1
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Ember K, Dallaire F, Plante A, Sheehy G, Guiot MC, Agarwal R, Yadav R, Douet A, Selb J, Tremblay JP, Dupuis A, Marple E, Urmey K, Rizea C, Harb A, McCarthy L, Schupper A, Umphlett M, Tsankova N, Leblond F, Hadjipanayis C, Petrecca K. In situ brain tumor detection using a Raman spectroscopy system-results of a multicenter study. Sci Rep 2024; 14:13309. [PMID: 38858389 PMCID: PMC11164901 DOI: 10.1038/s41598-024-62543-9] [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: 04/19/2024] [Accepted: 05/17/2024] [Indexed: 06/12/2024] Open
Abstract
Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection.
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Affiliation(s)
- Katherine Ember
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Arthur Plante
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Marie-Christine Guiot
- Division of Neuropathology, Department of Pathology, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Frédéric Leblond
- Polytechnique Montréal, Montreal, Canada.
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada.
- Institut du Cancer de Montréal, Montreal, Canada.
| | - Constantinos Hadjipanayis
- Mount Sinai Hospital, New York, NY, USA.
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
| | - Kevin Petrecca
- Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.
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2
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Cheng N, Gao Y, Ju S, Kong X, Lyu J, Hou L, Jin L, Shen B. Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124054. [PMID: 38382221 DOI: 10.1016/j.saa.2024.124054] [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: 09/12/2023] [Revised: 02/08/2024] [Accepted: 02/17/2024] [Indexed: 02/23/2024]
Abstract
Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as "fingerprint", in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.
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Affiliation(s)
- Nuo Cheng
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Yan Gao
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China; Chinese Academy of Science, Shenzhen Institutes of Advanced and Technology, Shenzhen 518000, PR China
| | - Shaowei Ju
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Xiangwei Kong
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Jiugong Lyu
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China; School of Biological Engineering, Dalian University of Technology, Dalian 116024, PR China
| | - Lijie Hou
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Lihong Jin
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Bingjun Shen
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
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3
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Klein K, Klamminger GG, Mombaerts L, Jelke F, Arroteia IF, Slimani R, Mirizzi G, Husch A, Frauenknecht KBM, Mittelbronn M, Hertel F, Kleine Borgmann FB. Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms. Molecules 2024; 29:979. [PMID: 38474491 DOI: 10.3390/molecules29050979] [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: 11/13/2023] [Revised: 12/24/2023] [Accepted: 02/07/2024] [Indexed: 03/14/2024] Open
Abstract
Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.
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Affiliation(s)
- Karoline Klein
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Gilbert Georg Klamminger
- Department of General and Special Pathology, Saarland University (USAAR), 66424 Homburg, Germany
- Department of General and Special Pathology, Saarland University Medical Center (UKS), 66424 Homburg, Germany
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
| | - Laurent Mombaerts
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Finn Jelke
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Isabel Fernandes Arroteia
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Rédouane Slimani
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
| | - Giulia Mirizzi
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Andreas Husch
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Katrin B M Frauenknecht
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
| | - Michel Mittelbronn
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Felix B Kleine Borgmann
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 1130 Luxembourg, Luxembourg
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4
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Liu X, Jiang S, Zhang T, Xu Z, Liu L, Zhang Z, Pan S, Li Y. "Magnet" Based on Activated Silver Nanoparticles Adsorbed Bacteria to Predict Refractory Apical Periodontitis Via Surface-Enhanced Raman Scattering. ACS APPLIED MATERIALS & INTERFACES 2024; 16:8499-8508. [PMID: 38335515 DOI: 10.1021/acsami.3c16677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
Refractory apical periodontitis (RAP) is an endodontic apical inflammatory disease caused by Enterococcus faecalis (E. faecalis). Bacterial detection using surface-enhanced Raman scattering (SERS) technology is a hot research topic, but the specific and direct detection of oral bacteria is a challenge, especially in real clinical samples. In this paper, we develop a novel SERS-based green platform for label-free detection of oral bacteria. The platform was built on silver nanoparticles with a two-step enhancement way using NaBH4 and sodium (Na+) to form "hot spots," which resulted in an enhanced SERS fingerprint of E. faecalis with fast, quantitative, lower-limit, reproducibility, and stability. In combination with machine learning, four different oral bacteria (E. faecalis, Porphyromonas gingivalis, Streptococcus mutans, and Escherichia coli) could be intelligently distinguished. The unlabeled detection method emphasized the specificity of E. faecalis in simulated saliva, serum, and even real samples from patients with clinical root periapical disease. In addition, the assay has been shown to be environmentally friendly and without secondary contamination through antimicrobial assays. The proposed label-free, rapid, safe, and green SERS detection strategy for oral bacteria provided an innovative solution for the early diagnosis and prevention of RAP and other perioral diseases.
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Affiliation(s)
- Xin Liu
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Shen Jiang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Ting Zhang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Inorganic Chemistry and Physical Chemistry, College of Pharmacy, Harbin Medical University, Heilongjiang 150081, P. R. China
| | - Ziming Xu
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Ling Liu
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Zhe Zhang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- College of Public Health, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Shuang Pan
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Yang Li
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine University of Oulu, 2125B, Aapistie 5A, Oulu 90220, Finland
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5
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Sarpe C, Ciobotea ER, Morscher CB, Zielinski B, Braun H, Senftleben A, Rüschoff J, Baumert T. Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning. Sci Rep 2023; 13:9250. [PMID: 37291175 PMCID: PMC10250396 DOI: 10.1038/s41598-023-36155-8] [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: 01/23/2023] [Accepted: 05/30/2023] [Indexed: 06/10/2023] Open
Abstract
In the treatment of most newly discovered solid cancerous tumors, surgery remains the first treatment option. An important factor in the success of these operations is the precise identification of oncological safety margins to ensure the complete removal of the tumor without affecting much of the neighboring healthy tissue. Here we report on the possibility of applying femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with Machine Learning algorithms as an alternative discrimination technique to differentiate cancerous tissue. The emission spectra following the ablation on thin fixed liver and breast postoperative samples were recorded with high spatial resolution; adjacent stained sections served as a reference for tissue identification by classical pathological analysis. In a proof of principle test performed on liver tissue, Artificial Neural Networks and Random Forest algorithms were able to differentiate both healthy and tumor tissue with a very high Classification Accuracy of around 0.95. The ability to identify unknown tissue was performed on breast samples from different patients, also providing a high level of discrimination. Our results show that LIBS with femtosecond lasers is a technique with potential to be used in clinical applications for rapid identification of tissue type in the intraoperative surgical field.
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Affiliation(s)
- Cristian Sarpe
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | - Elena Ramela Ciobotea
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | | | - Bastian Zielinski
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | - Hendrike Braun
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | - Arne Senftleben
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | - Josef Rüschoff
- Institut für Pathologie Nordhessen, Germaniastr. 7, 34119, Kassel, Germany
| | - Thomas Baumert
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany.
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6
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Corden C, Boitor R, Dusanjh PK, Harwood A, Mukherjee A, Gomez D, Notingher I. Autofluorescence-Raman Spectroscopy for Ex Vivo Mapping Colorectal Liver Metastases and Liver Tissue. J Surg Res 2023; 288:10-20. [PMID: 36940563 DOI: 10.1016/j.jss.2023.02.014] [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: 07/22/2022] [Revised: 01/15/2023] [Accepted: 02/17/2023] [Indexed: 03/23/2023]
Abstract
INTRODUCTION Identifying colorectal liver metastases (CRLM) during liver resection could assist in achieving clear surgical margins, which is an important prognostic variable for both disease-free and overall survival. The aim of this study was to investigate the effect of auto-fluorescence (AF) and Raman spectroscopy for ex vivo label-free discrimination of CRLMs from normal liver tissue. Secondary aims include exploring options for multimodal AF-Raman integration with respect to diagnosis accuracy and imaging speed on human liver tissue and CRLM. METHODS Liver samples were obtained from patients undergoing liver surgery for CRLM who provided informed consent (15 patients were recruited). AF and Raman spectroscopy was performed on CRLM and normal liver tissue samples and then compared to histology. RESULTS AF emission spectra demonstrated that the 671 nm and 775/785 nm excitation wavelengths provided the highest contrast, as normal liver tissue elicited on average around eight-fold higher AF intensity compared to CRLM. The use of the 785 nm wavelength had the advantage of enabling Raman spectroscopy measurements from CRLM regions, allowing discrimination of CRLM from regions of normal liver tissue eliciting unusual low AF intensity, preventing misclassification. Proof-of-concept experiments using small pieces of CRLM samples covered by large normal liver tissue demonstrated the feasibility of a dual-modality AF-Raman for detection of positive margins within few minutes. CONCLUSIONS AF imaging and Raman spectroscopy can discriminate CRLM from normal liver tissue in an ex vivo setting. These results suggest the potential for developing integrated multimodal AF-Raman imaging techniques for intraoperative assessment of surgical margins.
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Affiliation(s)
- Christopher Corden
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Radu Boitor
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Palminder Kaur Dusanjh
- Histopathology Department, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK
| | - Andrew Harwood
- Histopathology Department, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK
| | - Abhik Mukherjee
- Histopathology Department, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK; School of Medicine, University of Nottingham, Nottingham, UK
| | - Dhanwant Gomez
- Department of Hepatobiliary and Pancreatic Surgery, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK
| | - Ioan Notingher
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
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7
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Fitzgerald S, Akhtar J, Schartner E, Ebendorff-Heidepriem H, Mahadevan-Jansen A, Li J. Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis. JOURNAL OF BIOPHOTONICS 2023; 16:e202200231. [PMID: 36308009 PMCID: PMC10082563 DOI: 10.1002/jbio.202200231] [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: 07/20/2022] [Revised: 10/19/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.
