1
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Luo X, Yan S, Chen G, Wang Y, Zhang X, Lan J, Chen J, Yao X. A cavity induced mode hybridization plasmonic sensor for portable detection of exosomes. Biosens Bioelectron 2024; 261:116492. [PMID: 38870828 DOI: 10.1016/j.bios.2024.116492] [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: 01/14/2024] [Revised: 03/20/2024] [Accepted: 06/08/2024] [Indexed: 06/15/2024]
Abstract
Exosomes have been considered as promising biomarkers for cancer diagnosis due to their abundant information from originating cells. However, sensitive and reliable detection of exosomes is still facing technically challenges due to the lack of a sensing platform with high sensitivity and reproducibility. To address the challenges, here we propose a portable surface plasmon resonance (SPR) sensing of exosomes with a three-layer Au mirror/SiO2 spacer/Au nanohole sensor, fabricated by an economical polystyrene nanosphere self-assembly method. The SiO2 spacer can act as an optical cavity and induce mode hybridization, leading to excellent optimization of both sensitivity and full width at half maximum compared with normal single layer Au nanohole sensors. When modified with CD63 or EpCAM aptamers, a detection of limit (LOD) of as low as 600 particles/μL was achieved. The sensors showed good capability to distinguish between non-tumor derived L02 exosomes and tumor derived HepG2 exosomes. Additionally, high reproducibility was also achieved in detection of artificial serum samples with RSD as low as 2%, making it feasible for clinical applications. This mode hybridization plasmonic sensor provides an effective approach to optimize the detection sensitivity of exosomes, pushing SPR sensing one step further towards cancer diagnosis.
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Affiliation(s)
- Xinming Luo
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, The School of Pharmacy, Fujian Medical University, Fuzhou, 350108, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Guanyu Chen
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, The School of Pharmacy, Fujian Medical University, Fuzhou, 350108, China
| | - Yuxin Wang
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, The School of Pharmacy, Fujian Medical University, Fuzhou, 350108, China
| | - Xi Zhang
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, The School of Pharmacy, Fujian Medical University, Fuzhou, 350108, China; Innovative Drug Research Institute, Fujian Medical University, Fuzhou, 350108, China
| | - Jianming Lan
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, The School of Pharmacy, Fujian Medical University, Fuzhou, 350108, China; Innovative Drug Research Institute, Fujian Medical University, Fuzhou, 350108, China
| | - Jinghua Chen
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, The School of Pharmacy, Fujian Medical University, Fuzhou, 350108, China; Innovative Drug Research Institute, Fujian Medical University, Fuzhou, 350108, China.
| | - Xu Yao
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, The School of Pharmacy, Fujian Medical University, Fuzhou, 350108, China; Innovative Drug Research Institute, Fujian Medical University, Fuzhou, 350108, China.
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2
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Lei H, Wang H, Wang X, Xiao Z, Tian T, Cui K. Surface-enhanced Raman scattering-based identification of breast cancer progression using extracellular vesicles-derived integrin α6β4. Talanta 2024; 275:126092. [PMID: 38615459 DOI: 10.1016/j.talanta.2024.126092] [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: 02/08/2024] [Revised: 04/07/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024]
Abstract
Detection of progression is of great importance to breast cancer treatment and can benefit patients. Limited by current detection technologies and biomarkers, early breast cancer progression diagnosis remains challenging. Researchers have found blood extracellular vesicles (EVs)-derived integrin α6β4 directly facilitate progression in breast cancer, enabling cancer detection. However, EVs size and heterogeneity hinder protein detection, masked by abundant background EVs. Hence, novel tools for efficient detection of EVs with high selectivity and low interference are significantly desired. Here, a new silver-coated gold nanorods SERS probe, termed as Au@Ag@IDA-B/4MSTP, based on DNA aptamer was established for the detection of integrin α6β4 derived from EVs. Validation of the Au@Ag@IDA-B/4MSTP probes using cell-culture-derived EVs revealed a LOD of 23 particles/μL for EVs detection. This tool was further confirmed to mimic the real state of cancer with subcutaneous tumor model and lung metastasis model in mice. With 10 μL of blood plasma and simple Raman analysis process, the test achieved 85.7 % sensitivity and 83.3 % specificity. Moreover, our method achieves a simplified approach that expedites the detection process. These results demonstrate the good detection performance of Au@Ag@IDA-B/4MSTP probes for EVs integrin α6β4, and suggest that this non-invasive approach could be a promising tool for early detection of breast cancer progression.
