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Zhang Y, Yue NN, Chen LY, Tian CM, Yao J, Wang LS, Liang YJ, Wei DR, Ma HL, Li DF. Exosomal biomarkers: A novel frontier in the diagnosis of gastrointestinal cancers. World J Gastrointest Oncol 2025; 17:103591. [DOI: 10.4251/wjgo.v17.i4.103591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2024] [Revised: 01/24/2025] [Accepted: 02/25/2025] [Indexed: 03/25/2025] Open
Abstract
Gastrointestinal (GI) cancers, which predominantly manifest in the stomach, colorectum, liver, esophagus, and pancreas, accounting for approximately 35% of global cancer-related mortality. The advent of liquid biopsy has introduced a pivotal diagnostic modality for the early identification of premalignant GI lesions and incipient cancers. This non-invasive technique not only facilitates prompt therapeutic intervention, but also serves as a critical adjunct in prognosticating the likelihood of tumor recurrence. The wealth of circulating exosomes present in body fluids is often enriched with proteins, lipids, microRNAs, and other RNAs derived from tumor cells. These specific cargo components are reflective of processes involved in GI tumorigenesis, tumor progression, and response to treatment. As such, they represent a group of promising biomarkers for aiding in the diagnosis of GI cancer. In this review, we delivered an exhaustive overview of the composition of exosomes and the pathways for cargo sorting within these vesicles. We laid out some of the clinical evidence that supported the utilization of exosomes as diagnostic biomarkers for GI cancers and discussed their potential for clinical application. Furthermore, we addressed the challenges encountered when harnessing exosomes as diagnostic and predictive instruments in the realm of GI cancers.
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Affiliation(s)
- Yuan Zhang
- Department of Gastroenterology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518000, Guangdong Province, China
- Department of Medical Administration, Huizhou Institute for Occupational Health, Huizhou 516000, Guangdong Province, China
| | - Ning-Ning Yue
- Department of Gastroenterology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University), Shenzhen 518000, Guangdong Province, China
| | - Li-Yu Chen
- Department of Gastroenterology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518000, Guangdong Province, China
| | - Cheng-Mei Tian
- Department of Emergency, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518000, Guangdong Province, China
| | - Jun Yao
- Department of Gastroenterology, Shenzhen People’s Hospital (Jinan University of Second Clinical Medical Sciences), Shenzhen 518000, Guangdong Province, China
| | - Li-Sheng Wang
- Department of Gastroenterology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518000, Guangdong Province, China
| | - Yu-Jie Liang
- Department of Child and Adolescent Psychiatry, Shenzhen Kangning Hospital, Shenzhen 518000, Guangdong Province, China
| | - Dao-Ru Wei
- Department of Rehabilitation, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518000, Guangdong Province, China
| | - Hua-Lin Ma
- Department of Nephrology, The Second Clinical Medical College, Jinan University, Shenzhen 518020, Guangdong Province, China
| | - De-Feng Li
- Department of Gastroenterology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518000, Guangdong Province, China
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Shah KA, Ali T, Hussain Y, Dormocara A, You B, Cui JH. Isolation, characterization and therapeutic potentials of exosomes in lung cancer: Opportunities and challenges. Biochem Biophys Res Commun 2025; 759:151707. [PMID: 40153996 DOI: 10.1016/j.bbrc.2025.151707] [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: 12/06/2024] [Revised: 02/08/2025] [Accepted: 03/25/2025] [Indexed: 04/01/2025]
Abstract
Lung cancer (LC) signifies the primary cause of cancer-related mortality, representing 24 % of all cancer fatalities. LC is intricate and necessitates innovative approaches for early detection, precise diagnosis, and tailored treatment. Exosomes (EXOs), a subclass of extracellular vesicles (EVs), are integral to LC advancement, intercellular communication, tumor spread, and resistance to anticancer therapies. EXOs represent a viable drug delivery strategy owing to their distinctive biological characteristics, such as natural origin, biocompatibility, stability in blood circulation, minimal immunogenicity, and potential for modification. They can function as vehicles for targeted pharmaceuticals and facilitate the advancement of targeted therapeutics. EXOs are pivotal in the metastatic cascade, facilitating communication between cancer cells and augmenting their invasive capacity. Nonetheless, obstacles such as enhancing cargo loading efficiency, addressing homogeneity concerns during preparation, and facilitating large-scale clinical translation persist. Interdisciplinary collaboration in research is crucial for enhancing the efficacy of EXOs drug delivery systems. This review explores the role of EXOs in LC, their potential as therapeutic agents, and challenges in their development, aiming to advance targeted treatments. Future research should concentrate on engineering optimization and developing innovative EXOs to improve flexibility and effectiveness in clinical applications.
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Affiliation(s)
- Kiramat Ali Shah
- College of Pharmaceutical Science, Soochow University, Renai Road 199, SIP, 215213, Suzhou, Jiangsu, China
| | - Tariq Ali
- Department of Civil and Environmental Engineering, Shantou University, Shantou, Guangdong, 515063, China
| | - Yaseen Hussain
- College of Pharmaceutical Science, Soochow University, Renai Road 199, SIP, 215213, Suzhou, Jiangsu, China
| | - Amos Dormocara
- College of Pharmaceutical Science, Soochow University, Renai Road 199, SIP, 215213, Suzhou, Jiangsu, China
| | - Bengang You
- College of Pharmaceutical Science, Soochow University, Renai Road 199, SIP, 215213, Suzhou, Jiangsu, China
| | - Jing-Hao Cui
- College of Pharmaceutical Science, Soochow University, Renai Road 199, SIP, 215213, Suzhou, Jiangsu, China.
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Bebesi T, Pálmai M, Szigyártó IC, Gaál A, Wacha A, Bóta A, Varga Z, Mihály J. Surface-enhanced infrared spectroscopic study of extracellular vesicles using plasmonic gold nanoparticles. Colloids Surf B Biointerfaces 2025; 246:114366. [PMID: 39531836 DOI: 10.1016/j.colsurfb.2024.114366] [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: 09/26/2024] [Revised: 10/30/2024] [Accepted: 11/06/2024] [Indexed: 11/16/2024]
Abstract
Extracellular vesicles (EVs), sub-micrometer lipid-bound particles released by most cells, are considered a novel area in both biology and medicine. Among characterization methods, infrared (IR) spectroscopy, especially attenuated total reflection (ATR), is a rapidly emerging label-free tool for molecular characterization of EVs. The relatively low number of vesicles in biological fluids (∼1010 particle/mL), however, and the complex content of the EVs' milieu (protein aggregates, lipoproteins, buffer molecules) might result in poor signal-to-noise ratio in the IR analysis of EVs. Exploiting the increment of the electromagnetic field at the surface of plasmonic nanomaterials, surface-enhanced infrared spectroscopy (SEIRS) provides an amplification of characteristic IR signals of EV samples. Negatively charged citrate-capped and positively charged cysteamine-capped gold nanoparticles with around 10 nm diameter were synthesized and tested with blood-derived EVs. Both types of gold nanoparticles contributed to an enhancement of the EVs' IR spectroscopic signature. Joint evaluation of UV-Vis and IR spectroscopic results, supported by FF-TEM images, revealed that proper interaction of gold nanoparticles with EVs is crucial, and an aggregation or clustering of gold nanoparticles is necessary to obtain the SEIRS effect. Positively charged gold nanoparticles resulted in higher enhancement, probably due to electrostatic interaction with EVs, commonly negatively charged.
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Affiliation(s)
- Tímea Bebesi
- Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences (RCNS), Magyar tudósok körútja 2, Budapest 1117, Hungary; Hevesy György PhD School of Chemistry, Eötvös Lóránd University, Pázmány Péter sétány 1/A, Budapest 1117, Hungary
| | - Marcell Pálmai
- Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences (RCNS), Magyar tudósok körútja 2, Budapest 1117, Hungary
| | - Imola Csilla Szigyártó
- Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences (RCNS), Magyar tudósok körútja 2, Budapest 1117, Hungary
| | - Anikó Gaál
- Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences (RCNS), Magyar tudósok körútja 2, Budapest 1117, Hungary
| | - András Wacha
- Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences (RCNS), Magyar tudósok körútja 2, Budapest 1117, Hungary
| | - Attila Bóta
- Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences (RCNS), Magyar tudósok körútja 2, Budapest 1117, Hungary
| | - Zoltán Varga
- Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences (RCNS), Magyar tudósok körútja 2, Budapest 1117, Hungary; Department of Physical Chemistry and Materials Science, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest 1111, Hungary
| | - Judith Mihály
- Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences (RCNS), Magyar tudósok körútja 2, Budapest 1117, Hungary; Department of Chemistry, Eszterházy Károly Catholic University, Leányka u. 6, Eger 3300, Hungary.
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Xie Y, Xu L, Zhang J, Zhang C, Hu Y, Zhang Z, Chen G, Qi S, Xu X, Wang J, Ren W, Lin J, Wu A. Precise diagnosis of tumor cells and hemocytes using ultrasensitive, stable, selective cuprous oxide composite SERS bioprobes assisted with high-efficiency separation microfluidic chips. MATERIALS HORIZONS 2024; 11:5752-5767. [PMID: 39264270 DOI: 10.1039/d4mh00791c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Efficient enrichment and accurate diagnosis of cancer cells from biological samples can guide effective treatment strategies. However, the accessibility and accuracy of rapid identification of tumor cells have been hampered due to the overlap of white blood cells (WBCs) and cancer cells in size. Therefore, a diagnosis system for the identification of tumor cells using reliable surface-enhanced Raman spectroscopy (SERS) bioprobes assisted with high-efficiency microfluidic chips for rapid enrichment of cancer cells was developed. According to this, a homogeneous flower-like Cu2O@Ag composite with high SERS performance was constructed. It showed a favorable spectral stability of 5.81% and can detect trace alizarin red (10-9 mol L-1). Finite-difference time-domain (FDTD) simulation of Cu2O, Ag and Cu2O@Ag, decreased the fluorescence lifetime of methylene blue after adsorption on Cu2O@Ag, and surface defects of Cu2O observed using a spherical aberration-corrected transmission electron microscope (AC-TEM) demonstrated that the combined effects of electromagnetic enhancement and promoted charge transfer endowed the Cu2O@Ag with good SERS activity. In addition, the modulation of the absorption properties of flower-like Cu2O@Ag composites significantly improved electromagnetic enhancement and charge transfer effects at 532 nm, providing a reliable basis for the label-free SERS detection. After the cancer cells in blood were separated by a spiral inertial microfluidic chip (purity >80%), machine learning-assisted linear discriminant analysis (LDA) successfully distinguished three types of cancer cells and WBCs with high accuracy (>90%). In conclusion, this study provides a profound reference for the rational design of SERS probes and the efficient diagnosis of malignant tumors.
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Affiliation(s)
- Yujiao Xie
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Lei Xu
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Jiahao Zhang
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Chenguang Zhang
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Yue Hu
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Zhouxu Zhang
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Guoxin Chen
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Shuyan Qi
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Xiawei Xu
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Jing Wang
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Wenzhi Ren
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Jie Lin
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
| | - Aiguo Wu
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, 315201, China
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Wang D, Shen Y, Qian H, Jiang J, Xu W. Emerging advanced approaches for liquid biopsy: in situ nucleic acid assays of extracellular vesicles. Theranostics 2024; 14:7309-7332. [PMID: 39659566 PMCID: PMC11626945 DOI: 10.7150/thno.102437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Accepted: 10/20/2024] [Indexed: 12/12/2024] Open
Abstract
Extracellular vesicles (EVs) have emerged as valuable biomarkers in liquid biopsies owing to their stability, accessibility, and ability to encapsulate nucleic acids. The majority of existing methodologies for detecting EV nucleic acid biomarkers require the lysis of EVs to extract DNA or RNA. This process is labor-intensive and may lead to the loss and degradation of nucleic acids. However, the emerging field of in situ EV assays offers innovative tools for liquid biopsy, facilitating direct profiling of nucleic acids within intact EVs and reducing sample handling procedures. This review focuses on the promising and innovative field of in situ EV nucleic acid analysis. It examines the translational potential of in situ EV nucleic acid analysis in liquid biopsies from detection strategies, diagnostic applications, and diagnostic aids for single EV analysis and machine learning techniques. We highlight the innovative approach of in situ EV nucleic acid assays and provide novel insights into advancing liquid biopsy technology. This approach shows a promising avenue for improving EV-based cancer diagnosis and guiding personalized treatment with minimal invasiveness.