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Affiliation(s)
- Sean Fitzgerald
- Vanderbilt Biophotonics Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Jobaida Akhtar
- School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia, Australia
| | - Erik Schartner
- School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia, Australia
| | - Heike Ebendorff-Heidepriem
- School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia, Australia
| | - Anita Mahadevan-Jansen
- Vanderbilt Biophotonics Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Jiawen Li
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia, Australia
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, South Australia, Australia
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8
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Walther A, Stepula E, Ditzel N, Kassem M, Bergholt MS, Hedegaard MAB. In Vivo Longitudinal Monitoring of Disease Progression in Inflammatory Arthritis Animal Models Using Raman Spectroscopy. Anal Chem 2023; 95:3720-3728. [PMID: 36757324 PMCID: PMC9949228 DOI: 10.1021/acs.analchem.2c04743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/23/2023] [Indexed: 02/10/2023]
Abstract
Current techniques for monitoring disease progression and testing drug efficacy in animal models of inflammatory arthritis are either destructive, time-consuming, subjective, or require ionizing radiation. To accommodate this, we have developed a non-invasive and label-free optical system based on Raman spectroscopy for monitoring tissue alterations in rodent models of arthritis at the biomolecular level. To test different sampling geometries, the system was designed to collect both transmission and reflection mode spectra. Mice with collagen antibody-induced arthritis and controls were subject to in vivo Raman spectroscopy at the tibiotarsal joint every 3 days for 14 days. Raman-derived measures of bone content correlated well with micro-computed tomography bone mineral densities. This allowed for time-resolved quantitation of bone densities, which indicated gradual bone erosion in mice with arthritis. Inflammatory pannus formation, bone erosion, and bone marrow inflammation were confirmed by histological analysis. In addition, using library-based spectral decomposition, we quantified the progression of bone and soft tissue components. In general, the tissue components followed significantly different tendencies in mice developing arthritis compared to the control group in line with the histological analysis. In total, this demonstrates Raman spectroscopy as a versatile technique for monitoring alterations to both mineralized and soft tissues simultaneously in rodent models of musculoskeletal disorders. Furthermore, the technique presented herein allows for objective repeated within-animal measurements potentially refining and reducing the use of animals in research while improving the development of novel antiarthritic therapeutics.
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Affiliation(s)
- Anders
R. Walther
- SDU
Chemical Engineering, University of Southern
Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Elzbieta Stepula
- Centre
for Craniofacial and Regenerative Biology, King’s College London, SE1 9RT London, UK
| | - Nicholas Ditzel
- Molecular
Endocrinology Unit (KMEB), Department of Endocrinology, Odense University Hospital and University of Southern
Denmark, J.B. Winsløwsvej
25, 5000 Odense, Denmark
| | - Moustapha Kassem
- Molecular
Endocrinology Unit (KMEB), Department of Endocrinology, Odense University Hospital and University of Southern
Denmark, J.B. Winsløwsvej
25, 5000 Odense, Denmark
| | - Mads S. Bergholt
- Centre
for Craniofacial and Regenerative Biology, King’s College London, SE1 9RT London, UK
| | - Martin A. B. Hedegaard
- SDU
Chemical Engineering, University of Southern
Denmark, Campusvej 55, 5230 Odense, Denmark
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9
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Wahl J, Klint E, Hallbeck M, Hillman J, Wårdell K, Ramser K. Impact of preprocessing methods on the Raman spectra of brain tissue. BIOMEDICAL OPTICS EXPRESS 2022; 13:6763-6777. [PMID: 36589553 PMCID: PMC9774863 DOI: 10.1364/boe.476507] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/04/2022] [Accepted: 11/10/2022] [Indexed: 06/01/2023]
Abstract
Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.
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Affiliation(s)
- Joel Wahl
- Department of Engineering Sciences and Mathematics, Luleå University of Technology, 971 87, Luleå, Sweden
| | - Elisabeth Klint
- Department of Biomedical Engineering, Linköping University, 581 85 Linköping, Sweden
| | - Martin Hallbeck
- Department of Clinical Pathology and Biomedical and Clinical Sciences, Linköping University, 581 85 Linköping, Sweden
| | - Jan Hillman
- Department of Neurosurgery and Biomedical and Clinical Sciences, Linköping University, 581 85 Linköping, Sweden
| | - Karin Wårdell
- Department of Biomedical Engineering, Linköping University, 581 85 Linköping, Sweden
| | - Kerstin Ramser
- Department of Engineering Sciences and Mathematics, Luleå University of Technology, 971 87, Luleå, Sweden
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10
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Mitsutake H, Rodrigues da Silva GH, Breitkreitz MC, de Paula E, Bordallo HN. Neither too little nor too much: Finding the ideal proportion of excipients using confocal Raman and chemometrics. Eur J Pharm Biopharm 2022; 181:136-146. [PMID: 36400252 DOI: 10.1016/j.ejpb.2022.11.008] [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: 09/14/2022] [Revised: 10/21/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022]
Abstract
The applications of Raman imaging in pharmaceutical field are ever-increasing due its ability to obtain spatial and spectral information simultaneously, once it allows determine the chemical distribution of compounds. In this sense, it is used to study homogeneity, of paramount importance during the development of pharmaceutical formulations due to its relation to stability, safety and efficacy. Commonly, just surface is analyzed, but confocal Raman spectroscopy can also characterize the inner part of samples, allowing to determine phase separation in the early stages. In this sense, confocal 3D Raman microscopy was crucial to obtain the optimal proportion of Apifil®, Capryol® 90 and Transcutol® to promote controlled release of the local anesthetic butamben (BTB). 3D chemical maps were obtained by classical least squares (CLS) using pure compound spectra as S matrix, showing that chemical distribution throughout the material was different. Knowing that the composition of samples affects the homogeneity parameter, standard deviation and distributional homogeneity index (DHI) were used in mixture experimental design (DoE). From this analysis, it was revealed that a correct amount of Capryol® 90 enhances both miscibility and solubility. Furthermore, suitable miscibility was observed in two ratio proportions of excipients with a desirability of 0.783 and 0.742. These results unequivocally demonstrated that confocal Raman microscopy combined to DoE can bring pharmaceutical development to a higher level.
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Affiliation(s)
- Hery Mitsutake
- Department of Biochemistry and Tissue Biology, Institute of Biology, Unicamp. Rua Monteiro Lobato, 255. bloco F sup., sala 9, Campinas, SP 13083-862, Brazil; Niels Bohr Institute, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark.
| | - Gustavo H Rodrigues da Silva
- Department of Biochemistry and Tissue Biology, Institute of Biology, Unicamp. Rua Monteiro Lobato, 255. bloco F sup., sala 9, Campinas, SP 13083-862, Brazil.
| | - Márcia C Breitkreitz
- Department of Analytical Chemistry, Institute of Chemistry, Unicamp, Rua Josué de Castro, s/n Cid. Universitária Zeferino Vaz, Campinas, SP 13084-970, Brazil.
| | - Eneida de Paula
- Department of Biochemistry and Tissue Biology, Institute of Biology, Unicamp. Rua Monteiro Lobato, 255. bloco F sup., sala 9, Campinas, SP 13083-862, Brazil.
| | - Heloisa N Bordallo
- Niels Bohr Institute, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark.
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11
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Maryam S, Nogueira MS, Gautam R, Krishnamoorthy S, Venkata Sekar SK, Kho KW, Lu H, Ni Riordain R, Feeley L, Sheahan P, Burke R, Andersson-Engels S. Label-Free Optical Spectroscopy for Early Detection of Oral Cancer. Diagnostics (Basel) 2022; 12:diagnostics12122896. [PMID: 36552903 PMCID: PMC9776497 DOI: 10.3390/diagnostics12122896] [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: 10/24/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Oral cancer is the 16th most common cancer worldwide. It commonly arises from painless white or red plaques within the oral cavity. Clinical outcome is highly related to the stage when diagnosed. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Therefore, there is a need to develop a non-invasive cost-effective diagnostic technique to differentiate non-malignant and early-stage malignant lesions. Optical spectroscopy may provide an appropriate solution to facilitate early detection of these lesions. It has many advantages over traditional approaches including cost, speed, objectivity, sensitivity, painlessness, and ease-of use in clinical setting for real-time diagnosis. This review consists of a comprehensive overview of optical spectroscopy for oral cancer diagnosis, epidemiology, and recent improvements in this field for diagnostic purposes. It summarizes major developments in label-free optical spectroscopy, including Raman, fluorescence, and diffuse reflectance spectroscopy during recent years. Among the wide range of optical techniques available, we chose these three for this review because they have the ability to provide biochemical information and show great potential for real-time deep-tissue point-based in vivo analysis. This review also highlights the importance of saliva-based potential biomarkers for non-invasive early-stage diagnosis. It concludes with the discussion on the scope of development and future demands from a clinical point of view.
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Affiliation(s)
- Siddra Maryam
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
- Correspondence:
| | | | - Rekha Gautam
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
| | | | | | - Kiang Wei Kho
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
| | - Huihui Lu
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
| | - Richeal Ni Riordain
- ENTO Research Institute, University College Cork, T12 R229 Cork, Ireland
- Cork University Dental School and Hospital, Wilton, T12 E8YV Cork, Ireland
| | - Linda Feeley
- ENTO Research Institute, University College Cork, T12 R229 Cork, Ireland
- Cork University Hospital, T12 DC4A Cork, Ireland
| | - Patrick Sheahan
- ENTO Research Institute, University College Cork, T12 R229 Cork, Ireland
- South Infirmary Victoria University Hospital, T12 X23H Cork, Ireland
| | - Ray Burke
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
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12
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Milligan K, Van Nest SJ, Deng X, Ali-Adeeb R, Shreeves P, Punch S, Costie N, Pavey N, Crook JM, Berman DM, Brolo AG, Lum JJ, Andrews JL, Jirasek A. Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer. JOURNAL OF BIOPHOTONICS 2022; 15:e202200121. [PMID: 35908273 DOI: 10.1002/jbio.202200121] [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: 04/20/2022] [Revised: 06/14/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.
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Affiliation(s)
- Kirsty Milligan
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Samantha J Van Nest
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
- Department of Radiation Oncology, Weill Cornell Medicine, New York, New York, USA
| | - Xinchen Deng
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Phillip Shreeves
- Department of Mathematics and Statistics, University of British Columbia, Kelowna, Canada
| | - Samantha Punch
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Nathalie Costie
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Nils Pavey
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Juanita M Crook
- Sindi Ahluwalia Hawkins Centre for the Southern Interior, BC Cancer, Kelowna, Canada
- Department of Radiation Oncology, University of British Columbia, Kelowna, Canada
| | - David M Berman
- Department of Pathology and Molecular Medicine, Queens University, Kingston, Canada
| | | | - Julian J Lum
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Mathematics and Statistics, University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, University of British Columbia, Kelowna, Canada
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13
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Milligan K, Deng X, Ali-Adeeb R, Shreeves P, Punch S, Costie N, Crook JM, Brolo AG, Lum JJ, Andrews JL, Jirasek A. Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study. Sci Rep 2022; 12:15104. [PMID: 36068275 PMCID: PMC9448740 DOI: 10.1038/s41598-022-19446-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/29/2022] [Indexed: 11/09/2022] Open
Abstract
This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.
