1
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Wu D, Fedorov Kukk A, Panzer R, Emmert S, Roth B. In vivo Raman spectroscopic and fluorescence study of suspected melanocytic lesions and surrounding healthy skin. JOURNAL OF BIOPHOTONICS 2024; 17:e202400050. [PMID: 38932707 DOI: 10.1002/jbio.202400050] [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: 02/15/2024] [Revised: 04/25/2024] [Accepted: 05/03/2024] [Indexed: 06/28/2024]
Abstract
Cutaneous melanoma is the most lethal skin cancer and noninvasively distinguishing it from benign tumor is a major challenge. Raman spectroscopic measurements were conducted on 65 suspected melanocytic lesions and surrounding healthy skin from 47 patients. Compared to the spectra of healthy skin, spectra of melanocytic lesions exhibited lower intensities in carotenoid bands and higher intensities in lipid and melanin bands, suggesting similar variations in the content of these components. Distinct variations were observed among the autofluorescence intensities of healthy skin, benign nevi and malignant melanoma. By incorporating autofluorescence information, the classification accuracy of the support vector machine for spectra of healthy skin, nevi, and melanoma reached 90.2%, surpassing the 87.9% accuracy achieved without autofluorescence, with this difference being statistically significant. These findings indicate the diagnostic value of autofluorescence intensity, which reflect differences in fluorophore content, chemical composition, and structure among healthy skin, nevi, and melanoma.
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Affiliation(s)
- Di Wu
- Hannover Centre for Optical Technologies, Leibniz University Hannover, Hanover, Germany
| | - Anatoly Fedorov Kukk
- Hannover Centre for Optical Technologies, Leibniz University Hannover, Hanover, Germany
| | | | | | - Bernhard Roth
- Hannover Centre for Optical Technologies, Leibniz University Hannover, Hanover, Germany
- Cluster of Excellence PhoenixD, Leibniz University Hannover, Hannover, Germany
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2
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Al Jedani S, Lima C, Smith CI, Gunning PJ, Shaw RJ, Barrett SD, Triantafyllou A, Risk JM, Goodacre R, Weightman P. An optical photothermal infrared investigation of lymph nodal metastases of oral squamous cell carcinoma. Sci Rep 2024; 14:16050. [PMID: 38992088 PMCID: PMC11239877 DOI: 10.1038/s41598-024-66977-z] [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: 01/29/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
In this study, optical photothermal infrared (O-PTIR) spectroscopy combined with machine learning algorithms were used to evaluate 46 tissue cores of surgically resected cervical lymph nodes, some of which harboured oral squamous cell carcinoma nodal metastasis. The ratios obtained between O-PTIR chemical images at 1252 cm-1 and 1285 cm-1 were able to reveal morphological details from tissue samples that are comparable to the information achieved by a pathologist's interpretation of optical microscopy of haematoxylin and eosin (H&E) stained samples. Additionally, when used as input data for a hybrid convolutional neural network (CNN) and random forest (RF) analyses, these yielded sensitivities, specificities and precision of 98.6 ± 0.3%, 92 ± 4% and 94 ± 5%, respectively, and an area under receiver operator characteristic (AUC) of 94 ± 2%. Our findings show the potential of O-PTIR technology as a tool to study cancer on tissue samples.
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Affiliation(s)
- Safaa Al Jedani
- Department of Physics, Oliver Lodge Laboratory, University of Liverpool, Liverpool, L69 7ZE, UK
- Department of Physics, University of Jeddah, Jeddah, Saudi Arabia
| | - Cassio Lima
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Caroline I Smith
- Department of Physics, Oliver Lodge Laboratory, University of Liverpool, Liverpool, L69 7ZE, UK
| | - Philip J Gunning
- Department of Molecular and Clinical Cancer Medicine, Liverpool Head and Neck Centre, University of Liverpool, Liverpool, L7 8TX, UK
| | - Richard J Shaw
- Department of Molecular and Clinical Cancer Medicine, Liverpool Head and Neck Centre, University of Liverpool, Liverpool, L7 8TX, UK
- Head and Neck Surgery, Liverpool University Foundation NHS Trust, Aintree Hospital, Liverpool, L9 7AL, UK
| | - Steve D Barrett
- Department of Physics, Oliver Lodge Laboratory, University of Liverpool, Liverpool, L69 7ZE, UK
| | - Asterios Triantafyllou
- Department of Cellular Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, L7 8YE, UK
| | - Janet M Risk
- Department of Molecular and Clinical Cancer Medicine, Liverpool Head and Neck Centre, University of Liverpool, Liverpool, L7 8TX, UK
| | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Peter Weightman
- Department of Physics, Oliver Lodge Laboratory, University of Liverpool, Liverpool, L69 7ZE, UK.
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3
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Dunnington EL, Wong BS, Fu D. Innovative Approaches for Drug Discovery: Quantifying Drug Distribution and Response with Raman Imaging. Anal Chem 2024; 96:7926-7944. [PMID: 38625100 PMCID: PMC11108735 DOI: 10.1021/acs.analchem.4c01413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Affiliation(s)
| | | | - Dan Fu
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
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Niehaus P, Gonzalez de Vega R, Haindl MT, Birkl C, Leoni M, Birkl-Toeglhofer AM, Haybaeck J, Ropele S, Seeba M, Goessler W, Karst U, Langkammer C, Clases D. Multimodal analytical tools for the molecular and elemental characterisation of lesions in brain tissue of multiple sclerosis patients. Talanta 2024; 270:125518. [PMID: 38128277 DOI: 10.1016/j.talanta.2023.125518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 12/23/2023]
Abstract
Multiple sclerosis (MS) is a prevalent immune-mediated inflammatory disease of the central nervous system inducing a widespread degradation of myelin and resulting in neurological deficits. Recent advances in molecular and atomic imaging provide the means to probe the microenvironment in affected brain tissues at an unprecedented level of detail and may provide new insights. This study showcases state-of-the-art spectroscopic and mass spectrometric techniques to compare distributions of molecular and atomic entities in MS lesions and surrounding brain tissues. MS brains underwent post-mortem magnetic resonance imaging (MRI) to locate and subsequently dissect MS lesions and surrounding white matter. Digests of lesions and unaffected white matter were analysed via ICP-MS/MS revealing significant differences in concentrations of Li, Mg, P, K, Mn, V, Rb, Ag, Gd and Bi. Micro x-ray fluorescence spectroscopy (μXRF) and laser ablation - inductively coupled plasma - time of flight - mass spectrometry (LA-ICP-ToF-MS) were used as micro-analytical imaging techniques to study distributions of both endogenous and xenobiotic elements. The essential trace elements Fe, Cu and Zn were subsequently calibrated using in-house manufactured gelatine standards. Lipid distributions were studied using IR-micro spectroscopy and matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI). MALDI-MSI was complemented with high-resolution tandem mass spectrometry and trapped ion mobility spectroscopy for the annotation of specified phospho- and sphingolipids, revealing specific lipid species decreased in MS lesions compared to surrounding white matter. This explorative study demonstrated that modern molecular and atomic mapping techniques provide high-resolution imaging for relevant bio-indicative entities which may complement our current understanding of the underlying pathophysiological processes.
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Affiliation(s)
- Peter Niehaus
- Institute of Inorganic and Analytical Chemistry, University of Münster, Germany
| | | | | | - Christoph Birkl
- Department of Radiology, Medical University of Innsbruck, Austria
| | - Marlene Leoni
- Diagnostic and Research Center for Molecular Biomedicine, Institute of Pathology, Medical University of Graz, Austria
| | - Anna Maria Birkl-Toeglhofer
- Diagnostic and Research Center for Molecular Biomedicine, Institute of Pathology, Medical University of Graz, Austria; Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Austria
| | - Johannes Haybaeck
- Diagnostic and Research Center for Molecular Biomedicine, Institute of Pathology, Medical University of Graz, Austria
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Austria
| | | | | | - Uwe Karst
- Institute of Inorganic and Analytical Chemistry, University of Münster, Germany
| | | | - David Clases
- Institute of Chemistry, University of Graz, Austria.
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5
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Galli R, Lehner F, Richter S, Kirsche K, Meinhardt M, Juratli TA, Temme A, Kirsch M, Warta R, Herold-Mende C, Ricklefs FL, Lamszus K, Sievers P, Sahm F, Eyüpoglu IY, Uckermann O. Prediction of WHO grade and methylation class of aggressive meningiomas: Extraction of diagnostic information from infrared spectroscopic data. Neurooncol Adv 2024; 6:vdae082. [PMID: 39006162 PMCID: PMC11245706 DOI: 10.1093/noajnl/vdae082] [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] [Indexed: 07/16/2024] Open
Abstract
Background Infrared (IR) spectroscopy allows intraoperative, optical brain tumor diagnosis. Here, we explored it as a translational technology for the identification of aggressive meningioma types according to both, the WHO CNS grading system and the methylation classes (MC). Methods Frozen sections of 47 meningioma were examined by IR spectroscopic imaging and different classification approaches were compared to discern samples according to WHO grade or MC. Results IR spectroscopic differences were more pronounced between WHO grade 2 and 3 than between MC intermediate and MC malignant, although similar spectral ranges were affected. Aggressive types of meningioma exhibited reduced bands of carbohydrates (at 1024 cm-1) and nucleic acids (at 1080 cm-1), along with increased bands of phospholipids (at 1240 and 1450 cm-1). While linear discriminant analysis was able to discern spectra of WHO grade 2 and 3 meningiomas (AUC 0.89), it failed for MC (AUC 0.66). However, neural network classifiers were effective for classification according to both WHO grade (AUC 0.91) and MC (AUC 0.83), resulting in the correct classification of 20/23 meningiomas of the test set. Conclusions IR spectroscopy proved capable of extracting information about the malignancy of meningiomas, not only according to the WHO grade, but also for a diagnostic system based on molecular tumor characteristics. In future clinical use, physicians could assess the goodness of the classification by considering classification probabilities and cross-measurement validation. This might enhance the overall accuracy and clinical utility, reinforcing the potential of IR spectroscopy in advancing precision medicine for meningioma characterization.
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Affiliation(s)
- Roberta Galli
- Faculty of Medicine, Medical Physics and Biomedical Engineering, Technische Universität Dresden, Dresden, Germany
| | - Franz Lehner
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Sven Richter
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany
| | - Katrin Kirsche
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Matthias Meinhardt
- Faculty of Medicine, Department of Pathology, Technische Universität Dresden, Dresden, Germany
| | - Tareq A Juratli
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | | | - Matthias Kirsch
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Rolf Warta
- Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Christel Herold-Mende
- Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Franz L Ricklefs
- Laboratory for Brain Tumor Research, Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katrin Lamszus
- Laboratory for Brain Tumor Research, Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Philipp Sievers
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
- CCU Neuropathology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
- CCU Neuropathology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ilker Y Eyüpoglu
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ortrud Uckermann
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany
- Department of Psychiatry and Psychotherapy, Division of Medical Biology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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Liu Y, Chen C, Xie X, Lv X, Chen C. For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123147. [PMID: 37517264 DOI: 10.1016/j.saa.2023.123147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 08/01/2023]
Abstract
Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.
