proteomics and metabolomics. First, iTRAQ-based proteomics and LC/MS metabolomics analyses were used to determine the protein and metabolite expression profiles in L929 cells after treatment with 100 μM Ni2+ for 12, 24 and 48 h. A total of 177, 2191 and 2109 proteins and 40, 60 and 74 metabolites were found to be differentially expressed. Then, the metabolic pathways in which both differentially expressed proteins and metabolites were involved were identified, and three pathways with proteins and metabolites showing upstream and downstream relationships were affected at all three time points. Furthermore, the protein-metabolite-metabolic pathway network was constructed, and two important metabolic pathways involving 4 metabolites and 17 proteins were identified. Finally, the functions of the important screened metabolic pathways, metabolites and proteins were investigated and experimentally verified. Ni2+ mainly affected the expression of upstream proteins in the glutathione metabolic pathway and the arginine and proline metabolic pathway, which further regulated the synthesis of downstream metabolites, reduced the antioxidant capacity of cells, increased the level of superoxide anions and the ratio of GSSG to GSH, led to oxidative stress, affected energy metabolism and induced apoptosis.
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Affiliation(s)
- Yajing Zhang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University , 2# Si Pailou, Nanjing 210096, China
| | - Yan Huang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University , 2# Si Pailou, Nanjing 210096, China
| | - Rong Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University , 2# Si Pailou, Nanjing 210096, China
| | - Shulin Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University , 2# Si Pailou, Nanjing 210096, China
| | - Xiaoying Lü
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University , 2# Si Pailou, Nanjing 210096, China
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35
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Plasmonic Gold Chip for Multiplexed Detection of Ovarian Cancer Biomarker in Urine. Chem Res Chin Univ 2022. [DOI: 10.1007/s40242-022-2117-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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36
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Wan X, Lan Z, Yang S, Yang S, Zhu Y, Wang F, Yang W, Chen J. Investigation of fragmentation pathways of norpimarane diterpenoids by mass spectrometry combined with computational chemistry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2022; 36:e9269. [PMID: 35156244 DOI: 10.1002/rcm.9269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 06/14/2023]
Abstract
RATIONALE Norpimarane diterpenes possess plentiful bioactivities and are widely distributed in herbs, such as Flickingeria fimbriata. Rapid characterization of these natural products in complicated plant extracts is of great importance, and electrospray ionization tandem mass spectrometry is a powerful tool for chemical constituent profiling. However, limited researches on their fragmentation mechanisms seriously hinder identification via mass spectrometry. METHODS Three norpimarane diterpenes isolated from F. fimbriata via multiple types of column chromatography served as reference compounds, and collision-induced dissociation experiments were performed on them with a series of variable collision energies. Plausible fragmentation pathways were proposed based on product ions. To further validate the fragmentation mechanisms, the proton affinity and product ion energy were simulated by density functional theory at the B3LYP/6-31+G(d, p) level. RESULTS Three main cleavage reactions induced skeleton breakage and resulted in characteristic ions, methyl (CH3 -20) migration, hydrogen arrangement and Retro-Diels-Alder reaction, among which methyl migration was firstly proposed for pimarane diterpenes. A series of common diagnostic ions were identified, such as m/z 133.1012, 121.1012, 119.0805 and 107.0855. Additionally, the constructed fragmentation mechanisms were successfully applied for fragment ion rationalization of previously reported isopimarane diterpenes. CONCLUSIONS Fragmentation mechanisms of norpimarane diterpenes have been uncovered. Carbocation located at the C ring tends to result in methyl (CH3 -20) migration which has been rarely reported before. This characteristic dissociation reaction allows multiple diagnostic ions to be rationalized and aids in rationalizing fragmentation patterns of other diterpenes. The uncovered mechanisms also shed light on rapid identification of norpimarane diterpenes.