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Affiliation(s)
- Kirsty Milligan
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Xinchen Deng
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Phillip Shreeves
- Department of Statistics, University of British Columbia, Kelowna, Canada
| | - Samantha Punch
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada
| | - Nathalie Costie
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada
| | - Juanita M Crook
- Department of Radiation Oncology, University of British Columbia, Kelowna, BC, Canada
| | - Alexandre G Brolo
- Department of Chemistry, University of Victoria, British Columbia, Canada
| | - Julian J Lum
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Statistics, University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, University of British Columbia, Kelowna, BC, Canada.
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14
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Gao W, Zhou L, Liu S, Guan Y, Gao H, Hu J. Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy. Carbohydr Polym 2022; 292:119635. [DOI: 10.1016/j.carbpol.2022.119635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/08/2022] [Accepted: 05/16/2022] [Indexed: 11/02/2022]
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15
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Walther AR, Ditzel N, Kassem M, Andersen MØ, Hedegaard MAB. In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy. BIOMATERIALS AND BIOSYSTEMS 2022; 7:100059. [PMID: 36824488 PMCID: PMC9934492 DOI: 10.1016/j.bbiosy.2022.100059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 07/04/2022] [Accepted: 07/25/2022] [Indexed: 10/16/2022] Open
Abstract
The development of novel biomaterials for regenerative therapy relies on the ability to assess tissue development, quality, and similarity with native tissue types in in vivo experiments. Non-invasive imaging modalities such as X-ray computed tomography offer high spatial resolution but limited biochemical information while histology and biochemical assays are destructive. Raman spectroscopy is a non-invasive, label-free and non-destructive technique widely applied for biochemical characterization. Here we demonstrate the use of fibre-optic Raman spectroscopy for in vivo quantitative monitoring of tissue development in subcutaneous calcium phosphate scaffolds in mice over 16 weeks. Raman spectroscopy was able to quantify the time dependency of different tissue components related to the presence, absence, and quantity of mesenchymal stem cells. Scaffolds seeded with stem cells produced 3-5 times higher amount of collagen-rich extracellular matrix after 16 weeks implantation compared to scaffolds without. These however, showed a 2.5 times higher amount of lipid-rich tissue compared to implants with stem cells. Ex vivo micro-computed tomography and histology showed stem cell mediated collagen and bone development. Histological measures of collagen correlated well with Raman derived quantifications (correlation coefficient in vivo 0.74, ex vivo 0.93). In the absence of stem cells, the scaffolds were largely occupied by adipocytes. The technique developed here could potentially be adapted for a range of small animal experiments for assessing tissue engineering strategies at the biochemical level.
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Affiliation(s)
- Anders Runge Walther
- SDU Biotechnology, Department of Green Technology, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
| | - Nicholas Ditzel
- Endocrine Research (KMEB), Department of Endocrinology, Odense University Hospital and University of Southern Denmark, J.B. Winsløws Vej 25, DK-5000 Odense, Denmark
| | - Moustapha Kassem
- Endocrine Research (KMEB), Department of Endocrinology, Odense University Hospital and University of Southern Denmark, J.B. Winsløws Vej 25, DK-5000 Odense, Denmark
| | - Morten Østergaard Andersen
- SDU Biotechnology, Department of Green Technology, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
| | - Martin Aage Barsøe Hedegaard
- SDU Biotechnology, Department of Green Technology, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
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16
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Taieb A, Berkovic G, Haifler M, Cheshnovsky O, Shaked NT. Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer. JOURNAL OF BIOPHOTONICS 2022; 15:e202200009. [PMID: 35488750 DOI: 10.1002/jbio.202200009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 03/25/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantitative phase imaging of the entire unstained tissue slice is performed using automated scanning. Then, pixel-wise segmentation of the tissue layers is performed by a kernelled structural support vector machine based on Haralick texture features, which are extracted from the quantitative phase profile, and used to find the best locations for performing the label-free localized Raman measurements. We use this multimodal label-free measurement approach for segmenting the urothelium in benign and malignant bladder cancer tissues by quantitative phase imaging, followed by location-guided Raman spectroscopy measurements. We then use sparse multinomial logistic regression (SMLR) on the Raman spectroscopy measurements to classify the tissue types, demonstrating that the prior segmentation of the urothelium done by label-free quantitative phase imaging improves the Raman spectra classification accuracy from 85.7% to 94.7%.
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Affiliation(s)
- Almog Taieb
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Garry Berkovic
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Soreq Nuclear Research Center, Yavne, Israel
| | - Miki Haifler
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Department of Urology, Chaim Sheba Medical Center, Tel Hashomer, Israel, Affiliated to Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ori Cheshnovsky
- School of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Natan T Shaked
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
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17
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Wilson BC, Eu D. Optical Spectroscopy and Imaging in Surgical Management of Cancer Patients. TRANSLATIONAL BIOPHOTONICS 2022. [DOI: 10.1002/tbio.202100009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Brian C. Wilson
- Princess Margaret Cancer Centre/University Health Network 101 College Street Toronto Ontario Canada
- Department of Medical Biophysics, Faculty of Medicine University of Toronto Canada
| | - Donovan Eu
- Department of Otolaryngology‐Head and Neck Surgery‐Surgical Oncology, Princess Margaret Cancer Centre/University Health Network University of Toronto Canada
- Department of Otolaryngology‐Head and Neck Surgery National University Hospital System Singapore
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18
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Ilchenko O, Pilhun Y, Kutsyk A. Towards Raman imaging of centimeter scale tissue areas for real-time opto-molecular visualization of tissue boundaries for clinical applications. LIGHT, SCIENCE & APPLICATIONS 2022; 11:143. [PMID: 35585059 PMCID: PMC9117314 DOI: 10.1038/s41377-022-00828-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Raman spectroscopy combined with augmented reality and mixed reality to reconstruct molecular information of tissue surface.
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Affiliation(s)
- Oleksii Ilchenko
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs, Lyngby, 2800, Denmark.
- Lightnovo ApS, Birkerød, 3460, Denmark.
| | - Yurii Pilhun
- Lightnovo ApS, Birkerød, 3460, Denmark
- Taras Shevchenko National University of Kyiv, Department of Quantum Radio Physics, Kyiv, Ukraine
| | - Andrii Kutsyk
- Lightnovo ApS, Birkerød, 3460, Denmark
- Taras Shevchenko National University of Kyiv, Department of Quantum Radio Physics, Kyiv, Ukraine
- Technical University of Denmark, Department of Energy Conversion and Storage, Kgs, Lyngby, 2800, Denmark
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19
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Yang W, Knorr F, Latka I, Vogt M, Hofmann GO, Popp J, Schie IW. Real-time molecular imaging of near-surface tissue using Raman spectroscopy. LIGHT, SCIENCE & APPLICATIONS 2022; 11:90. [PMID: 35396506 PMCID: PMC8993924 DOI: 10.1038/s41377-022-00773-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 03/09/2022] [Accepted: 03/19/2022] [Indexed: 05/08/2023]
Abstract
The steady progress in medical diagnosis and treatment of diseases largely hinges on the steady development and improvement of modern imaging modalities. Raman spectroscopy has attracted increasing attention for clinical applications as it is label-free, non-invasive, and delivers molecular fingerprinting information of a sample. In combination with fiber optic probes, it also allows easy access to different body parts of a patient. However, image acquisition with fiber optic probes is currently not possible. Here, we introduce a fiber optic probe-based Raman imaging system for the real-time molecular virtual reality data visualization of chemical boundaries on a computer screen and the physical world. The approach is developed around a computer vision-based positional tracking system in conjunction with photometric stereo and augmented and mixed chemical reality, enabling molecular imaging and direct visualization of molecular boundaries of three-dimensional surfaces. The proposed approach achieves a spatial resolution of 0.5 mm in the transverse plane and a topology resolution of 0.6 mm, with a spectral sampling frequency of 10 Hz, and can be used to image large tissue areas in a few minutes, making it highly suitable for clinical tissue-boundary demarcation. A variety of applications on biological samples, i.e., distribution of pharmaceutical compounds, brain-tumor phantom, and various types of sarcoma have been characterized, showing that the system enables rapid and intuitive assessment of molecular boundaries.
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Affiliation(s)
- Wei Yang
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Straße 9, 07745, Jena, Germany
| | - Florian Knorr
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Straße 9, 07745, Jena, Germany
| | - Ines Latka
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Straße 9, 07745, Jena, Germany
| | - Matthias Vogt
- Department of Trauma, Hand and Reconstructive Surgery, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany
| | - Gunther O Hofmann
- Department of Trauma, Hand and Reconstructive Surgery, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller University Jena, Helmholtzweg 4, 07743, Jena, Germany
| | - Iwan W Schie
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Straße 9, 07745, Jena, Germany.
- Department of Medical Engineering and Biotechnology, University of Applied Sciences - Jena, Carl-Zeiss-Promenade 2, 07745, Jena, Germany.
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20
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Cao Z, Pan X, Yu H, Hua S, Wang D, Chen DZ, Zhou M, Wu J. A Deep Learning Approach for Detecting Colorectal Cancer via Raman Spectra. BME FRONTIERS 2022; 2022:9872028. [PMID: 37850174 PMCID: PMC10521640 DOI: 10.34133/2022/9872028] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 03/01/2022] [Indexed: 10/19/2023] Open
Abstract
Objective and Impact Statement. Distinguishing tumors from normal tissues is vital in the intraoperative diagnosis and pathological examination. In this work, we propose to utilize Raman spectroscopy as a novel modality in surgery to detect colorectal cancer tissues. Introduction. Raman spectra can reflect the substance components of the target tissues. However, the feature peak is slight and hard to detect due to environmental noise. Collecting a high-quality Raman spectroscopy dataset and developing effective deep learning detection methods are possibly viable approaches. Methods. First, we collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with the Raman shift ranging from 385 to 1545 cm - 1 . Second, a one-dimensional residual convolutional neural network (1D-ResNet) architecture is designed to classify the tumor tissues of colorectal cancer. Third, we visualize and interpret the fingerprint peaks found by our deep learning model. Results. Experimental results show that our deep learning method achieves 98.5% accuracy in the detection of colorectal cancer and outperforms traditional methods. Conclusion. Overall, Raman spectra are a novel modality for clinical detection of colorectal cancer. Our proposed ensemble 1D-ResNet could effectively classify the Raman spectra obtained from colorectal tumor tissues or normal tissues.