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Affiliation(s)
- Haozhi Lei
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China; Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Shanghai, 200127, China
| | - Haoze Wang
- Department of Pharmacology and Chemical Biology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China; College of Chemistry and Materials Science, Shanghai Normal University, Shanghai, 200233, China
| | - Xiqiu Wang
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Zeyu Xiao
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China; Department of Pharmacology and Chemical Biology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Tian Tian
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
| | - Kai Cui
- Department of Pharmacology and Chemical Biology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China.
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3
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Zhang Y, Chang K, Ogunlade B, Herndon L, Tadesse LF, Kirane AR, Dionne JA. From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis. ACS NANO 2024. [PMID: 38950145 DOI: 10.1021/acsnano.4c04282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.
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Affiliation(s)
- Yirui Zhang
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Kai Chang
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States
| | - Babatunde Ogunlade
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Liam Herndon
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Loza F Tadesse
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Jameel Clinic for AI & Healthcare, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Amanda R Kirane
- Department of Surgery, Stanford University, Stanford, California 94305, United States
| | - Jennifer A Dionne
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California 94305, United States
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4
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Kneipp J, Seifert S, Gärber F. SERS microscopy as a tool for comprehensive biochemical characterization in complex samples. Chem Soc Rev 2024. [PMID: 38934892 DOI: 10.1039/d4cs00460d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Surface enhanced Raman scattering (SERS) spectra of biomaterials such as cells or tissues can be used to obtain biochemical information from nanoscopic volumes in these heterogeneous samples. This tutorial review discusses the factors that determine the outcome of a SERS experiment in complex bioorganic samples. They are related to the SERS process itself, the possibility to selectively probe certain regions or constituents of a sample, and the retrieval of the vibrational information in order to identify molecules and their interaction. After introducing basic aspects of SERS experiments in the context of biocompatible environments, spectroscopy in typical microscopic settings is exemplified, including the possibilities to combine SERS with other linear and non-linear microscopic tools, and to exploit approaches that improve lateral and temporal resolution. In particular the great variation of data in a SERS experiment calls for robust data analysis tools. Approaches will be introduced that have been originally developed in the field of bioinformatics for the application to omics data and that show specific potential in the analysis of SERS data. They include the use of simulated data and machine learning tools that can yield chemical information beyond achieving spectral classification.
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Affiliation(s)
- Janina Kneipp
- Department of Chemistry, Humboldt-Universität zu Berlin, Brook-Taylor-Str. 2, 12489 Berlin, Germany.
| | - Stephan Seifert
- Hamburg School of Food Science, Department of Chemistry, Universität Hamburg, Grindelallee 117, 20146 Hamburg, Germany
| | - Florian Gärber
- Hamburg School of Food Science, Department of Chemistry, Universität Hamburg, Grindelallee 117, 20146 Hamburg, Germany
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5
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Wang Z, Zhou X, Kong Q, He H, Sun J, Qiu W, Zhang L, Yang M. Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2401069. [PMID: 38874129 DOI: 10.1002/advs.202401069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/11/2024] [Indexed: 06/15/2024]
Abstract
In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.
