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Lee S, Jue M, Lee K, Paulson B, Oh J, Cho M, Kim JK. Early-stage diagnosis of bladder cancer using surface-enhanced Raman spectroscopy combined with machine learning algorithms in a rat model. Biosens Bioelectron 2024; 246:115915. [PMID: 38081101 DOI: 10.1016/j.bios.2023.115915] [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: 04/24/2023] [Revised: 07/24/2023] [Accepted: 12/04/2023] [Indexed: 12/30/2023]
Abstract
Early diagnosis and accurate assessment of tumor development facilitate early bladder cancer resection and initiation of drug therapy. This study enabled an early, accurate, label-free, noninvasive diagnosis of bladder tumors by analyzing nano-biomarkers in a single drop of urine through surface-enhanced Raman spectroscopy (SERS). In a standard N-butyl-N-4-hydroxybutyl nitrosamine-induced rat model of bladder cancer, cancer stage and polyp tumor development were monitored using a small endoscope with a diameter of 1.2 mm in a minimally invasive manner without the need to kill the rats. Samples were divided into cancer-free, early-stage, and polyp-form cancer. Training data were classified according to micro-cystoscopic 5-aminolevulinic acid fluorescence diagnosis, and specimens were postmortem verified through histopathological analysis. A drop of urine from each sample group was placed on an Au-coated zinc oxide nanoporous chip to filter nano-biomaterials and selectively enhance the Raman signals of nanoscale analytes via SERS. Principal component analysis was used to reduce the dimensionality of the collected Raman spectra, and partial least squares discriminant analysis was used to find diagnostic clusters based on the labeled samples. The combination of SERS and machine learning achieved an accuracy ≥99.6% in diagnosing both early- and polyp-stage bladder tumors. With an area under the receiver operating characteristic curve greater than 0.996, the accuracy of the diagnosis in the rat model suggests that SERS-based diagnostic methods are promising when coupled with machine learning. Low-cost, label-free, and noninvasive surface-enhanced Raman spectra are ideal for developing clinically relevant point-of-care diagnostic techniques.
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Affiliation(s)
- Sanghwa Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea
| | - Miyeon Jue
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea; Apollon, Inc., 68 Achasan-ro, Seongdong-gu, Seoul, 05505, Republic of Korea
| | - Kwanhee Lee
- Department of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea
| | - Bjorn Paulson
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea; Morgridge Institute for Research, Madison, WI, 53715, USA
| | - Jeongmin Oh
- Department of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea
| | - Minju Cho
- Department of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea
| | - Jun Ki Kim
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea; Department of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea.
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Oshima Y, Haruki T, Koizumi K, Yonezawa S, Taketani A, Kadowaki M, Saito S. Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy. Int J Mol Sci 2023; 24:12170. [PMID: 37569541 PMCID: PMC10418989 DOI: 10.3390/ijms241512170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/23/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.
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Affiliation(s)
- Yusuke Oshima
- Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Medicine, Oita University, Yufu 879-5593, Japan
| | - Takayuki Haruki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Sustainable Design, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-8555, Japan
| | - Shota Yonezawa
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Akinori Taketani
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
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Lee S, Jue M, Cho M, Lee K, Paulson B, Jo H, Song JS, Kang S, Kim JK. Label-free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface-enhanced Raman spectroscopy validated by machine learning algorithm. Bioeng Transl Med 2023; 8:e10529. [PMID: 37476064 PMCID: PMC10354754 DOI: 10.1002/btm2.10529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/28/2023] [Accepted: 04/12/2023] [Indexed: 07/22/2023] Open
Abstract
The direct preventative detection of flow-induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface-enhanced Raman spectroscopy (SERS) from single-drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high-fat diet to rapidly induce disturbed flow-induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro-magnetic resonance imaging (micro-MRI) and histology in comparison to the right carotid artery. Single-drop blood samples are deposited on chips of gold-coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA-based analysis was validated against an independent test sample and compared against the PCA-PLS-DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification-based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.
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Affiliation(s)
- Sanghwa Lee
- Biomedical Engineering Research CenterAsan Medical CenterSeoulRepublic of Korea
| | - Miyeon Jue
- Biomedical Engineering Research CenterAsan Medical CenterSeoulRepublic of Korea
| | - Minju Cho
- Biomedical Engineering Research CenterAsan Medical CenterSeoulRepublic of Korea
| | - Kwanhee Lee
- Biomedical Engineering Research CenterAsan Medical CenterSeoulRepublic of Korea
| | - Bjorn Paulson
- Biomedical Engineering Research CenterAsan Medical CenterSeoulRepublic of Korea
| | - Hanjoong Jo
- Wallace H. Coulter Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Joon Seon Song
- Department of PathologyUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulRepublic of Korea
| | - Soo‐Jin Kang
- Department of CardiologyUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulRepublic of Korea
| | - Jun Ki Kim
- Biomedical Engineering Research CenterAsan Medical CenterSeoulRepublic of Korea
- Department of Biomedical EngineeringUniversity of Ulsan, College of MedicineSeoulRepublic of Korea
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