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Affiliation(s)
- Zheng Cao
- RealDoctor AI Research Center, College of Computer Science and Technology, Zhejiang University, China
| | - Xiang Pan
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Hongyun Yu
- RealDoctor AI Research Center, College of Computer Science and Technology, Zhejiang University, China
| | - Shiyuan Hua
- Institute of Translational Medicine and the Cancer Institute of the Second Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Da Wang
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Danny Z. Chen
- Department of Computer Science and Engineering, University of Notre Dame, USA
| | - Min Zhou
- Institute of Translational Medicine and the Cancer Institute of the Second Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Jian Wu
- Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, China
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21
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Cameron JM, Rinaldi C, Rutherford SH, Sala A, G Theakstone A, Baker MJ. Clinical Spectroscopy: Lost in Translation? APPLIED SPECTROSCOPY 2022; 76:393-415. [PMID: 34041957 DOI: 10.1177/00037028211021846] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This Focal Point Review paper discusses the developments of biomedical Raman and infrared spectroscopy, and the recent strive towards these technologies being regarded as reliable clinical tools. The promise of vibrational spectroscopy in the field of biomedical science, alongside the development of computational methods for spectral analysis, has driven a plethora of proof-of-concept studies which convey the potential of various spectroscopic approaches. Here we report a brief review of the literature published over the past few decades, with a focus on the current technical, clinical, and economic barriers to translation, namely the limitations of many of the early studies, and the lack of understanding of clinical pathways, health technology assessments, regulatory approval, clinical feasibility, and funding applications. The field of biomedical vibrational spectroscopy must acknowledge and overcome these hurdles in order to achieve clinical efficacy. Current prospects have been overviewed with comment on the advised future direction of spectroscopic technologies, with the aspiration that many of these innovative approaches can ultimately reach the frontier of medical diagnostics and many clinical applications.
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Affiliation(s)
| | - Christopher Rinaldi
- WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, Glasgow, UK
| | - Samantha H Rutherford
- WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, Glasgow, UK
| | - Alexandra Sala
- WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, Glasgow, UK
| | - Ashton G Theakstone
- WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, Glasgow, UK
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22
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Shapey J, Xie Y, Nabavi E, Ebner M, Saeed SR, Kitchen N, Dorward N, Grieve J, McEvoy AW, Miserocchi A, Grover P, Bradford R, Lim YM, Ourselin S, Brandner S, Jaunmuktane Z, Vercauteren T. Optical properties of human brain and tumour tissue: An ex vivo study spanning the visible range to beyond the second near-infrared window. JOURNAL OF BIOPHOTONICS 2022; 15:e202100072. [PMID: 35048541 DOI: 10.1002/jbio.202100072] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Neuro-oncology surgery would benefit from detailed intraoperative tissue characterization provided by noncontact, contrast-agent-free, noninvasive optical imaging methods. In-depth knowledge of target tissue optical properties across a wide-wavelength spectrum could inform the design of optical imaging and computational methods to enable robust tissue analysis during surgery. We adapted a dual-beam integrating sphere to analyse small tissue samples and investigated ex vivo optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue across a wavelength spectrum of 400 to 1800 nm. Fresh and frozen tissue samples were analysed. All tissue types demonstrated similar absorption spectra, but the reduced scattering coefficients of tumours show visible differences in the obtained optical spectrum compared to those of surrounding normal tissue. These results underline the potential of optical imaging technologies for intraoperative tissue characterization.
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Affiliation(s)
- Jonathan Shapey
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Yijing Xie
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Elham Nabavi
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Michael Ebner
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Shakeel R Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- The Ear Institute, University College London, London, UK
- The Royal National Throat, Nose and Ear Hospital, London, UK
| | - Neil Kitchen
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Neil Dorward
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Joan Grieve
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Andrew W McEvoy
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Anna Miserocchi
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Patrick Grover
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Yau-Mun Lim
- Division of Neuropathology, UCL Queen Square Institute of Neurology, and The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Foundation Trust, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sebastian Brandner
- Division of Neuropathology, UCL Queen Square Institute of Neurology, and The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Foundation Trust, London, UK
| | - Zane Jaunmuktane
- Division of Neuropathology, UCL Queen Square Institute of Neurology, and The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Foundation Trust, London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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23
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He C, Zhu S, Wu X, Zhou J, Chen Y, Qian X, Ye J. Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning. ACS OMEGA 2022; 7:10458-10468. [PMID: 35382336 PMCID: PMC8973095 DOI: 10.1021/acsomega.1c07263] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 03/09/2022] [Indexed: 05/04/2023]
Abstract
Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been demonstrated to be a powerful tool for tumor identification. However, the analysis and classification of Raman spectra for biological samples with complex compositions are still challenges. In addition, the signal-to-noise ratio of the spectra also influences the accuracy of the classification. Herein, we applied the variational autoencoder (VAE) to Raman spectra for downscaling and noise reduction simultaneously. We validated the performance of the VAE algorithm at the cellular and tissue levels. VAE successfully downscaled high-dimensional Raman spectral data to two-dimensional (2D) data for three subtypes of non-small cell lung cancer cells and two subtypes of kidney cancer tissues. Gaussian naïve bayes was applied to subtype discrimination with the 2D data after VAE encoding at both the cellular and tissue levels, significantly outperforming the discrimination results using original spectra. Therefore, the analysis of Raman spectroscopy based on VAE and machine learning has great potential for rapid diagnosis of tumor subtypes.
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Affiliation(s)
- Chang He
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Shuo Zhu
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Xiaorong Wu
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Jiale Zhou
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Yonghui Chen
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Xiaohua Qian
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Jian Ye
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
- Shanghai
Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of
Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
- Institute
of Medical Robotics, Shanghai Jiao Tong
University, Shanghai 200240, P.R. China
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Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance. Cancers (Basel) 2022; 14:cancers14051144. [PMID: 35267451 PMCID: PMC8909093 DOI: 10.3390/cancers14051144] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 12/23/2022] Open
Abstract
Simple Summary Cancer still constitutes one of the main global health challenges. Novel approaches towards understanding the molecular composition of the disease can be employed as adjuvant tools to current oncological applications. Raman spectroscopy has been contemplated and pursued to serve as a noninvasive, real time, in vivo tool which may uncover the molecular basis of cancer and simultaneously offer high specificity, sensitivity, and multiplexing capacity, as well as high spatial and temporal resolution. In this review, the potential impact of Spontaneous Raman spectroscopy in clinical applications related to cancer diagnosis and surgical removal is analyzed. Moreover, the coupling of Raman systems with modern instrumentation and machine learning methods has been explored as a prominent enhancement factor towards a personalized approach promoting objectivity and accuracy in surgical oncology. Abstract Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.
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Ruiz JJ, Marro M, Galván I, Bernabeu-Wittel J, Conejo-Mir J, Zulueta-Dorado T, Guisado-Gil AB, Loza-Álvarez P. Novel Non-Invasive Quantification and Imaging of Eumelanin and DHICA Subunit in Skin Lesions by Raman Spectroscopy and MCR Algorithm: Improving Dysplastic Nevi Diagnosis. Cancers (Basel) 2022; 14:cancers14041056. [PMID: 35205803 PMCID: PMC8870175 DOI: 10.3390/cancers14041056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 12/17/2022] Open
Abstract
Malignant melanoma (MM) is the most aggressive form of skin cancer, and around 30% of them may develop from pre-existing dysplastic nevi (DN). Diagnosis of DN is a relevant clinical challenge, as these are intermediate lesions between benign and malignant tumors, and, up to date, few studies have focused on their diagnosis. In this study, the accuracy of Raman spectroscopy (RS) is assessed, together with multivariate analysis (MA), to classify 44 biopsies of MM, DN and compound nevus (CN) tumors. For this, we implement a novel methodology to non-invasively quantify and localize the eumelanin pigment, considered as a tumoral biomarker, by means of RS imaging coupled with the Multivariate Curve Resolution-Alternative Least Squares (MCR-ALS) algorithm. This represents a step forward with respect to the currently established technique for melanin analysis, High-Performance Liquid Chromatography (HPLC), which is invasive and cannot provide information about the spatial distribution of molecules. For the first time, we show that the 5, 6-dihydroxyindole (DHI) to 5,6-dihydroxyindole-2-carboxylic acid (DHICA) ratio is higher in DN than in MM and CN lesions. These differences in chemical composition are used by the Partial Least Squares-Discriminant Analysis (PLS-DA) algorithm to identify DN lesions in an efficient, non-invasive, fast, objective and cost-effective method, with sensitivity and specificity of 100% and 94.1%, respectively.
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Affiliation(s)
- José Javier Ruiz
- ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Castelldefels, 08860 Barcelona, Spain;
| | - Monica Marro
- ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Castelldefels, 08860 Barcelona, Spain;
- Correspondence: (M.M.); (P.L.-Á.)
| | - Ismael Galván
- Department of Evolutionary Ecology, National Museum of Natural Sciences, CSIC, 28006 Madrid, Spain;
| | - José Bernabeu-Wittel
- Department of Dermatology, University Hospital Virgen del Rocio, 41013 Sevilla, Spain; (J.B.-W.); (J.C.-M.); (T.Z.-D.); (A.B.G.-G.)
| | - Julián Conejo-Mir
- Department of Dermatology, University Hospital Virgen del Rocio, 41013 Sevilla, Spain; (J.B.-W.); (J.C.-M.); (T.Z.-D.); (A.B.G.-G.)
| | - Teresa Zulueta-Dorado
- Department of Dermatology, University Hospital Virgen del Rocio, 41013 Sevilla, Spain; (J.B.-W.); (J.C.-M.); (T.Z.-D.); (A.B.G.-G.)
| | - Ana Belén Guisado-Gil
- Department of Dermatology, University Hospital Virgen del Rocio, 41013 Sevilla, Spain; (J.B.-W.); (J.C.-M.); (T.Z.-D.); (A.B.G.-G.)
| | - Pablo Loza-Álvarez
- ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Castelldefels, 08860 Barcelona, Spain;
- Correspondence: (M.M.); (P.L.-Á.)