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Affiliation(s)
- Xunda Wan
- School of Resources Environmental & Chemical Engineering, Nanchang University, Nanchang, Jiangxi, China
| | - Ziqiang Lan
- Paediatrics College, Nanchang University, Nanchang, Jiangxi, China
| | - Shushu Yang
- School of Resources Environmental & Chemical Engineering, Nanchang University, Nanchang, Jiangxi, China
| | - Shanzheng Yang
- Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Yangjian Zhu
- School of Resources Environmental & Chemical Engineering, Nanchang University, Nanchang, Jiangxi, China
| | - Feng Wang
- CSPC Jiangxi Jinfurong Pharmaceutical Co. Ltd, Jiujiang, Jiangxi, China
| | - Weiran Yang
- School of Resources Environmental & Chemical Engineering, Nanchang University, Nanchang, Jiangxi, China
| | - Jinlong Chen
- School of Resources Environmental & Chemical Engineering, Nanchang University, Nanchang, Jiangxi, China
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37
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Müller WH, McCann A, Arias AA, Malherbe C, Quinton L, De Pauw E, Eppe G. Imaging Metabolites in Agar‐Based Bacterial Co‐Cultures with Minimal Sample Preparation using a DIUTHAME Membrane in Surface‐Assisted Laser Desorption/Ionization Mass Spectrometry**. ChemistrySelect 2022. [DOI: 10.1002/slct.202200734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Wendy H. Müller
- Mass Spectrometry Laboratory MolSys Research Unit Department of Chemistry University of Liège Liège Belgium
| | - Andréa McCann
- Mass Spectrometry Laboratory MolSys Research Unit Department of Chemistry University of Liège Liège Belgium
| | - Anthony Argüelles Arias
- Microbial Processes and Interactions Laboratory Terra Teaching and Research Center Gembloux Agro-Bio Tech University of Liège Gembloux Belgium
| | - Cedric Malherbe
- Mass Spectrometry Laboratory MolSys Research Unit Department of Chemistry University of Liège Liège Belgium
| | - Loïc Quinton
- Mass Spectrometry Laboratory MolSys Research Unit Department of Chemistry University of Liège Liège Belgium
| | - Edwin De Pauw
- Mass Spectrometry Laboratory MolSys Research Unit Department of Chemistry University of Liège Liège Belgium
| | - Gauthier Eppe
- Mass Spectrometry Laboratory MolSys Research Unit Department of Chemistry University of Liège Liège Belgium
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38
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Müller WH, Verdin A, De Pauw E, Malherbe C, Eppe G. Surface-assisted laser desorption/ionization mass spectrometry imaging: A review. MASS SPECTROMETRY REVIEWS 2022; 41:373-420. [PMID: 33174287 PMCID: PMC9292874 DOI: 10.1002/mas.21670] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/22/2020] [Accepted: 10/24/2020] [Indexed: 05/04/2023]
Abstract
In the last decades, surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has attracted increasing interest due to its unique capabilities, achievable through the nanostructured substrates used to promote the analyte desorption/ionization. While the most widely recognized asset of SALDI-MS is the untargeted analysis of small molecules, this technique also offers the possibility of targeted approaches. In particular, the implementation of SALDI-MS imaging (SALDI-MSI), which is the focus of this review, opens up new opportunities. After a brief discussion of the nomenclature and the fundamental mechanisms associated with this technique, which are still highly controversial, the analytical strategies to perform SALDI-MSI are extensively discussed. Emphasis is placed on the sample preparation but also on the selection of the nanosubstrate (in terms of chemical composition and morphology) as well as its functionalization possibilities for the selective analysis of specific compounds in targeted approaches. Subsequently, some selected applications of SALDI-MSI in various fields (i.e., biomedical, biological, environmental, and forensic) are presented. The strengths and the remaining limitations of SALDI-MSI are finally summarized in the conclusion and some perspectives of this technique, which has a bright future, are proposed in this section.
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Affiliation(s)
- Wendy H. Müller
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
| | - Alexandre Verdin
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
| | - Edwin De Pauw
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
| | - Cedric Malherbe
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
| | - Gauthier Eppe
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
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39
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Zheng P, Wu L, Raj P, Mizutani T, Szabo M, Hanson WA, Barman I. A Dual-Modal Single-Antibody Plasmonic Spectro-Immunoassay for Detection of Small Molecules. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2200090. [PMID: 35373504 PMCID: PMC9302383 DOI: 10.1002/smll.202200090] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/15/2022] [Indexed: 05/03/2023]
Abstract
Small molecules play a pivotal role in regulating physiological processes and serve as biomarkers to uncover pathological conditions and the effects of therapeutic treatments. However, it remains a significant challenge to detect small molecules given the size as compared to macromolecules. Recently, the newly emerging plasmonic immunoassays based on surface-enhanced Raman scattering (SERS) offer great promise to deliver extraordinary sensitivity. Nevertheless, they are limited by the intrinsic SERS intensity fluctuations associated with the SERS uncertainty principle. The single transducer that relies on the intensity change is also prone to false signals. Additionally, the prevailing sandwich immunoassay format proves less effective towards detecting small molecules. To circumvent these critical issues, a dual-modal single-antibody approach that synergizes both the intensity and shift of the peak-based immunoassay with Raman enhancement, coined as the INSPIRE assay, is developed for small molecules detection. With two independent transduction mechanisms, it allows better prediction of analyte concentration and attenuation of signal artifacts, providing a new and robust strategy for molecular analysis. With a proof-of-concept demonstration for detection of free T4 and testosterone in serum matrix, the authors envision that the INSPIRE assay could be expanded for a wide spectrum of applications in biomedical diagnosis, discovery of new biopharmaceuticals, food safety, and environmental monitoring.