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Affiliation(s)
- Zesheng Wang
- Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, Guangdong, 518000, P. R. China
- Department of Biomedical Sciences, and Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen, 518057, P. R. China
| | - Xiaoyu Zhou
- Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, Guangdong, 518000, P. R. China
- Department of Biomedical Sciences, and Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen, 518057, P. R. China
| | - Qinglong Kong
- The Second Department of Thoracic Surgery, Dalian Municipal Central Hospital, Dalian, 116033, P. R. China
| | - Huimin He
- Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, Guangdong, 518000, P. R. China
- Department of Biomedical Sciences, and Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen, 518057, P. R. China
| | - Jiayu Sun
- Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, Guangdong, 518000, P. R. China
- Department of Biomedical Sciences, and Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Wenting Qiu
- Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, Guangdong, 518000, P. R. China
- Department of Biomedical Sciences, and Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Liang Zhang
- Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, Guangdong, 518000, P. R. China
- Department of Biomedical Sciences, and Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen, 518057, P. R. China
| | - Mengsu Yang
- Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, Guangdong, 518000, P. R. China
- Department of Biomedical Sciences, and Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute of City University of Hong Kong, Shenzhen, 518057, P. R. China
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Wang Y, Ma Q, Wang T, Xing J, Li Q, Wang D, Wang G. The involvement and application potential of exosomes in breast cancer immunotherapy. Front Immunol 2024; 15:1384946. [PMID: 38835784 PMCID: PMC11148227 DOI: 10.3389/fimmu.2024.1384946] [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: 02/11/2024] [Accepted: 04/29/2024] [Indexed: 06/06/2024] Open
Abstract
Breast cancer has a high incidence and a heightened propensity for metastasis. The absence of precise targets for effective intervention makes it imperative to devise enhanced treatment strategies. Exosomes, characterized by a lipid bilayer and ranging in size from 30 to 150 nm, can be actively released by various cells, including those in tumors. Exosomes derived from distinct subsets of immune cells have been shown to modulate the immune microenvironment within tumors and influence breast cancer progression. In addition, tumor-derived exosomes have been shown to contribute to breast cancer development and progression and may become a new target for breast cancer immunotherapy. Tumor immunotherapy has become an option for managing tumors, and exosomes have become therapeutic vectors that can be used for various pathological conditions. Edited exosomes can be used as nanoscale drug delivery systems for breast cancer therapy, contributing to the remodeling of immunosuppressive tumor microenvironments and influencing the efficacy of immunotherapy. This review discusses the regulatory role of exosomes from different cells in breast cancer and the latest applications of exosomes as nanoscale drug delivery systems and immunotherapeutic agents in breast cancer, showing the development prospects of exosomes in the clinical treatment of breast cancer.
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Affiliation(s)
- Yun Wang
- Department of Thoracic Surgery, The Affliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Qiji Ma
- Department of Breast and Thyroid Surgery, The Affliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Tielin Wang
- College of Acupuncture-Moxibustion and Tuina, Changchun University of Chinese Medicine, Changchun, China
| | - Jie Xing
- Department of Breast and Thyroid Surgery, The Affliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Qirong Li
- Laboratory Animal Center, College of Animal Science, Jilin University, Changchun, China
| | - Dongxu Wang
- Laboratory Animal Center, College of Animal Science, Jilin University, Changchun, China
| | - Gang Wang
- Department of Breast and Thyroid Surgery, The Affliated Hospital to Changchun University of Chinese Medicine, Changchun, China
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7
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Li X, Liu Y, Fan Y, Tian G, Shen B, Zhang S, Fu X, He W, Tao X, Ding X, Li X, Ding S. Advanced Nanoencapsulation-Enabled Ultrasensitive Analysis: Unraveling Tumor Extracellular Vesicle Subpopulations for Differential Diagnosis of Hepatocellular Carcinoma via DNA Cascade Reactions. ACS NANO 2024; 18:11389-11403. [PMID: 38628141 DOI: 10.1021/acsnano.4c01310] [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: 05/01/2024]
Abstract
Tumor-derived extracellular vesicles (tEVs) hold immense promise as potential biomarkers for the precise diagnosis of hepatocellular carcinoma (HCC). However, their clinical translation is hampered by their inherent characteristics, such as small size and high heterogeneity and complex environment, including non-EV particles and normal cell-derived EVs, which prolong separation procedures and compromise detection accuracy. In this study, we devised a DNA cascade reaction-triggered individual EV nanoencapsulation (DCR-IEVN) strategy to achieve the ultrasensitive and specific detection of tEV subpopulations via routine flow cytometry in a one-pot, one-step fashion. DCR-IEVN enables the direct and selective packaging of multiple tEV subpopulations in clinical serum samples into flower-like particles exceeding 600 nm. This approach bypasses the need for EV isolation, effectively reducing interference from non-EV particles and nontumor EVs. Compared with conventional analytical technologies, DCR-IEVN exhibits superior efficacy in diagnosing HCC owing to its high selectivity for tEVs. Integration of machine learning algorithms with DCR-IEVN resulted in differential diagnosis accuracy of 96.7% for the training cohort (n = 120) and 93.3% for the validation cohort (n = 30), effectively distinguishing HCC, cirrhosis, and healthy donors. This strategy offers a streamlined workflow and rapid assay completion and requires only small-volume serum samples and routine clinical devices, facilitating the clinical translation of tEV-based tumor diagnosis.