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Intraductal Carcinoma of the Prostate as a Cause of Prostate Cancer Metastasis: A Molecular Portrait. Cancers (Basel) 2022; 14:cancers14030820. [PMID: 35159086 PMCID: PMC8834356 DOI: 10.3390/cancers14030820] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Most men with prostate cancer will live as long as those who do not have prostate cancer. However, some men will die early of their disease due to a particular type of prostate cancer associated with recurrence and metastasis: intraductal carcinoma of the prostate. In this review, we discuss the associations between intraductal carcinoma of the prostate and metastasis, and the contemporary knowledge about the molecular alterations of intraductal carcinoma of the prostate. Abstract Intraductal carcinoma of the prostate (IDC-P) is one of the most aggressive types of prostate cancer (PCa). IDC-P is identified in approximately 20% of PCa patients and is associated with recurrence, metastasis, and PCa-specific death. The main feature of this histological variant is the colonization of benign glands by PCa cells. Although IDC-P is a well-recognized independent parameter for metastasis, mechanisms by which IDC-P cells can spread and colonize other tissues are not fully known. In this review, we discuss the molecular portraits of IDC-P determined by immunohistochemistry and genomic approaches and highlight the areas in which more research is needed.
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27
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Yaari Z, Horoszko CP, Antman-Passig M, Kim M, Nguyen FT, Heller DA. Emerging technologies in cancer detection. Cancer Biomark 2022. [DOI: 10.1016/b978-0-12-824302-2.00011-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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28
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Jabarkheel R, Ho CS, Rodrigues AJ, Jin MC, Parker JJ, Mensah-Brown K, Yecies D, Grant GA. Rapid intraoperative diagnosis of pediatric brain tumors using Raman spectroscopy: A machine learning approach. Neurooncol Adv 2022; 4:vdac118. [PMID: 35919071 PMCID: PMC9341441 DOI: 10.1093/noajnl/vdac118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background Surgical resection is a mainstay in the treatment of pediatric brain tumors to achieve tissue diagnosis and tumor debulking. While maximal safe resection of tumors is desired, it can be challenging to differentiate normal brain from neoplastic tissue using only microscopic visualization, intraoperative navigation, and tactile feedback. Here, we investigate the potential for Raman spectroscopy (RS) to accurately diagnose pediatric brain tumors intraoperatively. Methods Using a rapid acquisition RS device, we intraoperatively imaged fresh ex vivo brain tissue samples from 29 pediatric patients at the Lucile Packard Children’s Hospital between October 2018 and March 2020 in a prospective fashion. Small tissue samples measuring 2-4 mm per dimension were obtained with each individual tissue sample undergoing multiple unique Raman spectra acquisitions. All tissue samples from which Raman spectra were acquired underwent individual histopathology review. A labeled dataset of 678 unique Raman spectra gathered from 160 samples was then used to develop a machine learning model capable of (1) differentiating normal brain from tumor tissue and (2) normal brain from low-grade glioma (LGG) tissue. Results Trained logistic regression model classifiers were developed using our labeled dataset. Model performance was evaluated using leave-one-patient-out cross-validation. The area under the curve (AUC) of the receiver-operating characteristic (ROC) curve for our tumor vs normal brain model was 0.94. The AUC of the ROC curve for LGG vs normal brain was 0.91. Conclusions Our work suggests that RS can be used to develop a machine learning-based classifier to differentiate tumor vs non-tumor tissue during resection of pediatric brain tumors.
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Affiliation(s)
- Rashad Jabarkheel
- Department of Neurosurgery, Stanford University , Stanford, California , USA
- Department of Neurosurgery, University of Pennsylvania , Philadelphia, Pennsylvania , USA
| | - Chi-Sing Ho
- Department of Applied Physics, Stanford University , Stanford, California , USA
| | - Adrian J Rodrigues
- Department of Neurosurgery, Stanford University , Stanford, California , USA
| | - Michael C Jin
- Department of Neurosurgery, Stanford University , Stanford, California , USA
| | - Jonathon J Parker
- Department of Neurosurgery, Stanford University , Stanford, California , USA
| | - Kobina Mensah-Brown
- Department of Neurosurgery, University of Pennsylvania , Philadelphia, Pennsylvania , USA
| | - Derek Yecies
- Department of Neurosurgery, Stanford University , Stanford, California , USA
| | - Gerald A Grant
- Department of Neurosurgery, Stanford University , Stanford, California , USA
- Department of Neurosurgery, Duke University , Durham, North Carolina , USA
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Plante A, Dallaire F, Grosset AA, Nguyen T, Birlea M, Wong J, Daoust F, Roy N, Kougioumoutzakis A, Azzi F, Aubertin K, Kadoury S, Latour M, Albadine R, Prendeville S, Boutros P, Fraser M, Bristow RG, van der Kwast T, Orain M, Brisson H, Benzerdjeb N, Hovington H, Bergeron A, Fradet Y, Têtu B, Saad F, Trudel D, Leblond F. Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210212R. [PMID: 34743445 PMCID: PMC8571651 DOI: 10.1117/1.jbo.26.11.116501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
SIGNIFICANCE Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85 % detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8 % accuracy. AIM To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. APPROACH A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l'Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. RESULTS Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4 % (+8 % ), +7 % (+9 % ), +2 % (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2 % (+1.7 % ), +4.5 % (+3.6 % ), +0 % (+0 % ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0 % (-2 % ), +0 % (-3 % ), +2 % (-2 % ), +4 (+3)], the AUC was improved in both testing sets. CONCLUSIONS Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.
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Affiliation(s)
- Arthur Plante
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Andrée-Anne Grosset
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
| | - Tien Nguyen
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Mirela Birlea
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Jahg Wong
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - François Daoust
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Noémi Roy
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - André Kougioumoutzakis
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Feryel Azzi
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Kelly Aubertin
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Computer Engineering and Software Engineering, Montreal, Quebec, Canada
| | - Mathieu Latour
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
- Centre hospitalier de l’Université de Montréal, Department of Pathology, Montreal, Quebec, Canada
| | - Roula Albadine
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
- Centre hospitalier de l’Université de Montréal, Department of Pathology, Montreal, Quebec, Canada
| | - Susan Prendeville
- University Health Network, Laboratory Medicine Program, Toronto, Ontario, Canada
| | - Paul Boutros
- Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- University of California, Los Angeles, Department of Human Genetics, Los Angeles, California, United States
- University of California, Los Angeles, Department of Urology, Los Angeles, California, United States
- University of California, Los Angeles, Institute for Precision Health, Los Angeles, California, United States
- University of California, Los Angeles, Jonsson Comprehensive Cancer Center, Los Angeles, California, United States
- University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Michael Fraser
- Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Rob G. Bristow
- University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | - Michèle Orain
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Hervé Brisson
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Nazim Benzerdjeb
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Hélène Hovington
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Alain Bergeron
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
- Université Laval, Department of Surgery, Quebec City, Quebec, Canada
| | - Yves Fradet
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
- Université Laval, Department of Surgery, Quebec City, Quebec, Canada
| | - Bernard Têtu
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Fred Saad
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
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Ettabib MA, Marti A, Liu Z, Bowden BM, Zervas MN, Bartlett PN, Wilkinson JS. Waveguide Enhanced Raman Spectroscopy for Biosensing: A Review. ACS Sens 2021; 6:2025-2045. [PMID: 34114813 DOI: 10.1021/acssensors.1c00366] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Waveguide enhanced Raman spectroscopy (WERS) utilizes simple, robust, high-index contrast dielectric waveguides to generate a strong evanescent field, through which laser light interacts with analytes residing on the surface of the waveguide. It offers a powerful tool for the direct identification and reproducible quantification of biochemical species and an alternative to surface enhanced Raman spectroscopy (SERS) without reliance on fragile noble metal nanostructures. The advent of low-cost laser diodes, compact spectrometers, and recent progress in material engineering, nanofabrication techniques, and software modeling tools have made realizing portable and cheap WERS Raman systems with high sensitivity a realistic possibility. This review highlights the latest progress in WERS technology and summarizes recent demonstrations and applications. Following an introduction to the fundamentals of WERS, the theoretical framework that underpins the WERS principles is presented. The main WERS design considerations are then discussed, and a review of the available approaches for the modification of waveguide surfaces for the attachment of different biorecognition elements is provided. The review concludes by discussing and contrasting the performance of recent WERS implementations, thereby providing a future roadmap of WERS technology where the key opportunities and challenges are highlighted.
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Affiliation(s)
- Mohamed A. Ettabib
- Zepler Institute for Photonics and Nanoelectronics, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Almudena Marti
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Zhen Liu
- Zepler Institute for Photonics and Nanoelectronics, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Bethany M. Bowden
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Michalis N. Zervas
- Zepler Institute for Photonics and Nanoelectronics, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Philip N. Bartlett
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - James S. Wilkinson
- Zepler Institute for Photonics and Nanoelectronics, University of Southampton, Southampton SO17 1BJ, United Kingdom
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Byrne HJ, Behl I, Calado G, Ibrahim O, Toner M, Galvin S, Healy CM, Flint S, Lyng FM. Biomedical applications of vibrational spectroscopy: Oral cancer diagnostics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 252:119470. [PMID: 33503511 DOI: 10.1016/j.saa.2021.119470] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/09/2021] [Accepted: 01/10/2021] [Indexed: 06/12/2023]
Abstract
Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.
