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Kepesidis K, Jacob P, Schweinberger W, Huber M, Feiler N, Fleischmann F, Trubetskov M, Voronina L, Aschauer J, Eissa T, Gigou L, Karandušovsky P, Pupeza I, Weigel A, Azzeer A, Stief CG, Chaloupka M, Reinmuth N, Behr J, Kolben T, Harbeck N, Reiser M, Krausz F, Žigman M. Electric-Field Molecular Fingerprinting to Probe Cancer. ACS CENTRAL SCIENCE 2025; 11:560-573. [PMID: 40290141 PMCID: PMC12022918 DOI: 10.1021/acscentsci.4c02164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Revised: 03/01/2025] [Accepted: 03/04/2025] [Indexed: 04/30/2025]
Abstract
Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for in vitro diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naïve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.
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Affiliation(s)
- Kosmas
V. Kepesidis
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
- Center
for Molecular Fingerprinting (CMF), 1093 Budapest, Hungary
| | - Philip Jacob
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
| | - Wolfgang Schweinberger
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Center
for Molecular Fingerprinting (CMF), 1093 Budapest, Hungary
- King
Saud University (KSU), Department of Physics
and Astronomy, 11451 Riyadh, Saudi Arabia
| | - Marinus Huber
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
| | - Nico Feiler
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
| | - Frank Fleischmann
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
| | - Michael Trubetskov
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
| | - Liudmila Voronina
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
| | - Jacqueline Aschauer
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
| | - Tarek Eissa
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
| | - Lea Gigou
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
- Center
for Molecular Fingerprinting (CMF), 1093 Budapest, Hungary
| | | | - Ioachim Pupeza
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
- Leibniz
Institute of Photonic Technology-Member of the Research Alliance “Leibniz
Health Technologies”, 07745 Jena, Germany
| | - Alexander Weigel
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
- Center
for Molecular Fingerprinting (CMF), 1093 Budapest, Hungary
| | - Abdallah Azzeer
- King
Saud University (KSU), Department of Physics
and Astronomy, 11451 Riyadh, Saudi Arabia
| | - Christian G. Stief
- University
Hospital of the Ludwig Maximilians University Munich (LMU), Department of Urology, LMU, 81377 Munich, Germany
| | - Michael Chaloupka
- University
Hospital of the Ludwig Maximilians University Munich (LMU), Department of Urology, LMU, 81377 Munich, Germany
| | - Niels Reinmuth
- Asklepios,
Department of Thoracic Surgery, Member of
the German Center for Lung Research, DZL, Asklepios Fachkliniken München-Gauting, 82131 Gauting, Germany
| | - Jürgen Behr
- Department
of Medicine V, LMU University Hospital,
Comprehensive Pneumology Center, German Center for Lung Research,
LMU, 81377 Munich, Germany
| | - Thomas Kolben
- University
Hospital of the Ludwig Maximilians University Munich (LMU), Department
of Obstetrics and Gynecology, Breast Cancer and Comprehensive Cancer
Center Munich (CCLMU), LMU, 81377 Munich, Germany
| | - Nadia Harbeck
- University
Hospital of the Ludwig Maximilians University Munich (LMU), Department
of Obstetrics and Gynecology, Breast Cancer and Comprehensive Cancer
Center Munich (CCLMU), LMU, 81377 Munich, Germany
| | - Maximilian Reiser
- University
Hospital of the Ludwig Maximilians University Munich (LMU), Department of Clinical Radiology, LMU, 81377 Munich, Germany
| | - Ferenc Krausz
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
- Center
for Molecular Fingerprinting (CMF), 1093 Budapest, Hungary
| | - Mihaela Žigman
- Ludwig-Maximilians-Universität
München (LMU), Chair of Experimental
Physics - Laser Physics, 85748 Garching, Germany
- Max
Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, 85748 Garching, Germany
- Center
for Molecular Fingerprinting (CMF), 1093 Budapest, Hungary
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2
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Shim JE, Kim YJ, Hahm E, Choe JH, Baek A, Kim RM, You EA. Ultrasensitive SERS nanoprobe-based multiplexed digital sensing platform for the simultaneous quantification of Alzheimer's disease biomarkers. Biosens Bioelectron 2025; 274:117216. [PMID: 39899917 DOI: 10.1016/j.bios.2025.117216] [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: 11/20/2024] [Revised: 01/18/2025] [Accepted: 01/27/2025] [Indexed: 02/05/2025]
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disease that requires early diagnosis to manage its progression. Although the simultaneous detection of multiple AD biomarkers is expected to facilitate early assessment of AD risk, the lack of multiplexed sensing platforms for accurately quantifying multiple AD biomarkers remains a challenge. Here, we present a multiplexed digital sensing platform based on bumpy core-shell (BCS) surface-enhanced Raman spectroscopy (SERS) nanoprobes for ultrasensitive, quantitative, and simultaneous detection of Aβ42 and Aβ40 as AD biomarkers, enabling the accurate determination of the Aβ42/Aβ40 ratio. We synthesized BCS SERS nanoprobes with distinct Raman reporters to generate unique, intense, and reproducible SERS signals, offering single-nanoparticle sensitivity and quantification capabilities. These nanoprobes were subsequently employed in SERS-based immunoassays combined with digital SERS analysis for multiplexed quantification. The proposed platform accurately and quantitatively detected Aβ42 and Aβ40 across a range of five orders of magnitude, with a limit of detection of 8.7× 10-17 g/mL (1.9 × 10-17 M) for Aβ42 and 1.0 × 10-15 g/mL (2.3 × 10-16 M) for Aβ40, surpassing the performance of conventional enzyme-linked immunosorbent assays. Based on the exclusive detection of Aβ42 and Aβ40 using distinct SERS nanoprobes, the proposed sensing platform can also accurately quantify Aβ42 and Aβ40 at clinically relevant levels in both cerebrospinal fluid and blood plasma. Therefore, this sensing platform can be used to accurately and reliably determine the Aβ42/Aβ40 ratio, thus serving as an effective tool for the early diagnosis of AD.
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Affiliation(s)
- Jae-Eul Shim
- Medical Metrology Group, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea
| | - Young Jun Kim
- Medical Metrology Group, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea
| | - Eunil Hahm
- Medical Metrology Group, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea
| | - Jong-Ho Choe
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Ahruem Baek
- Nanobio Measurement Group, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea
| | - Ryeong Myeong Kim
- Medical Metrology Group, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea
| | - Eun-Ah You
- Medical Metrology Group, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea.
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Jiao B, Ouyang Z, Xiao X, Zhang C, Xu T, Yang Q, Zhu Y, Liu Y, Liu X, Zhou Y, Liao X, Luo S, Tang B, Li Z, Shen L. Development and validation of machine learning models with blood-based digital biomarkers for Alzheimer's disease diagnosis: a multicohort diagnostic study. EClinicalMedicine 2025; 81:103142. [PMID: 40115175 PMCID: PMC11925590 DOI: 10.1016/j.eclinm.2025.103142] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2024] [Revised: 02/17/2025] [Accepted: 02/18/2025] [Indexed: 03/23/2025] Open
Abstract
Background Alzheimer's disease (AD) involves complex alterations in biological pathways, making comprehensive blood biomarkers crucial for accurate and earlier diagnosis. However, the cost-effectiveness and operational complexity of method using blood-based biomarkers significantly limit its availability in clinical practice. Methods We developed low-cost, convenient machine learning-based with digital biomarkers (MLDB) using plasma spectra data to detect AD or mild cognitive impairment (MCI) from healthy controls (HCs) and discriminate AD from different types of neurodegenerative diseases. Retrospective data were gathered for 1324 individuals, including 293 with amyloid beta positive AD, 151 with mild cognitive impairment (MCI), 106 with Lewy body dementia (DLB), 106 with frontotemporal dementia (FTD), 135 with progressive supranuclear palsy (PSP) and 533 healthy controls (HCs) between July 2017 and August 2023. Findings Random forest classifier and feature selection procedures were used to select digital biomarkers. MLDB achieved area under the curves (AUCs) of 0.92 (AD vs. HC, Sensitivity 88.2%, specificity 84.1%), 0.89 (MCI vs. HC, Sensitivity 88.8%, specificity 86.4%), 0.83 (AD vs. DLB, Sensitivity 77.2%, specificity 74.6%), 0.80 (AD vs. FTD, sensitivity 74.2%, specificity 72.4%), and 0.93 (AD vs. PSP, sensitivity 76.1%, specificity 75.7%). Digital biomarkers distinguishing AD from HC were negatively correlated with plasma p-tau217 (r = -0.22, p < 0.05) and glial fibrillary acidic protein (GFAP) (r = -0.09, p < 0.05). Interpretation The ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared) plasma spectra features can identify AD-related pathological changes. These spectral features serve as digital biomarkers, providing valuable support in the early screening and diagnosis of AD. Funding The National Natural Science Foundation of China, STI2030-Major Projects, National Key R&D Program of China, Outstanding Youth Fund of Hunan Provincial Natural Science Foundation, Hunan Health Commission Grant, Science and Technology Major Project of Hunan Province, Hunan Innovative Province Construction Project, Grant of National Clinical Research Center for Geriatric Disorders, Xiangya Hospital and Postdoctoral Fellowship Program of CPSF.