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Affiliation(s)
- Peng Zheng
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Lintong Wu
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Piyush Raj
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Takayuki Mizutani
- Beckman Coulter Diagnostics – Immunoassay Business Unit, 1000 Lake Hazeltine Dr, Chaska, MN 55318
| | - Miklos Szabo
- Beckman Coulter Diagnostics – Immunoassay Business Unit, 1000 Lake Hazeltine Dr, Chaska, MN 55318
| | - William A. Hanson
- Beckman Coulter Diagnostics – Immunoassay Business Unit, 1000 Lake Hazeltine Dr, Chaska, MN 55318
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
- To whom the correspondence should be addressed.
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40
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Wu J, Xu M, Liu W, Huang Y, Wang R, Chen W, Feng L, Liu N, Sun X, Zhou M, Qian K. Glaucoma Characterization by Machine Learning of Tear Metabolic Fingerprinting. SMALL METHODS 2022; 6:e2200264. [PMID: 35388987 DOI: 10.1002/smtd.202200264] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/17/2022] [Indexed: 06/14/2023]
Abstract
Glaucoma is a common optic neuropathy disease affecting over 76 million people. Both timely diagnosis and progression monitoring are critical but challenging. Conventional characterization of glaucoma needs a combination of methods, calling for tedious procedures and experienced doctors. Herein, a platform through machine learning of tear metabolic fingerprinting (TMF) using nanoparticle enhanced laser desorption-ionization mass spectrometry is built. Direct TMF is obtained noninvasively, with fast speed and high reproducibility, using trace tear samples (down to 10 nL). Consequently, glaucoma patients are screened against healthy controls with the area under the curve (AUC) of 0.866, through machine learning of TMF. Further, primary open-angle glaucoma (POAG) is differentiated from primary angle-closure glaucoma (PACG) and an early-stage POAG is identified. Finally, a biomarker panel of six metabolites for glaucoma characterization (including screening, subtyping, and early diagnosis) with AUC of 0.827-0.891 is constructed, showing related metabolic pathways. The work will provide insights into eye diseases not limited to glaucoma.
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Affiliation(s)
- Jiao Wu
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Mengqiao Xu
- Department of Ophthalmology, Shanghai General Hospital, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, P. R. China
| | - Wanshan Liu
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Yida Huang
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Ruimin Wang
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Wei Chen
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Lei Feng
- Instrumental Analysis Center, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Ning Liu
- School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, P. R. China
| | - Minwen Zhou
- Department of Ophthalmology, Shanghai General Hospital, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
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Liu X, Chen Z, Wang T, Jiang X, Qu X, Duan W, Xi F, He Z, Wu J. Tissue Imprinting on 2D Nanoflakes-Capped Silicon Nanowires for Lipidomic Mass Spectrometry Imaging and Cancer Diagnosis. ACS NANO 2022; 16:6916-6928. [PMID: 35416655 DOI: 10.1021/acsnano.2c02616] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Spatially resolved tissue lipidomics is essential for accurate intraoperative and postoperative cancer diagnosis by revealing molecular information in the tumor microenvironment. Matrix-free laser desorption ionization mass spectrometry imaging (LDI-MSI) is an emerging attractive technology for label-free visualization of metabolites distributions in biological specimens. However, the development of LDI-MSI technology that could conveniently and authentically reveal molecular distribution on tissue samples is still a challenge. Herein, we present a tissue imprinting technology by retaining tissue lipids on 2D nanoflakes-capped silicon nanowires (SiNWs) for further mass spectrometry imaging and cancer diagnosis. The 2D nanoflakes were prepared by liquid exfoliation of molybdenum disulfide (MoS2) with nitrogen-doped graphene quantum dots (NGQDs), which serve as both intercalation agent and dispersant. The obtained NGQD@MoS2 nanoflakes were then decorated on the tip of vertical SiNWs, forming a hybrid NGQD@MoS2/SiNWs nanostructure, which display excellent lipid extraction ability, enhanced LDI efficiency and molecule imaging capability. The peak number and total ion intensity of different lipids species on animal lung tissues obtained by tissue imprinting LDI-MSI on NGQD@MoS2/SiNWs were ∼4-5 times greater than those on SiNWs substrate. As a proof-of-concept demonstration, the NGQD@MoS2/SiNWs nanostructure was further applied to visualize phospholipids on sliced non small cell lung cancer (NSCLC) tissue along with the adjacent normal tissue. On the basis of selected feature lipids and machine learning algorithm, a prediction model was constructed to discriminate NSCLC tissues from the adjacent normal tissues with an accuracy of 100% for the discovery cohort and 91.7% for the independent validation cohort.