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Affiliation(s)
- Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Dublin 8, Ireland.
| | - Isha Behl
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, City Campus, Dublin 8, Ireland; Radiation and Environmental Science Centre, FOCAS Research Institute, Technological University Dublin, City Campus, Dublin 8, Ireland
| | - Genecy Calado
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, City Campus, Dublin 8, Ireland; Radiation and Environmental Science Centre, FOCAS Research Institute, Technological University Dublin, City Campus, Dublin 8, Ireland
| | - Ola Ibrahim
- School of Dental Science, Trinity College Dublin, Lincoln Place, Dublin 2, Ireland
| | - Mary Toner
- Central Pathology Laboratory, St. James Hospital, James Street, Dublin 8, Ireland
| | - Sheila Galvin
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Lincoln Place, Dublin 2, Ireland
| | - Claire M Healy
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Lincoln Place, Dublin 2, Ireland
| | - Stephen Flint
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Lincoln Place, Dublin 2, Ireland
| | - Fiona M Lyng
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, City Campus, Dublin 8, Ireland; Radiation and Environmental Science Centre, FOCAS Research Institute, Technological University Dublin, City Campus, Dublin 8, Ireland
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Prajapati N, Niu Z, Novikova I. Quantum-enhanced two-photon spectroscopy using two-mode squeezed light. OPTICS LETTERS 2021; 46:1800-1803. [PMID: 33857073 DOI: 10.1364/ol.418398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
We investigate the prospects of using two-mode intensity squeezed twin beams, generated in Rb vapor, to improve the sensitivity of spectroscopic measurements by engaging two-photon Raman transitions. As a proof-of-principle demonstration, we recorded quantum-enhanced measurements of the Rb 5D3/2 hyperfine structure with reduced requirements for the Raman pump laser power and Rb vapor number density.
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The Potential of Raman Spectroscopy in the Diagnosis of Dysplastic and Malignant Oral Lesions. Cancers (Basel) 2021; 13:cancers13040619. [PMID: 33557195 PMCID: PMC7913942 DOI: 10.3390/cancers13040619] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 02/01/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Raman spectroscopy, a light scattering technique that provides the biochemical fingerprint of a sample, was used on samples taken from patients with cancer and precancerous lesions. This information was then used to build a classifier to identify cancer and the precancerous phases. The ability to distinguish cancerous tissue from normal and precancerous tissue is diagnostically crucial as it can alter the patients’ prognosis and management. Moreover, as cellular changes are often present at the tumour margin, the ability to distinguish these changes from cancer can help in preserving more of the tissue and maintaining aesthetics and functionality for the patient. Abstract Early diagnosis, treatment and/or surveillance of oral premalignant lesions are important in preventing progression to oral squamous cell carcinoma (OSCC). The current gold standard is through histopathological diagnosis, which is limited by inter- and intra-observer errors and sampling errors. The objective of this work was to use Raman spectroscopy to discriminate between benign, mild, moderate and severe dysplasia and OSCC in formalin fixed paraffin preserved (FFPP) tissues. The study included 72 different pathologies from which 17 were benign lesions, 20 mildly dysplastic, 20 moderately dysplastic, 10 severely dysplastic and 5 invasive OSCC. The glass substrate and paraffin wax background were digitally removed and PLSDA with LOPO cross-validation was used to differentiate the pathologies. OSCC could be differentiated from the other pathologies with an accuracy of 70%, while the accuracy of the classifier for benign, moderate and severe dysplasia was ~60%. The accuracy of the classifier was lowest for mild dysplasia (~46%). The main discriminating features were increased nucleic acid contributions and decreased protein and lipid contributions in the epithelium and decreased collagen contributions in the connective tissue. Smoking and the presence of inflammation were found to significantly influence the Raman classification with respective accuracies of 76% and 94%.
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Vibrational Spectroscopy for In Vitro Monitoring Stem Cell Differentiation. Molecules 2020; 25:molecules25235554. [PMID: 33256146 PMCID: PMC7729886 DOI: 10.3390/molecules25235554] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 12/12/2022] Open
Abstract
Stem cell technology has attracted considerable attention over recent decades due to its enormous potential in regenerative medicine and disease therapeutics. Studying the underlying mechanisms of stem cell differentiation and tissue generation is critical, and robust methodologies and different technologies are required. Towards establishing improved understanding and optimised triggering and control of differentiation processes, analytical techniques such as flow cytometry, immunohistochemistry, reverse transcription polymerase chain reaction, RNA in situ hybridisation analysis, and fluorescence-activated cell sorting have contributed much. However, progress in the field remains limited because such techniques provide only limited information, as they are only able to address specific, selected aspects of the process, and/or cannot visualise the process at the subcellular level. Additionally, many current analytical techniques involve the disruption of the investigation process (tissue sectioning, immunostaining) and cannot monitor the cellular differentiation process in situ, in real-time. Vibrational spectroscopy, as a label-free, non-invasive and non-destructive analytical technique, appears to be a promising candidate to potentially overcome many of these limitations as it can provide detailed biochemical fingerprint information for analysis of cells, tissues, and body fluids. The technique has been widely used in disease diagnosis and increasingly in stem cell technology. In this work, the efforts regarding the use of vibrational spectroscopy to identify mechanisms of stem cell differentiation at a single cell and tissue level are summarised. Both infrared absorption and Raman spectroscopic investigations are explored, and the relative merits, and future perspectives of the techniques are discussed.
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Picot F, Daoust F, Sheehy G, Dallaire F, Chaikho L, Bégin T, Kadoury S, Leblond F. Data consistency and classification model transferability across biomedical Raman spectroscopy systems. TRANSLATIONAL BIOPHOTONICS 2020. [DOI: 10.1002/tbio.202000019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Fabien Picot
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - François Daoust
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Guillaume Sheehy
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Frédérick Dallaire
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Layal Chaikho
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
| | - Théophile Bégin
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
| | - Samuel Kadoury
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Frédéric Leblond
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
- Institut du Cancer de Montréal Montreal Quebec Canada
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Zhang W, Ma J, Sun DW. Raman spectroscopic techniques for detecting structure and quality of frozen foods: principles and applications. Crit Rev Food Sci Nutr 2020; 61:2623-2639. [DOI: 10.1080/10408398.2020.1828814] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Wenyang Zhang
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
- State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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Akbarzadeh A, Edjlali E, Sheehy G, Selb J, Agarwal R, Weber J, Leblond F. Experimental validation of a spectroscopic Monte Carlo light transport simulation technique and Raman scattering depth sensing analysis in biological tissue. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:JBO-200202R. [PMID: 33111509 PMCID: PMC7720906 DOI: 10.1117/1.jbo.25.10.105002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/16/2020] [Indexed: 05/15/2023]
Abstract
SIGNIFICANCE Raman spectroscopy (RS) applied to surgical guidance is attracting attention among scientists in biomedical optics. Offering a computational platform for studying depth-resolved RS and probing molecular specificity of different tissue layers is of crucial importance to increase the precision of these techniques and facilitate their clinical adoption. AIM The aim of this work was to present a rigorous analysis of inelastic scattering depth sampling and elucidate the relationship between sensing depth of the Raman effect and optical properties of the tissue under interrogation. APPROACH A new Monte Carlo (MC) package was developed to simulate absorption, fluorescence, elastic, and inelastic scattering of light in tissue. The validity of the MC algorithm was demonstrated by comparison with experimental Raman spectra in phantoms of known optical properties using nylon and polydimethylsiloxane as Raman-active compounds. A series of MC simulations were performed to study the effects of optical properties on Raman sensing depth for an imaging geometry consistent with single-point detection using a handheld fiber optics probe system. RESULTS The MC code was used to estimate the Raman sensing depth of a handheld fiber optics system. For absorption and reduced scattering coefficients of 0.001 and 1 mm - 1, the sensing depth varied from 105 to 225 μm for a range of Raman probabilities from 10 - 6 to 10 - 3. Further, for a realistic Raman probability of 10 - 6, the sensing depth ranged between 10 and 600 μm for the range of absorption coefficients 0.001 to 1.4 mm - 1 and reduced scattering coefficients of 0.5 to 30 mm - 1. CONCLUSIONS A spectroscopic MC light transport simulation platform was developed and validated against experimental measurements in tissue phantoms and used to predict depth sensing in tissue. It is hoped that the current package and reported results provide the research community with an effective simulating tool to improve the development of clinical applications of RS.
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Affiliation(s)
- Alireza Akbarzadeh
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Ehsan Edjlali
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | | | | | - Jessie Weber
- Institut National d’Optique, Quebec, Quebec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Address all correspondence to Frédéric Leblond,
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Grosset AA, Dallaire F, Nguyen T, Birlea M, Wong J, Daoust F, Roy N, Kougioumoutzakis A, Azzi F, Aubertin K, Kadoury S, Latour M, Albadine R, Prendeville S, Boutros P, Fraser M, Bristow RG, van der Kwast T, Orain M, Brisson H, Benzerdjeb N, Hovington H, Bergeron A, Fradet Y, Têtu B, Saad F, Leblond F, Trudel D. Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation. PLoS Med 2020; 17:e1003281. [PMID: 32797086 PMCID: PMC7428053 DOI: 10.1371/journal.pmed.1003281] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 07/20/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (RμS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories. METHODS AND FINDINGS We used RμS to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l'Université de Montréal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Québec-Université Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%-69%), and pT3a (22%-49%) was more frequent than pT3b (9%-12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% ± 5%, 86% ± 6%, and 89% ± 8%, respectively, to differentiate PC from benign tissue, and 95% ± 2%, 96% ± 4%, and 94% ± 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets. CONCLUSIONS In this study, we developed classification models for the analysis of RμS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RμS could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.