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Affiliation(s)
- Bin Jiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Brain Research Center, Central South University, Changsha, China
- FuRong Laboratory, Changsha, China
| | - Ziyu Ouyang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xuewen Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Cong Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Tianyan Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qijie Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuan Zhu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yiliang Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xixi Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Yafang Zhou
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Brain Research Center, Central South University, Changsha, China
- FuRong Laboratory, Changsha, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Xinxin Liao
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Brain Research Center, Central South University, Changsha, China
- FuRong Laboratory, Changsha, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Shilin Luo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Brain Research Center, Central South University, Changsha, China
- FuRong Laboratory, Changsha, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Brain Research Center, Central South University, Changsha, China
- FuRong Laboratory, Changsha, China
| | - Zhigang Li
- College of Information Science and Engineering, Northeastern University, Shenyang, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Brain Research Center, Central South University, Changsha, China
- FuRong Laboratory, Changsha, China
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Huang W, Zeng R, Li Y, Hua Y, Liu L, Chen M, Xue M, Tu S, Huang F, Hu J. Identification of Alzheimer's disease and vascular dementia based on a Deep Forest and near-infrared spectroscopy analysis method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025; 326:125209. [PMID: 39340951 DOI: 10.1016/j.saa.2024.125209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 09/14/2024] [Accepted: 09/22/2024] [Indexed: 09/30/2024]
Abstract
Alzheimer's disease (AD) and vascular dementia (VaD) typically do not exhibit distinct differences in clinical manifestations and auxiliary examination results, which leads to a high misdiagnosis rate. However, significant differences in treatment approaches and prognosis between these two diseases underscore the critical need for an accurate diagnosis of AD and VaD. In this study, serum samples from 33 patients with AD patients, 37 patients with VaD, and 130 healthy individuals were collected, employing near-infrared aquaphotomics technology in combination with deep learning for differential diagnoses. Through an analysis of water absorption patterns among different diseases via aquaphotomics, the efficacies of traditional machine learning methods (Support Vector Machine and Decision Trees) and deep learning approaches (Deep Forest) in modeling were compared. Ultimately, by leveraging feature extraction techniques in conjunction with deep learning, a differential diagnostic model for AD and VaD was successfully developed. The results revealed that aquaphotomics could identify a certain correlation between the number of hydrogen bonds in water molecules and the development of AD and VaD; the deep learning model was found to be superior to traditional machine learning models, achieving an accuracy of 98.67 %, sensitivity of 97.33 %, and specificity of 100.00 %. The bands identified using the Competitive Adaptive Reweighting Algorithm method, primarily located at approximately 1300-1500 nm, showed a significant correlation with water molecules containing four hydrogen bonds. These results highlighted the potential role of the water molecule hydrogen-bond network in disease development and were consistent with the aquaphotomics analysis results. Therefore, the differential diagnostic model developed by integrating near-infrared spectroscopy and deep learning was proven to be effective and feasible, providing accurate and rapid diagnostic methods for AD and VaD diagnoses.
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Affiliation(s)
- Wenchang Huang
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Rui Zeng
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Yuanpeng Li
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University, 541004, China.
| | - Yisheng Hua
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Lingli Liu
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Meiyuan Chen
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Mengjiao Xue
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Shan Tu
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University, 541004, China.
| | - Furong Huang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, Guangdong 510632, China.
| | - Junhui Hu
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University, 541004, China
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Chechekina OG, Tropina EV, Fatkhutdinova LI, Zyuzin MV, Bogdanov AA, Ju Y, Boldyrev KN. Machine learning assisted rapid approach for quantitative prediction of biochemical parameters of blood serum with FTIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025; 326:125283. [PMID: 39418681 DOI: 10.1016/j.saa.2024.125283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Revised: 08/25/2024] [Accepted: 10/10/2024] [Indexed: 10/19/2024]
Abstract
This study develops regression models for predicting blood biochemical data using Fourier-transform infrared spectroscopy (FTIR) analysis. Absorption at specific wavelengths of blood serum is revealed to have strong correlations with biochemical parameters, such as ALT, amylase, AST, protein, bilirubin, Gamma-GT, iron, calcium, uric acid, triglycerides, phosphatase and cholesterol, were shown. The results consistently demonstrate that Random Forest Regression outperforms other models, delivering impressive outcomes for the majority of the analyzed parameters. For some parameters we obtained a coefficient of determination of 0.95 and more (amylase, AST, iron, calcium, protein, uric acid and cholesterol), which makes this approach to be applicable in clinical diagnostics. These findings highlight the potential of FTIR analysis combined with regression models for precise assessment of blood biochemistry.
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Affiliation(s)
- O G Chechekina
- Institute of Spectroscopy, Russian Academy of Sciences, 108840 Troitsk, Russia; National Research University Higher School of Economics, 101000 Moscow, Russia
| | - E V Tropina
- Institute of Spectroscopy, Russian Academy of Sciences, 108840 Troitsk, Russia; National Research University Higher School of Economics, 101000 Moscow, Russia
| | - L I Fatkhutdinova
- School of Physics and Engineering, ITMO University, 197101 St. Petersburg, Russia
| | - M V Zyuzin
- School of Physics and Engineering, ITMO University, 197101 St. Petersburg, Russia
| | - A A Bogdanov
- School of Physics and Engineering, ITMO University, 197101 St. Petersburg, Russia; Qingdao Innovation and Development Center, Harbin Engineering University, 266000 Qingdao, China
| | - Y Ju
- Advanced Research Institute of Multidisciplinary, Beijing Institute of Technology, 100081 Beijing, China
| | - K N Boldyrev
- Institute of Spectroscopy, Russian Academy of Sciences, 108840 Troitsk, Russia; National Research University Higher School of Economics, 101000 Moscow, Russia; Advanced Research Institute of Multidisciplinary, Beijing Institute of Technology, 100081 Beijing, China.
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6
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Eissa T, Voronina L, Huber M, Fleischmann F, Žigman M. The Perils of Molecular Interpretations from Vibrational Spectra of Complex Samples. Angew Chem Int Ed Engl 2024:e202411596. [PMID: 39508580 DOI: 10.1002/anie.202411596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Indexed: 11/15/2024]
Abstract
Vibrational spectroscopy is a widely used technique for chemical characterizations across various analytical sciences. Its applications are increasingly extending to the analysis of complex samples such as biofluids, providing high-throughput molecular profiling. While powerful, the technique suffers from an inherent limitation: The overlap of absorption information across different spectral domains hinders the capacity to identify individual molecular substances contributing to measured signals. Despite the awareness of this challenge, the difficulty of analyzing multi-molecular spectra is often underestimated, leading to unsubstantiated molecular interpretations. Here, we examine the prevalent overreliance on spectral band assignment and illuminate the pitfalls of correlating spectral signals to discrete molecular entities or physiological states without rigorous validation. Focusing on blood-based infrared spectroscopy, we provide examples illustrating how peak overlap among different substances, relative substance concentrations, and preprocessing steps can lead to erroneous interpretations. We advocate for a viewpoint shift towards a more careful understanding of complex spectra, which shall lead to either accepting their fingerprinting nature and leveraging machine learning analysis - or involving additional measurement modalities for robust molecular interpretations. Aiming to help translate and improve analytical practices within the field, we highlight the limitations of molecular interpretations and feature their viable applications.
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Affiliation(s)
- Tarek Eissa
- Ludwig-Maximilians-Universität München (LMU), Chair of Exper-imental Physics - Laser Physics, Garching, Germany
- Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany
- Technical University of Munich (TUM), School of Computation, Information and Technology, Garching, Germany
| | - Liudmila Voronina
- Ludwig-Maximilians-Universität München (LMU), Chair of Exper-imental Physics - Laser Physics, Garching, Germany
| | - Marinus Huber
- Ludwig-Maximilians-Universität München (LMU), Chair of Exper-imental Physics - Laser Physics, Garching, Germany
- Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany
| | - Frank Fleischmann
- Ludwig-Maximilians-Universität München (LMU), Chair of Exper-imental Physics - Laser Physics, Garching, Germany
- Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany
| | - Mihaela Žigman
- Ludwig-Maximilians-Universität München (LMU), Chair of Exper-imental Physics - Laser Physics, Garching, Germany
- Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany
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7
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Kaur M, Singh S, Kaur A. Structural changes in amide I and amide II regions of PCOS women analyzed by ATR-FTIR spectroscopy. Heliyon 2024; 10:e33494. [PMID: 39040335 PMCID: PMC11261041 DOI: 10.1016/j.heliyon.2024.e33494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/24/2024] Open
Abstract
The etiology of PCOS is complex and frequently mis or undiagnosed, which may enhance morbidity and reduce the quality of life. Attenuated total reflection- Fourier transform infrared (ATR-FTIR) spectroscopy examines the structural fingerprints of the biochemical compounds and can provide distinct FTIR spectra of the PCOS cases and controls. The present study recruited 61 PCOS cases and 38 control women. The student's t-test was used to compare BMI, WHR, and lipid profile. The FTIR spectral region was compared among both groups using the Mann-Whitney U test and multivariate analysis involved principal component analysis (PCA) and hierarchical cluster analysis (HCA). FTIR spectra of different phenotypes of PCOS were also analyzed using multivariate analysis. In univariate analysis, PCOS women had significantly higher WHR (p = 0.007), BMI (p = 0.04), triglycerides (p = 0.04), and VLDL (p = 0.02) than the controls. The spectral regions of amide I (1700-1600 cm-1) and amide II (1580-1480 cm-1), were significantly greater in the PCOS group than in the controls (p < 0.01 and p < 0.001, respectively). The PCA and HCA revealed a distinct molecular fingerprint for phenotype A (PCOM + OA + HA) and phenotype B (HA + OA). Our study postulated that the spectral regions of amide I and amide II can distinguish between PCOS cases and control women and it may be used for the diagnosis of cases.
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Affiliation(s)
- Mandeep Kaur
- Department of Human Genetics, Guru Nanak Dev University, Amritsar, Punjab, 143005, India
| | - Sukhjashanpreet Singh
- Department of Human Genetics, Guru Nanak Dev University, Amritsar, Punjab, 143005, India
| | - Anupam Kaur
- Department of Human Genetics, Guru Nanak Dev University, Amritsar, Punjab, 143005, India
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8
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Augustyniak K, Lesniak M, Latka H, Golan MP, Kubiak JZ, Zdanowski R, Malek K. Adipose-derived mesenchymal stem cells' adipogenesis chemistry analyzed by FTIR and Raman metrics. J Lipid Res 2024; 65:100573. [PMID: 38844049 PMCID: PMC11260339 DOI: 10.1016/j.jlr.2024.100573] [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: 03/22/2024] [Revised: 05/08/2024] [Accepted: 05/28/2024] [Indexed: 07/01/2024] Open
Abstract
The full understanding of molecular mechanisms of cell differentiation requires a holistic view. Here we combine label-free FTIR and Raman hyperspectral imaging with data mining to detect the molecular cell composition enabling noninvasive monitoring of cell differentiation and identifying biochemical heterogeneity. Mouse adipose-derived mesenchymal stem cells (AD-MSCs) undergoing adipogenesis were followed by Raman and FT-IR imaging, Oil Red, and immunofluorescence. A workflow of the data analysis (IRRSmetrics4stem) was designed to identify spectral predictors of adipogenesis and test machine-learning (ML) methods (hierarchical clustering, PCA, PLSR) for the control of the AD-MSCs differentiation degree. IRRSmetrics4stem provided insights into the chemism of adipogenesis. With single-cell tracking, we established IRRS metrics for lipids, proteins, and DNA variations during AD-MSCs differentiation. The over 90% predictive efficiency of the selected ML methods proved the high sensitivity of the IRRS metrics. Importantly, the IRRS metrics unequivocally recognize a switch from proliferation to differentiation. This study introduced a new bioassay identifying molecular markers indicating molecular transformations and delivering rapid and machine learning-based monitoring of adipogenesis that can be relevant to other differentiation processes. Thus, we introduce a novel, rapid, machine learning-based bioassay to identify molecular markers of adipogenesis. It can be relevant to identification of differentiation-related molecular processes in other cell types, and beyond the cell differentiation including progression of different cellular pathophysiologies reconstituted in vitro.