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Affiliation(s)
- Xinyu Li
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Yuanjie Liu
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Yunpeng Fan
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Department of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400016, China
| | - Gang Tian
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Bo Shen
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Department of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400016, China
| | - Songzhi Zhang
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Xuhuai Fu
- Department of Clinical Laboratory, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital and Chongqing Cancer Institute, Chongqing 400030, China
| | - Wen He
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Xingyu Tao
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Xiaojuan Ding
- Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xinmin Li
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Department of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400016, China
| | - Shijia Ding
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
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Liu Q, Zheng J, Xie A, Chen M, Gong RY, Sheng Y, Chen HL, Qi CB. Exosome, a Rising Biomarkers in Liquid Biopsy: Advances of Label-Free and Label Strategy for Diagnosis of Cancer. Crit Rev Anal Chem 2024:1-12. [PMID: 38669199 DOI: 10.1080/10408347.2024.2339961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
Cancer is commonly considered as one of the most severe diseases, posing a significant threat to human health and society due to various serious challenges. These challenges include difficulties in accurate diagnosis and a high propensity to form metastasis. Tissue biopsy remains the gold standard for diagnosing and subtyping cancer. However, concerns arise from its invasive nature and the potential risk of metastasis during these complex diagnostic procedures. Meanwhile, liquid biopsy has recently witnessed the rapid advancements with the emergence of three prominent detection biomarkers: circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Whereas, the very low abundance of CTCs combined with the instability of ctDNA intensify the challenges and decrease the accuracy of these two biomarkers for cancer diagnosis. While exosomes have gained widespread recognition as a promising biomarker in liquid biopsy due to their relatively low-invasive detection method, excellent biostability, rich resources, high abundance, and ability to provide valuable information about cancer. Therefore, it is crucial to systematically summarize recent advancements mainly in exosome-based detection methods for early cancer diagnosis. Specifically, this review will primarily focus on label-based and label-free strategies for detecting cancer using exosomes. We anticipate that this comprehensive analysis will enhance readers' understanding of the significance and value of exosomes in the fields of cancer diagnosis and therapy.
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Affiliation(s)
- Qian Liu
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Jing Zheng
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - An Xie
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Min Chen
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Rui-Yue Gong
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Yuan Sheng
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Hong-Lei Chen
- Department of Pathology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
| | - Chu-Bo Qi
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
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9
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Wei J, Zhu K, Wang T, Qi T, Wang Z, Li J, Zong S, Cui Y. Highly Accurate Profiling of Exosome Phenotypes Using Super-resolution Tricolor Fluorescence Co-localization. ACS NANO 2024; 18:10206-10215. [PMID: 38536943 DOI: 10.1021/acsnano.4c00534] [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: 04/10/2024]
Abstract
Exosomes contain a wealth of proteomic information, presenting promising biomarkers for the noninvasive early diagnosis of diseases, especially cancer. However, it remains a great challenge to accurately and reliably distinguish exosomes secreted from different types of cell lines. Fluorescence immunoassay is frequently used for exosome detection. Nonspecific adsorption in immunoassays is unavoidable and affects the reliability of assay results. Despite the fact that various methods have been proposed to reduce nonspecific adsorption, a more effective method that can eliminate the influence of nonspecific adsorption is still lacking. Here, we report a more convenient way (named SR-TFC) to remove the artifacts caused by nonspecific adsorption, which combines tricolor fluorescence labeling of target exosomes, tricolor super-resolution imaging, and pixel counting. The pixel counting method (named CFPP) is realized by MATLAB and can eliminate nonspecific binding sites at the single-pixel level, which has never been achieved before and could improve the reliability of detection to the maximum extent. Furthermore, as a proof-of-concept, profiling of exosomal membrane proteins and identification of breast cancer subpopulations are demonstrated. To enable multiplex breast cancer phenotypic analysis, three kinds of specific proteins are labeled to obtain the 3D phenotypic information on various exosomes. Breast cancer subtypes can be accurately identified according to the super-resolution images of some clinically relevant exosomal proteins. Worth mentioning is that, by selecting other biomarkers, classification of other cancers could also be realized using SR-TFC. Hence, the present work holds great potential in clinical cancer diagnosis and precision medicine.