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Affiliation(s)
- Yang Liu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Xiaodong Xie
- Xinjiang Uygur Autonomous Region People's Hospital, Urumqi 830046, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
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7
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Shaik TA, Ramoji A, Milis N, Popp J, Krafft C. Optical photothermal infrared spectroscopy and discrete wavenumber imaging for high content screening of single cells. Analyst 2023; 148:5627-5635. [PMID: 37842964 DOI: 10.1039/d3an00902e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Major drawbacks of direct mid-infrared spectroscopic imaging of single cells in an aqueous buffer are strong water absorption, low resolution typically above 10 μm, and Mie scattering effects. This study demonstrates how an indirect detection principle can overcome these drawbacks using the optical photothermal infrared (O-PTIR) technique for high-resolution discrete wavenumber imaging and fingerprint spectroscopy of cultivated cells as a model system in a simple liquid sample chamber. The O-PTIR spectra of six leukemia- and cancer-derived cell lines showed main IR bands near 1648, 1547, 1447, 1400, 1220, and 1088 cm-1. Five spectra of approximately 260 single cells per cell type were averaged, the O-PTIR data set was divided into leukemia-derived cells (THP-1, HL 60, Jurkat, and Raji) and cancer cells (HeLa and HepaRG), and partial least squares linear discriminant analysis (PLS-LDA) was applied in the spectral range 800-1800 cm-1 to train three classification models. A leukemia versus cancer cell model showed an accuracy of 90.0%, the HeLa versus HepaRG cell model had an accuracy of 95.4%, and the model for the distinction of leukemia cells had an accuracy of 75.4%. IR bands in linear discriminants (LDs) of the models were correlated with second derivative spectra that resolved more than 25 subbands. The IR and second derivative spectra of proteins, DNA, RNA and lipids were collected as references to confirm band assignments. O-PTIR images of single cells at a 200 nm step size were acquired at 1086, 1548, and 1746 cm-1 to visualize the nucleic acid, protein, and lipid distribution, respectively. Variations in subcellular features and in the lipid-to-protein and nucleic acid-to-protein ratios were identified that were consistent with biomolecular information in LDs. In conclusion, O-PTIR can provide high-quality spectra and images with submicron resolution of single cells in aqueous buffers that offer prospects in high-content screening applications.
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Affiliation(s)
- Tanveer Ahmed Shaik
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of Photonics, Member of the Leibniz Center for Photonics in Infection Research, 07743 Jena, Germany
| | - Anuradha Ramoji
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of Photonics, Member of the Leibniz Center for Photonics in Infection Research, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Research Alliance Leibniz Health Technologies, Member of the Leibniz Center for Photonics in Infection Research, 07745 Jena, Germany.
- Jena University Hospital, Center for Sepsis Control and Care (CSCC), Member of the Leibniz Center for Photonics in Infection Research, Friedrich-Schiller University Jena, 07747 Jena, Germany
| | - Nils Milis
- Leibniz Institute of Photonic Technology, Member of Leibniz Research Alliance Leibniz Health Technologies, Member of the Leibniz Center for Photonics in Infection Research, 07745 Jena, Germany.
| | - Jürgen Popp
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of Photonics, Member of the Leibniz Center for Photonics in Infection Research, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Research Alliance Leibniz Health Technologies, Member of the Leibniz Center for Photonics in Infection Research, 07745 Jena, Germany.
| | - Christoph Krafft
- Leibniz Institute of Photonic Technology, Member of Leibniz Research Alliance Leibniz Health Technologies, Member of the Leibniz Center for Photonics in Infection Research, 07745 Jena, Germany.
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8
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Ellis BG, Ingham J, Whitley CA, Al Jedani S, Gunning PJ, Gardner P, Shaw RJ, Barrett SD, Triantafyllou A, Risk JM, Smith CI, Weightman P. Metric-based analysis of FTIR data to discriminate tissue types in oral cancer. Analyst 2023; 148:1948-1953. [PMID: 37067098 PMCID: PMC10152457 DOI: 10.1039/d3an00258f] [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: 02/16/2023] [Accepted: 04/08/2023] [Indexed: 04/18/2023]
Abstract
A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.
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Affiliation(s)
- Barnaby G Ellis
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - James Ingham
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Conor A Whitley
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Safaa Al Jedani
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Philip J Gunning
- Liverpool Head and Neck Centre, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, L7 8TX, UK
| | - Peter Gardner
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Richard J Shaw
- Liverpool Head and Neck Centre, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, L7 8TX, UK
- Head and Neck Surgery, Liverpool University Foundation NHS Trust, Aintree Hospital, Liverpool, L9 7AL, UK
| | - Steve D Barrett
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Asterios Triantafyllou
- Department of Cellular Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, L7 8YE, UK
| | - Janet M Risk
- Liverpool Head and Neck Centre, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, L7 8TX, UK
| | | | - Peter Weightman
- Department of Physics, University of Liverpool, L69 7ZE, UK.
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9
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Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning. Pharmaceutics 2023; 15:pharmaceutics15010203. [PMID: 36678833 PMCID: PMC9862809 DOI: 10.3390/pharmaceutics15010203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 01/11/2023] Open
Abstract
The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.
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10
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Bench C, Nallala J, Wang CC, Sheridan H, Stone N. Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders. BIOMEDICAL OPTICS EXPRESS 2022; 13:6373-6388. [PMID: 36589581 PMCID: PMC9774878 DOI: 10.1364/boe.476233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/25/2022] [Accepted: 10/25/2022] [Indexed: 06/17/2023]
Abstract
Information about the structure and composition of biopsy specimens can assist in disease monitoring and diagnosis. In principle, this can be acquired from Raman and infrared (IR) hyperspectral images (HSIs) that encode information about how a sample's constituent molecules are arranged in space. Each tissue section/component is defined by a unique combination of spatial and spectral features, but given the high dimensionality of HSI datasets, extracting and utilising them to segment images is non-trivial. Here, we show how networks based on deep convolutional autoencoders (CAEs) can perform this task in an end-to-end fashion by first detecting and compressing relevant features from patches of the HSI into low-dimensional latent vectors, and then performing a clustering step that groups patches containing similar spatio-spectral features together. We showcase the advantages of using this end-to-end spatio-spectral segmentation approach compared to i) the same spatio-spectral technique not trained in an end-to-end manner, and ii) a method that only utilises spectral features (spectral k-means) using simulated HSIs of porcine tissue as test examples. Secondly, we describe the potential advantages/limitations of using three different CAE architectures: a generic 2D CAE, a generic 3D CAE, and a 2D convolutional encoder-decoder architecture inspired by the recently proposed UwU-net that is specialised for extracting features from HSI data. We assess their performance on IR HSIs of real colon samples. We find that all architectures are capable of producing segmentations that show good correspondence with HE stained adjacent tissue slices used as approximate ground truths, indicating the robustness of the CAE-driven spatio-spectral clustering approach for segmenting biomedical HSI data. Additionally, we stress the need for more accurate ground truth information to enable a precise comparison of the advantages offered by each architecture.
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Affiliation(s)
- Ciaran Bench
- School of Physics and Astronomy, University of Exeter, Exeter, Devon, EX4 4PY, United Kingdom
| | - Jayakrupakar Nallala
- School of Physics and Astronomy, University of Exeter, Exeter, Devon, EX4 4PY, United Kingdom
| | - Chun-Chin Wang
- School of Physics and Astronomy, University of Exeter, Exeter, Devon, EX4 4PY, United Kingdom
| | - Hannah Sheridan
- School of Physics and Astronomy, University of Exeter, Exeter, Devon, EX4 4PY, United Kingdom
| | - Nicholas Stone
- School of Physics and Astronomy, University of Exeter, Exeter, Devon, EX4 4PY, United Kingdom
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11
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Matys J, Turska-Szewczuk A, Gieroba B, Kurzylewska M, Pękala-Safińska A, Sroka-Bartnicka A. Evaluation of Proteomic and Lipidomic Changes in Aeromonas-Infected Trout Kidney Tissue with the Use of FT-IR Spectroscopy and MALDI Mass Spectrometry Imaging. Int J Mol Sci 2022; 23:ijms232012551. [PMID: 36293421 PMCID: PMC9604335 DOI: 10.3390/ijms232012551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/13/2022] [Accepted: 10/15/2022] [Indexed: 11/16/2022] Open
Abstract
Aeromonas species are opportunistic bacteria causing a vast spectrum of human diseases, including skin and soft tissue infections, meningitis, endocarditis, peritonitis, gastroenteritis, and finally hemorrhagic septicemia. The aim of our research was to indicate the molecular alterations in proteins and lipids profiles resulting from Aeromonas sobria and A. salmonicida subsp. salmonicida infection in trout kidney tissue samples. We successfully applied FT-IR (Fourier transform infrared) spectroscopy and MALDI-MSI (matrix-assisted laser desorption/ionization mass spectrometry imaging) to monitor changes in the structure and compositions of lipids, secondary conformation of proteins, and provide useful information concerning disease progression. Our findings indicate that the following spectral bands’ absorbance ratios (spectral biomarkers) can be used to discriminate healthy tissue from pathologically altered tissue, for example, lipids (CH2/CH3), amide I/amide II, amide I/CH2 and amide I/CH3. Spectral data obtained from 10 single measurements of each specimen indicate numerous abnormalities concerning proteins, lipids, and phospholipids induced by Aeromonas infection, suggesting significant disruption of the cell membranes. Moreover, the increase in the content of lysolipids such as lysophosphosphatidylcholine was observed. The results of this study suggest the application of both methods MALDI-MSI and FT-IR as accurate methods for profiling biomolecules and identifying biochemical changes in kidney tissue during the progression of Aeromonas infection.
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Affiliation(s)
- Joanna Matys
- Department of Biopharmacy, Medical University of Lublin, Chodźki 4a, 20-093 Lublin, Poland
- Correspondence: (J.M.); (A.S.-B.)
| | - Anna Turska-Szewczuk
- Department of Genetics and Microbiology, Institute of Biological Sciences, Maria Curie-Skłodowska University, Akademicka 19, 20-033 Lublin, Poland
| | - Barbara Gieroba
- Independent Unit of Spectroscopy and Chemical Imaging, Medical University of Lublin, Chodźki 4a, 20-093 Lublin, Poland
| | - Maria Kurzylewska
- Department of Genetics and Microbiology, Institute of Biological Sciences, Maria Curie-Skłodowska University, Akademicka 19, 20-033 Lublin, Poland
| | - Agnieszka Pękala-Safińska
- Department of Preclinical Sciences and Infectious Diseases, Poznan University of Life Sciences, Wołyńska 35, 60-637 Poznań, Poland
| | - Anna Sroka-Bartnicka
- Department of Genetics and Microbiology, Institute of Biological Sciences, Maria Curie-Skłodowska University, Akademicka 19, 20-033 Lublin, Poland
- Independent Unit of Spectroscopy and Chemical Imaging, Medical University of Lublin, Chodźki 4a, 20-093 Lublin, Poland
- Correspondence: (J.M.); (A.S.-B.)
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12
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Yousefi-Darani A, Paquet-Durand O, Von Wrochem A, Classen J, Tränkle J, Mertens M, Snelders J, Chotteau V, Mäkinen M, Handl A, Kadisch M, Lang D, Dumas P, Hitzmann B. Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra. SENSORS (BASEL, SWITZERLAND) 2022; 22:5581. [PMID: 35898085 PMCID: PMC9332195 DOI: 10.3390/s22155581] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.