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Affiliation(s)
- Karolina Augustyniak
- Department of Chemical Physics, Faculty of Chemistry, Jagiellonian University in Krakow, Krakow, Poland; Doctoral School of Exact and Natural Sciences, Jagiellonian University in Krakow, Krakow, Poland
| | - Monika Lesniak
- Laboratory of Molecular Oncology and Innovative Therapies, Military Institute of Medicine - National Research Institute, Warszawa, Poland
| | - Hubert Latka
- Department of Chemical Physics, Faculty of Chemistry, Jagiellonian University in Krakow, Krakow, Poland
| | - Maciej P Golan
- Laboratory of Molecular Oncology and Innovative Therapies, Military Institute of Medicine - National Research Institute, Warszawa, Poland; Institute of Psychology, The Maria Grzegorzewska University, Warsaw, Poland
| | - Jacek Z Kubiak
- Laboratory of Molecular Oncology and Innovative Therapies, Military Institute of Medicine - National Research Institute, Warszawa, Poland; Dynamics and Mechanics of Epithelia Group, Institute of Genetics and Development of Rennes (IGDR), Faculty of Medicine, University of Rennes, CNRS, UMR 6290, Rennes, France.
| | - Robert Zdanowski
- Laboratory of Molecular Oncology and Innovative Therapies, Military Institute of Medicine - National Research Institute, Warszawa, Poland.
| | - Kamilla Malek
- Department of Chemical Physics, Faculty of Chemistry, Jagiellonian University in Krakow, Krakow, Poland.
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9
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Ranasinghe JC, Wang Z, Huang S. Unveiling brain disorders using liquid biopsy and Raman spectroscopy. NANOSCALE 2024; 16:11879-11913. [PMID: 38845582 PMCID: PMC11290551 DOI: 10.1039/d4nr01413h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.
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Affiliation(s)
- Jeewan C Ranasinghe
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Ziyang Wang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Shengxi Huang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
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10
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Zhang X, Xiao J, Yang F, Qu H, Ye C, Chen S, Guo Y. Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning. Int J Legal Med 2024; 138:1139-1148. [PMID: 38047927 DOI: 10.1007/s00414-023-03118-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/25/2023] [Indexed: 12/05/2023]
Abstract
OBJECTIVE The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice. METHODS ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD. RESULTS A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%. CONCLUSION Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.
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Affiliation(s)
- Xiangyan Zhang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Jiao Xiao
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Fengqin Yang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Hongke Qu
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, China
| | - Chengxin Ye
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Sile Chen
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Yadong Guo
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
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11
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Alix JJP, Plesia M, Dudgeon AP, Kendall CA, Hewamadduma C, Hadjivassiliou M, Gorman GS, Taylor RW, McDermott CJ, Shaw PJ, Mead RJ, Day JC. Conformational fingerprinting with Raman spectroscopy reveals protein structure as a translational biomarker of muscle pathology. Analyst 2024; 149:2738-2746. [PMID: 38533726 PMCID: PMC11056770 DOI: 10.1039/d4an00320a] [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: 02/28/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024]
Abstract
Neuromuscular disorders are a group of conditions that can result in weakness of skeletal muscles. Examples include fatal diseases such as amyotrophic lateral sclerosis and conditions associated with high morbidity such as myopathies (muscle diseases). Many of these disorders are known to have abnormal protein folding and protein aggregates. Thus, easy to apply methods for the detection of such changes may prove useful diagnostic biomarkers. Raman spectroscopy has shown early promise in the detection of muscle pathology in neuromuscular disorders and is well suited to characterising the conformational profiles relating to protein secondary structure. In this work, we assess if Raman spectroscopy can detect differences in protein structure in muscle in the setting of neuromuscular disease. We utilise in vivo Raman spectroscopy measurements from preclinical models of amyotrophic lateral sclerosis and the myopathy Duchenne muscular dystrophy, together with ex vivo measurements of human muscle samples from individuals with and without myopathy. Using quantitative conformation profiling and matrix factorisation we demonstrate that quantitative 'conformational fingerprinting' can be used to identify changes in protein folding in muscle. Notably, myopathic conditions in both preclinical models and human samples manifested a significant reduction in α-helix structures, with concomitant increases in β-sheet and, to a lesser extent, nonregular configurations. Spectral patterns derived through non-negative matrix factorisation were able to identify myopathy with a high accuracy (79% in mouse, 78% in human tissue). This work demonstrates the potential of conformational fingerprinting as an interpretable biomarker for neuromuscular disorders.
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Affiliation(s)
- James J P Alix
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
- Neuroscience Institute, University of Sheffield, Western Bank, Sheffield, UK
- National Institute for Health and Care Research Sheffield Biomedical Research Centre, Sheffield, UK
| | - Maria Plesia
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
| | - Alexander P Dudgeon
- Biophotonics Research Unit, Gloucestershire Hospitals NHS Foundation Trust, UK
- Department of Physics and Astronomy, University of Exeter, UK
| | - Catherine A Kendall
- Biophotonics Research Unit, Gloucestershire Hospitals NHS Foundation Trust, UK
| | - Channa Hewamadduma
- National Institute for Health and Care Research Sheffield Biomedical Research Centre, Sheffield, UK
- Department of Neurology, Academic Directorate of Neurosciences, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, UK
| | - Marios Hadjivassiliou
- National Institute for Health and Care Research Sheffield Biomedical Research Centre, Sheffield, UK
- Department of Neurology, Academic Directorate of Neurosciences, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, UK
| | - Gráinne S Gorman
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NHS Highly Specialised Service for Rare Mitochondrial Disorders, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- National Institute for Health and Care Research Newcastle Biomedical Research Centre, Newcastle upon Tyne, UK
| | - Robert W Taylor
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NHS Highly Specialised Service for Rare Mitochondrial Disorders, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Christopher J McDermott
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
- Neuroscience Institute, University of Sheffield, Western Bank, Sheffield, UK
- National Institute for Health and Care Research Sheffield Biomedical Research Centre, Sheffield, UK
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
- Neuroscience Institute, University of Sheffield, Western Bank, Sheffield, UK
- National Institute for Health and Care Research Sheffield Biomedical Research Centre, Sheffield, UK
| | - Richard J Mead
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
- Neuroscience Institute, University of Sheffield, Western Bank, Sheffield, UK
| | - John C Day
- Interface Analysis Centre, School of Physics, University of Bristol, UK
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12
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Li YP, Lu TY, Huang FR, Zhang WM, Chen ZQ, Guang PW, Deng LY, Yang XH. Differential diagnosis of Crohn's disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning. World J Gastroenterol 2024; 30:1377-1392. [PMID: 38596500 PMCID: PMC11000079 DOI: 10.3748/wjg.v30.i10.1377] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/02/2024] [Accepted: 02/06/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Crohn's disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed. AIM To develop a method to identify CD and ITB with high accuracy, specificity, and speed. METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis. RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB. CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.
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Affiliation(s)
- Yuan-Peng Li
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Tian-Yu Lu
- Department of Gastroenterology, The Affiliated Hospital of South University of Science and Technology, Shenzhen 518000, Guangdong Province, China
| | - Fu-Rong Huang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, Guangdong Province, China
| | - Wei-Min Zhang
- Department of Gastroenterology, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou 510632, Guangdong Province, China
| | - Zhen-Qiang Chen
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, Guangdong Province, China
| | - Pei-Wen Guang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, Guangdong Province, China
| | - Liang-Yu Deng
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, Guangdong Province, China
| | - Xin-Hao Yang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, Guangdong Province, China
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13
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Liu X, Su X, Chen M, Xie Y, Li M. Self-calibrating surface-enhanced Raman scattering-lateral flow immunoassay for determination of amyloid-β biomarker of Alzheimer's disease. Biosens Bioelectron 2024; 245:115840. [PMID: 37988777 DOI: 10.1016/j.bios.2023.115840] [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: 07/11/2023] [Revised: 10/24/2023] [Accepted: 11/12/2023] [Indexed: 11/23/2023]
Abstract
Rapid early diagnosis of Alzheimer's disease (AD) is critical for its effective and prompt treatment since the clinically available treatments can only relieve the symptoms or slow the disease progression. However, it is still a grand challenge to accurately diagnose AD at its early stage because of the indiscernible early symptoms and the lack of sensitive detection tools. Here, we develop a self-calibrating surface-enhanced Raman scattering (SERS)-lateral flow immunoassay (LFIA) biosensor for quantitative analysis of amyloid-β1-42 (Aβ1-42) biomarker in biofluids, enabling accurate AD diagnosis. The designed SERS-LFIA biosensor makes full use of the unique aspects of the LFIA format and the SERS technique to quantify the Aβ1-42 level in complex biofluids with high sensitivity, excellent anti-interference capability, low-cost, and operation simplicity. The key aspect of the design of this biosensor is that internal standard (IS)-SERS nanoparticles are embedded in the test line of the test strip as a self-calibration unit for correction of fluctuations of SERS signals caused by various external factors such as test parameters and sample heterogeneity. We demonstrate significant improvement of the detection performance of the SERS-LFIA biosensor for ratiometric quantification of Aβ1-42 owing to the built-in IS in the test line. We expect that the present IS-based biosensing strategy provides a promising tool for accurate AD diagnosis and longitudinal monitoring of therapeutic response with great promises for clinical translation.
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Affiliation(s)
- Xinyu Liu
- School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, China
| | - Xiaoming Su
- School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, China
| | - Mingyang Chen
- School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, China
| | - Yangcenzi Xie
- School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, China
| | - Ming Li
- School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, China.