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Affiliation(s)
- Jinxiu Wei
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Kai Zhu
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Tingyu Wang
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Tongsheng Qi
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Zhuyuan Wang
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Jia Li
- Department of Ultrasonography, Zhongda Hospital, Medical School Southeast University, Nanjing, Jiangsu 210009, China
| | - Shenfei Zong
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Yiping Cui
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
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10
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Vázquez-Iglesias L, Stanfoca Casagrande GM, García-Lojo D, Ferro Leal L, Ngo TA, Pérez-Juste J, Reis RM, Kant K, Pastoriza-Santos I. SERS sensing for cancer biomarker: Approaches and directions. Bioact Mater 2024; 34:248-268. [PMID: 38260819 PMCID: PMC10801148 DOI: 10.1016/j.bioactmat.2023.12.018] [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: 09/21/2023] [Revised: 12/14/2023] [Accepted: 12/21/2023] [Indexed: 01/24/2024] Open
Abstract
These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.
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Affiliation(s)
- Lorena Vázquez-Iglesias
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | | | - Daniel García-Lojo
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Letícia Ferro Leal
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil
- Barretos School of Medicine Dr. Paulo Prata—FACISB, Barretos, 14785-002, Brazil
| | - Tien Anh Ngo
- Vinmec Tissue Bank, Vinmec Health Care System, Hanoi, Viet Nam
| | - Jorge Pérez-Juste
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Rui Manuel Reis
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil
- Life and Health Sciences Research Institute (ICVS), School of Medicine, Campus de Gualtar, University of Minho, 4710-057 Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, 4710-057, Braga, Portugal
| | - Krishna Kant
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Isabel Pastoriza-Santos
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
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11
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Jiang Z, Luo K, Zeng H, Li J. Monitoring of Medical Wastewater by Sensitive, Convenient, and Low-Cost Determination of Small Extracellular Vesicles Using a Glycosyl-Imprinted Sensor. ACS Sens 2024; 9:1252-1260. [PMID: 38373338 DOI: 10.1021/acssensors.3c02091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
The monitoring of small extracellular vesicles (sEVs) in medical waste is of great significance for the prevention of the spread of infectious diseases and the treatment of environmental pollutants in medical waste. Highly sensitive and selective detection methods are urgently needed due to the low content of sEVs in waste samples and the complex sample composition. Herein, a glycosyl-imprinted electrochemical sensor was constructed and a novel strategy for rapid, sensitive, and selective sEVs detection was proposed. The characteristic trisaccharide at the end of the glycosyl chain of the glycoprotein carried on the surface of the sEVs was used as the template molecule. The glycosyl-imprinted polymer films was then prepared by electropolymerization with o-phenylenediamine (o-PD) and 3-aminophenylboronic acid (m-APBA) as functional monomers. sEVs were captured by the imprinted cavities through the recognition and adsorption of glycosyl chains of glycoproteins on sEVs. The m-APBA molecule also acted as a signal probe and was then attached on the immobilized glycoprotein on the surface of sEVs by boric acid affinity. The electrochemical signal of m-APBA was amplificated due to the abundant glycoproteins on the surface of sEVs. The detection range of the sensor was 2.1 × 104 to 8.7 × 107 particles/mL, and the limit of detection was 1.7 × 104 particles/mL. The sensor was then applied to the determination of sEVs in medical wastewater and urine, which showed good selectivity, low detection cost, and good sensitivity.
