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Kazemzadeh M, Martinez-Calderon M, Otupiri R, Artuyants A, Lowe M, Ning X, Reategui E, Schultz ZD, Xu W, Blenkiron C, Chamley LW, Broderick NGR, Hisey CL. Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures. BIOMEDICAL OPTICS EXPRESS 2024; 15:4220-4236. [PMID: 39022543 PMCID: PMC11249694 DOI: 10.1364/boe.522376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 07/20/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.
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Affiliation(s)
- Mohammadrahim Kazemzadeh
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland 1010, New Zealand
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin 9016, New Zealand
| | | | - Robert Otupiri
- Photon Factory, University of Auckland, Auckland 1010, New Zealand
| | - Anastasiia Artuyants
- Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland 1023, New Zealand
| | - MoiMoi Lowe
- Photon Factory, University of Auckland, Auckland 1010, New Zealand
| | - Xia Ning
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| | - Eduardo Reategui
- Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Zachary D Schultz
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA
| | - Weiliang Xu
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland 1010, New Zealand
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin 9016, New Zealand
| | - Cherie Blenkiron
- Auckland Cancer Society Research Centre, Auckland 1023, New Zealand
| | - Lawrence W Chamley
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland 1023, New Zealand
- Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland 1023, New Zealand
| | - Neil G R Broderick
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin 9016, New Zealand
- Photon Factory, University of Auckland, Auckland 1010, New Zealand
| | - Colin L Hisey
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland 1023, New Zealand
- Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland 1023, New Zealand
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
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Kazemzadeh M, Martinez-Calderon M, Otupiri R, Artuyants A, Lowe MM, Ning X, Reategui E, Schultz ZD, Xu W, Blenkiron C, Chamley LW, Broderick NGR, Hisey CL. Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533481. [PMID: 36993759 PMCID: PMC10055277 DOI: 10.1101/2023.03.20.533481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.
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Zhou H, Xu L, Ren Z, Zhu J, Lee C. Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics. NANOSCALE ADVANCES 2023; 5:538-570. [PMID: 36756499 PMCID: PMC9890940 DOI: 10.1039/d2na00608a] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/06/2022] [Indexed: 06/17/2023]
Abstract
The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.
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Affiliation(s)
- Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Liangge Xu
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Zhihao Ren
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Jiaqi Zhu
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- NUS Suzhou Research Institute (NUSRI) Suzhou 215123 China
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Ding Y, Sun Y, Liu C, Jiang Q, Chen F, Cao Y. SERS-Based Biosensors Combined with Machine Learning for Medical Application. ChemistryOpen 2023; 12:e202200192. [PMID: 36627171 PMCID: PMC9831797 DOI: 10.1002/open.202200192] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.
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Affiliation(s)
- Yan Ding
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Yang Sun
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Cheng Liu
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Qiao‐Yan Jiang
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Feng Chen
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Yue Cao
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
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Kazemzadeh M, Martinez-Calderon M, Xu W, Chamley LW, Hisey CL, Broderick NGR. Cascaded Deep Convolutional Neural Networks as Improved Methods of Preprocessing Raman Spectroscopy Data. Anal Chem 2022; 94:12907-12918. [PMID: 36067379 DOI: 10.1021/acs.analchem.2c03082] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Machine learning has had a significant impact on the value of spectroscopic characterization tools, particularly in biomedical applications, due to its ability to detect latent patterns within complex spectral data. However, it often requires extensive data preprocessing, including baseline correction and denoising, which can lead to an unintentional bias during classification. To address this, we developed two deep learning methods capable of fully preprocessing raw Raman spectroscopy data without any human input. First, cascaded deep convolutional neural networks (CNN) based on either ResNet or U-Net architectures were trained on randomly generated spectra with augmented defects. Then, they were tested using simulated Raman spectra, surface-enhanced Raman spectroscopy (SERS) imaging of chemical species, low resolution Raman spectra of human bladder cancer tissue, and finally, classification of SERS spectra from human placental extracellular vesicles (EVs). Both approaches resulted in faster training and complete spectral preprocessing in a single step, with more speed, defect tolerance, and classification accuracy compared to conventional methods. These findings indicate that cascaded CNN preprocessing is ideal for biomedical Raman spectroscopy applications in which large numbers of heterogeneous spectra with diverse defects need to be automatically, rapidly, and reproducibly preprocessed.
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Affiliation(s)
- Mohammadrahim Kazemzadeh
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland1010, New Zealand.,Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin9054, New Zealand
| | | | - Weiliang Xu
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland1010, New Zealand
| | - Lawrence W Chamley
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland1023, New Zealand.,Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland1023, New Zealand
| | - Colin L Hisey
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland1023, New Zealand.,Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland1023, New Zealand.,Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio43210, United States
| | - Neil G R Broderick
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin9054, New Zealand.,Department of Physics, University of Auckland, Auckland1061, New Zealand
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