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14
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Pang W, Xing Y, Morais CLM, Lao Q, Li S, Qiao Z, Li Y, Singh MN, Barauna VG, Martin FL, Zhang Z. Serum-based ATR-FTIR spectroscopy combined with multivariate analysis for the diagnosis of pre-diabetes and diabetes. Analyst 2024; 149:497-506. [PMID: 38063458 DOI: 10.1039/d3an01519j] [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: 01/16/2024]
Abstract
Diabetes mellitus (DM) is a metabolic disease with an increasing prevalence that is causing worldwide concern. The pre-diabetes stage is the only reversible stage in the patho-physiological process towards DM. Due to the limitations of traditional methods, the diagnosis and detection of DM and pre-diabetes are complicated, expensive, and time-consuming. Therefore, it would be of great benefit to develop a simple, rapid and inexpensive diagnostic test. Herein, the infrared (IR) spectra of serum samples from 111 DM patients, 111 pre-diabetes patients and 333 healthy volunteers were collected using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy and this was combined with the multivariate analysis of principal component analysis linear discriminant analysis (PCA-LDA) to develop a discriminant model to verify the diagnostic potential of this approach. The study found that the accuracy of the test model established by ATR-FTIR spectroscopy combined with PCA-LDA was 97%, and the sensitivity and specificity were 100% and 100% in the control group, 94% and 98% in the pre-diabetes group, and 91% and 98% in the DM group, respectively. This indicates that this method can effectively diagnose DM and pre-diabetes, which has far-reaching clinical significance.
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Affiliation(s)
- Weiyi Pang
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, 541199, Guangxi, China.
- School of Public Health, Guilin Medical University, Guilin, 541199, Guangxi, China
- School of Humanities and Management, Guilin Medical University, Guilin, 541199, Guangxi, China
| | - Yu Xing
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, 541199, Guangxi, China.
- School of Public Health, Guilin Medical University, Guilin, 541199, Guangxi, China
| | - Camilo L M Morais
- Center for Education, Science and Technology of the Inhamuns Region, State University of Ceará, Tauá 63660-000, Brazil
| | - Qiufeng Lao
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, 541199, Guangxi, China.
- School of Public Health, Guilin Medical University, Guilin, 541199, Guangxi, China
| | - Shengle Li
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, 541199, Guangxi, China.
- School of Public Health, Guilin Medical University, Guilin, 541199, Guangxi, China
| | - Zipeng Qiao
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, 541199, Guangxi, China.
- School of Public Health, Guilin Medical University, Guilin, 541199, Guangxi, China
| | - You Li
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, 541199, Guangxi, China.
- School of Public Health, Guilin Medical University, Guilin, 541199, Guangxi, China
| | - Maneesh N Singh
- Biocel UK Ltd, Hull HU10 6TS, UK.
- Chesterfield Royal Hospital, Chesterfield Road, Calow, Chesterfield S44 5BL, UK
| | - Valério G Barauna
- Department of Physiological Sciences, Federal University of Espírito Santo, Vitoria, Brazil
| | - Francis L Martin
- Biocel UK Ltd, Hull HU10 6TS, UK.
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
| | - Zhiyong Zhang
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, 541199, Guangxi, China.
- School of Public Health, Guilin Medical University, Guilin, 541199, Guangxi, China
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15
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Soares Martins T, Ferreira M, Magalhães S, Leandro K, de Almeida LP, Vogelgsang J, Breitling B, Hansen N, Esselmann H, Wiltfang J, da Cruz e Silva OA, Nunes A, Henriques AG. FTIR Spectroscopy and Blood-Derived Extracellular Vesicles Duo in Alzheimer's Disease. J Alzheimers Dis 2024; 98:1157-1167. [PMID: 38489187 PMCID: PMC11091593 DOI: 10.3233/jad-231239] [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] [Accepted: 02/09/2024] [Indexed: 03/17/2024]
Abstract
Background Alzheimer's disease (AD) diagnosis is difficult, and new accurate tools based on peripheral biofluids are urgently needed. Extracellular vesicles (EVs) emerged as a valuable source of biomarker profiles for AD, since their cargo is disease-specific and these can be easily isolated from easily accessible biofluids, as blood. Fourier Transform Infrared (FTIR) spectroscopy can be employed to analyze EVs and obtain the spectroscopic profiles from different regions of the spectra, simultaneously characterizing carbohydrates, nucleic acids, proteins, and lipids. Objective The aim of this study was to identify blood-derived EVs (bdEVs) spectroscopic signatures with AD discriminatory potential. Methods Herein, FTIR spectra of bdEVs from two biofluids (serum and plasma) and distinct sets of Controls and AD cases were acquired, and EVs' spectra analyzed. Results Analysis of bdEVs second derivative peaks area revealed differences between Controls and AD cases in distinct spectra regions, assigned to carbohydrates and nucleic acids, amides, and lipids. Conclusions EVs' spectroscopic profiles presented AD discriminatory value, supporting the use of bdEVs combined with FTIR as a screening or complementary tool for AD diagnosis.
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Affiliation(s)
- Tânia Soares Martins
- Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Maria Ferreira
- Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Sandra Magalhães
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
- Department of Chemistry, CICECO – Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
- Faculty of Medicine, UnIC@RISE – Cardiovascular Research and Development Center, University of Porto, Porto, Portugal
| | - Kevin Leandro
- Faculty of Pharmacy, Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
- ViraVector–Viral Vector for Gene Transfer Core Facility, University of Coimbra, Coimbra, Portugal
| | - Luís P. de Almeida
- Faculty of Pharmacy, Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
- ViraVector–Viral Vector for Gene Transfer Core Facility, University of Coimbra, Coimbra, Portugal
| | - Jonathan Vogelgsang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Georg-August University, Goettingen, Germany
- Translational Neuroscience Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Benedict Breitling
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Georg-August University, Goettingen, Germany
| | - Niels Hansen
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Georg-August University, Goettingen, Germany
| | - Hermann Esselmann
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Georg-August University, Goettingen, Germany
| | - Jens Wiltfang
- Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Georg-August University, Goettingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
| | - Odete A.B. da Cruz e Silva
- Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Alexandra Nunes
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Ana Gabriela Henriques
- Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
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16
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Xie L, Luo S, Liu Y, Ruan X, Gong K, Ge Q, Li K, Valev VK, Liu G, Zhang L. Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18203-18214. [PMID: 37399235 DOI: 10.1021/acs.est.3c03210] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.
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Affiliation(s)
- Lifang Xie
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Siheng Luo
- 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
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yangyang Liu
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Xuejun Ruan
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Kedong Gong
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Qiuyue Ge
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Kejian Li
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
| | - Ventsislav Kolev Valev
- Centre for Photonics and Photonic Materials and Centre for Nanoscience and Nanotechnology, Department of Physics, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
| | - Guokun 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
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Liwu Zhang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, Peoples' Republic of China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples' Republic of China
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17
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Condino F, Crocco MC, Pirritano D, Petrone A, Del Giudice F, Guzzi R. A Linear Predictor Based on FTIR Spectral Biomarkers Improves Disease Diagnosis Classification: An Application to Multiple Sclerosis. J Pers Med 2023; 13:1596. [PMID: 38003911 PMCID: PMC10672539 DOI: 10.3390/jpm13111596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/02/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing interest in the medical field to integrate and improve diagnostic procedures. Here we present a statistical approach where we combine a number of spectral biomarkers derived from the ATR-FTIR spectra of blood plasma samples of healthy control subjects and MS patients, to obtain a linear predictor useful for discriminating between the two groups of individuals. This predictor provides a simple tool in which the contribution of different molecular components is summarized and, as a result, the sensitivity (80%) and specificity (93%) of the identification are significantly improved compared to those obtained with typical classification algorithms. The strategy proposed can be very helpful when applied to the diagnosis of diseases whose presence is reflected in a minimal way in the analyzed biofluids (blood and its derivatives), as it is for MS as well as for other neurological disorders.
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Affiliation(s)
- Francesca Condino
- Department of Economics, Statistics and Finance ”Giovanni Anania”, University of Calabria, 87036 Rende, Italy;
| | - Maria Caterina Crocco
- STAR Research Infrastructure, University of Calabria, 87036 Rende, Italy;
- Department of Physics, Molecular Biophysics Laboratory, University of Calabria, 87036 Rende, Italy
| | - Domenico Pirritano
- SOC Neurologia, Azienda Ospedaliero-Universitaria Renato Dulbecco, 88100 Catanzaro, Italy;
- UOC Neurologia, Azienda Ospedaliera dell’Annunziata, 87100 Cosenza, Italy; (A.P.); (F.D.G.)
| | - Alfredo Petrone
- UOC Neurologia, Azienda Ospedaliera dell’Annunziata, 87100 Cosenza, Italy; (A.P.); (F.D.G.)
| | - Francesco Del Giudice
- UOC Neurologia, Azienda Ospedaliera dell’Annunziata, 87100 Cosenza, Italy; (A.P.); (F.D.G.)
- SOC Neurologia, Ospedale Jazzolino, Azienda Ospedaliera Provinciale, 89900 Vibo Valentia, Italy
| | - Rita Guzzi
- STAR Research Infrastructure, University of Calabria, 87036 Rende, Italy;
- CNR-NANOTEC, Department of Physics, University of Calabria, 87036 Rende, Italy
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18
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Martin FL. Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics. J Pers Med 2023; 13:1511. [PMID: 37888122 PMCID: PMC10608143 DOI: 10.3390/jpm13101511] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023] Open
Abstract
As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...].
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Affiliation(s)
- Francis L Martin
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
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19
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Alix JJP, Plesia M, Shaw PJ, Mead RJ, Day JCC. Combining electromyography and Raman spectroscopy: optical EMG. Muscle Nerve 2023; 68:464-470. [PMID: 37477391 PMCID: PMC10952815 DOI: 10.1002/mus.27937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 06/21/2023] [Accepted: 06/25/2023] [Indexed: 07/22/2023]
Abstract
INTRODUCTION/AIMS Electromyography (EMG) remains a key component of the diagnostic work-up for suspected neuromuscular disease, but it does not provide insight into the molecular composition of muscle which can provide diagnostic information. Raman spectroscopy is an emerging neuromuscular biomarker capable of generating highly specific, molecular fingerprints of tissue. Here, we present "optical EMG," a combination of EMG and Raman spectroscopy, achieved using a single needle. METHODS An optical EMG needle was created to collect electrophysiological and Raman spectroscopic data during a single insertion. We tested functionality with in vivo recordings in the SOD1G93A mouse model of amyotrophic lateral sclerosis (ALS), using both transgenic (n = 10) and non-transgenic (NTg, n = 7) mice. Under anesthesia, compound muscle action potentials (CMAPs), spontaneous EMG activity and Raman spectra were recorded from both gastrocnemius muscles with the optical EMG needle. Standard concentric EMG needle recordings were also undertaken. Electrophysiological data were analyzed with standard univariate statistics, Raman data with both univariate and multivariate analyses. RESULTS A significant difference in CMAP amplitude was observed between SOD1G93A and NTg mice with optical EMG and standard concentric needles (p = .015 and p = .011, respectively). Spontaneous EMG activity (positive sharp waves) was detected in transgenic SOD1G93A mice only. Raman spectra demonstrated peaks associated with key muscle components. Significant differences in molecular composition between SOD1G93A and NTg muscle were identified through the Raman spectra. DISCUSSION Optical EMG can provide standard electrophysiological data and molecular Raman data during a single needle insertion and represents a potential biomarker for neuromuscular disease.