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Affiliation(s)
- Abdolrahim Yousefi-Darani
- Department of Process Analytics und Cereal Science, Institute for Food Science and Biotechnology, University of Hohenheim, Garbenstr. 23, 70599 Stuttgart, Germany; (A.Y.-D.); (A.V.W.); (B.H.)
| | - Olivier Paquet-Durand
- Department of Process Analytics und Cereal Science, Institute for Food Science and Biotechnology, University of Hohenheim, Garbenstr. 23, 70599 Stuttgart, Germany; (A.Y.-D.); (A.V.W.); (B.H.)
| | - Almut Von Wrochem
- Department of Process Analytics und Cereal Science, Institute for Food Science and Biotechnology, University of Hohenheim, Garbenstr. 23, 70599 Stuttgart, Germany; (A.Y.-D.); (A.V.W.); (B.H.)
| | - Jens Classen
- Bayer AG, L Kaiser-Wilhelm-Allee 1, 51373 Leverkusen, Germany; (J.C.); (J.T.)
| | - Jens Tränkle
- Bayer AG, L Kaiser-Wilhelm-Allee 1, 51373 Leverkusen, Germany; (J.C.); (J.T.)
| | - Mario Mertens
- Sanofi, Cipalstraat 8, 2440 Geel, Belgium; (M.M.); (J.S.)
| | | | - Veronique Chotteau
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology (KTH), 109 06 Stockholm, Sweden; (V.C.); (M.M.)
| | - Meeri Mäkinen
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology (KTH), 109 06 Stockholm, Sweden; (V.C.); (M.M.)
| | - Alina Handl
- Rentschler Biopharma SE, Erwin-Rentschler-Street 21, 88471 Laupheim, Germany; (A.H.); (M.K.); (D.L.)
| | - Marvin Kadisch
- Rentschler Biopharma SE, Erwin-Rentschler-Street 21, 88471 Laupheim, Germany; (A.H.); (M.K.); (D.L.)
| | - Dietmar Lang
- Rentschler Biopharma SE, Erwin-Rentschler-Street 21, 88471 Laupheim, Germany; (A.H.); (M.K.); (D.L.)
| | | | - Bernd Hitzmann
- Department of Process Analytics und Cereal Science, Institute for Food Science and Biotechnology, University of Hohenheim, Garbenstr. 23, 70599 Stuttgart, Germany; (A.Y.-D.); (A.V.W.); (B.H.)
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13
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Lima C, Muhamadali H, Goodacre R. Simultaneous Raman and Infrared Spectroscopy of Stable Isotope Labelled Escherichia coli. SENSORS (BASEL, SWITZERLAND) 2022; 22:3928. [PMID: 35632337 PMCID: PMC9145054 DOI: 10.3390/s22103928] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
We report the use of a novel technology based on optical photothermal infrared (O-PTIR) spectroscopy for obtaining simultaneous infrared and Raman spectra from the same location of the sample allowing us to study bacterial metabolism by monitoring the incorporation of 13C- and 15N-labeled compounds. Infrared data obtained from bulk populations and single cells via O-PTIR spectroscopy were compared to conventional Fourier transform infrared (FTIR) spectroscopy in order to evaluate the reproducibility of the results achieved by all three approaches. Raman spectra acquired were concomitant with infrared data from bulk populations as well as infrared spectra collected from single cells, and were subjected to principal component analysis in order to evaluate any specific separation resulting from the isotopic incorporation. Similar clustering patterns were observed in infrared data acquired from single cells via O-PTIR spectroscopy as well as from bulk populations via FTIR and O-PTIR spectroscopies, indicating full incorporation of heavy isotopes by the bacteria. Satisfactory discrimination between unlabeled (viz. 12C14N), 13C14N- and 13C15N-labeled bacteria was also obtained using Raman spectra from bulk populations. In this report, we also discuss the limitations of O-PTIR technology to acquire Raman data from single bacterial cells (with typical dimensions of 1 × 2 µm) as well as spectral artifacts induced by thermal damage when analyzing very small amounts of biomass (a bacterium tipically weighs ~ 1 pg).
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Affiliation(s)
| | | | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK; (C.L.); (H.M.)
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14
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Lin H, Wang Z, Luo Y, Sun Q, Shen Y, Huang P. Post-mortem evaluation of the pathological degree of myocardial infarction by Fourier transform infrared microspectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 268:120630. [PMID: 34815176 DOI: 10.1016/j.saa.2021.120630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/18/2021] [Accepted: 11/11/2021] [Indexed: 06/13/2023]
Abstract
In clinical and forensic investigations, accurate post-mortem diagnosis of the pathological degree of myocardial infarction (MI) is critical. However, because of the observer variability, the diagnosis cannot be made objectively. Many studies have shown that Fourier transform infrared (FTIR) microspectroscopy is non-invasive, observer-independent, and label-free when analyzing biological tissues. In this study, we used FTIR microspectroscopy in combination with intelligent algorithms to identify the pathological phases in human infarcted cardiac tissues, including ischemia, necrotic, granulation, and fibrotic stages. First, a comparison of infrared spectra corresponding to infarcted tissue pathological categories revealed various spectral properties. The results of unsupervised principal component analysis (PCA) revealed a clear distinction between these four pathological stages and the normal stage. Then, to identify these five stages, an automatic artificial neural network (ANN) classifier was effectively created. Finally, two-dimensional pseudo-color images of two infarcted cardiac tissue sections visualized via the ANN classifier showed great agreement with their histological images. These findings demonstrate that FTIR microspectroscopy has the potential for the post-mortem evaluation of the pathological degree of MI.
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Affiliation(s)
- Hancheng Lin
- Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Zhenyuan Wang
- Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an 710061, China
| | - Yiwen Luo
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, PRC, Shanghai 200063, China
| | - Qiran Sun
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, PRC, Shanghai 200063, China
| | - Yiwen Shen
- Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
| | - Ping Huang
- Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, PRC, Shanghai 200063, China. @ssfjd.cn
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15
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Lima C, Ahmed S, Xu Y, Muhamadali H, Parry C, McGalliard RJ, Carrol ED, Goodacre R. Simultaneous Raman and infrared spectroscopy: a novel combination for studying bacterial infections at the single cell level. Chem Sci 2022; 13:8171-8179. [PMID: 35919437 PMCID: PMC9278432 DOI: 10.1039/d2sc02493d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
Sepsis is a life-threatening clinical condition responsible for approximately 11 million deaths worldwide. Rapid and accurate identification of pathogenic bacteria and its antimicrobial susceptibility play a critical role in reducing the morbidity and mortality rates related to sepsis. Raman and infrared spectroscopies have great potential to be used as diagnostic tools for rapid and culture-free detection of bacterial infections. Despite numerous reports using both methods to analyse bacterial samples, there is to date no study collecting both Raman and infrared signatures from clinical samples simultaneously due to instrument incompatibilities. Here, we report for the first time the use of an emerging technology that provides infrared signatures via optical photothermal infrared (O-PTIR) spectroscopy and Raman spectra simultaneously. We use this approach to analyse 12 bacterial clinical isolates including six isolates of Gram-negative and six Gram-positive bacteria commonly associated with bloodstream infection in humans. To benchmark the single cell spectra obtained by O-PTIR spectroscopy, infrared signatures were also collected from bulk samples via both FTIR and O-PTIR spectroscopies. Our findings showed significant similarity and high reproducibility in the infrared signatures obtained by all three approaches, including similar discrimination patterns when subjected to clustering algorithms. Principal component analysis (PCA) showed that O-PTIR and Raman data acquired simultaneously from bulk bacterial isolates displayed different clustering patterns due to the ability of both methods to probe metabolites produced by bacteria. By contrast, signatures of microbial pigments were identified in Raman spectra, providing complementary and orthogonal information compared to infrared, which may be advantageous as it has been demonstrated that certain pigments play an important role in bacterial virulence. We found that infrared spectroscopy showed higher sensitivity than Raman for the analysis of individual cells. Despite the different patterns obtained by using Raman and infrared spectral data as input for clustering algorithms, our findings showed high data reproducibility in both approaches as the biological replicates from each bacterial strain clustered together. Overall, we show that Raman and infrared spectroscopy offer both advantages and disadvantages and, therefore, having both techniques combined in one single technology is a powerful tool with promising applications in clinical microbiology. O-PTIR was used for simultaneous collection of infrared and Raman spectra from clinical pathogens associated with bloodstream infections.![]()
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Affiliation(s)
- Cassio Lima
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Shwan Ahmed
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
- Department of Environment and Quality Control, Kurdistan Institution for Strategic Studies and Scientific Research, Kurdistan Region, Iraq
| | - Yun Xu
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Howbeer Muhamadali
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Christopher Parry
- Department of Clinical Infection, Microbiology and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, L69 7BE, UK
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
| | - Rachel J. McGalliard
- Department of Clinical Infection, Microbiology and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, L69 7BE, UK
| | - Enitan D. Carrol
- Department of Clinical Infection, Microbiology and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, L69 7BE, UK
| | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
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16
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Kujdowicz M, Mech B, Chrabaszcz K, Chlosta P, Okon K, Malek K. FTIR Spectroscopic Imaging Supports Urine Cytology for Classification of Low- and High-Grade Bladder Carcinoma. Cancers (Basel) 2021; 13:cancers13225734. [PMID: 34830887 PMCID: PMC8616357 DOI: 10.3390/cancers13225734] [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: 10/13/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Human urine cytological samples were investigated using Fourier transform infrared spectroscopic imaging in terms of recognition of bladder cancer. The clustering of IR spectra of whole cytological smears revealed very good spectral correlation with normal urothelial cell features. Next, the combination of spectral information derived from unsupervised hierarchical cluster analysis and partial least square discriminant analysis (PLS-DA) classified normal vs. low- and high-grade bladder urothelial carcinoma with sensitivity and specificity of 90–97%. Abstract Bladder urothelial carcinoma (BC) is a common, recurrent, life-threatening, and unpredictable disease which is difficult to diagnose. These features make it one of the costliest malignancies. Although many possible diagnostic methods are available, molecular heterogeneity and difficulties in cytological or histological examination induce an urgent need to improve diagnostic techniques. Herein, we applied Fourier transform infrared spectroscopy in imaging mode (FTIR) to investigate patients’ cytology samples assigned to normal (N), low-grade (LG) and high-grade (HG) BC. With unsupervised hierarchical cluster analysis (UHCA) and hematoxylin-eosin (HE) staining, we observed a correlation between N cell types and morphology. High-glycogen superficial (umbrella) and low-glycogen piriform urothelial cells, both with normal morphology, were observed. Based on the spectra derived from UHCA, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed, indicating a variation of protein content between the patient groups. Moreover, BC spectral cytology identified a low number of high-glycogen cells for which a shift of the carbohydrate/phosphate bands was also observed. Despite high cellular heterogeneity, PLS-DA was able to classify the spectra obtained. The voided urine FTIR cytology is one of the options that might be helpful in BC diagnosis, as high sensitivity and specificity up to 97% were determined.
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Affiliation(s)
- Monika Kujdowicz
- Department of Pathomorphology, Faculty of Medicine, Jagiellonian University Medical College, Grzegorzecka 16, 31-531 Krakow, Poland;
- Faculty of Chemistry, Jagiellonian University in Krakow, Gronostajowa 2, 30-387 Krakow, Poland; (B.M.); (K.C.)
| | - Brygida Mech
- Faculty of Chemistry, Jagiellonian University in Krakow, Gronostajowa 2, 30-387 Krakow, Poland; (B.M.); (K.C.)
| | - Karolina Chrabaszcz
- Faculty of Chemistry, Jagiellonian University in Krakow, Gronostajowa 2, 30-387 Krakow, Poland; (B.M.); (K.C.)
- Department of Experimental Physics of Complex Systems, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Krakow, Poland
| | - Piotr Chlosta
- Department of Urology, Faculty of Medicine, Jagiellonian University Medical College, Jakubowskiego 2, 30-688 Krakow, Poland;
| | - Krzysztof Okon
- Department of Pathomorphology, Faculty of Medicine, Jagiellonian University Medical College, Grzegorzecka 16, 31-531 Krakow, Poland;
- Correspondence: (K.O.); (K.M.)
| | - Kamilla Malek
- Faculty of Chemistry, Jagiellonian University in Krakow, Gronostajowa 2, 30-387 Krakow, Poland; (B.M.); (K.C.)