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Affiliation(s)
- Zejun Jiang
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China
| | - Kui Luo
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China
| | - Honghu Zeng
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China
| | - Jianping Li
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China
- College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541004, China
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12
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Reichstein J, Müssig S, Wintzheimer S, Mandel K. Communicating Supraparticles to Enable Perceptual, Information-Providing Matter. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2306728. [PMID: 37786273 DOI: 10.1002/adma.202306728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Indexed: 10/04/2023]
Abstract
Materials are the fundament of the physical world, whereas information and its exchange are the centerpieces of the digital world. Their fruitful synergy offers countless opportunities for realizing desired digital transformation processes in the physical world of materials. Yet, to date, a perfect connection between these worlds is missing. From the perspective, this can be achieved by overcoming the paradigm of considering materials as passive objects and turning them into perceptual, information-providing matter. This matter is capable of communicating associated digitally stored information, for example, its origin, fate, and material type as well as its intactness on demand. Herein, the concept of realizing perceptual, information-providing matter by integrating customizable (sub-)micrometer-sized communicating supraparticles (CSPs) is presented. They are assembled from individual nanoparticulate and/or (macro)molecular building blocks with spectrally differentiable signals that are either robust or stimuli-susceptible. Their combination yields functional signal characteristics that provide an identification signature and one or multiple stimuli-recorder features. This enables CSPs to communicate associated digital information on the tagged material and its encountered stimuli histories upon signal readout anywhere across its life cycle. Ultimately, CSPs link the materials and digital worlds with numerous use cases thereof, in particular fostering the transition into an age of sustainability.
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Affiliation(s)
- Jakob Reichstein
- Department of Chemistry and Pharmacy, Inorganic Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstraße 1, D-91058, Erlangen, Germany
| | - Stephan Müssig
- Department of Chemistry and Pharmacy, Inorganic Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstraße 1, D-91058, Erlangen, Germany
| | - Susanne Wintzheimer
- Department of Chemistry and Pharmacy, Inorganic Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstraße 1, D-91058, Erlangen, Germany
- Fraunhofer-Institute for Silicate Research ISC, Neunerplatz 2, D-97082, Würzburg, Germany
| | - Karl Mandel
- Department of Chemistry and Pharmacy, Inorganic Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstraße 1, D-91058, Erlangen, Germany
- Fraunhofer-Institute for Silicate Research ISC, Neunerplatz 2, D-97082, Würzburg, Germany
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13
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Roerig J, Schulz-Siegmund M. Standardization Approaches for Extracellular Vesicle Loading with Oligonucleotides and Biologics. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2301763. [PMID: 37287374 DOI: 10.1002/smll.202301763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/13/2023] [Indexed: 06/09/2023]
Abstract
Extracellular vesicles (EVs) are widely recognized for their potential as drug delivery systems. EVs are membranous nanoparticles shed from cells. Among their natural features are their ability to shield cargo molecules against degradation and enable their functional internalization into target cells. Especially biological or bio-inspired large molecules (LMs), like nucleic acids, proteins, peptides, and others, may profit from encapsulation in EVs for drug delivery purposes. In the last years, a variety of loading protocols are explored for different LMs. The lack of standardization in the EV drug delivery field has impeded their comparability so far. Currently, the first reporting frameworks and workflows for EV drug loading are proposed. The aim of this review is to summarize these evolving standardization approaches and set recently developed methods into context. This will allow for enhanced comparability of future work on EV drug loading with LMs.