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Affiliation(s)
- James J. P. Alix
- Sheffield Institute for Translational NeuroscienceUniversity of SheffieldSheffieldUK
- Cross‐Faculty Neuroscience InstituteUniversity of SheffieldSheffieldUK
| | - Maria Plesia
- Sheffield Institute for Translational NeuroscienceUniversity of SheffieldSheffieldUK
| | - Pamela J. Shaw
- Sheffield Institute for Translational NeuroscienceUniversity of SheffieldSheffieldUK
- Cross‐Faculty Neuroscience InstituteUniversity of SheffieldSheffieldUK
| | - Richard J. Mead
- Sheffield Institute for Translational NeuroscienceUniversity of SheffieldSheffieldUK
- Cross‐Faculty Neuroscience InstituteUniversity of SheffieldSheffieldUK
| | - John C. C. Day
- Interface Analysis Centre, School of PhysicsUniversity of BristolBristolUK
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20
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Freitas RVDM, de Freitas DLD, de Oliveira IRD, Dos Santos Gomes C, Guerra GCB, Dantas PMS, da Silva TG, Duque G, de Lima KMG, Guerra RO. Fourier-Transform Infrared Spectroscopy as a Screening Tool for Osteosarcopenia in Community-Dwelling Older Women. J Gerontol A Biol Sci Med Sci 2023; 78:1543-1549. [PMID: 36905160 DOI: 10.1093/gerona/glad081] [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: 07/09/2022] [Indexed: 03/12/2023] Open
Abstract
Osteosarcopenia is a complex geriatric syndrome characterized by the presence of both sarcopenia and osteopenia/osteoporosis. This condition increases rates of disability, falls, fractures, mortality, and mobility impairments in older adults. The purpose of this study was to analyze the Fourier-transform infrared (FTIR) spectroscopy diagnostic power for osteosarcopenia in community-dwelling older women (n = 64; 32 osteosarcopenic and 32 non-osteosarcopenia). FTIR is a fast and reproducible technique highly sensitive to biological tissues, and a mathematical model was created using multivariate classification techniques that denoted the graphic spectra of the molecular groups. Genetic algorithm and support vector machine regression (GA-SVM) was the most feasible model, achieving 80.0% of accuracy. GA-SVM identified 15 wave numbers responsible for class differentiation, in which several amino acids (responsible for the proper activation of the mammalian target of rapamycin) and hydroxyapatite (an inorganic bone component) were observed. Imaging tests and low availability of instruments that allow the observation of osteosarcopenia involve high health costs for patients and restrictive indications. Therefore, FTIR can be used to diagnose osteosarcopenia due to its efficiency and low cost and to enable early detection in geriatric services, contributing to advances in science and technology that are potential "conventional" methods in the future.
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Affiliation(s)
| | | | | | | | | | - Paulo Moreira Silva Dantas
- Postgraduation Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Brazil
- Postgraduation Program in Physical Education, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Tales Gomes da Silva
- Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Gustavo Duque
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Bone, Muscle & Geroscience Group, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Kassio Michell Gomes de Lima
- Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ricardo Oliveira Guerra
- Postgraduation Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Brazil
- Postgraduation Program in Physiotherapy, Federal University of Rio Grande do Norte, Natal, Brazil
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21
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Kavungal D, Magalhães P, Kumar ST, Kolla R, Lashuel HA, Altug H. Artificial intelligence-coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases. SCIENCE ADVANCES 2023; 9:eadg9644. [PMID: 37436975 PMCID: PMC10337894 DOI: 10.1126/sciadv.adg9644] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/08/2023] [Indexed: 07/14/2023]
Abstract
Diagnosis of neurodegenerative disorders (NDDs) including Parkinson's disease and Alzheimer's disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progression of NDDs, thus underscoring the need for structural biomarker-based diagnostics. We developed an immunoassay-coupled nanoplasmonic infrared metasurface sensor that detects proteins linked to NDDs, such as alpha-synuclein, with specificity and differentiates the distinct structural species using their unique absorption signatures. We augmented the sensor with an artificial neural network enabling unprecedented quantitative prediction of oligomeric and fibrillar protein aggregates in their mixture. The microfluidic integrated sensor can retrieve time-resolved absorbance fingerprints in the presence of a complex biomatrix and is capable of multiplexing for the simultaneous monitoring of multiple pathology-associated biomarkers. Thus, our sensor is a promising candidate for the clinical diagnosis of NDDs, disease monitoring, and evaluation of novel therapies.
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Affiliation(s)
- Deepthy Kavungal
- Bionanophotonic Systems Laboratory, Institute of Bioengineering, School of Engineering, EPFL, Lausanne, Switzerland
- Laboratory of Molecular and Chemical Biology of Neurodegeneration, Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland
| | - Pedro Magalhães
- Laboratory of Molecular and Chemical Biology of Neurodegeneration, Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland
| | - Senthil T. Kumar
- Laboratory of Molecular and Chemical Biology of Neurodegeneration, Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland
| | - Rajasekhar Kolla
- Laboratory of Molecular and Chemical Biology of Neurodegeneration, Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland
| | - Hilal A. Lashuel
- Laboratory of Molecular and Chemical Biology of Neurodegeneration, Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland
| | - Hatice Altug
- Bionanophotonic Systems Laboratory, Institute of Bioengineering, School of Engineering, EPFL, Lausanne, Switzerland
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22
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Martin FL, Dickinson AW, Saba T, Bongers T, Singh MN, Bury D. ATR-FTIR Spectroscopy with Chemometrics for Analysis of Saliva Samples Obtained in a Lung-Cancer-Screening Programme: Application of Swabs as a Paradigm for High Throughput in a Clinical Setting. J Pers Med 2023; 13:1039. [PMID: 37511652 PMCID: PMC10381591 DOI: 10.3390/jpm13071039] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/30/2023] Open
Abstract
There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab "dip" test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot study, a total of 211 saliva samples (n = 170 benign, 41 designated cancer-positive) were randomly taken during the course of this prospective lung-cancer-screening programme. The samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. An exploratory analysis using principal component analysis (PCA,) with or without linear discriminant analysis (LDA), was then undertaken. Three pairwise comparisons were undertaken including: (1) benign vs. cancer following swab analysis; (2) benign vs. cancer following swab analysis with the subtraction of dry swab spectra; and (3) benign vs. cancer following swab analysis with the subtraction of wet swab spectra. Consistent and remarkably similar patterns of clustering for the benign control vs. cancer categories, irrespective of whether the swab plus saliva sample was analysed or whether there was a subtraction of wet or dry swab spectra, was observed. In each case, MANOVA demonstrated that this segregation of categories is highly significant. A k-NN (using three nearest neighbours) machine-learning algorithm also showed that the specificity (90%) and sensitivity (75%) are consistent for each pairwise comparison. In detailed analyses, the swab as a substrate did not alter the level of spectral discrimination between benign control vs. cancer saliva samples. These results demonstrate a novel swab "dip" test using saliva as a biofluid that is highly applicable to be rolled out into a larger lung-cancer-screening programme.
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Affiliation(s)
- Francis L Martin
- Biocel UK Ltd., Hull HU10 6TS, UK
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
| | - Andrew W Dickinson
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
| | - Tarek Saba
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
| | - Thomas Bongers
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
| | - Maneesh N Singh
- Biocel UK Ltd., Hull HU10 6TS, UK
- Chesterfield Royal Hospital, Chesterfield Road, Calow, Chesterfield S44 5BL, UK
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23
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Shukla MK, Wilkes P, Bargary N, Meagher K, Khamar D, Bailey D, Hudson SP. Identification of monoclonal antibody drug substances using non-destructive Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122872. [PMID: 37209478 DOI: 10.1016/j.saa.2023.122872] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/05/2023] [Accepted: 05/10/2023] [Indexed: 05/22/2023]
Abstract
Monoclonal antibodies provide highly specific and effective therapies for the treatment of chronic diseases. These protein-based therapeutics, or drug substances, are transported in single used plastic packaging to fill finish sites. According to good manufacturing practice guidelines, each drug substance needs to be identified before manufacturing of the drug product. However, considering their complex structure, it is challenging to correctly identify therapeutic proteins in an efficient manner. Common analytical techniques for therapeutic protein identification are SDS-gel electrophoresis, enzyme linked immunosorbent assays, high performance liquid chromatography and mass spectrometry-based assays. Although effective in correctly identifying the protein therapeutic, most of these techniques need extensive sample preparation and removal of samples from their containers. This step not only risks contamination but the sample taken for the identification is destroyed and cannot be re-used. Moreover, these techniques are often time consuming, sometimes taking several days to process. Here, we address these challenges by developing a rapid and non-destructive identification technique for monoclonal antibody-based drug substances. Raman spectroscopy in combination with chemometrics were used to identify three monoclonal antibody drug substances. This study explored the impact of laser exposure, time out of refrigerator and multiple freeze thaw cycles on the stability of monoclonal antibodies. and demonstrated the potential of using Raman spectroscopy for the identification of protein-based drug substances in the biopharmaceutical industry.
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Affiliation(s)
- Mahendra K Shukla
- SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals & Bernal Institute, University of Limerick, Limerick V94 T9PX, Ireland; Department of Chemical Sciences, University of Limerick, Limerick V94 T9PX, Ireland
| | - Philippa Wilkes
- SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals & Bernal Institute, University of Limerick, Limerick V94 T9PX, Ireland; Department of Mathematics and Statistics, University of Limerick, Limerick V94 T9PX, Ireland
| | - Norma Bargary
- SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals & Bernal Institute, University of Limerick, Limerick V94 T9PX, Ireland; Department of Mathematics and Statistics, University of Limerick, Limerick V94 T9PX, Ireland
| | - Katherine Meagher
- Manufacturing Science and Technology, Sanofi Ireland, Old Kilmeaden Road, Waterford, Ireland
| | - Dikshitkumar Khamar
- Manufacturing Science and Technology, Sanofi Ireland, Old Kilmeaden Road, Waterford, Ireland
| | - Donal Bailey
- Manufacturing Science and Technology, Sanofi Ireland, Old Kilmeaden Road, Waterford, Ireland
| | - Sarah P Hudson
- SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals & Bernal Institute, University of Limerick, Limerick V94 T9PX, Ireland; Department of Mathematics and Statistics, University of Limerick, Limerick V94 T9PX, Ireland.