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Affiliation(s)
- Xingyue Liu
- Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Zhao Chen
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, P. R. China
| | - Tao Wang
- Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Xinrong Jiang
- Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Xuetong Qu
- Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Wei Duan
- Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Fengna Xi
- Department of Chemistry, Key Laboratory of Surface & Interface Science of Polymer Materials of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou 310018, P. R. China
| | - Zhengfu He
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, P. R. China
| | - Jianmin Wu
- Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
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Dendrimer-Modified Silica Nanoparticles for Efficient Enrichment of Low-Concentration Peptides. Appl Biochem Biotechnol 2022; 194:3419-3434. [PMID: 35366184 DOI: 10.1007/s12010-022-03892-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/14/2022] [Indexed: 11/02/2022]
Abstract
Peptide profiling based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is of particular interest as it can provide physiologically and pathologically related information of the bio-samples. Due to the complexity of real biological samples, MALDI-TOF MS-based peptide mapping methods rely strongly on particular enrichment methods to improve the signal intensity. This paper introduces third-generation dendrimer-modified SBA-15 with the surface functionalization of amino and carboxyl group, respectively (denoted as SBA-15/G3-NH2 and SBA-15/G3-COOH), for the efficient capture of low-abundance peptides. The enrichment ability of the nanocomposites was evaluated by standard peptides digests and real biological samples. The synthesized nanocomposites incorporated the benefit of dendrimers and mesoporous silica nanomaterial SBA-15, showing enhanced peptide enrichment ability. Therefore, this work may provide a new class of nanomaterials for peptide mapping from biological samples.
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43
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Exponential isothermal amplification coupled MALDI-TOF MS for microRNAs detection. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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44
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Wang Y, Shu W, Lin S, Wu J, Jiang M, Li S, Liu C, Li R, Pei C, Ding Y, Wan J, Di W. Hollow Cobalt Oxide/Carbon Hybrids Aid Metabolic Encoding for Active Systemic Lupus Erythematosus during Pregnancy. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2106412. [PMID: 35064740 DOI: 10.1002/smll.202106412] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/16/2021] [Indexed: 06/14/2023]
Abstract
A noninvasive, easy operation, and accurate diagnostic protocol is highly demanded to assess systemic lupus erythematosus (SLE) activity during pregnancy, promising real-time activity monitoring during the whole gestational period to reduce adverse pregnancy outcomes. Here, machine learning of serum metabolic fingerprints (SMFs) is developed to assess the SLE activity for pregnant women. The SMFs are directly extracted through a hollow-cobalt oxide/carbon (Co3 O4 /C)-composite-assisted laser desorption/ionization mass spectrometer (LDI MS) platform. The Co3 O4 /C composite owns enhanced light absorption, size-selective trapping, and better charge-hole separation, enabling improved ionization efficiency and selectivity for LDI MS detection toward small molecules. Metabolic fingerprints are collected from ≈0.1 µL serum within 1 s without enrichment and encoded by the optimized elastic net algorithm. The averaged area under the curve (AUC) value in the differentiation of active SLE from inactive SLE and healthy controls reaches 0.985 and 0.990, respectively. Further, a simplified panel based on four identified metabolites is built to distinguish SLE flares in pregnant women with the highest AUC value of 0.875 for the blind test. This work sets an accurate and practical protocol for SLE activity assessment during pregnancy, promoting precision diagnosis of disease status transitions in clinics.