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Affiliation(s)
- Andrée-Anne Grosset
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montreal, Quebec, Canada
| | - Tien Nguyen
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Department of Engineering Physics, Polytechnique Montréal, Montreal, Quebec, Canada
| | - Mirela Birlea
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Jahg Wong
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - François Daoust
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Department of Engineering Physics, Polytechnique Montréal, Montreal, Quebec, Canada
| | - Noémi Roy
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - André Kougioumoutzakis
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Feryel Azzi
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Kelly Aubertin
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montreal, Quebec, Canada
| | - Mathieu Latour
- Department of Pathology and Cellular Biology, Université de Montréal, Montreal, Quebec, Canada
- Department of Pathology, Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Roula Albadine
- Department of Pathology and Cellular Biology, Université de Montréal, Montreal, Quebec, Canada
- Department of Pathology, Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Susan Prendeville
- Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada
| | - Paul Boutros
- Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America
- Department of Urology, University of California, Los Angeles, Los Angeles, California, United States of America
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, California, United States of America
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Michael Fraser
- Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Rob G. Bristow
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | | | - Michèle Orain
- Oncology Division, Centre de recherche du Centre hospitalier universitaire de Québec–Université Laval, Quebec City, Quebec, Canada
- Centre de recherche sur le cancer, Université Laval, Quebec City, Quebec, Canada
| | - Hervé Brisson
- Oncology Division, Centre de recherche du Centre hospitalier universitaire de Québec–Université Laval, Quebec City, Quebec, Canada
- Centre de recherche sur le cancer, Université Laval, Quebec City, Quebec, Canada
| | - Nazim Benzerdjeb
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Oncology Division, Centre de recherche du Centre hospitalier universitaire de Québec–Université Laval, Quebec City, Quebec, Canada
- Centre de recherche sur le cancer, Université Laval, Quebec City, Quebec, Canada
| | - Hélène Hovington
- Oncology Division, Centre de recherche du Centre hospitalier universitaire de Québec–Université Laval, Quebec City, Quebec, Canada
- Centre de recherche sur le cancer, Université Laval, Quebec City, Quebec, Canada
| | - Alain Bergeron
- Oncology Division, Centre de recherche du Centre hospitalier universitaire de Québec–Université Laval, Quebec City, Quebec, Canada
- Centre de recherche sur le cancer, Université Laval, Quebec City, Quebec, Canada
- Department of Surgery, Université Laval, Quebec City, Quebec, Canada
| | - Yves Fradet
- Oncology Division, Centre de recherche du Centre hospitalier universitaire de Québec–Université Laval, Quebec City, Quebec, Canada
- Centre de recherche sur le cancer, Université Laval, Quebec City, Quebec, Canada
- Department of Surgery, Université Laval, Quebec City, Quebec, Canada
| | - Bernard Têtu
- Oncology Division, Centre de recherche du Centre hospitalier universitaire de Québec–Université Laval, Quebec City, Quebec, Canada
- Centre de recherche sur le cancer, Université Laval, Quebec City, Quebec, Canada
| | - Fred Saad
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Department of Engineering Physics, Polytechnique Montréal, Montreal, Quebec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, Montreal, Quebec, Canada
- Department of Pathology, Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
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Alghazeer R, Burwaiss AA, Howell NK, Alansari WS, Shamlan G, Eskandrani AA. Determining the Cytotoxicity of Oxidized Lipids in Cultured Caco-2 Cells Using Bioimaging Techniques. Molecules 2020; 25:molecules25071693. [PMID: 32272768 PMCID: PMC7180719 DOI: 10.3390/molecules25071693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 04/01/2020] [Accepted: 04/04/2020] [Indexed: 12/02/2022] Open
Abstract
Fish lipids are comprised of considerable quantities of polyunsaturated acids and are prone to oxidation, producing reactive oxygen species and hydroperoxides. This study aimed to evaluate the biochemical and structural alterations in Caco-2 cells following exposure to 100 μg/mL methyl linoleate or fish oil, and then radiated for 24, 48 or 72 h. Electron spin resonance spectroscopy detected free radicals in the lipid membrane, Raman microscopy observed biochemical alterations and atomic force microscopy identified changes in morphology, such as the breakdown of DNA bonds. The study showed that bioimaging and biochemical techniques can be effective at detecting and diagnosing cellular injuries incurred by lipid peroxidation.
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Affiliation(s)
- Rabia Alghazeer
- Chemistry Department, Faculty of Science, University of Tripoli, Tripoli 50676, Libya
- Correspondence:
| | - Abdullah A. Burwaiss
- Medicine Department, Faculty of Human Medicine, University of Tripoli, Tripoli 50676, Libya;
| | - Nazlin K. Howell
- School of Biomedical and Molecular Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK;
| | - Wafa S. Alansari
- Biochemistry Department, Faculty of Science, University of Jeddah, Jeddah 21577, Saudi Arabia;
| | - Ghalia Shamlan
- Department of Food Science and Nutrition, College of Food and agriculture Sciences, King Saud University, Riyadh 11362, Saudi Arabia;
| | - Areej A. Eskandrani
- Chemistry Department, Faculty of Science, Taibah University, Medina 30002, Saudi Arabia;
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Surface-enhanced Raman spectroscopy of preoperative serum samples predicts Gleason grade group upgrade in biopsy Gleason grade group 1 prostate cancer. Urol Oncol 2020; 38:601.e1-601.e9. [PMID: 32241690 DOI: 10.1016/j.urolonc.2020.02.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 12/26/2019] [Accepted: 02/05/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE To predict Gleason grade group (GG) upgrade in biopsy Gleason grade group 1 (GG1) prostate cancer (CaP) patients using surface-enhanced Raman spectroscopy (SERS). MATERIALS AND METHODS Preoperative serum samples of patients with biopsy GG1 and subsequent radical prostatectomy were analyzed using SERS. The role of clinical variables and distinctive SERS spectra in the prediction of GG upgrade were evaluated. Principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage spectral data and develop diagnostic algorithms. RESULTS A total of 342 preoperative serum SERS spectra from 114 patients were obtained. SERS detected a higher level of circulating free nucleic acid bases and a lower level of lipids in patients with GG upgrade to GG3 and higher, presenting as SERS spectral peaks of 728 cm-1 and 1,655 cm-1, respectively. Both spectral peaks were independent predictors of GG upgrade and their addition to clinical predictors of PSA and positive core percent significantly improved predictive power of the logistic regression model with area under curve improved from 0.65 to 0.80 (P = 0.0045). Meanwhile, PCA-LDA diagnostic model based on serum SERS spectra showed a high accuracy of 91.2% in predicted groups and remained stable with a sensitivity, specificity, and accuracy of 65%, 97.3%, 86.0%, respectively when validated by leave-one-out cross-validation method. CONCLUSIONS By analyzing preoperative serum samples, SERS combined with PCA-LDA model could be a promising tool for prediction of Gleason GG upgrade in biopsy GG1 CaP and assist in treatment decision-making in clinical practice.
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D'Acunto M, Gaeta R, Capanna R, Franchi A. Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors. Sci Rep 2020; 10:2155. [PMID: 32034187 PMCID: PMC7005702 DOI: 10.1038/s41598-020-58848-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 01/19/2020] [Indexed: 12/21/2022] Open
Abstract
In the last decade, Raman Spectroscopy has demonstrated to be a label-free and non-destructive optical spectroscopy able to improve diagnostic accuracy in cancer diagnosis. This is because Raman spectroscopic measurements can reveal a deep molecular understanding of the biochemical changes in cancer tissues in comparison with non-cancer tissues. In this pilot study, we apply Raman spectroscopy imaging to the diagnosis and grading of chondrogenic tumors, including enchondroma and chondrosarcomas of increasing histologic grades. The investigation included the analysis of areas of 50×50 μm2 to approximately 200×200 μm2, respectively. Multivariate statistical analysis, based on unsupervised (Principal Analysis Components) and supervised (Linear Discriminant Analysis) methods, differentiated between the various tumor samples, between cells and extracellular matrix, and between collagen and non-collagenous components. The results dealt out basic biochemical information on tumor progression giving the possibility to grade with certainty the malignant cartilaginous tumors under investigation. The basic processes revealed by Raman Spectroscopy are the progressive degrading of collagen type-II components, the formation of calcifications and the cell proliferation in tissues ranging from enchondroma to chondrosarcomas. This study highlights that Raman spectroscopy is particularly effective when cartilaginous tumors need to be subjected to histopathological analysis.
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Affiliation(s)
- Mario D'Acunto
- IBF-CNR, Istituto di Biofisica, Consiglio Nazionale delle Ricerche, Area della Ricerca di Pisa, via Moruzzi 1, I-56124, Pisa, Italy.
| | - Raffaele Gaeta
- Department of Translational Research and of New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Rodolfo Capanna
- Department of Translational Research and of New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Alessandro Franchi
- Department of Translational Research and of New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
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42
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Ralbovsky NM, Lednev IK. Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chem Soc Rev 2020; 49:7428-7453. [DOI: 10.1039/d0cs01019g] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.
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Affiliation(s)
| | - Igor K. Lednev
- Department of Chemistry
- University at Albany
- SUNY
- Albany
- USA
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43
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Rangan S, Schulze HG, Vardaki MZ, Blades MW, Piret JM, Turner RFB. Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives. Analyst 2020; 145:2070-2105. [DOI: 10.1039/c9an01811e] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.
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Affiliation(s)
- Shreyas Rangan
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- School of Biomedical Engineering
| | - H. Georg Schulze
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
| | - Martha Z. Vardaki
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
| | - Michael W. Blades
- Department of Chemistry
- The University of British Columbia
- Vancouver
- Canada
| | - James M. Piret
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- School of Biomedical Engineering
| | - Robin F. B. Turner
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- Department of Chemistry
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44
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Krafft C, Popp J. Medical needs for translational biophotonics with the focus on Raman‐based methods. TRANSLATIONAL BIOPHOTONICS 2019. [DOI: 10.1002/tbio.201900018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena Germany
- Institute of Physical Chemistry and Abbe Center of PhotonicsFriedrich Schiller University Jena Jena Germany
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45
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Quantitative Histopathology of Stained Tissues using Color Spatial Light Interference Microscopy (cSLIM). Sci Rep 2019; 9:14679. [PMID: 31604963 PMCID: PMC6789107 DOI: 10.1038/s41598-019-50143-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 08/31/2019] [Indexed: 01/22/2023] Open
Abstract
Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light interference microscopy (cSLIM) as a new whole-slide imaging modality that performs interferometric imaging on stained tissue, with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide stain-independent prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.
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46
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Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies. Molecules 2019; 24:molecules24193577. [PMID: 31590270 PMCID: PMC6804209 DOI: 10.3390/molecules24193577] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 09/30/2019] [Accepted: 10/03/2019] [Indexed: 12/14/2022] Open
Abstract
Pituitary adenomas are neoplasia of the anterior pituitary gland and can be subdivided into hormone-producing tumors (lactotroph, corticotroph, gonadotroph, somatotroph, thyreotroph or plurihormonal) and hormone-inactive tumors (silent or null cell adenomas) based on their hormonal status. We therefore developed a line scan Raman microspectroscopy (LSRM) system to detect, discriminate and hyperspectrally visualize pituitary gland from pituitary adenomas based on molecular differences. By applying principal component analysis followed by a k-nearest neighbor algorithm, specific hormone states were identified and a clear discrimination between pituitary gland and various adenoma subtypes was achieved. The classifier yielded an accuracy of 95% for gland tissue and 84–99% for adenoma subtypes. With an overall accuracy of 92%, our LSRM system has proven its potential to differentiate pituitary gland from pituitary adenomas. LSRM images based on the presence of specific Raman bands were created, and such images provided additional insight into the spatial distribution of particular molecular compounds. Pathological states could be molecularly differentiated and characterized with texture analysis evaluating Grey Level Cooccurrence Matrices for each LSRM image, as well as correlation coefficients between LSRM images.