- Correspondence: (K.O.); (K.M.)
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17
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Lovergne L, Ghosh D, Schuck R, Polyzos AA, Chen AD, Martin MC, Barnard ES, Brown JB, McMurray CT. An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms. Sci Rep 2021; 11:15598. [PMID: 34341363 PMCID: PMC8329289 DOI: 10.1038/s41598-021-93686-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 06/24/2021] [Indexed: 12/29/2022] Open
Abstract
Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells.
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Affiliation(s)
- Lila Lovergne
- Division of Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Dhruba Ghosh
- Department of Statistics, University of California, Berkeley, CA, 94720, USA
| | - Renaud Schuck
- Division of Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Aris A Polyzos
- Division of Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Andrew D Chen
- Department of Statistics, University of California, Berkeley, CA, 94720, USA
| | - Michael C Martin
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Edward S Barnard
- Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - James B Brown
- Department of Statistics, University of California, Berkeley, CA, 94720, USA
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Cynthia T McMurray
- Division of Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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18
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Ellis BG, Whitley CA, Al Jedani S, Smith CI, Gunning PJ, Harrison P, Unsworth P, Gardner P, Shaw RJ, Barrett SD, Triantafyllou A, Risk JM, Weightman P. Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM. Analyst 2021; 146:4895-4904. [PMID: 34241603 PMCID: PMC8311263 DOI: 10.1039/d1an00922b] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/01/2021] [Indexed: 12/14/2022]
Abstract
A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the ratio of Fourier transform infrared (FTIR) absorption intensities at 1252 cm-1 and 1285 cm-1. The metric yields discriminating sensitivities, specificities and precision of 98.8 ± 0.1%, 99.89 ± 0.01% and 99.78 ± 0.02% respectively, and an area under receiver operator characteristic (AUC) of 0.9935 ± 0.0006. The delineation of the OSCC and lymphoid tissue revealed by the image formed from the metric is in better agreement with an immunohistochemistry (IHC) stained image than are either of the FTIR images obtained at the individual wavenumbers. Scanning near-field optical microscopy (SNOM) images of the tissue obtained at a number of key wavenumbers, with high spatial resolution, show variations in the chemical structure of the tissue with a feature size down to ∼4 μm. The image formed from the ratio of the SNOM images obtained at 1252 cm-1 and 1285 cm-1 shows more contrast than the SNOM images obtained at these or a number of other individual wavenumbers. The discrimination between the two tissue types is dominated by the contribution from the 1252 cm-1 signal, which is representative of nucleic acids, and this shows the OSCC tissue to be accompanied by two wide arcs of tissue which are particularly low in nucleic acids. Haematoxylin and eosin (H&E) staining shows the tumour core in this specimen to be ∼40 μm wide and the SNOM topography shows that the core centre is raised by ∼1 μm compared to the surrounding tissue. Line profiles of the SNOM signal intensity taken through the highly keratinised core show that the increase in height correlates with an increase in the protein signal. SNOM line profiles show that the nucleic acids signal decreases at the centre of the tumour core between two peaks of higher intensity. All these nucleic acid features are ∼25 μm wide, roughly the width of two cancer cells.
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Affiliation(s)
- Barnaby G Ellis
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Conor A Whitley
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Safaa Al Jedani
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | | | - Philip J Gunning
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, UK
| | - Paul Harrison
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Paul Unsworth
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Peter Gardner
- Manchester Institute of Biotechnology, 131 Princess Street, University of Manchester, Manchester, M1 7DN, UK
| | - Richard J Shaw
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, UK and Regional Maxillofacial Unit, Aintree University Hospital, Liverpool, L9 7AL, UK
| | - Steve D Barrett
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Asterios Triantafyllou
- Department of Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, L69 3GA, UK
| | - Janet M Risk
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, UK
| | - Peter Weightman
- Department of Physics, University of Liverpool, L69 7ZE, UK.
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19
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Lin J, Graziotto ME, Lay PA, New EJ. A Bimodal Fluorescence-Raman Probe for Cellular Imaging. Cells 2021; 10:cells10071699. [PMID: 34359866 PMCID: PMC8303253 DOI: 10.3390/cells10071699] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/30/2021] [Accepted: 07/02/2021] [Indexed: 11/24/2022] Open
Abstract
Biochemical changes in specific organelles underpin cellular function, and studying these changes is crucial to understand health and disease. Fluorescent probes have become important biosensing and imaging tools as they can be targeted to specific organelles and can detect changes in their chemical environment. However, the sensing capacity of fluorescent probes is highly specific and is often limited to a single analyte of interest. A novel approach to imaging organelles is to combine fluorescent sensors with vibrational spectroscopic imaging techniques; the latter provides a comprehensive map of the relative biochemical distributions throughout the cell to gain a more complete picture of the biochemistry of organelles. We have developed NpCN1, a bimodal fluorescence-Raman probe targeted to the lipid droplets, incorporating a nitrile as a Raman tag. NpCN1 was successfully used to image lipid droplets in 3T3-L1 cells in both fluorescence and Raman modalities, reporting on the chemical composition and distribution of the lipid droplets in the cells.
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Affiliation(s)
- Jiarun Lin
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia; (J.L.); (M.E.G.)
- The University of Sydney Nano Institute (Sydney Nano), The University of Sydney, Sydney, NSW 2006, Australia
| | - Marcus E. Graziotto
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia; (J.L.); (M.E.G.)
| | - Peter A. Lay
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia; (J.L.); (M.E.G.)
- The University of Sydney Nano Institute (Sydney Nano), The University of Sydney, Sydney, NSW 2006, Australia
- Sydney Analytical, The University of Sydney, Sydney, NSW 2006, Australia
- Correspondence: (P.A.L.); (E.J.N.); Tel.: +61-2-9351-4269 (P.A.L.); + 61-2-9351-3329 (E.J.N.)
| | - Elizabeth J. New
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia; (J.L.); (M.E.G.)
- The University of Sydney Nano Institute (Sydney Nano), The University of Sydney, Sydney, NSW 2006, Australia
- Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Sydney, Sydney, NSW 2006, Australia
- Correspondence: (P.A.L.); (E.J.N.); Tel.: +61-2-9351-4269 (P.A.L.); + 61-2-9351-3329 (E.J.N.)
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20
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Luo SH, Wang X, Chen GY, Xie Y, Zhang WH, Zhou ZF, Zhang ZM, Ren B, Liu GK, Tian ZQ. Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy. Anal Chem 2021; 93:8408-8413. [PMID: 34110787 DOI: 10.1021/acs.analchem.0c05391] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In spectroscopic analysis, push-to-the-limit sensitivity is one of the important topics, particularly when facing the qualitative and quantitative analyses of the trace target. Normally, the effective recognition and extraction of weak signals are the first key steps, for which there has been considerable effort in developing various denoising algorithms for decades. Nevertheless, the lower the signal-to-noise ratio (SNR), the greater the deviation of the peak height and shape during the denoising process. Therefore, we propose a denoising algorithm along with peak extraction and retention (PEER). First, both the first and second derivatives of the Raman spectrum are used to determine Raman peaks with a high SNR whose peak information is kept away from the denoising process. Second, an optimized window smoothing algorithm is applied to the left part of the Raman spectrum, which is combined with the untreated Raman peaks to obtain the denoised Raman spectrum. The PEER algorithm is demonstrated with much better signal extraction and retention and successfully improves the temporal resolution of Raman imaging of a living cell by at least 1 order of magnitude higher than those by traditional algorithms.
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Affiliation(s)
- Si-Heng Luo
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.,State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Xin Wang
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, Fujian 361102, China
| | - Gan-Yu Chen
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Yi Xie
- Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Wen-Han Zhang
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Zhi-Fan Zhou
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Zhi-Min Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan 410083, China
| | - Bin Ren
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Zhong-Qun Tian
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
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21
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Design of a Multimodal Imaging System and Its First Application to Distinguish Grey and White Matter of Brain Tissue. A Proof-of-Concept-Study. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11114777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Multimodal imaging gains increasing popularity for biomedical applications. This article presents the design of a novel multimodal imaging system. The centerpiece is a light microscope operating in the incident and transmitted light mode. Additionally, Raman spectroscopy and VIS/NIR reflectance spectroscopy are adapted. The proof-of-concept is realized to distinguish between grey matter (GM) and white matter (WM) of normal mouse brain tissue. Besides Raman and VIS/NIR spectroscopy, the following optical microscopy techniques are applied in the incident light mode: brightfield, darkfield, and polarization microscopy. To complement the study, brightfield images of a hematoxylin and eosin (H&E) stained cryosection in the transmitted light mode are recorded using the same imaging system. Data acquisition based on polarization microscopy and Raman spectroscopy gives the best results regarding the tissue differentiation of the unstained section. In addition to the discrimination of GM and WM, both modalities are suited to highlight differences in the density of myelinated axons. For Raman spectroscopy, this is achieved by calculating the sum of two intensity peak ratios (I2857 + I2888)/I2930 in the high-wavenumber region. For an optimum combination of the modalities, it is recommended to apply the molecule-specific but time-consuming Raman spectroscopy to smaller regions of interest, which have previously been identified by the microscopic modes.
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22
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Goertzen N, Pappesch R, Fassunke J, Brüning T, Ko YD, Schmidt J, Großerueschkamp F, Buettner R, Gerwert K. Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 191:1269-1280. [PMID: 34004158 DOI: 10.1016/j.ajpath.2021.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/09/2021] [Accepted: 04/22/2021] [Indexed: 12/28/2022]
Abstract
Therapeutic decisions in lung cancer critically depend on the determination of histologic types and oncogene mutations. Therefore, tumor samples are subjected to standard histologic and immunohistochemical analyses and examined for relevant mutations using comprehensive molecular diagnostics. In this study, an alternative diagnostic approach for automatic and label-free detection of mutations in lung adenocarcinoma tissue using quantum cascade laser-based infrared imaging is presented. For this purpose, a five-step supervised classification algorithm was developed, which was not only able to detect tissue types and tumor lesions, but also the tumor type and mutation status of adenocarcinomas. Tumor detection was verified on a data set of 214 patient samples with a specificity of 97% and a sensitivity of 95%. Furthermore, histology typing was verified on samples from 203 of the 214 patients with a specificity of 97% and a sensitivity of 94% for adenocarcinoma. The most frequently occurring mutations in adenocarcinoma (KRAS, EGFR, and TP53) were differentiated by this technique. Detection of mutations was verified in 60 patient samples from the data set with a sensitivity and specificity of 95% for each mutation. This demonstrates that quantum cascade laser infrared imaging can be used to analyze morphologic differences as well as molecular changes. Therefore, this single, one-step measurement provides comprehensive diagnostics of lung cancer histology types and most frequent mutations.
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Affiliation(s)
- Nina Goertzen
- Center for Protein Diagnostics, Biospectroscopy, Germany; Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany
| | | | - Jana Fassunke
- Institut für Pathologie, Universitätsklinikum Köln, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum, Bochum, Germany
| | - Yon-Dschun Ko
- Department of Internal Medicine, Johanniter-Kliniken Bonn GmbH, Johanniter Krankenhaus, Bonn, Germany
| | - Joachim Schmidt
- Lung Cancer Center Bonn, Department of Thoracic Surgery, Helios Klinikum Bonn/Rhein-Sieg and Department of Surgery, Division of Thoracic Surgery, Universitätsklinikum Bonn, Germany
| | - Frederik Großerueschkamp
- Center for Protein Diagnostics, Biospectroscopy, Germany; Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany
| | | | - Klaus Gerwert
- Center for Protein Diagnostics, Biospectroscopy, Germany; Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany.