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Affiliation(s)
- Dongli Wang
- Aoyang Institute of Cancer, Affiliated Aoyang Hospital of Jiangsu University, 279 Jingang Road, Suzhou Jiangsu 215600, China
- Zhenjiang Key Laboratory of High Technology Research on Exosomes Foundation and Transformation Application, Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang Jiangsu 212013, China
| | - Ye Shen
- Aoyang Institute of Cancer, Affiliated Aoyang Hospital of Jiangsu University, 279 Jingang Road, Suzhou Jiangsu 215600, China
- Zhenjiang Key Laboratory of High Technology Research on Exosomes Foundation and Transformation Application, Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang Jiangsu 212013, China
| | - Hui Qian
- Zhenjiang Key Laboratory of High Technology Research on Exosomes Foundation and Transformation Application, Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang Jiangsu 212013, China
| | - Jiajia Jiang
- Aoyang Institute of Cancer, Affiliated Aoyang Hospital of Jiangsu University, 279 Jingang Road, Suzhou Jiangsu 215600, China
| | - Wenrong Xu
- Aoyang Institute of Cancer, Affiliated Aoyang Hospital of Jiangsu University, 279 Jingang Road, Suzhou Jiangsu 215600, China
- Zhenjiang Key Laboratory of High Technology Research on Exosomes Foundation and Transformation Application, Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang Jiangsu 212013, China
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Hong Y, Yang J, Liu X, Huang S, Liang T, Bai X. Deciphering extracellular vesicles protein cargo in pancreatic cancer. Biochim Biophys Acta Rev Cancer 2024; 1879:189142. [PMID: 38914240 DOI: 10.1016/j.bbcan.2024.189142] [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: 01/13/2024] [Revised: 06/12/2024] [Accepted: 06/19/2024] [Indexed: 06/26/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) presents a significant therapeutic challenge as it is frequently diagnosed at advanced inoperable stages. Therefore, the development of a reliable screening tool for PDAC is crucial for effective prevention and treatment. Extracellular vesicles (EVs), characterized by their cup-shaped lipid bilayer structure and ubiquitous release from various cell types, offer notable advantages as an emerging liquid biopsy technique that is rapid, minimally invasive, easily sampled, and cost-effective. While EVs play a substantial role in cancer progression, EV proteins serve as direct mediators of diverse cellular behaviors and have immense potential as biomarkers for PDAC diagnosis and prognostication. This review provides an overview of EV proteins regarding PDAC diagnosis and prognostic implications as well as disease progression.
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Affiliation(s)
- Yifan Hong
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou, China; Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, China
| | - Jiaqi Yang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou, China; Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, China; Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, China
| | - Xinyuan Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou, China; Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, China
| | - Sicong Huang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou, China; Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, China
| | - Tingbo Liang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou, China; Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, China; Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, China; Cancer Center, Zhejiang University, Hangzhou, China.
| | - Xueli Bai
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou, China; Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, China; Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, China; Cancer Center, Zhejiang University, Hangzhou, China.
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Lyu N, Hassanzadeh-Barforoushi A, Rey Gomez LM, Zhang W, Wang Y. SERS biosensors for liquid biopsy towards cancer diagnosis by detection of various circulating biomarkers: current progress and perspectives. NANO CONVERGENCE 2024; 11:22. [PMID: 38811455 PMCID: PMC11136937 DOI: 10.1186/s40580-024-00428-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/09/2024] [Indexed: 05/31/2024]
Abstract
Liquid biopsy has emerged as a promising non-invasive strategy for cancer diagnosis, enabling the detection of various circulating biomarkers, including circulating tumor cells (CTCs), circulating tumor nucleic acids (ctNAs), circulating tumor-derived small extracellular vesicles (sEVs), and circulating proteins. Surface-enhanced Raman scattering (SERS) biosensors have revolutionized liquid biopsy by offering sensitive and specific detection methodologies for these biomarkers. This review comprehensively examines the application of SERS-based biosensors for identification and analysis of various circulating biomarkers including CTCs, ctNAs, sEVs and proteins in liquid biopsy for cancer diagnosis. The discussion encompasses a diverse range of SERS biosensor platforms, including label-free SERS assay, magnetic bead-based SERS assay, microfluidic device-based SERS system, and paper-based SERS assay, each demonstrating unique capabilities in enhancing the sensitivity and specificity for detection of liquid biopsy cancer biomarkers. This review critically assesses the strengths, limitations, and future directions of SERS biosensors in liquid biopsy for cancer diagnosis.
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Affiliation(s)
- Nana Lyu
- School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | | | - Laura M Rey Gomez
- School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Wei Zhang
- School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Yuling Wang
- School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia.
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Zhang Q, Ren T, Cao K, Xu Z. Advances of machine learning-assisted small extracellular vesicles detection strategy. Biosens Bioelectron 2024; 251:116076. [PMID: 38340580 DOI: 10.1016/j.bios.2024.116076] [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: 09/05/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.
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Affiliation(s)
- Qi Zhang
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Tingju Ren
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Ke Cao
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Zhangrun Xu
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China.
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Chen X, Shen J, Liu C, Shi X, Feng W, Sun H, Zhang W, Zhang S, Jiao Y, Chen J, Hao K, Gao Q, Li Y, Hong W, Wang P, Feng L, Yue S. Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification. Anal Chem 2024; 96:6158-6169. [PMID: 38602477 DOI: 10.1021/acs.analchem.3c04930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.
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Affiliation(s)
- Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Beijing 100191, China
| | - Jianghao Shen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chang Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiaoyu Shi
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Weichen Feng
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Hongyi Sun
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Weifeng Zhang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Shengpai Zhang
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Yuqing Jiao
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Jing Chen
- Su Zhou Surgi-Master High Tech Co., Ltd., Zhangjiagang, Suzhou 215626, China
| | - Kun Hao
- Research and Development Center, Beijing Yaogen Biotechnology Co., Ltd., Beijing 102600, China
| | - Qi Gao
- Research and Development Center, Beijing Yaogen Biotechnology Co., Ltd., Beijing 102600, China
| | - Yitong Li
- Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100021, China
| | - Weili Hong
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Pu Wang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Limin Feng
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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10
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Yang Y, Gao X, Zhang H, Chao F, Jiang H, Huang J, Lin J. Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 308:123764. [PMID: 38134653 DOI: 10.1016/j.saa.2023.123764] [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: 10/11/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023]
Abstract
The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.
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Affiliation(s)
- Yang Yang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
| | - Xingen Gao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
| | - Hongyi Zhang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
| | - Fei Chao
- Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China
| | - Huali Jiang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
| | - Junqi Huang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
| | - Juqiang Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
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11
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Wang Y, Fang L, Wang Y, Xiong Z. Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2300668. [PMID: 38072672 PMCID: PMC10870035 DOI: 10.1002/advs.202300668] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 09/08/2023] [Indexed: 02/17/2024]
Abstract
Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.
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Affiliation(s)
- Yumei Wang
- Department of NephrologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
| | - Liuru Fang
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| | - Yuhua Wang
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| | - Zuzhao Xiong
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
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12
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Schneider N, Hermann PC, Eiseler T, Seufferlein T. Emerging Roles of Small Extracellular Vesicles in Gastrointestinal Cancer Research and Therapy. Cancers (Basel) 2024; 16:567. [PMID: 38339318 PMCID: PMC10854789 DOI: 10.3390/cancers16030567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Discovered in the late eighties, sEVs are small extracellular nanovesicles (30-150 nm diameter) that gained increasing attention due to their profound roles in cancer, immunology, and therapeutic approaches. They were initially described as cellular waste bins; however, in recent years, sEVs have become known as important mediators of intercellular communication. They are secreted from cells in substantial amounts and exert their influence on recipient cells by signaling through cell surface receptors or transferring cargos, such as proteins, RNAs, miRNAs, or lipids. A key role of sEVs in cancer is immune modulation, as well as pro-invasive signaling and formation of pre-metastatic niches. sEVs are ideal biomarker platforms, and can be engineered as drug carriers or anti-cancer vaccines. Thus, sEVs further provide novel avenues for cancer diagnosis and treatment. This review will focus on the role of sEVs in GI-oncology and delineate their functions in cancer progression, diagnosis, and therapeutic use.
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Affiliation(s)
- Nora Schneider
- Department for Internal Medicine 1, University Clinic Ulm, 89081 Ulm, Germany; (P.C.H.); (T.S.)
| | | | - Tim Eiseler
- Correspondence: (N.S.); (T.E.); Tel.: +49-731-500-44678 (N.S.); +49-731-500-44523 (T.E.)
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13
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Tharrun Daniel Paul L, Munuswamy-Ramanujam G, Kumar RCS, Ramachandran V, Gnanasampanthapandian D, Palaniyandi K. Recent advancement in molecular markers of pancreatic cancer. BIOMARKERS IN CANCER DETECTION AND MONITORING OF THERAPEUTICS 2024:121-149. [DOI: 10.1016/b978-0-323-95114-2.00025-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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14
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Lee H, Liao JD, Wong TW, Wu CW, Huang BY, Wu SC, Shao PL, Wei YH, Cheng MH. Detection of micro-plasma-induced exosomes secretion in a fibroblast-melanoma co-culture model. Anal Chim Acta 2023; 1281:341910. [PMID: 38783745 DOI: 10.1016/j.aca.2023.341910] [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] [Received: 10/08/2023] [Accepted: 10/12/2023] [Indexed: 05/25/2024]
Abstract
BACKGROUND Melanoma is a highly aggressive tumor and a significant cause of skin cancer-related death. Timely diagnosis and treatment require identification of specific biomarkers in exosomes secreted by melanoma cells. In this study, label-free surface-enhanced Raman spectroscopy (SERS) method with size-matched selectivity was used to detect membrane proteins in exosomes released from a stimulated environment of fibroblasts (L929) co-cultured with melanoma cells (B16-F10). To promote normal secretion of exosomes, micro-plasma treatment was used to gently induce the co-cultured cells and slightly increase the stress level around the cells for subsequent detection using the SERS method. RESULTS AND DISCUSSION Firstly, changes in reactive oxygen species/reactive nitrogen species (ROS/RNS) concentrations in the cellular microenvironment and the viability and proliferation of healthy cells are assessed. Results showed that micro-plasma treatment increased extracellular ROS/RNS levels while modestly reducing cell proliferation without significantly affecting cell survival. Secondly, the particle size of secreted exosomes isolated from the culture medium of L929, B16-F10, and co-cultured cells with different micro-plasma treatment time did not increase significantly under single-cell conditions at short treatment time but might be changed under co-culture condition or longer treatment time. Third, for SERS signals related to membrane protein biomarkers, exosome markers CD9, CD63, and CD81 can be assigned to significant Raman shifts in the range of 943-1030 and 1304-1561 cm-1, while the characteristics SERS peaks of L929 and B16-F10 cells are most likely located at 1394/1404, 1271 and 1592 cm-1 respectively. SIGNIFICANCE AND NOVELTY Therefore, this micro-plasma-induced co-culture model provides a promising preclinical approach to understand the diagnostic potential of exosomes secreted by cutaneous melanoma/fibroblasts. Furthermore, the label-free SERS method with size-matched selectivity provides a novel approach to screen biomarkers in exosomes secreted by melanoma cells, aiming to reduce the use of labeling reagents and the processing time traditionally required.