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Affiliation(s)
- Josepha Roerig
- Pharmaceutical Technology, Institute of Pharmacy, Medical Faculty, Leipzig University, 04317, Leipzig, Germany
| | - Michaela Schulz-Siegmund
- Pharmaceutical Technology, Institute of Pharmacy, Medical Faculty, Leipzig University, 04317, Leipzig, Germany
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14
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Erdem Ö, Eş I, Saylan Y, Atabay M, Gungen MA, Ölmez K, Denizli A, Inci F. In situ synthesis and dynamic simulation of molecularly imprinted polymeric nanoparticles on a micro-reactor system. Nat Commun 2023; 14:4840. [PMID: 37563147 PMCID: PMC10415298 DOI: 10.1038/s41467-023-40413-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 07/26/2023] [Indexed: 08/12/2023] Open
Abstract
Current practices in synthesizing molecularly imprinted polymers face challenges-lengthy process, low-productivity, the need for expensive and sophisticated equipment, and they cannot be controlled in situ synthesis. Herein, we present a micro-reactor for in situ and continuously synthesizing trillions of molecularly imprinted polymeric nanoparticles that contain molecular fingerprints of bovine serum albumin in a short period of time (5-30 min). Initially, we performed COMSOL simulation to analyze mixing efficiency with altering flow rates, and experimentally validated the platform for synthesizing nanoparticles with sizes ranging from 52-106 nm. Molecular interactions between monomers and protein were also examined by molecular docking and dynamics simulations. Afterwards, we benchmarked the micro-reactor parameters through dispersity and concentration of molecularly imprinted polymers using principal component analysis. Sensing assets of molecularly imprinted polymers were examined on a metamaterial sensor, resulting in 81% of precision with high selectivity (4.5 times), and three cycles of consecutive use. Overall, our micro-reactor stood out for its high productivity (48-288 times improvement in assay-time and 2 times improvement in reagent volume), enabling to produce 1.4-1.5 times more MIPs at one-single step, and continuous production compared to conventional strategy.
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Affiliation(s)
- Özgecan Erdem
- UNAM-National Nanotechnology Research Center, Bilkent University, 06800, Ankara, Turkey
| | - Ismail Eş
- UNAM-National Nanotechnology Research Center, Bilkent University, 06800, Ankara, Turkey
| | - Yeşeren Saylan
- Department of Chemistry, Hacettepe University, 06800, Ankara, Turkey
| | - Maryam Atabay
- UNAM-National Nanotechnology Research Center, Bilkent University, 06800, Ankara, Turkey
- Department of Chemistry, Hacettepe University, 06800, Ankara, Turkey
| | - Murat Alp Gungen
- UNAM-National Nanotechnology Research Center, Bilkent University, 06800, Ankara, Turkey
- Institute of Materials Science and Nanotechnology, Bilkent University, 06800, Ankara, Turkey
| | - Kadriye Ölmez
- UNAM-National Nanotechnology Research Center, Bilkent University, 06800, Ankara, Turkey
- Institute of Materials Science and Nanotechnology, Bilkent University, 06800, Ankara, Turkey
| | - Adil Denizli
- Department of Chemistry, Hacettepe University, 06800, Ankara, Turkey
| | - Fatih Inci
- UNAM-National Nanotechnology Research Center, Bilkent University, 06800, Ankara, Turkey.
- Institute of Materials Science and Nanotechnology, Bilkent University, 06800, Ankara, Turkey.
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15
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Lyu Y, Guo Y, Okeoma CM, Yan Z, Hu N, Li Z, Zhou S, Zhao X, Li J, Wang X. Engineered extracellular vesicles (EVs): Promising diagnostic/therapeutic tools for pediatric high-grade glioma. Biomed Pharmacother 2023; 163:114630. [PMID: 37094548 DOI: 10.1016/j.biopha.2023.114630] [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: 01/31/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/26/2023] Open
Abstract
Diffuse intrinsic pontine glioma (DIPG) is a highly malignant brain tumor that mainly occurs in children with extremely low overall survival. Traditional therapeutic strategies, such as surgical resection and chemotherapy, are not feasible mostly due to the special location and highly diffused features. Radiotherapy turns out to be the standard treatment method but with limited benefits of overall survival. A broad search for novel and targeted therapies is in the progress of both preclinical investigations and clinical trials. Extracellular vesicles (EVs) emerged as a promising diagnostic and therapeutic candidate due to their distinct biocompatibility, excellent cargo-loading-delivery capacity, high biological barrier penetration efficiency, and ease of modification. The utilization of EVs in various diseases as biomarker diagnoses or therapeutic agents is revolutionizing modern medical research and practice. In this review, we will briefly talk about the research development of DIPG, and present a detailed description of EVs in medical applications, with a discussion on the application of engineered peptides on EVs. The possibility of applying EVs as a diagnostic tool and drug delivery system in DIPG is also discussed.