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23
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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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24
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Theakstone AG, Rinaldi C, Butler HJ, Cameron JM, Confield LR, Rutherford SH, Sala A, Sangamnerkar S, Baker MJ. Fourier‐transform infrared spectroscopy of biofluids: A practical approach. TRANSLATIONAL BIOPHOTONICS 2021. [DOI: 10.1002/tbio.202000025] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Affiliation(s)
- Ashton G. Theakstone
- WestCHEM, Department of Pure and Applied Chemistry Technology and Innovation Centre Glasgow UK
| | - Christopher Rinaldi
- WestCHEM, Department of Pure and Applied Chemistry Technology and Innovation Centre Glasgow UK
| | | | | | - Lily Rose Confield
- WestCHEM, Department of Pure and Applied Chemistry Technology and Innovation Centre Glasgow UK
- CDT Medical Devices, Department of Biomedical Engineering Wolfson 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
- ClinSpec Diagnostics Ltd, Royal College Building Glasgow UK
| | - Sayali Sangamnerkar
- WestCHEM, Department of Pure and Applied Chemistry Technology and Innovation Centre Glasgow UK
| | - Matthew J. Baker
- WestCHEM, Department of Pure and Applied Chemistry Technology and Innovation Centre Glasgow UK
- ClinSpec Diagnostics Ltd, Royal College Building Glasgow UK
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25
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Pereira TM, Diem M, Bachmann L, Bird B, Miljković M, Zezell DM. Evaluating biochemical differences in thyroglobulin from normal and goiter tissues by infrared spectral imaging. Analyst 2021; 145:7907-7915. [PMID: 33016272 DOI: 10.1039/d0an00700e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Thyroglobulin is a glycoiodoprotein that is produced by thyroid follicular cells; it is stored in follicles in structures known as colloids. The main function of this protein is to stock the hormones triiodothyronine (T3) and thyroxine (T4) until the body requires them. This study aims to demonstrate that infrared spectral imaging with appropriate multivariate analysis can reveal biochemical changes in this glycoprotein. The results achieved herein point out biochemical differences in the colloid samples obtained from normal and goiter patients including glycosylation and changes in the secondary conformational structure. We have presented the first spectral histopathology-based method to detect biochemical differences in thyroid colloids, such as TG iodination, glycosylation, and changes in the secondary structure in normal and goiter patients. The observed changes in the colloids were mainly due to the alterations in amide I and amide II (secondary conformation of proteins) and there is a correlation with different glycosylation between normal and goiter tissues.
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Affiliation(s)
- Thiago Martini Pereira
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, Rua Talim, 330-12231-280 - São José dos Campos, Brazil.
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26
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In Vitro Spectroscopy-Based Profiling of Urothelial Carcinoma: A Fourier Transform Infrared and Raman Imaging Study. Cancers (Basel) 2021; 13:cancers13010123. [PMID: 33401726 PMCID: PMC7796146 DOI: 10.3390/cancers13010123] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/17/2020] [Accepted: 12/21/2020] [Indexed: 11/21/2022] Open
Abstract
Simple Summary The mortality and recurrence associated with urothelial carcinoma are high. High heterogeneity makes it hard to detect with currently available methods such as cytology and histology. We propose here vibrational spectroscopic imaging as an additional diagnostic tool for the classification of bladder cancer. Our study revealed that chemism-induced spectroscopic features of the cancer cells of various stages and invasiveness were specifically detected. Abstract Markers of bladder cancer cells remain elusive, which is a major cause of the low recognition of this malignant neoplasm and its recurrence. This implies an urgent need for additional diagnostic tools which are based on the identification of the chemism of bladder cancer. In this study, we employed label-free techniques of molecular imaging—Fourier Transform Infrared and Raman spectroscopic imaging—to investigate bladder cancer cell lines of various invasiveness (T24a, T24p, HT-1376, and J82). The urothelial HCV-29 cell line was the healthy control. Specific biomolecules discriminated spatial distribution of the nucleus and cytoplasm and indicated the presence of lipid bodies and graininess in some cell lines. The most prominent discriminators are the total content of lipids and sugar moieties as well as the presence of glycogen and other carbohydrates, un/saturated lipids, cytochromes, and a level of S-S bridges in proteins. The combination of the obtained hyperspectral database and chemometric methods showed a clear differentiation of each cell line at the level of the nuclei and cytoplasm and pointed out spectral signals which differentiated bladder cancer cells. Registered spectral markers correlated with biochemical composition changes can be associated with pathogenesis and potentially used for the diagnosis of bladder cancer and response to experimental therapies.
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27
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Samolis PD, Langley D, O’Reilly BM, Oo Z, Hilzenrat G, Erramilli S, Sgro AE, McArthur S, Sander MY. Label-free imaging of fibroblast membrane interfaces and protein signatures with vibrational infrared photothermal and phase signals. BIOMEDICAL OPTICS EXPRESS 2021; 12:303-319. [PMID: 33520386 PMCID: PMC7818956 DOI: 10.1364/boe.411888] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 05/19/2023]
Abstract
Label-free vibrational imaging of biological samples has attracted significant interest due to its integration of structural and chemical information. Vibrational infrared photothermal amplitude and phase signal (VIPPS) imaging provide label-free chemical identification by targeting the characteristic resonances of biological compounds that are present in the mid-infrared fingerprint region (3 µm - 12 µm). High contrast imaging of subcellular features and chemical identification of protein secondary structures in unlabeled and labeled fibroblast cells embedded in a collagen-rich extracellular matrix is demonstrated by combining contrast from absorption signatures (amplitude signals) with sensitive detection of different heat properties (lock-in phase signals). We present that the detectability of nano-sized cell membranes is enhanced to well below the optical diffraction limit since the membranes are found to act as thermal barriers. VIPPS offers a novel combination of chemical imaging and thermal diffusion characterization that paves the way towards label-free imaging of cell models and tissues as well as the study of intracellular heat dynamics.
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Affiliation(s)
- Panagis D. Samolis
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
- Photonics Center, Boston University, Boston, MA 02215, USA
| | - Daniel Langley
- Bioengineering Research Group Engineering and Technology, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, CSIRO Manufacturing, Melbourne, VIC, Australia
| | - Breanna M. O’Reilly
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
| | - Zay Oo
- Bioengineering Research Group Engineering and Technology, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, CSIRO Manufacturing, Melbourne, VIC, Australia
| | - Geva Hilzenrat
- Bioengineering Research Group Engineering and Technology, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, CSIRO Manufacturing, Melbourne, VIC, Australia
| | - Shyamsunder Erramilli
- Photonics Center, Boston University, Boston, MA 02215, USA
- Department of Physics, Boston University, Boston, MA 02215, USA
- Division of Materials Science and Engineering, Boston University, Brookline, MA 02446, USA
| | - Allyson E. Sgro
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
- Department of Physics, Boston University, Boston, MA 02215, USA
| | - Sally McArthur
- Bioengineering Research Group Engineering and Technology, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, CSIRO Manufacturing, Melbourne, VIC, Australia
| | - Michelle Y. Sander
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
- Photonics Center, Boston University, Boston, MA 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Division of Materials Science and Engineering, Boston University, Brookline, MA 02446, USA
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28
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Marschall M, Hornemann A, Wübbeler G, Hoehl A, Rühl E, Kästner B, Elster C. Compressed FTIR spectroscopy using low-rank matrix reconstruction. OPTICS EXPRESS 2020; 28:38762-38772. [PMID: 33379438 DOI: 10.1364/oe.404959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
Fourier transform infrared (FTIR) spectroscopy is a powerful technique in analytical chemistry. Typically, spatially distributed spectra of the substance of interest are conducted simultaneously using FTIR spectrometers equipped with array detectors. Scanning-based methods such as near-field FTIR spectroscopy, on the other hand, are a promising alternative providing higher spatial resolution. However, serial recording severely limits their application due to the long acquisition times involved and the resulting stability issues. We demonstrate that it is possible to significantly reduce the measurement time of scanning methods by applying the mathematical technique of low-rank matrix reconstruction. Data from a previous pilot study of Leishmania strains are analyzed by randomly selecting 5% of the interferometer samples. The results obtained for bioanalytical fingerprinting using the proposed approach are shown to be essentially the same as those obtained from the full set of data. This finding can significantly foster the practical applicability of high-resolution serial scanning techniques in analytical chemistry and is also expected to improve other applications of FTIR spectroscopy and spectromicroscopy.
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29
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Introduction to Infrared and Raman-Based Biomedical Molecular Imaging and Comparison with Other Modalities. Molecules 2020; 25:molecules25235547. [PMID: 33256052 PMCID: PMC7731440 DOI: 10.3390/molecules25235547] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/20/2020] [Accepted: 11/22/2020] [Indexed: 01/18/2023] Open
Abstract
Molecular imaging has rapidly developed to answer the need of image contrast in medical diagnostic imaging to go beyond morphological information to include functional differences in imaged tissues at the cellular and molecular levels. Vibrational (infrared (IR) and Raman) imaging has rapidly emerged among the molecular imaging modalities available, due to its label-free combination of high spatial resolution with chemical specificity. This article presents the physical basis of vibrational spectroscopy and imaging, followed by illustration of their preclinical in vitro applications in body fluids and cells, ex vivo tissues and in vivo small animals and ending with a brief discussion of their clinical translation. After comparing the advantages and disadvantages of IR/Raman imaging with the other main modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography/single-photon emission-computed tomography (PET/SPECT), ultrasound (US) and photoacoustic imaging (PAI), the design of multimodal probes combining vibrational imaging with other modalities is discussed, illustrated by some preclinical proof-of-concept examples.
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30
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Behl I, Calado G, Malkin A, Flint S, Galvin S, Healy CM, Pimentel ML, Byrne HJ, Lyng FM. A pilot study for early detection of oral premalignant diseases using oral cytology and Raman micro-spectroscopy: Assessment of confounding factors. JOURNAL OF BIOPHOTONICS 2020; 13:e202000079. [PMID: 32686263 DOI: 10.1002/jbio.202000079] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 06/21/2020] [Accepted: 06/22/2020] [Indexed: 06/11/2023]
Abstract
This study demonstrates the efficacy of Raman micro-spectroscopy of oral cytological samples for differentiating dysplastic, potentially malignant lesions from those of normal, healthy donors. Cells were collected using brush biopsy from healthy donors (n = 20) and patients attending a Dysplasia Clinic (n = 20). Donors were sampled at four different sites (buccal mucosa, tongue, alveolus, gingiva), to ensure matched normal sites for all lesions, while patient samples were taken from clinically evident, histologically verified dysplastic lesions. Spectra were acquired from the nucleus and cytoplasm of individual cells of all samples and subjected to partial least squares-discriminant analysis. Discriminative sensitivities of 94% and 86% and specificity of 85% were achieved for the cytoplasm and nucleus, respectively, largely based on lipidic contributions of dysplastic cells. Alveolar/gingival samples were differentiated from tongue/buccal samples, indicating that anatomical site is potentially a confounding factor, while age, gender, smoking and alcohol consumption were confirmed not to be.