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Affiliation(s)
- You Wang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Weikang Shu
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Sihan Lin
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Jiayue Wu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Meng Jiang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Shumin Li
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Chao Liu
- 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
| | - Congcong Pei
- 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
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Wen Di
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
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45
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Yin X, Yang J, Zhang M, Wang X, Xu W, Price CAH, Huang L, Liu W, Su H, Wang W, Chen H, Hou G, Walker M, Zhou Y, Shen Z, Liu J, Qian K, Di W. Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring. ACS NANO 2022; 16:2852-2865. [PMID: 35099942 PMCID: PMC9007521 DOI: 10.1021/acsnano.1c09864] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Chemotherapy is a primary cancer treatment strategy, the monitoring of which is critical to enhancing the survival rate and quality of life of cancer patients. However, current chemotherapy monitoring mainly relies on imaging tools with inefficient sensitivity and radiation invasiveness. Herein, we develop the bowl-shaped submicroreactor chip of Au-loaded 3-aminophenol formaldehyde resin (denoted as APF-bowl&Au) with a specifically designed structure and Au loading content. The obtained APF-bowl&Au, used as the matrix of laser desorption/ionization mass spectrometry (LDI MS), possesses an enhanced localized electromagnetic field for strengthened small metabolite detection. The APF-bowl&Au enables the extraction of serum metabolic fingerprints (SMFs), and machine learning of the SMFs achieves chemotherapy monitoring of ovarian cancer with area-under-the-curve (AUC) of 0.81-0.98. Furthermore, a serum metabolic biomarker panel is preliminarily identified, exhibiting gradual changes as the chemotherapy cycles proceed. This work provides insights into the development of nanochips and contributes to a universal detection platform for chemotherapy monitoring.
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Affiliation(s)
- Xia Yin
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
| | - Jing Yang
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
- School
of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China
| | - Mengji Zhang
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
- School
of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China
| | - Xinyao Wang
- State
Key Laboratory of Catalysis, Dalian Institute
of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, P.R. China
| | - Wei Xu
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
- School
of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China
| | - Cameron-Alexander H. Price
- The
University of Manchester at Harwell, Diamond
Light Source, Didcot, Oxfordshire OX11 0DE, U.K.
- UK Catalysis
Hub, Research Complex at Harwell, Rutherford
Appleton Laboratories, Harwell Campus, Didcot, Oxfordshire OX11 0FA, U.K.
| | - Lin Huang
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
- School
of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China
| | - Wanshan Liu
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
- School
of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China
| | - Haiyang Su
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
- School
of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China
| | - Wenjing Wang
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
| | - Hongyu Chen
- State
Key Laboratory of Catalysis, Dalian Institute
of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, P.R. China
| | - Guangjin Hou
- State
Key Laboratory of Catalysis, Dalian Institute
of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, P.R. China
| | - Mark Walker
- Department
of Obstetrics and Gynecology, University
of Ottawa, Ottawa, Ontario ON K1H 8L6, Canada
| | - Ying Zhou
- Department
of Obstetrics and Gynecology, The First Affiliated Hospital of USTC,
Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Auhui 230001, P.R. China
| | - Zhen Shen
- Department
of Obstetrics and Gynecology, The First Affiliated Hospital of USTC,
Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Auhui 230001, P.R. China
| | - Jian Liu
- State
Key Laboratory of Catalysis, Dalian Institute
of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, P.R. China
- DICP-Surrey
Joint Centre for Future Materials, Department of Chemical and Process
Engineering, and Advanced Technology Institute, University of Surrey, Guilford, Surrey GU2 7XH, U.K.
| | - Kun Qian
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
- School
of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China
| | - Wen Di
- State
Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory
of Gynecologic Oncology, Department of Obstetrics and Gynecology,
Renji Hospital, School of Medicine, Shanghai
Jiao Tong University, Shanghai, 200127, P.R. China
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46
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Automation and Standardization-A Coupled Approach towards Reproducible Sample Preparation Protocols for Nanomaterial Analysis. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27030985. [PMID: 35164246 PMCID: PMC8838799 DOI: 10.3390/molecules27030985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/23/2022] [Accepted: 01/25/2022] [Indexed: 11/16/2022]
Abstract
Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by automation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been developed, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air-liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.