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24
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Rotondi SMC, Canepa P, Angeli E, Canepa M, Cavalleri O. DNA Sensing Platforms: Novel Insights into Molecular Grafting Using Low Perturbative AFM Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094557. [PMID: 37177760 PMCID: PMC10181596 DOI: 10.3390/s23094557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/01/2023] [Accepted: 05/06/2023] [Indexed: 05/15/2023]
Abstract
By using AFM as a nanografting tool, we grafted micrometer-sized DNA platforms into inert alkanethiol SAMs. Tuning the grafting conditions (surface density of grafting lines and scan rate) allowed us to tailor the molecular density of the DNA platforms. Following the nanografting process, AFM was operated in the low perturbative Quantitative Imaging (QI) mode. The analysis of QI AFM images showed the coexistence of molecular domains of different heights, and thus different densities, within the grafted areas, which were not previously reported using contact AFM imaging. Thinner domains corresponded to low-density DNA regions characterized by loosely packed, randomly oriented DNA strands, while thicker domains corresponded to regions with more densely grafted DNA. Grafting with densely spaced and slow scans increased the size of the high-density domains, resulting in an overall increase in patch height. The structure of the grafted DNA was compared to self-assembled DNA, which was assessed through nanoshaving experiments. Exposing the DNA patches to the target sequence produced an increase in the patch height, indicating that hybridization was accomplished. The relative height increase of the DNA patches upon hybridization was higher in the case of lower density patches due to hybridization leading to a larger molecular reorganization. Low density DNA patches were therefore the most suitable for targeting oligonucleotide sequences.
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Affiliation(s)
| | - Paolo Canepa
- Dipartimento di Fisica and Optmatlab, Università di Genova, Via Dodecaneso 33, 16146 Genova, Italy
| | - Elena Angeli
- Dipartimento di Fisica, Università di Genova, Via Dodecaneso 33, 16146 Genova, Italy
| | - Maurizio Canepa
- Dipartimento di Fisica and Optmatlab, Università di Genova, Via Dodecaneso 33, 16146 Genova, Italy
- INFN, Sezione di Genova, Via Dodecaneso 33, 16146 Genova, Italy
| | - Ornella Cavalleri
- Dipartimento di Fisica and Optmatlab, Università di Genova, Via Dodecaneso 33, 16146 Genova, Italy
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25
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Dawuti W, Dou J, Zheng X, Lü X, Zhao H, Yang L, Lin R, Lü G. Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier-transform infrared spectroscopy combined with machine learning algorithms. JOURNAL OF BIOPHOTONICS 2023; 16:e202200320. [PMID: 36707914 DOI: 10.1002/jbio.202200320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/20/2023] [Accepted: 01/25/2023] [Indexed: 05/17/2023]
Abstract
Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data. Among all the bands, 1500-1700 cm-1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.
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Affiliation(s)
- Wubulitalifu Dawuti
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Jingrui Dou
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaoyi Lü
- College of Software, Xinjiang University, Urumqi, China
| | - Hui Zhao
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Lingfei Yang
- Department of Abdominal Ultrasound Diagnosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Renyong Lin
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Guodong Lü
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
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26
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Caixeta DC, Lima C, Xu Y, Guevara-Vega M, Espindola FS, Goodacre R, Zezell DM, Sabino-Silva R. Monitoring glucose levels in urine using FTIR spectroscopy combined with univariate and multivariate statistical methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 290:122259. [PMID: 36584643 DOI: 10.1016/j.saa.2022.122259] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 12/12/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
The development of novel platforms for non-invasive continuous glucose monitoring applied in the screening and monitoring of diabetes is crucial to improve diabetes surveillance systems. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy of urine can be an alternative as a sustainable, label-free, fast, non-invasive, and highly sensitive analysis to detect changes in urine promoted by diabetes and insulin treatment. In this study, we used ATR-FTIR to evaluate the urinary components of non-diabetic (ND), diabetic (D), and diabetic insulin-treated (D + I) rats. As expected, insulin treatment was capable to revert changes in glycemia, 24-h urine collection volume, urine creatinine, urea, and glucose excretion promoted by diabetes. Several differences in the urine spectra of ND, D, and D + I were observed, with urea, creatinine, and glucose analytes being related to these changes. Principal components analysis (PCA) scores plots allowed for the discrimination of ND and D + I from D with an accuracy of ∼ 99 %. The PCA loadings associated with PC1 confirmed the importance of urea and glucose vibrational modes for this discrimination. Univariate analysis of second derivative spectra showed a high correlation (r: 0.865, p < 0.0001) between the height of 1074 cm-1 vibrational mode with urinary glucose concentration. In order to estimate the amount of glucose present in the infrared spectra from urine, multivariate curve resolution-alternating least square (MCR-ALS) was applied and a higher predicted concentration of glucose in the urine was observed with a correlation of 78.9 % compared to urinary glucose concentration assessed using enzyme assays. In summary, ATR-FTIR combined with univariate and multivariate chemometric analyses provides an innovative, non-invasive, and sustainable approach to diabetes surveillance.
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Affiliation(s)
- Douglas Carvalho Caixeta
- Innovation Center in Salivary Diagnostics and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia, Brazil.
| | - Cassio Lima
- Center for Lasers and Applications, Nuclear and Energy Research Institute, IPEN-CNEN/SP, São Paulo, Brazil; Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom.
| | - Yun Xu
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom.
| | - Marco Guevara-Vega
- Innovation Center in Salivary Diagnostics and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia, Brazil.
| | | | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom.
| | - Denise Maria Zezell
- Center for Lasers and Applications, Nuclear and Energy Research Institute, IPEN-CNEN/SP, São Paulo, Brazil.
| | - Robinson Sabino-Silva
- Innovation Center in Salivary Diagnostics and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia, Brazil.
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Srisaikaew P, Chad JA, Mahakkanukrauh P, Anderson ND, Chen JJ. Effect of sex on the APOE4-aging interaction in the white matter microstructure of cognitively normal older adults using diffusion-tensor MRI with orthogonal-tensor decomposition (DT-DOME). Front Neurosci 2023; 17:1049609. [PMID: 36908785 PMCID: PMC9992882 DOI: 10.3389/fnins.2023.1049609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 02/06/2023] [Indexed: 02/24/2023] Open
Abstract
The influence of the apolipoprotein E ε4 allele (APOE4) on brain microstructure of cognitively normal older adults remains incompletely understood, in part due to heterogeneity within study populations. In this study, we examined white-matter microstructural integrity in cognitively normal older adults as a function of APOE4 carrier status using conventional diffusion-tensor imaging (DTI) and the novel orthogonal-tensor decomposition (DT-DOME), accounting for the effects of age and sex. Age associations with white-matter microstructure did not significantly depend on APOE4 status, but did differ between sexes, emphasizing the importance of accounting for sex differences in APOE research. Moreover, we found the DT-DOME to be more sensitive than conventional DTI metrics to such age-related and sex effects, especially in crossing WM fiber regions, and suggest their use in further investigation of white matter microstructure across the life span in health and disease.
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Affiliation(s)
- Patcharaporn Srisaikaew
- Ph.D. Program in Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
- Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Jordan A. Chad
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Pasuk Mahakkanukrauh
- Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
- Excellence in Osteology Research and Training Center, Chiang Mai University, Chiang Mai, Thailand
| | - Nicole D. Anderson
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| | - J. Jean Chen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Crocco MC, Moyano MFH, Annesi F, Bruno R, Pirritano D, Del Giudice F, Petrone A, Condino F, Guzzi R. ATR-FTIR spectroscopy of plasma supported by multivariate analysis discriminates multiple sclerosis disease. Sci Rep 2023; 13:2565. [PMID: 36782055 PMCID: PMC9924868 DOI: 10.1038/s41598-023-29617-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
Multiple sclerosis (MS) is one of the most common neurodegenerative diseases showing various symptoms both of physical and cognitive type. In this work, we used attenuated total reflection Fourier transformed infrared (ATR-FTIR) spectroscopy to analyze plasma samples for discriminating MS patients from healthy control individuals, and identifying potential spectral biomarkers helping the diagnosis through a quick non-invasive blood test. The cohort of the study consists of 85 subjects, including 45 MS patients and 40 healthy controls. The differences in the spectral features both in the fingerprint region (1800-900 cm-1) and in the high region (3050-2800 cm-1) of the infrared spectra were highlighted also with the support of different chemometric methods, to capture the most significant wavenumbers for the differentiation. The results show an increase in the lipid/protein ratio in MS patients, indicating changes in the level (metabolism) of these molecular components in the plasma. Moreover, the multivariate tools provided a promising rate of success in the diagnosis, with 78% sensitivity and 83% specificity obtained through the random forest model in the fingerprint region. The MS diagnostic tools based on biomarkers identification on blood (and blood component, like plasma or serum) are very challenging and the specificity and sensitivity values obtained in this work are very encouraging. Overall, the results obtained suggest that ATR-FTIR spectroscopy on plasma samples, requiring minimal or no manipulation, coupled with statistical multivariate approaches, is a promising analytical tool to support MS diagnosis through the identification of spectral biomarkers.
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Affiliation(s)
- Maria Caterina Crocco
- Molecular Biophysics Laboratory, Department of Physics, University of Calabria, 87036, Rende, Italy
- STAR Research Infrastructure, University of Calabria, 87036, Rende, CS, Italy
| | | | | | - Rosalinda Bruno
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036, Rende, CS, Italy
| | - Domenico Pirritano
- Neurological and Stroke Unit, Multiple Sclerosis Clinic, Annunziata Hospital, 87100, Cosenza, Italy
- SOC Neurologia-Azienda Ospedaliera Pugliese-Ciaccio, 88100, Catanzaro, Italy
| | - Francesco Del Giudice
- Neurological and Stroke Unit, Multiple Sclerosis Clinic, Annunziata Hospital, 87100, Cosenza, Italy
- SOC Neurologia-Ospedale Jazzolino, Azienda Ospedaliera Provinciale, 89900, Vibo Valentia, Italy
| | - Alfredo Petrone
- Neurological and Stroke Unit, Multiple Sclerosis Clinic, Annunziata Hospital, 87100, Cosenza, Italy
| | - Francesca Condino
- Department of Economics, Statistics and Finance "Giovanni Anania", University of Calabria, Arcavacata di Rende, CS, Italy
| | - Rita Guzzi
- Molecular Biophysics Laboratory, Department of Physics, University of Calabria, 87036, Rende, Italy.