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Affiliation(s)
- Isha Behl
- Centre for Radiation and Environmental Science, FOCAS Research Institute, Technological University Dublin. City Campus, Dublin, Ireland
- School of Physics & Clinical & Optometric Sciences, Technological University Dublin, City Campus, Dublin, Ireland
| | - Genecy Calado
- Centre for Radiation and Environmental Science, FOCAS Research Institute, Technological University Dublin. City Campus, Dublin, Ireland
- School of Physics & Clinical & Optometric Sciences, Technological University Dublin, City Campus, Dublin, Ireland
| | - Alison Malkin
- School of Biological and Health Sciences, Technological University Dublin, City Campus, Dublin, Ireland
| | - Stephen Flint
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Sheila Galvin
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Claire M Healy
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Marina Leite Pimentel
- Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Dublin, Ireland
| | - Fiona M Lyng
- Centre for Radiation and Environmental Science, FOCAS Research Institute, Technological University Dublin. City Campus, Dublin, Ireland
- School of Physics & Clinical & Optometric Sciences, Technological University Dublin, City Campus, Dublin, Ireland
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31
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Nguyen TNQ, Maguire A, Mooney C, Jackson N, Lynam‐Lennon N, Weldon V, Muldoon C, Maguire AA, O'Toole D, Ravi N, Reynolds JV, O'Sullivan J, Meade AD. Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy. TRANSLATIONAL BIOPHOTONICS 2020. [DOI: 10.1002/tbio.202000014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Thi N. Q. Nguyen
- Centre for Radiation and Environmental Science, Focas Research Institute Technological University Dublin Dublin Ireland
- School of Physics and Clinical and Optometric Sciences Technological University Dublin Dublin Ireland
| | - Adrian Maguire
- Centre for Radiation and Environmental Science, Focas Research Institute Technological University Dublin Dublin Ireland
| | - Catherine Mooney
- School of Computer Science University College Dublin Dublin Ireland
| | - Naomi Jackson
- Centre for Radiation and Environmental Science, Focas Research Institute Technological University Dublin Dublin Ireland
| | - Niamh Lynam‐Lennon
- Trinity Translational Medicine Institute, Department of Surgery, Trinity College Dublin St James's Hospital Dublin Ireland
| | - Vicki Weldon
- Centre for Radiation and Environmental Science, Focas Research Institute Technological University Dublin Dublin Ireland
- School of Physics and Clinical and Optometric Sciences Technological University Dublin Dublin Ireland
| | - Cian Muldoon
- Department of Histopathology St. James's Hospital Dublin Ireland
| | - Aoife A. Maguire
- Department of Histopathology St. James's Hospital Dublin Ireland
| | - D. O'Toole
- Department of Histopathology St. James's Hospital Dublin Ireland
| | - Narayanasamy Ravi
- Trinity Translational Medicine Institute, Department of Surgery, Trinity College Dublin St James's Hospital Dublin Ireland
| | - John V. Reynolds
- Trinity Translational Medicine Institute, Department of Surgery, Trinity College Dublin St James's Hospital Dublin Ireland
| | - Jacintha O'Sullivan
- Trinity Translational Medicine Institute, Department of Surgery, Trinity College Dublin St James's Hospital Dublin Ireland
| | - Aidan D. Meade
- Centre for Radiation and Environmental Science, Focas Research Institute Technological University Dublin Dublin Ireland
- School of Physics and Clinical and Optometric Sciences Technological University Dublin Dublin Ireland
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32
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Gala de Pablo J, Chisholm DR, Ambler CA, Peyman SA, Whiting A, Evans SD. Detection and time-tracking activation of a photosensitiser on live single colorectal cancer cells using Raman spectroscopy. Analyst 2020; 145:5878-5888. [PMID: 32662453 DOI: 10.1039/d0an01023e] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Raman spectroscopy has been used to observe uptake, metabolism and response of single-cells to drugs. Photodynamic therapy is based on the use of light, a photosensitiser and oxygen to destroy tumour tissue. Here, we used single-cell Raman spectroscopy to study the uptake and intracellular degradation of a novel photosensitiser with a diphenylacetylene structure, DC473, in live single-cells from colorectal adenocarcinoma cell lines SW480, HT29 and SW620. DC473 was seen to predominantly accumulate in lipid droplets, showing higher accumulation in HT29 and SW620 cells than in SW480 cells, with a broader DC473 peak shifted to higher wavenumbers. DC473 activation and effects were tracked on live single-cells for 5 minutes. Upon exposure to UV light, the DC473 signal intensity dropped, with remaining DC473 shifting towards higher wavenumbers and widening, with a lifetime of approximately 50 seconds. Morphologically, SW480 and SW620 cells showed changes upon photodynamic therapy, whereas HT29 cells showed no changes. Morphological changes correlated with higher remaining DC473 signal after UV exposure. Our research suggests that DC473 forms aggregates within the cells that disaggregate following activation, showing the potential of Raman spectroscopy for the study of time-dependent single-cell pharmacodynamics.
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Affiliation(s)
- Julia Gala de Pablo
- Molecular and Nanoscale Physics Group, School of Physics and Astronomy, University of Leeds, Leeds, UK.
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33
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Morais CLM, Giamougiannis P, Grabowska R, Wood NJ, Martin-Hirsch PL, Martin FL. A three-dimensional discriminant analysis approach for hyperspectral images. Analyst 2020; 145:5915-5924. [PMID: 32687140 DOI: 10.1039/d0an01328e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques.
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Affiliation(s)
- Camilo L M Morais
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK.
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Matter MT, Li J, Lese I, Schreiner C, Bernard L, Scholder O, Hubeli J, Keevend K, Tsolaki E, Bertero E, Bertazzo S, Zboray R, Olariu R, Constantinescu MA, Figi R, Herrmann IK. Multiscale Analysis of Metal Oxide Nanoparticles in Tissue: Insights into Biodistribution and Biotransformation. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:2000912. [PMID: 32775166 PMCID: PMC7404155 DOI: 10.1002/advs.202000912] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/22/2020] [Indexed: 05/05/2023]
Abstract
Metal oxide nanoparticles have emerged as exceptionally potent biomedical sensors and actuators due to their unique physicochemical features. Despite fascinating achievements, the current limited understanding of the molecular interplay between nanoparticles and the surrounding tissue remains a major obstacle in the rationalized development of nanomedicines, which is reflected in their poor clinical approval rate. This work reports on the nanoscopic characterization of inorganic nanoparticles in tissue by the example of complex metal oxide nanoparticle hybrids consisting of crystalline cerium oxide and the biodegradable ceramic bioglass. A validated analytical method based on semiquantitative X-ray fluorescence and inductively coupled plasma spectrometry is used to assess nanoparticle biodistribution following intravenous and topical application. Then, a correlative multiscale analytical cascade based on a combination of microscopy and spectroscopy techniques shows that the topically applied hybrid nanoparticles remain at the initial site and are preferentially taken up into macrophages, form apatite on their surface, and lead to increased accumulation of lipids in their surroundings. Taken together, this work displays how modern analytical techniques can be harnessed to gain unprecedented insights into the biodistribution and biotransformation of complex inorganic nanoparticles. Such nanoscopic characterization is imperative for the rationalized engineering of safe and efficacious nanoparticle-based systems.
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Affiliation(s)
- Martin T. Matter
- Particles‐Biology Interactions, Department of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
- Nanoparticle Systems Engineering LaboratoryInstitute of Process EngineeringDepartment of Mechanical and Process EngineeringETH ZurichSonneggstrasse 3Zurich8092Switzerland
| | - Jian‐Hao Li
- Particles‐Biology Interactions, Department of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
- Nanoparticle Systems Engineering LaboratoryInstitute of Process EngineeringDepartment of Mechanical and Process EngineeringETH ZurichSonneggstrasse 3Zurich8092Switzerland
| | - Ioana Lese
- Department of Plastic and Hand SurgeryUniversity Hospital Bern (Inselspital)University of BernBern3010Switzerland
| | - Claudia Schreiner
- Advanced Analytical TechnologiesSwiss Federal Laboratories for Materials Science and Technology (Empa)Uberlandstrasse 129Dubendorf8600Switzerland
| | - Laetitia Bernard
- Nanoscale MaterialsDepartment of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Uberlandstrasse 129Dubendorf8600Switzerland
| | - Olivier Scholder
- Nanoscale MaterialsDepartment of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Uberlandstrasse 129Dubendorf8600Switzerland
| | - Jasmin Hubeli
- Advanced Analytical TechnologiesSwiss Federal Laboratories for Materials Science and Technology (Empa)Uberlandstrasse 129Dubendorf8600Switzerland
| | - Kerda Keevend
- Particles‐Biology Interactions, Department of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
- Nanoparticle Systems Engineering LaboratoryInstitute of Process EngineeringDepartment of Mechanical and Process EngineeringETH ZurichSonneggstrasse 3Zurich8092Switzerland
| | - Elena Tsolaki
- Particles‐Biology Interactions, Department of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
- Nanoparticle Systems Engineering LaboratoryInstitute of Process EngineeringDepartment of Mechanical and Process EngineeringETH ZurichSonneggstrasse 3Zurich8092Switzerland
- Department of Medical Physics and Biomedical EngineeringUniversity College London (UCL)Malet Place Engineering BuildingLondonWC1E 6BTUK
| | - Enrico Bertero
- Mechanics of Materials and NanostructuresSwiss Federal Laboratories for Materials Science and Technology (Empa)Feuerwerkerstrasse 39Thun3602Switzerland
| | - Sergio Bertazzo
- Department of Medical Physics and Biomedical EngineeringUniversity College London (UCL)Malet Place Engineering BuildingLondonWC1E 6BTUK
| | - Robert Zboray
- Center for X‐ray AnalyticsSwiss Federal Laboratories for Materials Science and Technology (Empa)Uberlandstrasse 129Dubendorf8600Switzerland
| | - Radu Olariu
- Department of Plastic and Hand SurgeryUniversity Hospital Bern (Inselspital)University of BernBern3010Switzerland
| | - Mihai A. Constantinescu
- Department of Plastic and Hand SurgeryUniversity Hospital Bern (Inselspital)University of BernBern3010Switzerland
| | - Renato Figi
- Advanced Analytical TechnologiesSwiss Federal Laboratories for Materials Science and Technology (Empa)Uberlandstrasse 129Dubendorf8600Switzerland
| | - Inge K. Herrmann
- Particles‐Biology Interactions, Department of Materials Meet LifeSwiss Federal Laboratories for Materials Science and Technology (Empa)Lerchenfeldstrasse 5St. Gallen9014Switzerland
- Nanoparticle Systems Engineering LaboratoryInstitute of Process EngineeringDepartment of Mechanical and Process EngineeringETH ZurichSonneggstrasse 3Zurich8092Switzerland
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Al Jedani S, Smith CI, Gunning P, Ellis BG, Gardner P, Barrett SD, Triantafyllou A, Risk JM, Weightman P. A de-waxing methodology for scanning probe microscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:3397-3403. [PMID: 32930228 DOI: 10.1039/d0ay00965b] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A de-waxing protocol that successfully removes paraffin from tissue microarray (TMA) cores of fixed tissue obtained from oral cancer is described. The success of the protocol is demonstrated by the comparison of Fourier transform infrared (FTIR) results obtained on paraffin-embedded and de-waxed tissue and the absence of any significant correlations between infrared scanning near-field optical microscopy (SNOM) images of de-waxed tissue obtained at the three main paraffin IR peaks. The success of the protocol in removing paraffin from tissue is also demonstrated by images obtained with scanning electron microscopy (SEM) and by energy dispersive spectra (EDS) of a de-waxed CaF2 disc which shows no significant contribution from carbon. The FTIR spectra of the de-waxed TMA core overlaps that obtained from OE19 oesophageal cancer cells which had never been exposed to paraffin.
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Affiliation(s)
- Safaa Al Jedani
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | | | - Philip Gunning
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, L3 9TA, UK
| | - Barnaby G Ellis
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Peter Gardner
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Steve D Barrett
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Asterios Triantafyllou
- Department of Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, UK
| | - Janet M Risk
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, L3 9TA, UK
| | - Peter Weightman
- Department of Physics, University of Liverpool, L69 7ZE, UK.