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Affiliation(s)
- Han Lee
- Department of Materials Science and Engineering, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan.
| | - Jiunn-Der Liao
- Department of Materials Science and Engineering, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan.
| | - Tak-Wah Wong
- Department of Dermatology, National Cheng Kung University Hospital, Department of Biochemistry and Molecular Biology, College of Medicine, Center of Applied Nanomedicine, National Cheng Kung University, Tainan, 70101, Taiwan.
| | - Che-Wei Wu
- Regenerative Medicine and Cell Therapy Research Center, Kaohsiung Medical University, Kaohsiung, 80701, Taiwan; Orthopaedic Research Center, Kaohsiung Medical University, Kaohsiung, 80701, Taiwan.
| | - Bo-Yao Huang
- Department of Materials Science and Engineering, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan.
| | - Shun-Cheng Wu
- Regenerative Medicine and Cell Therapy Research Center, Kaohsiung Medical University, Kaohsiung, 80701, Taiwan; Orthopaedic Research Center, Kaohsiung Medical University, Kaohsiung, 80701, Taiwan.
| | - Pei-Lin Shao
- Department of Nursing, Asia University, 500 Liou Feng Road, Taichung, 413, Taiwan.
| | - Yu-Han Wei
- Department of Materials Science and Engineering, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan.
| | - Ming-Hsien Cheng
- Department of Materials Science and Engineering, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan.
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15
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Altıntaş Ö, Saylan Y. Exploring the Versatility of Exosomes: A Review on Isolation, Characterization, Detection Methods, and Diverse Applications. Anal Chem 2023; 95:16029-16048. [PMID: 37874907 DOI: 10.1021/acs.analchem.3c02224] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Extracellular vesicles (EVs) are crucial mediators of intercellular communication and can be classified based on their physical properties, biomolecular structure, and origin. Among EVs, exosomes have garnered significant attention due to their potential as therapeutic and diagnostic tools. Exosomes are released via fusion of multivesicular bodies on plasma membranes and can be isolated from various biofluids using methods such as differential ultracentrifugation, immune affinity capture, ultrafiltration, and size exclusion chromatography. Herein, an overview of different techniques for exosome characterization and isolation, as well as the diverse applications of exosome detection, including their potential use in drug delivery and disease diagnosis, is provided. Additionally, we discuss the emerging field of exosome detection by sensors, which offers an up-and-coming avenue for point-of-care diagnostic tools development. Overall, this review aims to provide a exhaustive and up-to-date summary of the current state of exosome research.
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Affiliation(s)
- Özge Altıntaş
- Hacettepe University, Department of Chemistry, 06800 Ankara, Turkey
| | - Yeşeren Saylan
- Hacettepe University, Department of Chemistry, 06800 Ankara, Turkey
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16
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Luo Z, Zhu G, Xu H, Lin D, Li J, Qu J. Combination of deep learning and 2D CARS figures for identification of amyloid-β plaques. OPTICS EXPRESS 2023; 31:34413-34427. [PMID: 37859198 DOI: 10.1364/oe.500136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/18/2023] [Indexed: 10/21/2023]
Abstract
In vivo imaging and accurate identification of amyloid-β (Aβ) plaque are crucial in Alzheimer's disease (AD) research. In this work, we propose to combine the coherent anti-Stokes Raman scattering (CARS) microscopy, a powerful detection technology for providing Raman spectra and label-free imaging, with deep learning to distinguish Aβ from non-Aβ regions in AD mice brains in vivo. The 1D CARS spectra is firstly converted to 2D CARS figures by using two different methods: spectral recurrence plot (SRP) and spectral Gramian angular field (SGAF). This can provide more learnable information to the network, improving the classification precision. We then devise a cross-stage attention network (CSAN) that automatically learns the features of Aβ plaques and non-Aβ regions by taking advantage of the computational advances in deep learning. Our algorithm yields higher accuracy, precision, sensitivity and specificity than the results of conventional multivariate statistical analysis method and 1D CARS spectra combined with deep learning, demonstrating its competence in identifying Aβ plaques. Last but not least, the CSAN framework requires no prior information on the imaging modality and may be applicable to other spectroscopy analytical fields.
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17
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Rogers NMK, Hicks E, Kan C, Martin E, Gao L, Limso C, Hendren CO, Kuehn M, Wiesner MR. Characterizing the Transport and Surface Affinity of Extracellular Vesicles Isolated from Yeast and Bacteria in Well-Characterized Porous Media. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:13182-13192. [PMID: 37606695 PMCID: PMC10483924 DOI: 10.1021/acs.est.3c03700] [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: 05/17/2023] [Revised: 07/26/2023] [Accepted: 08/08/2023] [Indexed: 08/23/2023]
Abstract
Extracellular vesicles (EVs) are membrane-bounded, nanosized particles, produced and secreted by all biological cell types. EVs are ubiquitous in the environment, operating in various roles including intercellular communication and plant immune modulation. Despite their ubiquity, the role of EV surface chemistry in determining transport has been minimally investigated. Using the zeta (ζ)-potential as a surrogate for surface charge, this work considers the deposition of EVs from the yeast, Saccharomyces cerevisiae, and two bacterial species, Staphylococcus aureus and Pseudomonas fluorescens, in well-characterized porous medium under various background conditions shown to influence the transport of other environmental colloidal particles: ionic strength and humic acid concentration. The affinity of S. cerevisiae EVs for the porous medium (glass beads) appeared to be sensitive to changes in ionic strength, as predicted by colloid stability (Derjaguin, Landau, Verwey, and Overbeek or DLVO) theory, and humic acid concentration, while P. fluorescens EVs deviated from DLVO predictions, suggesting that mechanisms other than charge stabilization may control the deposition of P. fluorescens. Calculations of attachment efficiency from these deposition studies were used to estimate EV transport using a clean-bed filtration model. Based on these calculations, EVs could be transported through such homogeneous porous media up to 15 m.
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Affiliation(s)
- Nicholas M. K. Rogers
- Department
of Mechanical Engineering, Porter School of Earth and Environmental
Studies, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ethan Hicks
- Center
for the Environmental Implications of Nanotechnology, Department of
Civil & Environmental Engineering, Duke
University, Durham, North Carolina 27708, United States
| | - Christopher Kan
- Department
of Civil & Environmental Engineering, Duke University, Durham, North Carolina 27708, United States
| | - Ethan Martin
- Department
of Civil & Environmental Engineering, Duke University, Durham, North Carolina 27708, United States
| | - Lijia Gao
- Department
of Civil & Environmental Engineering, Duke University, Durham, North Carolina 27708, United States
| | - Clariss Limso
- Department
of Biochemistry, Duke University Medical
Center, Durham, North Carolina 27710, United States
| | - Christine Ogilvie Hendren
- Department
of Geological and Environmental Sciences, Research Institute for Environment,
Energy and Economics, Appalachian State
University, Boone, North Carolina 28608, United States
| | - Meta Kuehn
- Department
of Biochemistry, Duke University Medical
Center, Durham, North Carolina 27710, United States
| | - Mark R. Wiesner
- Center
for the Environmental Implications of Nanotechnology, Department of
Civil & Environmental Engineering, Duke
University, Durham, North Carolina 27708, United States
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18
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Kshirsagar PG, De Matteis V, Pal S, Sangaru SS. Silver-Gold Alloy Nanoparticles (AgAu NPs): Photochemical Synthesis of Novel Biocompatible, Bimetallic Alloy Nanoparticles and Study of Their In Vitro Peroxidase Nanozyme Activity. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2471. [PMID: 37686979 PMCID: PMC10490118 DOI: 10.3390/nano13172471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/15/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023]
Abstract
Facile synthesis of metal nanoparticles with controlled physicochemical properties using environment-friendly reagents can open new avenues in biomedical applications. Nanomaterials with controlled physicochemical properties have opened new prospects for a variety of applications. In the present study, we report a single-step photochemical synthesis of ~5 nm-sized silver (Ag) and gold (Au) nanoparticles (NPs), and Ag-Au alloy nanoparticles using L-tyrosine. The physicochemical and surface properties of both monometallic and bimetallic NPs were investigated by analytical, spectroscopic, and microscopic techniques. Our results also displayed an interaction between L-tyrosine and surface atoms that leads to the formation of AgAu NPs by preventing the growth and aggregation of the NPs. This method efficiently produced monodispersed NPs, with a narrow-sized distribution and good stability in an aqueous solution. The cytotoxicity assessment performed on breast cancer cell lines (MCF-7) revealed that the biofriendly L-tyrosine-capped AgNPs, AuNPs, and bimetallic AgAu NPs were biocompatible. Interestingly, AgAu NPs have also unveiled controlled cytotoxicity, cell viability, and in vitro peroxidase nanozyme activity reliant on metal composition and surface coating.
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Affiliation(s)
- Prakash G. Kshirsagar
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Valeria De Matteis
- Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, 73100 Lecce, Italy;
| | - Sudipto Pal
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy;
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19
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Mallick MS, Misbah I, Ohannesian N, Shih WC. Single-Exosome Counting and 3D, Subdiffraction Limit Localization Using Dynamic Plasmonic Nanoaperture Label-Free Imaging. ADVANCED NANOBIOMED RESEARCH 2023; 3:2300039. [PMID: 38384588 PMCID: PMC10878166 DOI: 10.1002/anbr.202300039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024] Open
Abstract
Blood-circulating exosomes as a disease biomarker have great potential in clinical applications as they contain molecular information about their parental cells. However, label-free characterization of exosomes is challenging due to their small size. Without labeling, exosomes are virtually indistinguishable from other entities of similar size. Over recent years, several techniques have been developed to overcome the existing challenges. This paper demonstrates a new label-free approach based on dynamic PlAsmonic NanO-apeRture lAbel-free iMAging (D-PANORAMA), a bright-field technique implemented on arrayed gold nanodisks on invisible substrates (AGNIS). PANORAMA provides high surface sensitivity and has been shown to count single 25 nm polystyrene beads (PSB) previously. Herein, we show that using the dynamic imaging mode, D-PANORAMA can yield 3-dimensional, sub-diffraction limited localization of individual 25 nm beads. Furthermore, we demonstrate D-PANORAMA's capability to size, count, and localize the 3-dimensional, sub-diffraction limited position of individual exosomes as they bind to the AGNIS surface. We emphasize the importance of both the in-plane and out-of-plane localization, which exploit the synergy of 2-dimensional imaging and the intensity contrast.
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Affiliation(s)
- Mohammad Sadman Mallick
- Department of Electrical and Computer Engineering, University of Houston, 4800 Calhoun Road, Houston, Texas 77204, United States of America
| | - Ibrahim Misbah
- Department of Electrical and Computer Engineering, University of Houston, 4800 Calhoun Road, Houston, Texas 77204, United States of America
| | - Nareg Ohannesian
- Department of Electrical and Computer Engineering, University of Houston, 4800 Calhoun Road, Houston, Texas 77204, United States of America
| | - Wei-Chuan Shih
- Department of Electrical and Computer Engineering, University of Houston, 4800 Calhoun Road, Houston, Texas 77204, United States of America
- Department of Biomedical Engineering, University of Houston, 4800 Calhoun Road, Houston, Texas 77204, United States of America
- Department of Chemistry, University of Houston, 4800 Calhoun Road, Houston, Texas 77204, United States of America
- Program of Materials Science and Engineering, University of Houston, 4800 Calhoun Road, Houston, Texas 77204, United States of America
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20
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Faur CI, Dinu C, Toma V, Jurj A, Mărginean R, Onaciu A, Roman RC, Culic C, Chirilă M, Rotar H, Fălămaș A, Știufiuc GF, Hedeșiu M, Almășan O, Știufiuc RI. A New Detection Method of Oral and Oropharyngeal Squamous Cell Carcinoma Based on Multivariate Analysis of Surface Enhanced Raman Spectra of Salivary Exosomes. J Pers Med 2023; 13:jpm13050762. [PMID: 37240933 DOI: 10.3390/jpm13050762] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/23/2023] [Accepted: 04/27/2023] [Indexed: 05/28/2023] Open
Abstract
Raman spectroscopy recently proved a tremendous capacity to identify disease-specific markers in various (bio)samples being a non-invasive, rapid, and reliable method for cancer detection. In this study, we first aimed to record vibrational spectra of salivary exosomes isolated from oral and oropharyngeal squamous cell carcinoma patients and healthy controls using surface enhancement Raman spectroscopy (SERS). Then, we assessed this method's capacity to discriminate between malignant and non-malignant samples by means of principal component-linear discriminant analysis (PC-LDA) and we used area under the receiver operating characteristics with illustration as the area under the curve to measure the power of salivary exosomes SERS spectra analysis to identify cancer presence. The vibrational spectra were collected on a solid plasmonic substrate developed in our group, synthesized using tangential flow filtered and concentrated silver nanoparticles, capable of generating very reproducible spectra for a whole range of bioanalytes. SERS examination identified interesting variations in the vibrational bands assigned to thiocyanate, proteins, and nucleic acids between the saliva of cancer and control groups. Chemometric analysis indicated discrimination sensitivity between the two groups up to 79.3%. The sensitivity is influenced by the spectral interval used for the multivariate analysis, being lower (75.9%) when the full-range spectra were used.