- CNR-Nanotec Rende, Via P. Bucci, 87036, Rende, Italy.
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Impaired Extracellular Proteostasis in Patients with Heart Failure. Arch Med Res 2023; 54:211-222. [PMID: 36797157 DOI: 10.1016/j.arcmed.2023.02.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 01/11/2023] [Accepted: 02/02/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND Proteostasis impairment and the consequent increase of amyloid burden in the myocardium have been associated with heart failure (HF) development and poor prognosis. A better knowledge of the protein aggregation process in biofluids could assist the development and monitoring of tailored interventions. AIM To compare the proteostasis status and protein's secondary structures in plasma samples of patients with HF with preserved ejection fraction (HFpEF), patients with HF with reduced ejection fraction (HFrEF), and age-matched individuals. METHODS A total of 42 participants were enrolled in 3 groups: 14 patients with HFpEF, 14 patients with HFrEF, and 14 age-matched individuals. Proteostasis-related markers were analyzed by immunoblotting techniques. Fourier Transform Infrared (FTIR) Spectroscopy in Attenuated Total Reflectance (ATR) was applied to assess changes in the protein's conformational profile. RESULTS Patients with HFrEF showed an elevated concentration of oligomeric proteic species and reduced clusterin levels. ATR-FTIR spectroscopy coupled with multivariate analysis allowed the discrimination of HF patients from age-matched individuals in the protein amide I absorption region (1700-1600 cm-1), reflecting changes in protein conformation, with a sensitivity of 73 and a specificity of 81%. Further analysis of FTIR spectra showed significantly reduced random coils levels in both HF phenotypes. Also, compared to the age-matched group, the levels of structures related to fibril formation were significantly increased in patients with HFrEF, whereas the β-turns were significantly increased in patients with HFpEF. CONCLUSION Both HF phenotypes showed a compromised extracellular proteostasis and different protein conformational changes, suggesting a less efficient protein quality control system.
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Prada P, Brunel B, Moulin D, Rouillon L, Netter P, Loeuille D, Slimano F, Bouche O, Peyrin-Biroulet L, Jouzeau JY, Piot O. Identification of circulating biomarkers of Crohn's disease and spondyloarthritis using Fourier transform infrared spectroscopy. JOURNAL OF BIOPHOTONICS 2023; 16:e202200200. [PMID: 36112612 DOI: 10.1002/jbio.202200200] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 06/15/2023]
Abstract
Crohn's disease (CD) and spondyloarthritis (SpA) are two inflammatory diseases sharing many common features (genetic polymorphism, armamentarium). Both diseases lack diagnostic markers of certainty. While the diagnosis of CD is made by a combination of clinical, and biological criteria, the diagnosis of SpA may take several years to be confirmed. Based on the hypothesis that CD and SpA alter the biochemical profile of plasma, the objective of this study was to evaluate the analytical capability of Fourier transform infrared spectroscopy (FTIR) in identifying spectral biomarkers. Plasma from 104 patients was analyzed. After data processing of the spectra by Extended Multiplicative Signal Correction and linear discriminant analysis, we demonstrated that it was possible to distinguish CD and SpA from controls with an accuracy of 97% and 85% respectively. Spectral differences were mainly associated with proteins and lipids. This study showed that FTIR analysis is efficient to identify plasma biosignatures specific to CD or SpA.
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Affiliation(s)
- Pierre Prada
- EA7506-BioSpectroscopie Translationnelle (BioSpecT), Université de Reims Champagne-Ardenne, Reims, France
| | - Benjamin Brunel
- EA7506-BioSpectroscopie Translationnelle (BioSpecT), Université de Reims Champagne-Ardenne, Reims, France
- FEMTO-ST Institute, CNRS UMR-6174, Université de Bourgogne Franche-Comté, Besançon, France
| | - David Moulin
- Ingénierie Moléculaire et Ingénierie Articulaire (IMoPA), UMR-7365 CNRS, Faculté de Médecine, Université de Lorraine et Hôpital Universitaire de Nancy, Nancy, France
| | - Lise Rouillon
- EA7506-BioSpectroscopie Translationnelle (BioSpecT), Université de Reims Champagne-Ardenne, Reims, France
| | - Patrick Netter
- Ingénierie Moléculaire et Ingénierie Articulaire (IMoPA), UMR-7365 CNRS, Faculté de Médecine, Université de Lorraine et Hôpital Universitaire de Nancy, Nancy, France
| | - Damien Loeuille
- Ingénierie Moléculaire et Ingénierie Articulaire (IMoPA), UMR-7365 CNRS, Faculté de Médecine, Université de Lorraine et Hôpital Universitaire de Nancy, Nancy, France
| | - Florian Slimano
- EA7506-BioSpectroscopie Translationnelle (BioSpecT), Université de Reims Champagne-Ardenne, Reims, France
| | - Olivier Bouche
- EA7506-BioSpectroscopie Translationnelle (BioSpecT), Université de Reims Champagne-Ardenne, Reims, France
| | - Laurent Peyrin-Biroulet
- Département de Gastroentérologie, Hôpital Universitaire de Nancy-Brabois, Vandœuvre-lès-Nancy, France
| | - Jean-Yves Jouzeau
- Ingénierie Moléculaire et Ingénierie Articulaire (IMoPA), UMR-7365 CNRS, Faculté de Médecine, Université de Lorraine et Hôpital Universitaire de Nancy, Nancy, France
| | - Olivier Piot
- EA7506-BioSpectroscopie Translationnelle (BioSpecT), Université de Reims Champagne-Ardenne, Reims, France
- Plateforme d'Imagerie Cellulaire ou Tissulaire (PICT), Université de Reims Champagne-Ardenne, Reims, France
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Li Z, Wu H, Ji Y, Shi Z, Liu S, Bao X, Shan P, Hu D, Li M. Toward Reagent-Free Discrimination of Alzheimer's Disease Using Blood Plasma Spectral Digital Biomarkers and Machine Learning. J Alzheimers Dis 2023; 95:1175-1188. [PMID: 37661884 DOI: 10.3233/jad-230248] [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] [Indexed: 09/05/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. The detection of early-stage AD is particularly desirable because it would allow early intervention. However, a minimally invasive, low-cost, and accurate discrimination or diagnostic method for AD is especially difficult in the earliest stage of AD. OBJECTIVE The aim of this research is to discover blood plasma spectral digital biomarkers of AD, develop a novel intelligent method for the discrimination of AD and accelerate the translation of Fourier transform infrared (FTIR) spectral-based disease discrimination methods from the laboratory to clinical practice. METHODS Since vibration spectroscopy can provide the structure and chemical composition information of biological samples at the molecular level, we investigated the potential of FTIR spectral biomarkers of blood plasma to differentiate between AD patients and healthy controls. Combined with machine learning technology, we designed a hierarchical discrimination system that provides reagent-free and accurate AD discrimination based on blood plasma spectral digital biomarkers of AD. RESULTS Accurate segregation between AD patients and healthy controls was achieved with 89.3% sensitivity and 85.7% specificity for early-stage AD patients, 92.8% sensitivity and 87.5% specificity for middle-stage AD patients, and 100% sensitivity and 100% specificity for late-stage AD patients. CONCLUSIONS Our results show that blood plasma spectral digital biomarkers hold great promise as discrimination markers of AD, indicating the potential for the development of an inexpensive, reagent-free, and less laborious clinical test. As a result, our research outcome will accelerate the clinical application of spectral digital biomarkers and machine learning.
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Affiliation(s)
- Zhigang Li
- College of Information Science and Engineering, Northeastern University, Shenyang, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao, China
| | - Hao Wu
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, China
| | - Yong Ji
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, China
| | - Zhihong Shi
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, China
| | - Shuai Liu
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, China
| | - Xinran Bao
- First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Peng Shan
- College of Information Science and Engineering, Northeastern University, Shenyang, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao, China
| | - Dean Hu
- College of Information Science and Engineering, Northeastern University, Shenyang, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao, China
| | - Meimei Li
- College of Information Science and Engineering, Northeastern University, Shenyang, China
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Wang J, Zhang YR, Shen XN, Han J, Cui M, Tan L, Dong Q, Zubarev RA, Yu JT. Deamidation-related blood biomarkers show promise for early diagnostics of neurodegeneration. Biomark Res 2022; 10:91. [PMID: 36575499 PMCID: PMC9795668 DOI: 10.1186/s40364-022-00435-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/13/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The strongest risk factor of neurodegenerative diseases (NDDs) is aging. Spontaneous asparaginyl deamidation leading to formation of isoaspartate (isoAsp) has been correlated with protein aggregation in NDDs. METHODS Two cohorts consisting of 140 subjects were studied. Cohort 1 contained patients with AD and healthy controls, while Cohort 2 recruited subjects with mild cognitive impairment (MCI), vascular dementia (VaD), frontotemporal dementia (FTD), Parkinson's disease (PD) and healthy controls. The levels of isoAsp in plasma human albumin (HSA), the most abundant protein in plasma, as well as the levels of immunoglobulin G (IgG) specific against deamidated HSA were measured. Apart from the memory tests, plasma biomarkers for NDDs reported in literature were also quantified, including amyloid beta (Aβ) peptides Aβ40 and Aβ42, neurofilament light protein (NfL), glial fibrillary acidic protein (GFAP) and phosphorylated tau 181 (p-tau181) protein. RESULTS Deamidation products of blood albumin were significantly elevated in vascular dementia and frontotemporal dementia (P < 0.05), but less so in PD. Intriguingly, the deamidation levels were significantly (P < 0.01) associated with the memory test scores for all tested subjects. Deamidation biomarkers performed superiorly (accuracy up to 92%) compared with blood biomarkers Aß42/Aß40, NfL, GFAP and p-tau181 in separating mild cognitive impairment from healthy controls. CONCLUSION We demonstrated the diagnostic capacity of deamidation-related biomarkers in predicting NDDs at the early stage of disease, and the biomarker levels significantly correlated with cognitive decline, strongly supporting the role of deamidation in triggering neurodegeneration and early stages of disease development. Prospective longitudinal studies with a longer observation period and larger cohorts should provide a more detailed picture of the deamidation role in NDD progression.