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Castro PAA, Lima CA, Morais MRPT, Zorn TMT, Zezell DM. Monitoring the Progress and Healing Status of Burn Wounds Using Infrared Spectroscopy. APPLIED SPECTROSCOPY 2020; 74:758-766. [PMID: 32419472 DOI: 10.1177/0003702820919446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Burns are one of the leading causes of morbidity worldwide and the most costly traumatic injuries. A better understanding of the molecular mechanisms in wound healing is required to accelerate tissue recovery and reduce the health economic impact. However, the standard techniques used to evaluate the biological events associated to wound repair are laborious, time-consuming, and/or require multiple assays/staining. Therefore, this study aims to evaluate the feasibility of Fourier transform infrared (FT-IR) spectroscopy to monitor the progress and healing status of burn wounds. Burn injuries were induced on Wistar rats by water vapor exposure and biopsied for further histopathological and spectroscopic evaluation at four time-points (3, 7, 14, and 21 days). Spectral data were preprocessed and compared by principal component analysis. Pairwise comparison of post-burn groups to each other revealed that metabolic activity induced by thermal injury decreases as the healing progresses. Higher amounts of carbohydrates, proteins, lipids, and nucleic acids were evidenced on days 3 and 7 compared to healthy skin and reduced amounts of these molecular structural units on days 14 and 21 post-burn. FT-IR spectroscopy was used to determine the healing status of a wound based on the biochemical information retained by spectral signatures in each phase of healing. Our findings demonstrate that FT-IR spectroscopy can monitor the biological events triggered by burn trauma as well as to detect the wound status including full recovery based on the spectral changes associated to the biochemical events in each phase.
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Affiliation(s)
- Pedro A A Castro
- Instituto de Pesquisas Energeticas e Nucleares (IPEN-CNEN/SP), University of Sao Paulo (USP), Sao Paulo, Brazil
| | - Cassio A Lima
- Instituto de Pesquisas Energeticas e Nucleares (IPEN-CNEN/SP), University of Sao Paulo (USP), Sao Paulo, Brazil
- Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool, UK
| | - Mychel R P T Morais
- Instituto de Ciencias Biomedicas (ICB), University of Sao Paulo (USP), Sao Paulo, Brazil
| | - Telma M T Zorn
- Instituto de Ciencias Biomedicas (ICB), University of Sao Paulo (USP), Sao Paulo, Brazil
| | - Denise M Zezell
- Instituto de Pesquisas Energeticas e Nucleares (IPEN-CNEN/SP), University of Sao Paulo (USP), Sao Paulo, Brazil
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Lin H, Luo Y, Sun Q, Deng K, Chen Y, Wang Z, Huang P. Determination of causes of death via spectrochemical analysis of forensic autopsies-based pulmonary edema fluid samples with deep learning algorithm. JOURNAL OF BIOPHOTONICS 2020; 13:e201960144. [PMID: 31957147 DOI: 10.1002/jbio.201960144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 12/22/2019] [Accepted: 01/15/2020] [Indexed: 06/10/2023]
Abstract
This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural network-based deep learning framework, named DeepIR and eight popular machine learning algorithms, were used to construct classifiers. The prediction performances of these classifiers demonstrated that DeepIR outperformed the machine learning algorithms in establishing classifiers to determine the causes of death. Moreover, DeepIR was generally less dependent on preprocessing procedures than were the machine learning algorithms; it provided the validation accuracy with a narrow range from 0.9661 to 0.9856 and the test accuracy ranging from 0.8774 to 0.9167 on the raw pulmonary edema fluid spectral dataset and the nine preprocessing protocol-based datasets in our study. In conclusion, this study demonstrates that the deep learning-equipped Fourier transform infrared spectroscopy technique has the potential to be an effective aid for determining causes of death.
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Affiliation(s)
- Hancheng Lin
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
- Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an, China
| | - Yiwen Luo
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Qiran Sun
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Kaifei Deng
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Yijiu Chen
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Zhenyuan Wang
- Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an, China
| | - Ping Huang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
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38
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Infrared and 2-Dimensional Correlation Spectroscopy Study of the Effect of CH 3NH 3PbI 3 and CH 3NH 3SnI 3 Photovoltaic Perovskites on Eukaryotic Cells. Molecules 2020; 25:molecules25020336. [PMID: 31947578 PMCID: PMC7024238 DOI: 10.3390/molecules25020336] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 01/03/2020] [Accepted: 01/09/2020] [Indexed: 11/30/2022] Open
Abstract
We studied the effect of the exposure of human A549 and SH-SY5Y cell lines to aqueous solutions of organic/inorganic halide perovskites CH3NH3PbI3 (MAPbI3) and CH3NH3SnI3 (MASnI3) at the molecular level by using Fourier transform infrared microspectroscopy. We monitored the infrared spectra of some cells over a few days following exposure to the metals and observed the spectroscopic changes dominated by the appearance of a strong band at 1627 cm−1. We used Infrared (IR) mapping to show that this change was associated with the cell itself or the cellular membrane. It is unclear whether the appearance of the 1627 cm−1 band and heavy metal exposure are related by a direct causal relationship. The spectroscopic response of exposure to MAPbI3 and MASnI3 was similar, indicating that it may arise from a general cellular response to stressful environmental conditions. We used 2D correlation spectroscopy (2DCOS) analysis to interpret spectroscopic changes. In a novel application of the method, we demonstrated the viability of 2DCOS for band assignment in spatially resolved spectra. We assigned the 1627 cm−1 band to the accumulation of an abundant amide or amine containing compound, while ruling out other hypotheses. We propose a few tentative assignments to specific biomolecules or classes of biomolecules, although additional biochemical characterization will be necessary to confirm such assignments.
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Bocklitz T, Silge A, Bae H, Rodewald M, Legesse FB, Meyer T, Popp J. Non-invasive Imaging Techniques: From Histology to In Vivo Imaging : Chapter of Imaging in Oncology. Recent Results Cancer Res 2020; 216:795-812. [PMID: 32594407 DOI: 10.1007/978-3-030-42618-7_25] [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] [Indexed: 12/19/2022]
Abstract
In this chapter, we will introduce and review molecular-sensitive imaging techniques, which close the gap between ex vivo and in vivo analysis. In detail, we will introduce spontaneous Raman spectral imaging, coherent anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS), second-harmonic generation (SHG) and third-harmonic generation (THG), two-photon excited fluorescence (TPEF), and fluorescence lifetime imaging (FLIM). After reviewing these imaging techniques, we shortly introduce chemometric methods and machine learning techniques, which are needed to use these imaging techniques in diagnostic applications.
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Affiliation(s)
- Thomas Bocklitz
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany.
| | - Anja Silge
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany
| | - Hyeonsoo Bae
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany
| | - Marko Rodewald
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany
| | | | - Tobias Meyer
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany
| | - Jürgen Popp
- University of Jena, IPC, Helmholtzweg 4, 07743, Jena, Germany.
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40
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Aguilar-Hernández I, Cárdenas-Chavez DL, López-Luke T, García-García A, Herrera-Domínguez M, Pisano E, Ornelas-Soto N. Discrimination of radiosensitive and radioresistant murine lymphoma cells by Raman spectroscopy and SERS. BIOMEDICAL OPTICS EXPRESS 2020; 11:388-405. [PMID: 32010523 PMCID: PMC6968773 DOI: 10.1364/boe.11.000388] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 11/22/2019] [Accepted: 12/02/2019] [Indexed: 05/10/2023]
Abstract
Intrinsic radiosensitivity is a biological parameter known to influence the response to radiation therapy in cancer treatment. In this study, Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) were successfully used in conjunction with principal component analysis (PCA) to discriminate between radioresistant (LY-R) and radiosensitive (LY-S) murine lymphoma sublines (L5178Y). PCA results for normal Raman analysis showed a differentiation between the radioresistant and radiosensitive cell lines based on their specific spectral fingerprint. In the case of SERS with gold nanoparticles (AuNPs), greater spectral enhancements were observed in the radioresistant subline in comparison to its radiosensitive counterpart, suggesting that each subline displays different interaction with AuNPs. Our results indicate that spectroscopic and chemometric techniques could be used as complementary tools for the prediction of intrinsic radiosensitivity of lymphoma samples.
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Affiliation(s)
- Iris Aguilar-Hernández
- Laboratorio de Nanotecnología Ambiental, Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L. 64849, Mexico
| | - Diana L. Cárdenas-Chavez
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Atlixcáyotl 5718, Puebla, Pue., México, 72453, Mexico
| | - Tzarara López-Luke
- Instituto de Investigación en Metalurgia y Materiales, Universidad Michoacana de San Nicolás de Hidalgo, Edificio U, Ciudad Universitaria, 58030 Morelia, Mich., Mexico
| | - Alejandra García-García
- Laboratorio de síntesis y Modificación de Nanoestructuras y Materiales Bidimensionales. Centro de Investigación en Materiales Avanzados S.C. Parque PIIT. C.P. 66628, Apodaca N.L., Mexico
| | - Marcela Herrera-Domínguez
- Laboratorio de Nanotecnología Ambiental, Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L. 64849, Mexico
| | - Eduardo Pisano
- Catedras CONACyT – Centro de Investigaciones en Óptica A.C., Alianza Centro 504, PIIT, Apodaca, N.L. 66629, Mexico
| | - Nancy Ornelas-Soto
- Laboratorio de Nanotecnología Ambiental, Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L. 64849, Mexico
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Label-free metabolic imaging by mid-infrared optoacoustic microscopy in living cells. Nat Biotechnol 2019; 38:293-296. [PMID: 31873214 DOI: 10.1038/s41587-019-0359-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 11/12/2019] [Indexed: 12/20/2022]
Abstract
We develop mid-infrared optoacoustic microscopy (MiROM) for label-free, bond-selective, live-cell metabolic imaging, enabling spatiotemporal monitoring of carbohydrates, lipids and proteins in cells and tissues. Using acoustic detection of optical absorption, MiROM converts mid-infrared sensing into a positive-contrast imaging modality with negligible photodamage and high sensitivity. We use MiROM to observe changes in intrinsic carbohydrate distribution from a diffusive spatial pattern to tight co-localization with lipid droplets during adipogenesis.
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42
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Galli R, Meinhardt M, Koch E, Schackert G, Steiner G, Kirsch M, Uckermann O. Rapid Label-Free Analysis of Brain Tumor Biopsies by Near Infrared Raman and Fluorescence Spectroscopy-A Study of 209 Patients. Front Oncol 2019; 9:1165. [PMID: 31750251 PMCID: PMC6848276 DOI: 10.3389/fonc.2019.01165] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 10/17/2019] [Indexed: 01/09/2023] Open
Abstract
In brain surgery, novel technologies are continuously developed to achieve better tumor delineation and maximize the extent of resection. Raman spectroscopy is an optical method that enables to retrieve a molecular signature of tissue biochemical composition in order to identify tumor and normal tissue. Here, the translation of Raman spectroscopy to the surgical practice for discerning a variety of different tumor entities from non-neoplastic brain parenchyma was investigated. Fresh unprocessed biopsies obtained from brain tumor surgery were analyzed over 1.5 years including all patients that gave consent. Measurements were performed with a Raman microscope by medical personnel as routine activity. The Raman and fluorescence signals of the acquired spectra were analyzed by principal component analysis, followed by supervised classification to discriminate non-tumor tissue vs. tumor and distinguish tumor entities. Histopathology of the measured biopsies was performed as reference. Classification led to the correct recognition of all non-neoplastic biopsies (7/7) and of 97% of the investigated tumor biopsies (195/202). For instance, GBM was recognized as tumor with a correct rate of 94% if primary, and of 100% if recurrent. Astrocytoma and oligodendroglioma were recognized as tumor with correct rates of 86 and 90%, respectively. All brain metastases, meningioma and schwannoma were correctly recognized as tumor and distinguished from non-neoplastic brain tissue. Furthermore, metastases were discerned from glioma with correct rate of 90%. Oligodendroglioma and astrocytoma IDH1-mutant, which differ in the presence of 1p/19q codeletion, were discerned with a correct rate of 81%. These results demonstrate the feasibility of rapid brain tumors recognition and extraction of diagnostic information by Raman spectroscopy, using a protocol that can be easily included in the routine surgical workflow.