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Affiliation(s)
- Cosmin Ioan Faur
- Department of Oral Radiology, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Cristian Dinu
- Department of Maxillofacial Surgery and Implantology, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Valentin Toma
- MedFuture-Research Center for Advanced Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Anca Jurj
- Research Center for Functional Genomics, Biomedicine and Translational Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Radu Mărginean
- MedFuture-Research Center for Advanced Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Anca Onaciu
- MedFuture-Research Center for Advanced Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Rareș Călin Roman
- Department of Oral and Craniomaxillofacial Surgery, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Carina Culic
- Department of Odontology, Endodontics, Oral Pathology, Faculty of Dentistry, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Magdalena Chirilă
- Department of Otorhinolaryngology, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Horațiu Rotar
- Department of Oral and Craniomaxillofacial Surgery, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Alexandra Fălămaș
- Department of Molecular and Biomolecular Physics, National Institute for Research and Development of Isotopic and Molecular Technologies, 400293 Cluj-Napoca, Romania
| | | | - Mihaela Hedeșiu
- Department of Oral Radiology, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Oana Almășan
- Department of Prosthodontics and Dental Materials, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Rares Ionuț Știufiuc
- Department of Maxillofacial Surgery and Implantology, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
- Department of Pharmaceutical Physics & Biophysics, Faculty of Pharmacy, "Iuliu Hațieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
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21
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Zou X, Huang Z, Guan C, Shi W, Gao J, Wang J, Cui Y, Wang M, Xu Y, Zhong X. Exosomal miRNAs in the microenvironment of pancreatic cancer. Clin Chim Acta 2023; 544:117360. [PMID: 37086943 DOI: 10.1016/j.cca.2023.117360] [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: 03/13/2023] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 04/24/2023]
Abstract
Pancreatic cancer (PC) is highly aggressive having an extremely poor prognosis. The tumor microenvironment (TME) of PC is complex and heterogeneous. Various cellular components in the microenvironment are capable of secreting different active substances that are involved in promoting tumor development. Their release may occur via exosomes, the most abundant extracellular vesicles (EVs), that can carry numerous factors as well as act as a mean of intercellular communication. Emerging evidence suggests that miRNAs are involved in the regulation and control of many pathological and physiological processes. They can also be transported by exosomes from donor cells to recipient cells, thereby regulating the TME. Exosomal miRNAs show promise for use as future targets for PC diagnosis and prognosis, which may reveal new treatment strategies for PC. In this paper, we review the important role of exosomal miRNAs in mediating cellular communication in the TME of PC as well as their potential use in clinical applications.
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Affiliation(s)
- Xinlei Zou
- Department of Hepatopancreatobiary Surgery, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Ziyue Huang
- Department of Hepatopancreatobiary Surgery, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Canghai Guan
- Department of Hepatopancreatobiary Surgery, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Wujiang Shi
- Department of Hepatopancreatobiary Surgery, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Jianjun Gao
- Department of Hepatopancreatobiary Surgery, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Jiangang Wang
- Central hospital of Baoji, Baoji, Shaanxi 721000, China
| | - Yunfu Cui
- Department of Hepatopancreatobiary Surgery, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Mei Wang
- Key Laboratory of Basic Pharmacology of Ministry of Education, Zunyi Medical University, Zunyi 563006, China
| | - Yi Xu
- Department of Hepatopancreatobiary Surgery, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China; Key Laboratory of Basic Pharmacology of Ministry of Education, Zunyi Medical University, Zunyi 563006, China; Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 999077, Hong Kong; Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang Province, Hangzhou 310000, China; State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
| | - Xiangyu Zhong
- Department of Hepatopancreatobiary Surgery, the 2nd Affiliated Hospital of Harbin Medical University, Harbin 150086, China.
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22
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Ren XD, Su N, Sun XG, Li WM, Li J, Li BW, Li RX, Lv J, Xu QY, Kong WL, Huang Q. Advances in liquid biopsy-based markers in NSCLC. Adv Clin Chem 2023; 114:109-150. [PMID: 37268331 DOI: 10.1016/bs.acc.2023.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Lung cancer is the second most-frequently occurring cancer and the leading cause of cancer-associated deaths worldwide. Non-small cell lung cancer (NSCLC), the most common type of lung cancer is often diagnosed in middle or advanced stages and have poor prognosis. Diagnosis of disease at an early stage is a key factor for improving prognosis and reducing mortality, whereas, the currently used diagnostic tools are not sufficiently sensitive for early-stage NSCLC. The emergence of liquid biopsy has ushered in a new era of diagnosis and management of cancers, including NSCLC, since analysis of circulating tumor-derived components, such as cell-free DNA (cfDNA), circulating tumor cells (CTCs), cell-free RNAs (cfRNAs), exosomes, tumor-educated platelets (TEPs), proteins, and metabolites in blood or other biofluids can enable early cancer detection, treatment selection, therapy monitoring and prognosis assessment. There have been great advances in liquid biopsy of NSCLC in the past few years. Hence, this chapter introduces the latest advances on the clinical application of cfDNA, CTCs, cfRNAs and exosomes, with a particular focus on their application as early markers in the diagnosis, treatment and prognosis of NSCLC.
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Affiliation(s)
- Xiao-Dong Ren
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Ning Su
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Xian-Ge Sun
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Wen-Man Li
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Jin Li
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Bo-Wen Li
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Ruo-Xu Li
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Jing Lv
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Qian-Ying Xu
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Wei-Long Kong
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China
| | - Qing Huang
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, P.R. China.
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23
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The Roles of Exosomes in Metastasis of Sarcoma: From Biomarkers to Therapeutic Targets. Biomolecules 2023; 13:biom13030456. [PMID: 36979391 PMCID: PMC10046038 DOI: 10.3390/biom13030456] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Sarcoma is a heterogeneous group of mesenchymal neoplasms with a high rate of lung metastasis. The cellular mechanisms responsible for sarcoma metastasis remain poorly understood. Furthermore, there are limited efficacious therapeutic strategies for treating metastatic sarcoma. Improved diagnostic and therapeutic modalities are of increasing importance for the treatment of sarcoma due to their high mortality in the advanced stages of the disease. Recent evidence demonstrates that the exosome, a type of extracellular vesicle released by virtually all cells in the body, is an important facilitator of intercellular communication between the cells and the surrounding environment. The exosome is gaining significant attention among the medical research community, but there is little knowledge about how the exosome affects sarcoma metastasis. In this review, we summarize the multifaceted roles of sarcoma-derived exosomes in promoting the process of metastasis via the formation of pre-metastatic niche (PMN), the regulation of immunity, angiogenesis, vascular permeability, and the migration of sarcoma cells. We also highlight the potential of exosomes as innovative diagnostic and prognostic biomarkers as well as therapeutic targets in sarcoma metastasis.
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24
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Huang X, Liu B, Guo S, Guo W, Liao K, Hu G, Shi W, Kuss M, Duryee MJ, Anderson DR, Lu Y, Duan B. SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis. Bioeng Transl Med 2023; 8:e10420. [PMID: 36925713 PMCID: PMC10013764 DOI: 10.1002/btm2.10420] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/02/2022] [Accepted: 09/18/2022] [Indexed: 11/12/2022] Open
Abstract
Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection.
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Affiliation(s)
- Xi Huang
- Department of Electrical and Computer EngineeringUniversity of Nebraska LincolnLincolnNebraskaUSA
| | - Bo Liu
- Mary & Dick Holland Regenerative Medicine ProgramUniversity of Nebraska Medical CenterOmahaNebraskaUSA
- Division of Cardiovascular Medicine, Department of Internal MedicineUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Shenghan Guo
- Department of Industrial and Systems EngineeringRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- School of Manufacturing Systems and NetworksArizona State UniversityMesaArizonaUSA
| | - Weihong Guo
- Department of Industrial and Systems EngineeringRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Ke Liao
- Department of Pharmacology and Experimental NeuroscienceUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Guoku Hu
- Department of Pharmacology and Experimental NeuroscienceUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Wen Shi
- Mary & Dick Holland Regenerative Medicine ProgramUniversity of Nebraska Medical CenterOmahaNebraskaUSA
- Division of Cardiovascular Medicine, Department of Internal MedicineUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Mitchell Kuss
- Mary & Dick Holland Regenerative Medicine ProgramUniversity of Nebraska Medical CenterOmahaNebraskaUSA
- Division of Cardiovascular Medicine, Department of Internal MedicineUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Michael J. Duryee
- Division of Rheumatology, Department of Internal MedicineUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Daniel R. Anderson
- Division of Cardiovascular Medicine, Department of Internal MedicineUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Yongfeng Lu
- Department of Electrical and Computer EngineeringUniversity of Nebraska LincolnLincolnNebraskaUSA
| | - Bin Duan
- Mary & Dick Holland Regenerative Medicine ProgramUniversity of Nebraska Medical CenterOmahaNebraskaUSA
- Department of Surgery, College of MedicineUniversity of Nebraska Medical CenterOmahaNebraskaUSA
- Department of Mechanical and Materials EngineeringUniversity of Nebraska‐LincolnLincolnNebraskaUSA
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25
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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: 20] [Impact Index Per Article: 10.0] [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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26
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Gerami MH, Khorram R, Rasoolzadegan S, Mardpour S, Nakhaei P, Hashemi S, Al-Naqeeb BZT, Aminian A, Samimi S. Emerging role of mesenchymal stem/stromal cells (MSCs) and MSCs-derived exosomes in bone- and joint-associated musculoskeletal disorders: a new frontier. Eur J Med Res 2023; 28:86. [PMID: 36803566 PMCID: PMC9939872 DOI: 10.1186/s40001-023-01034-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 01/26/2023] [Indexed: 02/22/2023] Open
Abstract
Exosomes are membranous vesicles with a 30 to 150 nm diameter secreted by mesenchymal stem/stromal cells (MSCs) and other cells, such as immune cells and cancer cells. Exosomes convey proteins, bioactive lipids, and genetic components to recipient cells, such as microRNAs (miRNAs). Consequently, they have been implicated in regulating intercellular communication mediators under physiological and pathological circumstances. Exosomes therapy as a cell-free approach bypasses many concerns regarding the therapeutic application of stem/stromal cells, including undesirable proliferation, heterogeneity, and immunogenic effects. Indeed, exosomes have become a promising strategy to treat human diseases, particularly bone- and joint-associated musculoskeletal disorders, because of their characteristics, such as potentiated stability in circulation, biocompatibility, low immunogenicity, and toxicity. In this light, a diversity of studies have indicated that inhibiting inflammation, inducing angiogenesis, provoking osteoblast and chondrocyte proliferation and migration, and negative regulation of matrix-degrading enzymes result in bone and cartilage recovery upon administration of MSCs-derived exosomes. Notwithstanding, insufficient quantity of isolated exosomes, lack of reliable potency test, and exosomes heterogeneity hurdle their application in clinics. Herein, we will deliver an outline respecting the advantages of MSCs-derived exosomes-based therapy in common bone- and joint-associated musculoskeletal disorders. Moreover, we will have a glimpse the underlying mechanism behind the MSCs-elicited therapeutic merits in these conditions.
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Affiliation(s)
- Mohammad Hadi Gerami
- grid.412571.40000 0000 8819 4698Bone and Joint Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Roya Khorram
- grid.412571.40000 0000 8819 4698Bone and Joint Diseases Research Center, Department of Orthopedic Surgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Soheil Rasoolzadegan
- grid.411600.2Department of Surgery, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saeid Mardpour
- grid.411705.60000 0001 0166 0922Department of Radiology, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Pooria Nakhaei
- grid.411705.60000 0001 0166 0922Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheyla Hashemi
- grid.411036.10000 0001 1498 685XObstetrician, Gynaecology & Infertility Department, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Amir Aminian
- Bone and Joint Reconstruction Research Center, Shafa Orthopedic Hospital, Iran University of Medical Sciences, Tehran, Iran.
| | - Sahar Samimi
- Tehran University of Medical Sciences, Tehran, Iran.