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47
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Wang Y, Li B, Tian T, Liu Y, Zhang J, Qian K. Advanced on-site and in vitro signal amplification biosensors for biomolecule analysis. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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48
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Liyanage T, Alharbi B, Quan L, Esquela-Kerscher A, Slaughter G. Plasmonic-Based Biosensor for the Early Diagnosis of Prostate Cancer. ACS OMEGA 2022; 7:2411-2418. [PMID: 35071928 PMCID: PMC8771705 DOI: 10.1021/acsomega.1c06479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/17/2021] [Indexed: 06/14/2023]
Abstract
A tapered optical fiber (TOF) plasmonic biosensor was fabricated and used for the sensitive detection of a panel of microRNAs (miRNAs) in human serum obtained from noncancer and prostate cancer (PCa) patients. Oncogenic and tumor suppressor miRNAs let-7a, let-7c, miR-200b, miR-141, and miR-21 were tested as predictive cancer biomarkers since multianalyte detection minimizes false-positive and false-negative rates and establishes a strong foundation for early PCa diagnosis. The biosensing platform integrates metallic gold triangular nanoprisms (AuTNPs) laminated on the TOF to excite surface plasmon waves in the supporting metallic layer and enhance the evanescent mode of the fiber surface. This sensitive TOF plasmonic biosensor as a point-of-care (POC) cancer diagnostic tool enabled the detection of the panel of miRNAs in seven patient serums without any RNA extraction or sample amplification. The TOF plasmonic biosensor could detect miRNAs in human serum with a limit of detection between 179 and 580 aM and excellent selectivity. Statistical studies were obtained to differentiate cancerous from noncancerous samples with a p-value <0.0001. This high-throughput TOF plasmonic biosensor has the potential to expand and advance POC diagnostics for the early diagnosis of cancer.
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Affiliation(s)
- Thakshila Liyanage
- Center
for Bioelectronics, Bioelectronics Laboratory, Department of Electrical
and Computer Engineering, Old Dominion University, Norfolk, Virginia 23508, United States
| | - Bayan Alharbi
- Center
for Bioelectronics, Bioelectronics Laboratory, Department of Electrical
and Computer Engineering, Old Dominion University, Norfolk, Virginia 23508, United States
| | - Linh Quan
- Leroy
T. Canoles Jr. Cancer Research Center, Department of Microbiology
and Molecular Cell Biology, Eastern Virginia
Medical School, Norfolk, Virginia 23507, United States
| | - Aurora Esquela-Kerscher
- Leroy
T. Canoles Jr. Cancer Research Center, Department of Microbiology
and Molecular Cell Biology, Eastern Virginia
Medical School, Norfolk, Virginia 23507, United States
| | - Gymama Slaughter
- Center
for Bioelectronics, Bioelectronics Laboratory, Department of Electrical
and Computer Engineering, Old Dominion University, Norfolk, Virginia 23508, United States
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49
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Sari B, Isik M, Eylem CC, Kilic C, Okesola BO, Karakaya E, Emregul E, Nemutlu E, Derkus B. Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions. Mol Omics 2022; 18:591-615. [DOI: 10.1039/d2mo00060a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...
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50
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Samarah LZ, Vertes A. Mass Spectrometry Imaging of Biological Tissues by Laser Desorption Ionization from Silicon Nanopost Arrays. Methods Mol Biol 2022; 2437:89-98. [PMID: 34902142 DOI: 10.1007/978-1-0716-2030-4_6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Mass spectrometry imaging (MSI) plays an expanding role in the label-free spatial mapping of hundreds of molecules simultaneously. Currently, matrix-assisted laser desorption ionization (MALDI) is among the most widely adopted MSI techniques. However, matrix application can impact the fidelity of spatial distributions, and matrix selection and related spectral interferences in the low mass range can lead to biased molecular coverage. Nanophotonic ionization from silicon nanopost arrays (NAPA) is an emerging matrix-free MSI platform with enhanced sensitivity for several molecular classes, for example, neutral lipids and biooligomers. Here, we describe a protocol with minimal sample preparation for NAPA-MSI of metabolites, lipids, and biooligomers from biological tissues.
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Affiliation(s)
- Laith Z Samarah
- Department of Chemistry, George Washington University, Washington, DC, USA.
| | - Akos Vertes
- Department of Chemistry, George Washington University, Washington, DC, USA.
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