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Affiliation(s)
- Yuan Lyu
- Medical Research Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Henan Joint International Laboratory of Glioma Metabolism and Microenvironment Research, Henan Provincial Department of Science and Technology, Zhengzhou, Henan 450052, China; Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yupei Guo
- Henan Joint International Laboratory of Glioma Metabolism and Microenvironment Research, Henan Provincial Department of Science and Technology, Zhengzhou, Henan 450052, China; Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan 450052, China; Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Chioma M Okeoma
- Department of Pathology, Microbiology, and Immunology, New York Medical College, Valhalla, NY 10595-1524, USA
| | - Zhaoyue Yan
- Department of Neurosurgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou 450003, China
| | - Nan Hu
- Henan Joint International Laboratory of Glioma Metabolism and Microenvironment Research, Henan Provincial Department of Science and Technology, Zhengzhou, Henan 450052, China; Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan 450052, China; Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Zian Li
- Henan Joint International Laboratory of Glioma Metabolism and Microenvironment Research, Henan Provincial Department of Science and Technology, Zhengzhou, Henan 450052, China; Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan 450052, China; Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Shaolong Zhou
- Henan Joint International Laboratory of Glioma Metabolism and Microenvironment Research, Henan Provincial Department of Science and Technology, Zhengzhou, Henan 450052, China; Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan 450052, China; Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Xin Zhao
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Junqi Li
- Medical Research Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Henan Joint International Laboratory of Glioma Metabolism and Microenvironment Research, Henan Provincial Department of Science and Technology, Zhengzhou, Henan 450052, China; Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan 450052, China.
| | - Xinjun Wang
- Henan Joint International Laboratory of Glioma Metabolism and Microenvironment Research, Henan Provincial Department of Science and Technology, Zhengzhou, Henan 450052, China; Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan 450052, China; Department of Neurosurgery, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China.
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16
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Kazemzadeh M, Martinez-Calderon M, Otupiri R, Artuyants A, Lowe MM, Ning X, Reategui E, Schultz ZD, Xu W, Blenkiron C, Chamley LW, Broderick NGR, Hisey CL. Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533481. [PMID: 36993759 PMCID: PMC10055277 DOI: 10.1101/2023.03.20.533481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.
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17
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Parlatan U, Ozen MO, Kecoglu I, Koyuncu B, Torun H, Khalafkhany D, Loc I, Ogut MG, Inci F, Akin D, Solaroglu I, Ozoren N, Unlu MB, Demirci U. Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2205519. [PMID: 36642804 DOI: 10.1002/smll.202205519] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.
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Affiliation(s)
- Ugur Parlatan
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| | - Mehmet Ozgun Ozen
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| | - Ibrahim Kecoglu
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
| | - Batuhan Koyuncu
- Department of Computer Engineering, Bogazici University, Istanbul, 34342, Turkey
| | - Hulya Torun
- Koc University Graduate School of Sciences and Engineering, Istanbul, 34450, Turkey
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, 34450, Turkey
| | - Davod Khalafkhany
- Department of Molecular Biology and Genetics, Center for Life Sciences and Technologies, Apoptosis and Cancer Immunology Laboratory (AKiL), Bogazici University, Istanbul, 34342, Turkey
| | - Irem Loc
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
| | - Mehmet Giray Ogut
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| | - Fatih Inci
- UNAM-National Nanotechnology Research Center, Bilkent University, Ankara, 06800, Turkey
- Institute of Materials Science and Nanotechnology, Bilkent University, Ankara, 06800, Turkey
| | - Demir Akin
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| | - Ihsan Solaroglu
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, 34450, Turkey
- School of Medicine, Koc University, Istanbul, 34450, Turkey
| | - Nesrin Ozoren
- Department of Molecular Biology and Genetics, Center for Life Sciences and Technologies, Apoptosis and Cancer Immunology Laboratory (AKiL), Bogazici University, Istanbul, 34342, Turkey
| | - Mehmet Burcin Unlu
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
- Faculty of Engineering, Hokkaido University, North-13 West-8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan
- Global Center for Biomedical Science and Engineering Quantum Medical Science and Engineering (GI-CoRE Cooperating Hub), Faculty of Medicine, Hokkaido University, Sapporo, 060-8638, Japan
| | - Utkan Demirci
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
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