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27
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Ye S, You Q, Song S, Wang H, Wang C, Zhu L, Yang Y. Nanostructures and Nanotechnologies for the Detection of Extracellular Vesicle. Adv Biol (Weinh) 2023; 7:e2200201. [PMID: 36394211 DOI: 10.1002/adbi.202200201] [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: 07/18/2022] [Revised: 10/17/2022] [Indexed: 11/19/2022]
Abstract
Liquid biopsy has been taken as a minimally invasive examination and a promising surrogate to the clinically applied tissue-based test for the diagnosis and molecular analysis of cancer. Extracellular vesicles (EVs) carry complex molecular information from the tumor, allowing for the multicomponent analysis of cancer and would be beneficial to personalized medicine. In this review, the advanced nanomaterials and nanotechniques for the detection and molecular profiling of EVs, highlight the advantages of nanotechnology in the high-purity isolation and the high-sensitive and high-specific identification of EVs, are summarized. An outlook on the clinical application of nanotechnology-based liquid biopsy in the diagnosis, prognostication, and surveillance of cancer is also provided. It provides information for developing liquid biopsy based on EVs by discussing the advantages and challenges of functionalized nanomaterials and various nanotechnologies.
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Affiliation(s)
- Siyuan Ye
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory of Biological Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, P. R. China.,University of Chinese Academy of Sciences, Beijing, 100049, P. R. China.,Department of Chemistry, Tsinghua University, Beijing, 100084, P. R. China
| | - Qing You
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory of Biological Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, P. R. China
| | - Shuya Song
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory of Biological Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, P. R. China.,University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Huayi Wang
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory of Biological Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, P. R. China.,Translational Medicine Center, Chinese Institute for Brain Research (CIBR), Beijing, 102206, P. R. China
| | - Chen Wang
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory of Biological Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, P. R. China.,University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Ling Zhu
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory of Biological Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, P. R. China.,University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yanlian Yang
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory of Biological Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, P. R. China.,University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
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28
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Del Real Mata C, Jeanne O, Jalali M, Lu Y, Mahshid S. Nanostructured-Based Optical Readouts Interfaced with Machine Learning for Identification of Extracellular Vesicles. Adv Healthc Mater 2023; 12:e2202123. [PMID: 36443009 DOI: 10.1002/adhm.202202123] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/14/2022] [Indexed: 11/30/2022]
Abstract
Extracellular vesicles (EVs) are shed from cancer cells into body fluids, enclosing molecular information about the underlying disease with the potential for being the target cancer biomarker in emerging diagnosis approaches such as liquid biopsy. Still, the study of EVs presents major challenges due to their heterogeneity, complexity, and scarcity. Recently, liquid biopsy platforms have allowed the study of tumor-derived materials, holding great promise for early-stage diagnosis and monitoring of cancer when interfaced with novel adaptations of optical readouts and advanced machine learning analysis. Here, recent advances in labeled and label-free optical techniques such as fluorescence, plasmonic, and chromogenic-based systems interfaced with nanostructured sensors like nanoparticles, nanoholes, and nanowires, and diverse machine learning analyses are reviewed. The adaptability of the different optical methods discussed is compared and insights are provided into prospective avenues for the translation of the technological approaches for cancer diagnosis. It is discussed that the inherent augmented properties of nanostructures enhance the sensitivity of the detection of EVs. It is concluded by reviewing recent integrations of nanostructured-based optical readouts with diverse machine learning models as novel analysis ventures that can potentially increase the capability of the methods to the point of translation into diagnostic applications.
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Affiliation(s)
| | - Olivia Jeanne
- McGill University, Department of Bioengineering, Montreal, QC, H3A 0E9, Canada
| | - Mahsa Jalali
- McGill University, Department of Bioengineering, Montreal, QC, H3A 0E9, Canada
| | - Yao Lu
- McGill University, Department of Bioengineering, Montreal, QC, H3A 0E9, Canada
| | - Sara Mahshid
- McGill University, Department of Bioengineering, Montreal, QC, H3A 0E9, Canada
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29
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Taylor ML, Giacalone AG, Amrhein KD, Wilson RE, Wang Y, Huang X. Nanomaterials for Molecular Detection and Analysis of Extracellular Vesicles. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:524. [PMID: 36770486 PMCID: PMC9920192 DOI: 10.3390/nano13030524] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
Extracellular vesicles (EVs) have emerged as a novel resource of biomarkers for cancer and certain other diseases. Probing EVs in body fluids has become of major interest in the past decade in the development of a new-generation liquid biopsy for cancer diagnosis and monitoring. However, sensitive and specific molecular detection and analysis are challenging, due to the small size of EVs, low amount of antigens on individual EVs, and the complex biofluid matrix. Nanomaterials have been widely used in the technological development of protein and nucleic acid-based EV detection and analysis, owing to the unique structure and functional properties of materials at the nanometer scale. In this review, we summarize various nanomaterial-based analytical technologies for molecular EV detection and analysis. We discuss these technologies based on the major types of nanomaterials, including plasmonic, fluorescent, magnetic, organic, carbon-based, and certain other nanostructures. For each type of nanomaterial, functional properties are briefly described, followed by the applications of the nanomaterials for EV biomarker detection, profiling, and analysis in terms of detection mechanisms.
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Affiliation(s)
| | | | | | | | | | - Xiaohua Huang
- Department of Chemistry, The University of Memphis, Memphis, TN 38152, USA
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30
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Isolation, Detection and Analysis of Circulating Tumour Cells: A Nanotechnological Bioscope. Pharmaceutics 2023; 15:pharmaceutics15010280. [PMID: 36678908 PMCID: PMC9864919 DOI: 10.3390/pharmaceutics15010280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/17/2022] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
Cancer is one of the dreaded diseases to which a sizeable proportion of the population succumbs every year. Despite the tremendous growth of the health sector, spanning diagnostics to treatment, early diagnosis is still in its infancy. In this regard, circulating tumour cells (CTCs) have of late grabbed the attention of researchers in the detection of metastasis and there has been a huge surge in the surrounding research activities. Acting as a biomarker, CTCs prove beneficial in a variety of aspects. Nanomaterial-based strategies have been devised to have a tremendous impact on the early and rapid examination of tumor cells. This review provides a panoramic overview of the different nanotechnological methodologies employed along with the pharmaceutical purview of cancer. Initiating from fundamentals, the recent nanotechnological developments toward the detection, isolation, and analysis of CTCs are comprehensively delineated. The review also includes state-of-the-art implementations of nanotechnological advances in the enumeration of CTCs, along with future challenges and recommendations thereof.
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31
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Lee HG, Roh S, Kim HJ, Kim S, Hong Y, Lee G, Jeon OH. Nanoscale biophysical properties of small extracellular vesicles from senescent cells using atomic force microscopy, surface potential microscopy, and Raman spectroscopy. NANOSCALE HORIZONS 2022; 7:1488-1500. [PMID: 36111604 DOI: 10.1039/d2nh00220e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Cells secrete extracellular vesicles (EVs) carrying cell-of-origin markers to communicate with surrounding cells. EVs regulate physiological processes ranging from intercellular signaling to waste management. However, when senescent cells (SnCs) secrete EVs, the EVs, which are newly regarded as senescence-associated secretory phenotype (SASP) factors, can evoke inflammation, senescence induction, and metabolic disorders in neighboring cells. Unlike other soluble SASP factors, the biophysical properties of EVs, including small EVs (sEVs), derived from SnCs have not yet been investigated. In this study, sEVs were extracted from a human IMR90 lung fibroblast in vitro senescence model. Their biomechanical properties were mapped using atomic force microscopy-based quantitative nanomechanical techniques, surface potential microscopy, and Raman spectroscopy. The surfaces of sEVs derived from SnCs are slightly stiffer but their cores are softer than those of sEVs secreted from non-senescent cells (non-SnCs). This inversely proportional relationship between deformation and stiffness, attributed to a decrease in the concentration of genetic and protein materials inside the vesicles and the adsorption of positively charged SASP factors onto the vesicle surfaces, respectively, was found to be a peculiar characteristic of SnC-derived sEVs. Our results demonstrate that the biomechanical properties of SnC-derived sEVs differ from those of non-SnC-derived sEVs and provide insight into the mechanisms underlying their formation and composition.
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Affiliation(s)
- Hyo Gyeong Lee
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul 02841, Republic of Korea.
| | - Seokbeom Roh
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea.
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong 30019, Republic of Korea
| | - Hyun Jung Kim
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea.
- Department of Medical Device, Korea Institute of Machinery and Materials (KIMM), Daegu 42994, Republic of Korea.
| | - Seokho Kim
- Animal Biotechnology Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea
| | - Yoochan Hong
- Department of Medical Device, Korea Institute of Machinery and Materials (KIMM), Daegu 42994, Republic of Korea.
| | - Gyudo Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea.
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong 30019, Republic of Korea
| | - Ok Hee Jeon
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul 02841, Republic of Korea.
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Zheng H, Ding Q, Li C, Chen W, Chen X, Lin Q, Wang D, Weng Y, Lin D. Recent progress in surface-enhanced Raman spectroscopy-based biosensors for the detection of extracellular vesicles. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:4161-4173. [PMID: 36254847 DOI: 10.1039/d2ay01339h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Extracellular vesicles (EVs) are a type of mediator that enables intercellular communication. Moreover, EVs carry critical molecular information from parental cells, making them ideal biomarkers for clinical screening and diagnosis. Currently, several sensing technologies have been established to sensitively detect EVs. Among them, surface-enhanced Raman spectroscopy (SERS) has become a powerful analytical tool with high sensitivity and low detection limits. In this review, we first cover the biological characteristics of EVs and the principle of SERS amplification. Then, we describe the recent progress in SERS technology applied to detect EVs, including direct label-free methods and indirect labeling strategies, in which substrate fabrication and nanoprobe assembly were emphasized. Furthermore, SERS technology could also be used to characterize or monitor the behavior of programmable EVs. Finally, we discuss the prospects and issues to be addressed for the development of SERS technology for EV analysis.
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Affiliation(s)
- Hong Zheng
- Department of Otolaryngology Head and Neck Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Qin Ding
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
| | - Chen Li
- Department of Otolaryngology Head and Neck Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Wei Chen
- Department of Otolaryngology Head and Neck Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Xiaoqiang Chen
- Department of Otolaryngology Head and Neck Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Qin Lin
- Department of Otolaryngology Head and Neck Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Desheng Wang
- Department of Otolaryngology Head and Neck Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Youliang Weng
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
| | - Duo Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
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Avci E, Yilmaz H, Sahiner N, Tuna BG, Cicekdal MB, Eser M, Basak K, Altıntoprak F, Zengin I, Dogan S, Çulha M. Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection. Cancers (Basel) 2022; 14:cancers14205021. [PMID: 36291805 PMCID: PMC9600112 DOI: 10.3390/cancers14205021] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/04/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Blood is considered a rich reservoir of biomarkers for disease diagnosis. Surface-enhanced Raman scattering (SERS) is known for its high sensitivity and has been successfully employed to differentiate blood samples from cancer patients versus healthy individuals. Different from previous reports, this study aims at investigating the reliability of the observed results by varying several parameters influencing the observed spectra. Thus, blood taken from 30 healthy individuals as the control group, 30 patients with different types of cancers, and 15 patients with various types of chronic diseases were used in the study. The results revealed that spectral differences in the cancer group was directly related to the presence of cancer-related biomarkers. Although data were obtained from only small group of patients, the recorded sensitivity and specificity values clearly show the power of the technique to detect cancer. Abstract Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.