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Affiliation(s)
- Roberta Galli
- Clinical Sensoring and Monitoring, Anesthesiology and Intensive Care Medicine, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Matthias Meinhardt
- Neuropathology, Institute of Pathology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Edmund Koch
- Clinical Sensoring and Monitoring, Anesthesiology and Intensive Care Medicine, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Gabriele Schackert
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Gerald Steiner
- Clinical Sensoring and Monitoring, Anesthesiology and Intensive Care Medicine, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Matthias Kirsch
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ortrud Uckermann
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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Raczkowska MK, Koziol P, Urbaniak-Wasik S, Paluszkiewicz C, Kwiatek WM, Wrobel TP. Influence of denoising on classification results in the context of hyperspectral data: High Definition FT-IR imaging. Anal Chim Acta 2019; 1085:39-47. [DOI: 10.1016/j.aca.2019.07.045] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/16/2019] [Accepted: 07/22/2019] [Indexed: 12/31/2022]
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Abstract
Abstract
A potential role of optical technologies in medicine including micro-Raman spectroscopy is diagnosis of bacteria, cells and tissues which is covered in this chapter. The main advantage of Raman-based methods to complement and augment diagnostic tools is that unsurpassed molecular specificity is achieved without labels and in a nondestructive way. Principles and applications of micro-Raman spectroscopy in the context of medicine will be described. First, Raman spectra of biomolecules representing proteins, nucleic acids, lipids and carbohydrates are introduced. Second, microbial applications are summarized with the focus on typing on species and strain level, detection of infections, antibiotic resistance and biofilms. Third, cytological applications are presented to classify single cells and study cell metabolism and drug–cell interaction. Fourth, applications to tissue characterization start with discussion of lateral resolution for Raman imaging followed by Raman-based detection of pathologies and combination with other modalities. Finally, an outlook is given to translate micro-Raman spectroscopy as a clinical tool to solve unmet needs in point-of-care applications and personalized treatment of diseases.
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da Silva WR, Silveira L, Fernandes AB. Diagnosing sickle cell disease and iron deficiency anemia in human blood by Raman spectroscopy. Lasers Med Sci 2019; 35:1065-1074. [PMID: 31637552 DOI: 10.1007/s10103-019-02887-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 09/23/2019] [Indexed: 12/18/2022]
Abstract
This work proposed the diagnosis of iron deficiency anemia (IDA) and sickle cell disease (SCD) in human blood caused by iron deficiency and hemoglobin S (HbS), which are among the most common anemias, by means of Raman spectroscopy. Whole blood samples from patients diagnosed with IDA and HbS, as well as from normal subjects (HbA), were obtained and submitted to Raman spectroscopy (830 nm, 150 mW, 400-1800 cm-1 spectral range, 4 cm-1 resolution). Difference spectra of IDA-HbA showed spectral features of hemoglobin with less intensity in the IDA, whereas the difference spectra of SCD-HbA showed spectral features of deoxyhemoglobin increased and of oxyhemoglobin decreased in SCD. An exploratory analysis by principal components analysis (PCA) showed that the peaks referred to oxy- and deoxyhemoglobin markedly differentiated SCD and HbA, as well as the increased amount of hemoglobin features in the SCD group, suggesting increased erythropoiesis. The IDA group showed hemoglobin features with lower intensities as well as peaks referred to the iron bonding to the porphyrin ring with reduced intensities when compared to the HbA. Discriminant analysis based on partial least squares (PLS-DA) and PCA (PCA-DA) showed that the IDA and SCD anemias could be discriminated from the HbA spectra with 95.0% and 93.8% of accuracy, for the PLS and PCA respectively, with sensitivity/specificity of 93.8%/95.7% for the PLS-DA model. The iron depletion and the sickling of erythrocytes could be identified by Raman spectroscopy and a spectral model based on PLS accurately discriminated these IDA and SCD samples from the normal HbA.
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Affiliation(s)
| | - Landulfo Silveira
- Center for Innovation Technology and Education-CITE, Universidade Anhembi Morumbi-UAM, Estr. Dr. Altino Bondensan, 500, São José dos Campos, SP, 12247-016, Brazil.
| | - Adriana Barrinha Fernandes
- Center for Innovation Technology and Education-CITE, Universidade Anhembi Morumbi-UAM, Estr. Dr. Altino Bondensan, 500, São José dos Campos, SP, 12247-016, Brazil
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Diem M, Ergin A, Mu X. Spectral histopathology of the lung: A review of two large studies. JOURNAL OF BIOPHOTONICS 2019; 12:e201900061. [PMID: 31177622 DOI: 10.1002/jbio.201900061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 05/06/2019] [Accepted: 06/04/2019] [Indexed: 06/09/2023]
Abstract
This paper summarizes results from two large lung cancer studies comprising over 700 samples that demonstrate the ability of spectral histopathology (SHP) to distinguish cancerous tissue regions from normal tissue, to differentiate benign lesions from normal tissue and cancerous lesions, and to classify lung cancer types. Furthermore, malignancy-associated changes can be identified in cancer-adjacent normal tissue. The ability to differentiate a multitude of normal cells and tissue types allow SHP to identify tumor margins and immune cell infiltration. Finally, SHP easily distinguishes small cell lung cancer (SCLC) from non-SCLC (NSCLC) and provides a further differentiation of NSCLC into adenocarcinomas and squamous cell carcinomas with an accuracy comparable of classical histopathology combined with immunohistochemistry. Case studies are presented that demonstrates that SHP can resolve interobserver discrepancies in standard histopathology.
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Affiliation(s)
- Max Diem
- CIRECA LLC, Cambridge, Massachusetts
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts
| | | | - Xinying Mu
- CIRECA LLC, Cambridge, Massachusetts
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts
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Shi J, Wong TT, He Y, Li L, Zhang R, Yung CS, Hwang J, Maslov K, Wang LV. High-resolution, high-contrast mid-infrared imaging of fresh biological samples with ultraviolet-localized photoacoustic microscopy. NATURE PHOTONICS 2019; 13:609-615. [PMID: 31440304 PMCID: PMC6705424 DOI: 10.1038/s41566-019-0441-3] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 04/09/2019] [Indexed: 05/18/2023]
Abstract
Mid-infrared (MIR) microscopy provides rich chemical and structural information about biological samples, without staining. Conventionally, the long MIR wavelength severely limits the lateral resolution owing to optical diffraction; moreover, the strong MIR absorption of water ubiquitous in fresh biological samples results in high background and low contrast. To overcome these limitations, we propose a method that employs photoacoustic detection highly localized with a pulsed ultraviolet (UV) laser on the basis of the Grüneisen relaxation effect. For cultured cells, our method achieves water-background suppressed MIR imaging of lipids and proteins at UV resolution, at least an order of magnitude finer than the MIR diffraction limits. Label-free histology using this method is also demonstrated in thick brain slices. Our approach provides convenient high-resolution and high-contrast MIR imaging, which can benefit diagnosis of fresh biological samples.
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Affiliation(s)
- Junhui Shi
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Terence T.W. Wong
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Optical Imaging Laboratory, Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Present address: Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | - Yun He
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Optical Imaging Laboratory, Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Lei Li
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ruiying Zhang
- Optical Imaging Laboratory, Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Christopher S. Yung
- Applied Physics Division, National Institute of Standards and Technology, 325 Broadway Street, Boulder, CO 80305, USA
| | - Jeeseong Hwang
- Applied Physics Division, National Institute of Standards and Technology, 325 Broadway Street, Boulder, CO 80305, USA
| | - Konstantin Maslov
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Lihong V. Wang
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Correspondence should be addressed to L.V.W. ()
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Morais CLM, Martin-Hirsch PL, Martin FL. A three-dimensional principal component analysis approach for exploratory analysis of hyperspectral data: identification of ovarian cancer samples based on Raman microspectroscopy imaging of blood plasma. Analyst 2019; 144:2312-2319. [PMID: 30714597 DOI: 10.1039/c8an02031k] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Hyperspectral imaging is a powerful tool to obtain both chemical and spatial information of biological systems. However, few algorithms are capable of working with full three-dimensional images, in which reshaping or averaging procedures are often performed to reduce the data complexity. Herein, we propose a new algorithm of three-dimensional principal component analysis (3D-PCA) for exploratory analysis of complete 3D spectrochemical images obtained through Raman microspectroscopy. Blood plasma samples of ten patients (5 healthy controls, 5 diagnosed with ovarian cancer) were analysed by acquiring hyperspectral imaging in the fingerprint region (∼780-1858 cm-1). Results show that 3D-PCA can clearly differentiate both groups based on its scores plot, where higher loadings coefficients were observed in amino acids, lipids and DNA regions. 3D-PCA is a new methodology for exploratory analysis of hyperspectral imaging, providing fast information for class differentiation.
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Affiliation(s)
- Camilo L M Morais
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK.
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Li J, Qin J, Zhang X, Wang R, Liang Z, He Q, Wang Z, Wang K, Wang S. Label-free Raman imaging of live osteosarcoma cells with multivariate analysis. Appl Microbiol Biotechnol 2019; 103:6759-6769. [DOI: 10.1007/s00253-019-09952-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 05/15/2019] [Accepted: 05/28/2019] [Indexed: 01/16/2023]
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A fully automated, faster noise rejection approach to increasing the analytical capability of chemical imaging for digital histopathology. PLoS One 2019; 14:e0205219. [PMID: 31017894 PMCID: PMC6481772 DOI: 10.1371/journal.pone.0205219] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 04/06/2019] [Indexed: 11/19/2022] Open
Abstract
Chemical hyperspectral imaging (HSI) data is naturally high dimensional and large. There are thus inherent manual trade-offs in acquisition time, and the quality of data. Minimum Noise Fraction (MNF) developed by Green et al. [1] has been extensively studied as a method for noise removal in HSI data. It too, however entails a manual speed-accuracy trade-off, namely the process of manually selecting the relevant bands in the MNF space. This process currently takes roughly around a month’s time for acquiring and pre-processing an entire TMA with acceptable signal to noise ratio. We present three approaches termed ‘Fast MNF’, ‘Approx MNF’ and ‘Rand MNF’ where the computational time of the algorithm is reduced, as well as the entire process of band selection is fully automated. This automated approach is shown to perform at the same level of accuracy as MNF with now large speedup factors, resulting in the same task to be accomplished in hours. The different approximations produced by the three algorithms, show the reconstruction accuracy vs storage (50×) and runtime speed (60×) trade-off. We apply the approach for automating the denoising of different tissue histology samples, in which the accuracy of classification (differentiating between the different histologic and pathologic classes) strongly depends on the SNR (signal to noise ratio) of recovered data. Therefore, we also compare the effect of the proposed denoising algorithms on classification accuracy. Since denoising HSI data is done unsupervised, we also use a metric that assesses the quality of denoising in the image domain between the noisy and denoised image in the absence of ground truth.
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