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47
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Eissa A, Zoeir A, Sighinolfi MC, Puliatti S, Bevilacqua L, Del Prete C, Bertoni L, Azzoni P, Reggiani Bonetti L, Micali S, Bianchi G, Rocco B. "Real-time" Assessment of Surgical Margins During Radical Prostatectomy: State-of-the-Art. Clin Genitourin Cancer 2019; 18:95-104. [PMID: 31784282 DOI: 10.1016/j.clgc.2019.07.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 07/14/2019] [Accepted: 07/15/2019] [Indexed: 01/18/2023]
Abstract
Histopathologic examination of the pathologic specimens using hematoxylin & eosin stains represents the backbone of the modern pathology. It is time-consuming; thus, "real-time" assessment of prostatic and periprostatic tissue has gained special interest in the diagnosis and management of prostate cancer. The current study focuses on the review of the different available techniques for "real-time" evaluation of surgical margins during radical prostatectomy (RP). We performed a comprehensive search of the Medline database to identify all the articles discussing "real-time" or intraoperative assessment of surgical margins during RP. Several filters were applied to the search to include only English articles performed on human subjects and published between January 2000 and March 2019. The search revealed several options for pathologic assessment of surgical margins including intraoperative frozen sections, confocal laser endomicroscopy, optical spectroscopy, photodynamic diagnosis, optical coherence tomography, multiphoton microscopy, structured illumination microscopy, 3D augmented reality, and ex vivo fluorescence confocal microscope. Frozen section represents the gold standard technique for real-time pathologic examinations of surgical margins during RP; however, several other options showed promising results in the initial clinical trials, and considering the rapid development in the field of molecular and cellular imaging, some of these options may serve as an alternative to frozen section.
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Affiliation(s)
- Ahmed Eissa
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy; Urology Department, Faculty of Medicine, Tanta University, Tanta, Egypt
| | - Ahmed Zoeir
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy; Urology Department, Faculty of Medicine, Tanta University, Tanta, Egypt
| | | | - Stefano Puliatti
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Luigi Bevilacqua
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Chiara Del Prete
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Laura Bertoni
- Department of Pathology, University of Modena and Reggio Emilia, Modena, Italy
| | - Paola Azzoni
- Department of Pathology, University of Modena and Reggio Emilia, Modena, Italy
| | | | - Salvatore Micali
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Giampaolo Bianchi
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Bernardo Rocco
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy.
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Sdobnov AY, Lademann J, Darvin ME, Tuchin VV. Methods for Optical Skin Clearing in Molecular Optical Imaging in Dermatology. BIOCHEMISTRY (MOSCOW) 2019; 84:S144-S158. [PMID: 31213200 DOI: 10.1134/s0006297919140098] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
This short review describes recent progress in using optical clearing (OC) technique in skin studies. Optical clearing is an efficient tool for enhancing the probing depth and data quality in multiphoton microscopy and Raman spectroscopy. Here, we discuss the main mechanisms of OC, its safety, advantages, and limitations. The data on the OC effect on the skin water content are presented. It was demonstrated that 70% glycerol and 100% OmnipaqueTM 300 reduce the water content in the skin. Both OC agents (OCAs) significantly affect the strongly bound and weakly bound water. However, OmnipaqueTM 300 causes considerably less skin dehydration than glycerol. In addition, the results of examination of the OC effect on autofluorescence in two-photon excitation and background fluorescence in Raman scattering at different skin depths are presented. It is shown that OmnipaqueTM 300 is a promising OCA due to its ability to reduce background fluorescence in the upper skin layers. The possibility of multimodal imaging combining optical methods and OC technique is discussed.
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Affiliation(s)
- A Yu Sdobnov
- Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, 90570, Finland. .,Research-Educational Institute of Optics and Biophotonics, Saratov State University, Saratov, 410012, Russia
| | - J Lademann
- Center of Experimental and Applied Cutaneous Physiology, Department of Dermatology, Venerology and Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 10117, Germany
| | - M E Darvin
- Center of Experimental and Applied Cutaneous Physiology, Department of Dermatology, Venerology and Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 10117, Germany
| | - V V Tuchin
- Research-Educational Institute of Optics and Biophotonics, Saratov State University, Saratov, 410012, Russia.,Laboratory of Laser Diagnostics of Technical and Living Systems, Institute of Precision Mechanics and Control, Russian Academy of Sciences, Saratov, 410028, Russia.,Interdisciplinary Laboratory of Biophotonics, Tomsk State University, Tomsk, 634050, Russia.,Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, 119071, Russia
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49
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Wang N, Cao H, Wang L, Ren F, Zeng Q, Xu X, Liang J, Zhan Y, Chen X. Recent Advances in Spontaneous Raman Spectroscopic Imaging: Instrumentation and Applications. Curr Med Chem 2019; 27:6188-6207. [PMID: 31237196 DOI: 10.2174/0929867326666190619114431] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 04/04/2019] [Accepted: 04/05/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Spectroscopic imaging based on the spontaneous Raman scattering effects can provide unique fingerprint information in relation to the vibration bands of molecules. Due to its advantages of high chemical specificity, non-invasive detection capability, low sensitivity to water, and no special sample pretreatment, Raman Spectroscopic Imaging (RSI) has become an invaluable tool in the field of biomedicine and medicinal chemistry. METHODS There are three methods to implement RSI, including point scanning, line scanning and wide-field RSI. Point-scanning can achieve two-and three-dimensional imaging of target samples. High spectral resolution, full spectral range and confocal features render this technique highly attractive. However, point scanning based RSI is a time-consuming process that can take several hours to map a small area. Line scanning RSI is an extension of point scanning method, with an imaging speed being 300-600 times faster. In the wide-field RSI, the laser illuminates the entire region of interest directly and all the images then collected for analysis. In general, it enables more accurate chemical imaging at faster speeds. RESULTS This review focuses on the recent advances in RSI, with particular emphasis on the latest developments on instrumentation and the related applications in biomedicine and medicinal chemistry. Finally, we prospect the development trend of RSI as well as its potential to translation from bench to bedside. CONCLUSION RSI is a powerful technique that provides unique chemical information, with a great potential in the fields of biomedicine and medicinal chemistry.
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Affiliation(s)
- Nan Wang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of China, Xi’an, Shaanxi 710126, China,School of Life Science and Technology, Xidian University, P.O. Box: 0528, Xi’an, Shaanxi 710126, China
| | - Honghao Cao
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of China, Xi’an, Shaanxi 710126, China,School of Life Science and Technology, Xidian University, P.O. Box: 0528, Xi’an, Shaanxi 710126, China
| | - Lin Wang
- School of Information Sciences and Techonlogy, Northwest University, Xi’an, Shaanxi 710127, China
| | - Feng Ren
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of China, Xi’an, Shaanxi 710126, China,School of Life Science and Technology, Xidian University, P.O. Box: 0528, Xi’an, Shaanxi 710126, China
| | - Qi Zeng
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of China, Xi’an, Shaanxi 710126, China,School of Life Science and Technology, Xidian University, P.O. Box: 0528, Xi’an, Shaanxi 710126, China
| | - Xinyi Xu
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of China, Xi’an, Shaanxi 710126, China,School of Life Science and Technology, Xidian University, P.O. Box: 0528, Xi’an, Shaanxi 710126, China
| | - Jimin Liang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of China, Xi’an, Shaanxi 710126, China,School of Life Science and Technology, Xidian University, P.O. Box: 0528, Xi’an, Shaanxi 710126, China
| | - Yonghua Zhan
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of China, Xi’an, Shaanxi 710126, China,School of Life Science and Technology, Xidian University, P.O. Box: 0528, Xi’an, Shaanxi 710126, China
| | - Xueli Chen
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of China, Xi’an, Shaanxi 710126, China,School of Life Science and Technology, Xidian University, P.O. Box: 0528, Xi’an, Shaanxi 710126, China
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50
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D'Acunto M. In Situ Surface-Enhanced Raman Spectroscopy of Cellular Components: Theory and Experimental Results. MATERIALS 2019; 12:ma12091564. [PMID: 31086033 PMCID: PMC6539138 DOI: 10.3390/ma12091564] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/17/2019] [Accepted: 05/09/2019] [Indexed: 12/30/2022]
Abstract
In the last decade, surface-enhanced Raman spectroscopy (SERS) met increasing interest in the detection of chemical and biological agents due to its rapid performance and ultra-sensitive features. Being SERS a combination of Raman spectroscopy and nanotechnology, it includes the advantages of Raman spectroscopy, providing rapid spectra collection, small sample sizes, characteristic spectral fingerprints for specific analytes. In addition, SERS overcomes low sensitivity or fluorescence interference that represents two major drawbacks of traditional Raman spectroscopy. Nanoscale roughened metal surfaces tremendously enhance the weak Raman signal due to electromagnetic field enhancement generated by localized surface plasmon resonances. In this paper, we detected label-free SERS signals for arbitrarily configurations of dimers, trimers, etc., composed of gold nanoshells (AuNSs) and applied to the mapping of osteosarcoma intracellular components. The experimental results combined to a theoretical model computation of SERS signal of specific AuNSs configurations, based on open cavity plasmonics, give the possibility to quantify SERS enhancement for overcoming spectral fluctuations. The results show that the Raman signal is locally enhanced inside the cell by AuNSs uptake and correspondent geometrical configuration generating dimers are able to enhance locally electromagnetic fields. The SERS signals inside such regions permit the unequivocal identification of cancer-specific biochemical components such as hydroxyapatite, phenylalanine, and protein denaturation due to disulfide bonds breaking between cysteine links or proline.
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Affiliation(s)
- Mario D'Acunto
- IBF-CNR, Istituto di Biofisica, Consiglio Nazionale delle Ricerche, Area della Ricerca CNR di Pisa, via Moruzzi 1, I-56124 Pisa, Italy.
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