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Affiliation(s)
- Ertug Avci
- Department of Genetics and Bioengineering, Faculty of Engineering, Yeditepe University, Istanbul 34755, Turkey
| | - Hulya Yilmaz
- Sabanci University Nanotechnology Research and Application Center (SUNUM), Istanbul 34956, Turkey
| | - Nurettin Sahiner
- Department of Ophthalmology, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
- Department of Chemistry, Canakkale Onsekiz Mart University, Canakkale 17020, Turkey
| | - Bilge Guvenc Tuna
- Department of Biophysics, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Munevver Burcu Cicekdal
- Department of Medical Biology, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Mehmet Eser
- Department of General Surgery, School of Medicine, Istinye University, Istanbul 34010, Turkey
| | - Kayhan Basak
- Department of Pathology, Kartal Dr. Lütfi Kırdar City Hospital, University of Health Sciences, Istanbul 34865, Turkey
| | - Fatih Altıntoprak
- Department of General Surgery, Research and Educational Hospital, Sakarya University, Serdivan 54100, Turkey
| | - Ismail Zengin
- Department of General Surgery, Research and Educational Hospital, Sakarya University, Serdivan 54100, Turkey
| | - Soner Dogan
- Department of Medical Biology, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Mustafa Çulha
- Sabanci University Nanotechnology Research and Application Center (SUNUM), Istanbul 34956, Turkey
- The Knight Cancer Institute, Cancer Early Detection Advanced Research Center (CEDAR), Oregon Health and Science University, Portland, OR 97239, USA
- Department of Chemistry and Physics, College of Science and Mathematics, Augusta University, Augusta, GA 30912, USA
- Correspondence: or or
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Qian K, Fu W, Li T, Zhao J, Lei C, Hu S. The roles of small extracellular vesicles in cancer and immune regulation and translational potential in cancer therapy. J Exp Clin Cancer Res 2022; 41:286. [PMID: 36167539 PMCID: PMC9513874 DOI: 10.1186/s13046-022-02492-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/08/2022] [Indexed: 11/23/2022] Open
Abstract
Extracellular vesicles (EVs) facilitate the extracellular transfer of proteins, lipids, and nucleic acids and mediate intercellular communication among multiple cells in the tumour environment. Small extracellular vesicles (sEVs) are defined as EVs range in diameter from approximately 50 to 150 nm. Tumour-derived sEVs (TDsEVs) and immune cell-derived sEVs have significant immunological activities and participate in cancer progression and immune responses. Cancer-specific molecules have been identified on TDsEVs and can function as biomarkers for cancer diagnosis and prognosis, as well as allergens for TDsEVs-based vaccination. Various monocytes, including but not limited to dendritic cells (DCs), B cells, T cells, natural killer (NK) cells, macrophages, and myeloid-derived suppressor cells (MDSCs), secrete sEVs that regulate immune responses in the complex immune network with either protumour or antitumour effects. After engineered modification, sEVs from immune cells and other donor cells can provide improved targeting and biological effects. Combined with their naïve characteristics, these engineered sEVs hold great potential as drug carriers. When used in a variety of cancer therapies, they can adjunctly enhance the safety and antitumor efficacy of multiple therapeutics. In summary, both naïve sEVs in the tumour environment and engineered sEVs with effector cargoes are regarded as showing promising potential for use in cancer diagnostics and therapeutics.
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Constantinou M, Hadjigeorgiou K, Abalde-Cela S, Andreou C. Label-Free Sensing with Metal Nanostructure-Based Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis. ACS APPLIED NANO MATERIALS 2022; 5:12276-12299. [PMID: 36210923 PMCID: PMC9534173 DOI: 10.1021/acsanm.2c02392] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/27/2022] [Indexed: 05/03/2023]
Abstract
Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for the detection of small analytes with great potential for medical diagnostic applications. Its high sensitivity and excellent molecular specificity, which stems from the unique fingerprint of molecular species, have been applied toward the detection of different types of cancer. The noninvasive and rapid detection offered by SERS highlights its applicability for point-of-care (PoC) deployment for cancer diagnosis, screening, and staging, as well as for predicting tumor recurrence and treatment monitoring. This review provides an overview of the progress in label-free (direct) SERS-based chemical detection for cancer diagnosis with the main focus on the advances in the design and preparation of SERS substrates on the basis of metal nanoparticle structures formed via bottom-up strategies. It begins by introducing a synopsis of the working principles of SERS, including key chemometric approaches for spectroscopic data analysis. Then it introduces the advances of label-free sensing with SERS in cancer diagnosis using biofluids (blood, urine, saliva, sweat) and breath as the detection media. In the end, an outlook of the advances and challenges in cancer diagnosis via SERS is provided.
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Affiliation(s)
- Marios Constantinou
- Department
of Electrical and Computer Engineering, University of Cyprus, Nicosia, 2112, Cyprus
| | - Katerina Hadjigeorgiou
- Department
of Electrical and Computer Engineering, University of Cyprus, Nicosia, 2112, Cyprus
| | - Sara Abalde-Cela
- International
Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga s/n, Braga 4715-330, Portugal
| | - Chrysafis Andreou
- Department
of Electrical and Computer Engineering, University of Cyprus, Nicosia, 2112, Cyprus
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Liu Z, Li T, Wang Z, Liu J, Huang S, Min BH, An JY, Kim KM, Kim S, Chen Y, Liu H, Kim Y, Wong DT, Huang TJ, Xie YH. Gold Nanopyramid Arrays for Non-Invasive Surface-Enhanced Raman Spectroscopy-Based Gastric Cancer Detection via sEVs. ACS APPLIED NANO MATERIALS 2022; 5:12506-12517. [PMID: 36185166 PMCID: PMC9513748 DOI: 10.1021/acsanm.2c01986] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/12/2022] [Indexed: 05/05/2023]
Abstract
Gastric cancer (GC) is one of the most common and lethal types of cancer affecting over one million people, leading to 768,793 deaths globally in 2020 alone. The key for improving the survival rate lies in reliable screening and early diagnosis. Existing techniques including barium-meal gastric photofluorography and upper endoscopy can be costly and time-consuming and are thus impractical for population screening. We look instead for small extracellular vesicles (sEVs, currently also referred as exosomes) sized ⌀ 30-150 nm as a candidate. sEVs have attracted a significantly higher level of attention during the past decade or two because of their potentials in disease diagnoses and therapeutics. Here, we report that the composition information of the collective Raman-active bonds inside sEVs of human donors obtained by surface-enhanced Raman spectroscopy (SERS) holds the potential for non-invasive GC detection. SERS was triggered by the substrate of gold nanopyramid arrays we developed previously. A machine learning-based spectral feature analysis algorithm was developed for objectively distinguishing the cancer-derived sEVs from those of the non-cancer sub-population. sEVs from the tissue, blood, and saliva of GC patients and non-GC participants were collected (n = 15 each) and analyzed. The algorithm prediction accuracies were reportedly 90, 85, and 72%. "Leave-a-pair-of-samples out" validation was further performed to test the clinical potential. The area under the curve of each receiver operating characteristic curve was 0.96, 0.91, and 0.65 in tissue, blood, and saliva, respectively. In addition, by comparing the SERS fingerprints of individual vesicles, we provided a possible way of tracing the biogenesis pathways of patient-specific sEVs from tissue to blood to saliva. The methodology involved in this study is expected to be amenable for non-invasive detection of diseases other than GC.
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Affiliation(s)
- Zirui Liu
- Department
of Materials Science and Engineering, University
of California Los Angeles, Los Angeles, California 90095, United States
| | - Tieyi Li
- Department
of Materials Science and Engineering, University
of California Los Angeles, Los Angeles, California 90095, United States
| | - Zeyu Wang
- Department
of Mechanical Engineering and Material Science, Duke University, Durham, North Carolina 27708, United States
| | - Jun Liu
- Department
of Materials Science and Engineering, University
of California Los Angeles, Los Angeles, California 90095, United States
| | - Shan Huang
- Department
of Materials Science and Engineering, University
of California Los Angeles, Los Angeles, California 90095, United States
| | - Byoung Hoon Min
- Department
of Medicine, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul 135-710, Korea
| | - Ji Young An
- Department
of Medicine, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul 135-710, Korea
| | - Kyoung Mee Kim
- Department
of Pathology and Translational Genomics, Sungkyunkwan University School
of Medicine, Samsung Medical Center, Seoul 135-710, Korea
| | - Sung Kim
- Department
of Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul 135-710, Korea
| | - Yiqing Chen
- Department
of Bioengineering, University of California, Riverside, Riverside, California 92521, United States
| | - Huinan Liu
- Department
of Bioengineering, University of California, Riverside, Riverside, California 92521, United States
| | - Yong Kim
- UCLA
School of Dentistry, 10833 Le Conte Ave. Box 951668, Los Angeles, California 90095-1668, United States
| | - David T.W. Wong
- UCLA
School of Dentistry, 10833 Le Conte Ave. Box 951668, Los Angeles, California 90095-1668, United States
| | - Tony Jun Huang
- Department
of Mechanical Engineering and Material Science, Duke University, Durham, North Carolina 27708, United States
| | - Ya-Hong Xie
- Department
of Materials Science and Engineering, University
of California Los Angeles, Los Angeles, California 90095, United States
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Bioprobes-regulated precision biosensing of exosomes: From the nanovesicle surface to the inside. Coord Chem Rev 2022. [DOI: 10.1016/j.ccr.2022.214538] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Imanbekova M, Suarasan S, Lu Y, Jurchuk S, Wachsmann-Hogiu S. Recent advances in optical label-free characterization of extracellular vesicles. NANOPHOTONICS 2022; 11:2827-2863. [PMID: 35880114 PMCID: PMC9128385 DOI: 10.1515/nanoph-2022-0057] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/16/2022] [Indexed: 05/04/2023]
Abstract
Extracellular vesicles (EVs) are complex biological nanoparticles endogenously secreted by all eukaryotic cells. EVs carry a specific molecular cargo of proteins, lipids, and nucleic acids derived from cells of origin and play a significant role in the physiology and pathology of cells, organs, and organisms. Upon release, they may be found in different body fluids that can be easily accessed via noninvasive methodologies. Due to the unique information encoded in their molecular cargo, they may reflect the state of the parent cell and therefore EVs are recognized as a rich source of biomarkers for early diagnostics involving liquid biopsy. However, body fluids contain a mixture of EVs released by different types of healthy and diseased cells, making the detection of the EVs of interest very challenging. Recent research efforts have been focused on the detection and characterization of diagnostically relevant subpopulations of EVs, with emphasis on label-free methods that simplify sample preparation and are free of interfering signals. Therefore, in this paper, we review the recent progress of the label-free optical methods employed for the detection, counting, and morphological and chemical characterization of EVs. We will first briefly discuss the biology and functions of EVs, and then introduce different optical label-free techniques for rapid, precise, and nondestructive characterization of EVs such as nanoparticle tracking analysis, dynamic light scattering, atomic force microscopy, surface plasmon resonance spectroscopy, Raman spectroscopy, and SERS spectroscopy. In the end, we will discuss their applications in the detection of neurodegenerative diseases and cancer and provide an outlook on the future impact and challenges of these technologies to the field of liquid biopsy via EVs.
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Affiliation(s)
- Meruyert Imanbekova
- Bioengineering, McGill University Faculty of Engineering, Montreal, QC, Canada
| | - Sorina Suarasan
- Nanobiophotonics and Laser Microspectroscopy Center, Interdisciplinary Research Institute in Bio-Nano-Sciences, Babes-Bolyai University, T. Laurian 42, 400271, Cluj-Napoca, Romania
| | - Yao Lu
- Bioengineering, McGill University Faculty of Engineering, 3480 Rue Universite, 1006, Montreal, QC, H3C6W1, Canada
| | - Sarah Jurchuk
- Bioengineering, McGill University Faculty of Engineering, 3480 Rue Universite, Rm#350, Montreal, QC, H3A 0E9, Canada
| | - Sebastian Wachsmann-Hogiu
- Bioengineering, McGill University Faculty of Engineering, 3480 University St., MC362, Montreal, H3A 0E9l, Canada
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Bragina VA, Khomyakova E, Orlov AV, Znoyko SL, Mochalova EN, Paniushkina L, Shender VO, Erbes T, Evtushenko EG, Bagrov DV, Lavrenova VN, Nazarenko I, Nikitin PI. Highly Sensitive Nanomagnetic Quantification of Extracellular Vesicles by Immunochromatographic Strips: A Tool for Liquid Biopsy. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:1579. [PMID: 35564289 PMCID: PMC9101557 DOI: 10.3390/nano12091579] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/18/2022] [Accepted: 05/02/2022] [Indexed: 01/27/2023]
Abstract
Extracellular vesicles (EVs) are promising agents for liquid biopsy-a non-invasive approach for the diagnosis of cancer and evaluation of therapy response. However, EV potential is limited by the lack of sufficiently sensitive, time-, and cost-efficient methods for their registration. This research aimed at developing a highly sensitive and easy-to-use immunochromatographic tool based on magnetic nanoparticles for EV quantification. The tool is demonstrated by detection of EVs isolated from cell culture supernatants and various body fluids using characteristic biomarkers, CD9 and CD81, and a tumor-associated marker-epithelial cell adhesion molecules. The detection limit of 3.7 × 105 EV/µL is one to two orders better than the most sensitive traditional lateral flow system and commercial ELISA kits. The detection specificity is ensured by an isotype control line on the test strip. The tool's advantages are due to the spatial quantification of EV-bound magnetic nanolabels within the strip volume by an original electronic technique. The inexpensive tool, promising for liquid biopsy in daily clinical routines, can be extended to other relevant biomarkers.
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Affiliation(s)
- Vera A. Bragina
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia; (V.A.B.); (E.K.); (A.V.O.); (S.L.Z.); (E.N.M.)
| | - Elena Khomyakova
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia; (V.A.B.); (E.K.); (A.V.O.); (S.L.Z.); (E.N.M.)
| | - Alexey V. Orlov
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia; (V.A.B.); (E.K.); (A.V.O.); (S.L.Z.); (E.N.M.)
- Moscow Institute of Physics and Technology, 9 Institutskii per., 141700 Dolgoprudny, Russia
| | - Sergey L. Znoyko
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia; (V.A.B.); (E.K.); (A.V.O.); (S.L.Z.); (E.N.M.)
| | - Elizaveta N. Mochalova
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia; (V.A.B.); (E.K.); (A.V.O.); (S.L.Z.); (E.N.M.)
- Sirius University of Science and Technology, 1 Olympic Ave., 354340 Sochi, Russia
| | - Liliia Paniushkina
- Institute for Infection Prevention and Hospital Epidemiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (L.P.); (I.N.)
| | - Victoria O. Shender
- Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, 1a Malaya Pirogovskaya St., 119992 Moscow, Russia; (V.O.S.); (V.N.L.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya St., 117997 Moscow, Russia
| | - Thalia Erbes
- Department of Obstetrics and Gynecology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany;
| | - Evgeniy G. Evtushenko
- Lomonosov Moscow State University, 1 Leninskie Gory, 119991 Moscow, Russia; (E.G.E.); (D.V.B.)
| | - Dmitry V. Bagrov
- Lomonosov Moscow State University, 1 Leninskie Gory, 119991 Moscow, Russia; (E.G.E.); (D.V.B.)
| | - Victoria N. Lavrenova
- Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, 1a Malaya Pirogovskaya St., 119992 Moscow, Russia; (V.O.S.); (V.N.L.)
- Lomonosov Moscow State University, 1 Leninskie Gory, 119991 Moscow, Russia; (E.G.E.); (D.V.B.)
| | - Irina Nazarenko
- Institute for Infection Prevention and Hospital Epidemiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (L.P.); (I.N.)
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Petr I. Nikitin
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia; (V.A.B.); (E.K.); (A.V.O.); (S.L.Z.); (E.N.M.)
- National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31 Kashirskoe Shosse, 115409 Moscow, Russia
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Exosome detection via surface-enhanced Raman spectroscopy for cancer diagnosis. Acta Biomater 2022; 144:1-14. [PMID: 35358734 DOI: 10.1016/j.actbio.2022.03.036] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/10/2022] [Accepted: 03/22/2022] [Indexed: 02/07/2023]
Abstract
As nanoscale extracellular vesicles, exosomes are secreted by various cell types, and they are widely distributed in multiple biological fluids. Studies have shown that tumor-derived exosomes can carry a variety of primary tumor-specific molecules, which may represent a novel tool for the early detection of cancer. However, the clinical translation of exosomes remains a challenge due to the requirement of large quantities of samples when enriching the cancer-related exosomes in biological fluids, the insufficiency of traditional techniques for exosome subpopulations, and the complex exosome isolation of the current commercially available exosome phenotype profiling approaches. The evolving surface-enhanced Raman scattering (SERS) technology, with properties of unique optoelectronics, easy functionalization, and the particular interaction between light and nanoscale metallic materials, can achieve sensitive detection of exosomes without large quantities of samples and multiplexed phenotype profiling, providing a new mode of real-time and noninvasive analysis for cancer patients. In the present review, we mainly discussed exosome detection based on SERS, especially SERS immunoassay. The basic structure and function of exosomes were firstly introduced. Then, recent studies using the SERS technique for cancer detection were critically reviewed, which mainly included various SERS substrates, biological modification of SERS substrates, SERS-based exosome detection, and the combination of SERS and other technologies for cancer diagnosis. This review systematically discussed the essential aspects, limitations, and considerations of applying SERS technology in the detection and analysis of cancer-derived exosomes, which could provide a valuable reference for the early diagnosis of cancer through SERS technology. STATEMENT OF SIGNIFICANCE: Surface-enhanced Raman scattering (SERS) has been applied to exosomes detection to obtain better diagnostic results. In past three years, several reviews have been published in exosome detection, which were narrowly focus on methods of exosome detection. Selection and surface functionalization of the substrate and the combination detection with different methods based on SERS will provide new strategies for the detection of exosomes. This review will focus on the above aspects. This emerging detection method is constantly evolving and contributing to the early discovery of diseases in the future.
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Lai CH, Lee CL, Vu CA, Vu VT, Tsai YH, Chen WY, Cheng CM. Paper-Based Devices for Capturing Exosomes and Exosomal Nucleic Acids From Biological Samples. Front Bioeng Biotechnol 2022; 10:836082. [PMID: 35497368 PMCID: PMC9039228 DOI: 10.3389/fbioe.2022.836082] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/08/2022] [Indexed: 12/13/2022] Open
Abstract
Exosomes, nanovesicles derived from cells, contain a variety of biomolecules that can be considered biomarkers for disease diagnosis, including microRNAs (miRNAs). Given knowledge and demand, inexpensive, robust, and easy-to-use tools that are compatible with downstream nucleic acid detection should be developed to replace traditional methodologies for point-of-care testing (POCT) applications. This study deploys a paper-based extraction kit for exosome and exosomal miRNA analytical system with some quantifying methods to serve as an easy sample preparation for a possible POCT process. Exosomes concentrated from HCT116 cell cultures were arrested on paper-based immunoaffinity devices, which were produced by immobilizing anti-CD63 antibodies on Whatman filter paper, before being subjected to paper-based silica devices for nucleic acids to be trapped by silica nanoparticles adsorbed onto Whatman filter paper. Concentrations of captured exosomes were quantified by enzyme-linked immunosorbent assay (ELISA), demonstrating that paper-based immunoaffinity devices succeeded in capturing and determining exosome levels from cells cultured in both neutral and acidic microenvironments, whereas microRNA 21 (miR-21), a biomarker for various types of cancers and among the nucleic acids absorbed onto the silica devices, was determined by reverse transcription quantitative polymerase chain reaction (RT-qPCR) to prove that paper-based silica devices were capable of trapping exosomal nucleic acids. The developed paper-based kit and the devised procedure was successfully exploited to isolate exosomes and exosomal nucleic acids from different biological samples (platelet-poor plasma and lesion fluid) as clinical applications.
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Affiliation(s)
- Chi-Hung Lai
- Department of Chemical and Materials Engineering, National Central University, Taoyuan, Taiwan
| | - Chih-Ling Lee
- Department of Chemical and Materials Engineering, National Central University, Taoyuan, Taiwan
| | - Cao-An Vu
- Department of Chemical and Materials Engineering, National Central University, Taoyuan, Taiwan
| | - Van-Truc Vu
- Department of Chemical and Materials Engineering, National Central University, Taoyuan, Taiwan
| | - Yao-Hung Tsai
- Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Wen-Yih Chen
- Department of Chemical and Materials Engineering, National Central University, Taoyuan, Taiwan
- *Correspondence: Chao-Min Cheng, ; Wen-Yih Chen,
| | - Chao-Min Cheng
- Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu, Taiwan
- *Correspondence: Chao-Min Cheng, ; Wen-Yih Chen,
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42
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Rojalin T, Antonio D, Kulkarni A, Carney RP. Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency. APPLIED SPECTROSCOPY 2022; 76:485-495. [PMID: 34342493 PMCID: PMC8880398 DOI: 10.1177/00037028211034543] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.
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Affiliation(s)
- Tatu Rojalin
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
| | - Dexter Antonio
- Department of Chemical Engineering, University of California, Davis, Davis, CA, USA
| | - Ambarish Kulkarni
- Department of Chemical Engineering, University of California, Davis, Davis, CA, USA
| | - Randy P. Carney
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
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Qi Y, Zhang G, Yang L, Liu B, Zeng H, Xue Q, Liu D, Zheng Q, Liu Y. High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning. Anal Chem 2022; 94:6491-6501. [PMID: 35271250 DOI: 10.1021/acs.analchem.1c05098] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Raman spectroscopy, as a label-free detection technology, has been widely used in tumor diagnosis. However, most tumor diagnosis procedures utilize multivariate statistical analysis methods for classification, which poses a major bottleneck toward achieving high accuracy. Here, we propose a concept called the two-dimensional (2D) Raman figure combined with convolutional neural network (CNN) to improve the accuracy. Two-dimensional Raman figures can be obtained from four transformation methods: spectral recurrence plot (SRP), spectral Gramian angular field (SGAF), spectral short-time Fourier transform (SSTFT), and spectral Markov transition field (SMTF). Two-dimensional CNN models all yield more than 95% accuracy, which is higher than the PCA-LDA method and the Raman-spectrum-CNN method, indicating that 2D Raman figure inputs combined with CNN may be one reason for gaining excellent performances. Among 2D-CNN models, the main difference is the conversion, where SRP is based on the structure of wavenumber series with the best performances (98.9% accuracy, 99.5% sensitivity, 98.3% specificity), followed by SGAF on the wavenumber series, SSTFT on wavenumber and intensity information, and SMTF on wavenumber position information. The inclusion of external information in the conversion may be another reason for improvement in the accuracy. The excellent capability shows huge potential for tumor diagnosis via 2D Raman figures and may be applied in other spectroscopy analytical fields.
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Affiliation(s)
- Yafeng Qi
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Guochao Zhang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bangxu Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Hui Zeng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Qi Xue
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Qingfeng Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuhong Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
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44
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Yu D, Li Y, Wang M, Gu J, Xu W, Cai H, Fang X, Zhang X. Exosomes as a new frontier of cancer liquid biopsy. Mol Cancer 2022; 21:56. [PMID: 35180868 PMCID: PMC8855550 DOI: 10.1186/s12943-022-01509-9] [Citation(s) in RCA: 401] [Impact Index Per Article: 133.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/15/2022] [Indexed: 02/08/2023] Open
Abstract
Liquid biopsy, characterized by minimally invasive detection through biofluids such as blood, saliva, and urine, has emerged as a revolutionary strategy for cancer diagnosis and prognosis prediction. Exosomes are a subset of extracellular vesicles (EVs) that shuttle molecular cargoes from donor cells to recipient cells and play a crucial role in mediating intercellular communication. Increasing studies suggest that exosomes have a great promise to serve as novel biomarkers in liquid biopsy, since large quantities of exosomes are enriched in body fluids and are involved in numerous physiological and pathological processes. However, the further clinical application of exosomes has been greatly restrained by the lack of high-quality separation and component analysis methods. This review aims to provide a comprehensive overview on the conventional and novel technologies for exosome isolation, characterization and content detection. Additionally, the roles of exosomes serving as potential biomarkers in liquid biopsy for the diagnosis, treatment monitoring, and prognosis prediction of cancer are summarized. Finally, the prospects and challenges of applying exosome-based liquid biopsy to precision medicine are evaluated.
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Affiliation(s)
- Dan Yu
- Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yixin Li
- Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Maoye Wang
- Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Jianmei Gu
- Department of Clinical Laboratory Medicine, Nantong Tumor Hospital, Nantong, 226361, Jiangsu, China
| | - Wenrong Xu
- Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Hui Cai
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Hospital of Jiangsu University, Lanzhou, 730000, Gansu, China
| | - Xinjian Fang
- Department of Oncology, Lianyungang Hospital Affiliated to Jiangsu University, Lianyungang, 222000, Jiangsu, China.
| | - Xu Zhang
- Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Hospital of Jiangsu University, Lanzhou, 730000, Gansu, China.
- Department of Oncology, Lianyungang Hospital Affiliated to Jiangsu University, Lianyungang, 222000, Jiangsu, China.
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Qi Y, Yang L, Liu B, Liu L, Liu Y, Zheng Q, Liu D, Luo J. Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120400. [PMID: 34547683 DOI: 10.1016/j.saa.2021.120400] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps including staining. In this paper, we present the use of Raman spectroscopy with deep learning to achieve accurate diagnosis with stain-free process. To make the spectrum more suitable for deep learning, we utilize an unusual way of thinking which regards Raman spectral signal as a sequence and then converts it into two-dimensional Raman spectrogram by short-time Fourier transform as input. The normal-adenocarcinoma deep learning model and normal-squamous carcinoma deep learning model both achieve more than 96% accuracy, 95% sensitivity and 98% specificity when test, which higher than the conventional principal components analysis-linear discriminant analysis method with normal-adenocarcinoma model (0.896 accuracy, 0.867 sensitivity, 0.926 specificity) and normal-squamous carcinoma model (0.821 accuracy, 0.776 sensitivity, 1.000 specificity). The high performance of deep learning models provides a reliable way for intraoperative detection of marginal tissue, and is expected to reduce the detection time and save human lives.
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Affiliation(s)
- Yafeng Qi
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bangxu Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Li Liu
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuhong Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Qingfeng Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Jianbin Luo
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
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46
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Yang L, Jia J, Li S. Advances in the Application of Exosomes Identification Using Surface-Enhanced Raman Spectroscopy for the Early Detection of Cancers. Front Bioeng Biotechnol 2022; 9:808933. [PMID: 35087806 PMCID: PMC8786808 DOI: 10.3389/fbioe.2021.808933] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/17/2021] [Indexed: 12/21/2022] Open
Abstract
Exosomes are small nanoscale vesicles with a double-layered lipid membrane structure secreted by cells, and almost all types of cells can secrete exosomes. Exosomes carry a variety of biologically active contents such as nucleic acids and proteins, and play an important role not only in intercellular information exchange and signal transduction, but also in various pathophysiological processes in the human body. Surface-enhanced Raman Spectroscopy (SERS) uses light to interact with nanostructured materials such as gold and silver to produce a strong surface plasmon resonance effect, which can significantly enhance the Raman signal of molecules adsorbed on the surface of nanostructures to obtain a rich fingerprint of the sample itself or Raman probe molecules with ultra-sensitivity. The unique advantages of SERS, such as non-invasive and high sensitivity, good selectivity, fast analysis speed, and low water interference, make it a promising technology for life science and clinical testing applications. In this paper, we briefly introduce exosomes and the current main detection methods. We also describe the basic principles of SERS and the progress of the application of unlabeled and labeled SERS in exosome detection. This paper also summarizes the value of SERS-based exosome assays for early tumor diagnosis.
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Affiliation(s)
- Lu Yang
- Department of Internal Medicine, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital and Institute), Shenyang, China
| | - Jingyuan Jia
- School of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian, China
- *Correspondence: Jingyuan Jia, ; Shenglong Li,
| | - Shenglong Li
- Department of Bone and Soft Tissue Tumor Surgery, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital and Institute), Shenyang, China
- *Correspondence: Jingyuan Jia, ; Shenglong Li,
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47
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Deng F, Ratri A, Deighan C, Daaboul G, Geiger PC, Christenson LK. Single-Particle Interferometric Reflectance Imaging Characterization of Individual Extracellular Vesicles and Population Dynamics. JOURNAL OF VISUALIZED EXPERIMENTS : JOVE 2022:10.3791/62988. [PMID: 35068480 PMCID: PMC8968924 DOI: 10.3791/62988] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Extracellular vesicles (EVs) are nanometer-sized vesicles with a lipid bilayer that are secreted by most cells. EVs carry a multitude of different biological molecules, including protein, lipid, DNA, and RNA, and are postulated to facilitate cell-to-cell communication in diverse tissues and organs. Recently, EVs have attracted significant attention as biomarkers for diagnostics and therapeutic agents for various diseases. Many methods have been developed for EV characterization. However, current methods for EV analysis all have different limitations. Thus, developing efficient and effective methods for EV isolation and characterization remains one of the crucial steps for this cutting-edge research field as it matures. Here, we provide a detailed protocol outlining a single-particle interferometric reflectance imaging sensor (SP-IRIS), as a method that is capable of detecting and characterizing EVs from unpurified biological sources and purified EVs by other methodologies. This advanced technique can be used for multi-level and comprehensive measurements for the analysis of EV size, EV count, EV phenotype, and biomarker colocalization.
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Affiliation(s)
- Fengyan Deng
- University of Kansas Medical Center, Department of Molecular and Integrative Physiology, Kansas City, KS
| | - Anamika Ratri
- University of Kansas Medical Center, Department of Molecular and Integrative Physiology, Kansas City, KS
| | | | | | - Paige C. Geiger
- University of Kansas Medical Center, Department of Molecular and Integrative Physiology, Kansas City, KS
| | - Lane K. Christenson
- University of Kansas Medical Center, Department of Molecular and Integrative Physiology, Kansas City, KS
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48
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Chen J, Yao D, Chen W, Li Z, Guo Y, Zhu F, Hu X. Serum exosomal miR-451a acts as a candidate marker for pancreatic cancer. Int J Biol Markers 2022; 37:74-80. [PMID: 35001683 DOI: 10.1177/17246008211070018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVES The aim of this study was to explore the diagnostic efficiency of serum exosomal miR-451a as a novel biomarker for pancreatic cancer. METHODS Serum samples were collected prior to treatment. First, we analyzed microRNA (miRNA) profiles in serum exosomes from eight pancreatic cancer patients and eight healthy volunteers. We then validated the usefulness of the selected exosomal miRNAs as biomarkers in another 191 pancreatic cancer patients, 95 pancreatic benign disease (PB) patients, and 90 healthy controls. RESULTS The expression of miR-451a in serum-derived exosomes from pancreatic cancer patients was significantly upregulated compared with those from PB patients and healthy individuals. Serum exosomal miR-451a showed excellent diagnostic power in identifying pancreatic cancer patients. In addition, exosomal miR-451a showed a significant association with clinical stage and distant metastasis in pancreatic cancer, and the expression level of serum exosomal miR-451a was sensitive to therapy and relapse. CONCLUSIONS Serum exosomal miR-451a might serve as a novel diagnostic marker for pancreatic cancer.
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Affiliation(s)
- Jia Chen
- Department of Laboratory Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China.,Co-first author
| | - Dongting Yao
- Department of Laboratory Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China.,Co-first author
| | - Weiqin Chen
- Department of Laboratory Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
| | - Zhen Li
- Department of Laboratory Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
| | - Yuanyuan Guo
- Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Kunming Medical University, Kunming 650000, China
| | - Fan Zhu
- Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Kunming Medical University, Kunming 650000, China
| | - Xiaobo Hu
- Department of Laboratory Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
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49
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File N, Carmicheal J, Krasnoslobodtsev AV, Japp NC, Souchek JJ, Chakravarty S, Hollingsworth MA, Sasson AA, Natarajan G, Kshirsagar PG, Jain M, Hayashi C, Junker WM, Kaur S, Batra SK. Substituent Effects Impact Surface Charge and Aggregation of Thiophenol-Labeled Gold Nanoparticles for SERS Biosensors. BIOSENSORS 2022; 12:25. [PMID: 35049653 PMCID: PMC8773556 DOI: 10.3390/bios12010025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/21/2021] [Accepted: 12/30/2021] [Indexed: 11/17/2022]
Abstract
SERS immunoassay biosensors hold immense potential for clinical diagnostics due to their high sensitivity and growing interest in multi-marker panels. However, their development has been hindered by difficulties in designing compatible extrinsic Raman labels. Prior studies have largely focused on spectroscopic characteristics in selecting Raman reporter molecules (RRMs) for multiplexing since the presence of well-differentiated spectra is essential for simultaneous detection. However, these candidates often induce aggregation of the gold nanoparticles used as SERS nanotags despite their similarity to other effective RRMs. Thus, an improved understanding of factors affecting the aggregation of RRM-coated gold nanoparticles is needed. Substituent electronic effects on particle stability were investigated using various para-substituted thiophenols. The inductive and resonant effects of functional group modifications were strongly correlated with nanoparticle surface charge and hence their stability. Treatment with thiophenols diminished the negative surface charge of citrate-stabilized gold nanoparticles, but electron-withdrawing substituents limited the magnitude of this diminishment. It is proposed that this phenomenon arises by affecting the interplay of competing sulfur binding modes. This has wide-reaching implications for the design of biosensors using thiol-modified gold surfaces. A proof-of-concept multiplexed SERS biosensor was designed according to these findings using the two thiophenol compounds with the most electron-withdrawing substitutions: NO2 and CN.
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Affiliation(s)
- Nolan File
- Sanguine Diagnostics and Therapeutics Inc., Omaha, NE 68106, USA; (N.F.); (N.C.J.); (J.J.S.); (M.A.H.); (A.A.S.); (W.M.J.)
- School of Chemistry, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Joseph Carmicheal
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
| | | | - Nicole C. Japp
- Sanguine Diagnostics and Therapeutics Inc., Omaha, NE 68106, USA; (N.F.); (N.C.J.); (J.J.S.); (M.A.H.); (A.A.S.); (W.M.J.)
| | - Joshua J. Souchek
- Sanguine Diagnostics and Therapeutics Inc., Omaha, NE 68106, USA; (N.F.); (N.C.J.); (J.J.S.); (M.A.H.); (A.A.S.); (W.M.J.)
| | - Sudesna Chakravarty
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
| | - Michael A. Hollingsworth
- Sanguine Diagnostics and Therapeutics Inc., Omaha, NE 68106, USA; (N.F.); (N.C.J.); (J.J.S.); (M.A.H.); (A.A.S.); (W.M.J.)
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Aaron A. Sasson
- Sanguine Diagnostics and Therapeutics Inc., Omaha, NE 68106, USA; (N.F.); (N.C.J.); (J.J.S.); (M.A.H.); (A.A.S.); (W.M.J.)
- Department of Surgery, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Gopalakrishnan Natarajan
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
| | - Prakash G. Kshirsagar
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
| | - Maneesh Jain
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Chihiro Hayashi
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
| | - Wade M. Junker
- Sanguine Diagnostics and Therapeutics Inc., Omaha, NE 68106, USA; (N.F.); (N.C.J.); (J.J.S.); (M.A.H.); (A.A.S.); (W.M.J.)
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
| | - Sukhwinder Kaur
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
| | - Surinder K. Batra
- Sanguine Diagnostics and Therapeutics Inc., Omaha, NE 68106, USA; (N.F.); (N.C.J.); (J.J.S.); (M.A.H.); (A.A.S.); (W.M.J.)
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.C.); (S.C.); (G.N.); (P.G.K.); (M.J.); (C.H.)
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
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50
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Penders J, Nagelkerke A, Cunnane EM, Pedersen SV, Pence IJ, Coombes RC, Stevens MM. Single Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers. ACS NANO 2021; 15:18192-18205. [PMID: 34735133 PMCID: PMC9286313 DOI: 10.1021/acsnano.1c07075] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysis─SPARTA─system through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (>95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.
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Affiliation(s)
- Jelle Penders
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - Anika Nagelkerke
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - Eoghan M. Cunnane
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - Simon V. Pedersen
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - Isaac J. Pence
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - R. Charles Coombes
- Department
of Surgery and Cancer, Hammersmith Hospital, Imperial College, London W120HS, United Kingdom
| | - Molly M. Stevens
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
- E-mail:
| |
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