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Affiliation(s)
- Jijing Wang
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Ya-Ru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
| | - Xue-Ning Shen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
| | - Jinming Han
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Mei Cui
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital Group, Qingdao University, Qingdao, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
| | - Roman A Zubarev
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- National Center for Neurological Disorders, Shanghai, China.
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Callery EL, Morais CLM, Nugent L, Rowbottom AW. Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing. Diagnostics (Basel) 2022; 12:diagnostics12123158. [PMID: 36553165 PMCID: PMC9777204 DOI: 10.3390/diagnostics12123158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/08/2022] [Accepted: 12/11/2022] [Indexed: 12/16/2022] Open
Abstract
The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.
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Affiliation(s)
- Emma L. Callery
- Department of Immunology, Royal Preston Hospital, Preston PR2 9HT, UK
- Correspondence: (E.L.C.); (A.W.R.)
| | - Camilo L. M. Morais
- Institute of Chemistry, Federal University of Rio Grande do Norte, Natal 59072-970, Brazil
| | - Lucy Nugent
- Department of Immunology, Whiston Hospital, Prescot L35 5DR, UK
| | - Anthony W. Rowbottom
- Department of Immunology, Royal Preston Hospital, Preston PR2 9HT, UK
- School of Medicine, University of Central Lancashire, Preston PR1 2HE, UK
- Correspondence: (E.L.C.); (A.W.R.)
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34
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On the use of spectroscopy, prediction machines and cybernetics for an affordable and proactive care approach for endometrial cancer. BIOMEDICAL ENGINEERING ADVANCES 2022. [DOI: 10.1016/j.bea.2022.100057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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35
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Rapid and sensitive detection of esophageal cancer by FTIR spectroscopy of serum and plasma. Photodiagnosis Photodyn Ther 2022; 40:103177. [PMID: 36602070 DOI: 10.1016/j.pdpdt.2022.103177] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 11/11/2022]
Abstract
Fourier transform infrared (FTIR) spectroscopy, as a platform technology for cancer detection, must be up to the challenge of clinical transformation. To this end, detection of esophageal squamous cell carcinoma (ESCC) was hereby explored using serum and plasma scrape-coated on barium fluoride (BaF2) disk by transmission FTIR method, and the classification model was built using six multivariate statistical analyses, including support vector machine (SVM), principal component linear discriminant analysis (PC-LDA), decision tree (DT), k-nearest neighbor (KNN) classification, ensemble algorithms (EA) and partial least squares for discriminant analysis (PLS-DA). All statistical analyses methods demonstrated that late-stage cancer could be well classified from healthy people employing either serum or plasma with different anticoagulants. Resulting PC-LDA model differentiated late-stage cancer from normal group with an accuracy of 99.26%, a sensitivity of 98.53%, and a specificity of 100%. The accuracy and sensitivity reached 97.08% and 91.43%, respectively for early-stage cancer discrimination from normal group. This pilot exploration demonstrated that transmission FTIR provided a rapid, cost effective and sensitive method for ESCC diagnosis using either serum or plasma.
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36
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Alix JJP, Verber NS, Schooling CN, Kadirkamanathan V, Turner MR, Malaspina A, Day JCC, Shaw PJ. Label-free fibre optic Raman spectroscopy with bounded simplex-structured matrix factorization for the serial study of serum in amyotrophic lateral sclerosis. Analyst 2022; 147:5113-5120. [PMID: 36222101 PMCID: PMC9639415 DOI: 10.1039/d2an00936f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease in urgent need of disease biomarkers for the assessment of promising therapeutic candidates in clinical trials. Raman spectroscopy is an attractive technique for identifying disease related molecular changes due to its simplicity. Here, we describe a fibre optic fluid cell for undertaking spontaneous Raman spectroscopy studies of human biofluids that is suitable for use away from a standard laboratory setting. Using this system, we examined serum obtained from patients with ALS at their first presentation to our centre (n = 66) and 4 months later (n = 27). We analysed Raman spectra using bounded simplex-structured matrix factorization (BSSMF), a generalisation of non-negative matrix factorisation which uses the distribution of the original data to limit the factorisation modes (spectral patterns). Biomarkers associated with ALS disease such as measures of symptom severity, respiratory function and inflammatory/immune pathways (C3/C-reactive protein) correlated with baseline Raman modes. Between visit spectral changes were highly significant (p = 0.0002) and were related to protein structure. Comparison of Raman data with established ALS biomarkers as a trial outcome measure demonstrated a reduction in required sample size with BSSMF Raman. Our portable, simple to use fibre optic system allied to BSSMF shows promise in the quantification of disease-related changes in ALS over short timescales.
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Affiliation(s)
- James J P Alix
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
- Neuroscience Institute, University of Sheffield, UK
| | - Nick S Verber
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
- Neuroscience Institute, University of Sheffield, UK
| | - Chlöe N Schooling
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
- Department of Automatic Control and Systems Engineering, University of Sheffield, UK
| | | | - Martin R Turner
- Nuffield Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - John C C Day
- Interface Analysis Centre, School of Physics, University of Bristol, UK
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
- Neuroscience Institute, University of Sheffield, UK
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37
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Alzheimer's disease diagnosis by blood plasma molecular fluorescence spectroscopy (EEM). Sci Rep 2022; 12:16199. [PMID: 36171258 PMCID: PMC9519548 DOI: 10.1038/s41598-022-20611-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/15/2022] [Indexed: 11/08/2022] Open
Abstract
Despite tremendous research advances in detecting Alzheimer's disease (AD), traditional diagnostic tests remain expensive, time-consuming or invasive. The search for a low-cost, rapid, and minimally invasive test has marked a new era of research and technological developments toward establishing blood-based AD biomarkers. The current study has employed excitation-emission matrices (EEM) of fluorescence spectroscopy combined with machine learning to diagnose AD using blood plasma samples from 230 individuals (83 AD patients from 147 healthy controls). To evaluate the performance of the classification algorithms, we calculated the commonly used figures of merit (accuracy, sensitivity and specificity) and figures of merit that take into account the samples unbalance and the discrimination power of the models, as F2-score (F2), Matthews correlation coefficient (MCC) and test effectiveness (\documentclass[12pt]{minimal}
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\begin{document}$$\delta$$\end{document}δ). The classification models achieved satisfactory results: Parallel Factor Analysis with Quadratic Discriminant Analysis (PARAFAC-QDA) with 83.33% sensitivity, 100% specificity, 86.21% F2; and Tucker3-QDA with 91.67% sensitivity, 95.45% specificity and 91.67% F2. In addition, the classifiers show high overall performance with 94.12% accuracy and 0.87 MCC. Regarding the discrimination power between healthy and AD patients, the classification algorithms showed high effectiveness with the mean scores separated by three or more standard deviations. The PARAFAC's spectral profiles and the wavelength values from both models loading profiles can be used in future research to relate this information to plasma AD biomarkers. Our results point to a rapid, low-cost and minimally invasive blood-based method for AD diagnosis.
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38
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Duckworth E, Hole A, Deshmukh A, Chaturvedi P, Chilakapati MK, Mora B, Roy D. Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer. Anal Chem 2022; 94:13642-13646. [PMID: 36161799 PMCID: PMC9558084 DOI: 10.1021/acs.analchem.2c02496] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
We report a novel
method with higher than 90% accuracy
in diagnosing
buccal mucosa cancer. We use Fourier transform infrared spectroscopic
analysis of human serum by suppressing confounding high molecular
weight signals, thus relatively enhancing the biomarkers’ signals.
A narrower range molecular weight window of the serum was also investigated
that yielded even higher accuracy on diagnosis. The most accurate
results were produced in the serum’s 10–30 kDa molecular
weight region to distinguish between the two hardest to discern classes,
i.e., premalignant and cancer patients. This work promises an avenue
for earlier diagnosis with high accuracy as well as greater insight
into the molecular origins of these signals by identifying a key molecular
weight region to focus on.
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Affiliation(s)
- Edward Duckworth
- Swansea University, Singleton Park, Swansea, SA28PP Wales, United Kingdom
| | - Arti Hole
- Advanced Centre for Treatment, Research and Education in Cancer, Kharghar, Navi Mumbai 410210, India
| | - Atul Deshmukh
- Center for Interdisciplinary Research, D. Y. Patil Dental College, Nerul, Navi Mumbai 400706, India
| | - Pankaj Chaturvedi
- Department of Life Sciences, Homi Bhaba National Institute, Mumbai 400094, India
| | - Murali Krishna Chilakapati
- Advanced Centre for Treatment, Research and Education in Cancer, Kharghar, Navi Mumbai 410210, India.,Tata Memorial Center, Head and Neck Surgical Oncology, Dr. E Borges Road, Parel, Mumbai 400012, India.,Department of Life Sciences, Homi Bhaba National Institute, Mumbai 400094, India
| | - Benjamin Mora
- Swansea University, Singleton Park, Swansea, SA28PP Wales, United Kingdom
| | - Debdulal Roy
- Swansea University, Singleton Park, Swansea, SA28PP Wales, United Kingdom
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39
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Galgani A, Vergallo A, Campese N, Lombardo F, Pavese N, Petrozzi L, LoGerfo A, Franzini M, Cecchetti D, Puglisi-Allegra S, Busceti CL, Siciliano G, Tognoni G, Baldacci F, Lista S, Hampel H, Fornai F, Giorgi FS. Biological determinants of blood-based cytokines in the Alzheimer's Disease clinical continuum. J Neurochem 2022; 163:40-52. [PMID: 35950445 DOI: 10.1111/jnc.15686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/31/2022] [Accepted: 08/06/2022] [Indexed: 11/29/2022]
Abstract
Converging translational and clinical research strongly indicates that altered immune and inflammatory homeostasis (neuroinflammation) plays a critical pathophysiological role in Alzheimer's disease (AD), across the clinical continuum. A dualistic role of neuroinflammation may account for a complex biological phenomenon, representing a potential pharmacological target. Emerging blood-based pathophysiological biomarkers, such as cytokines (Cyt) and interleukins (ILs) have been studied as indicators of neuroinflammation in AD. However, inconsistent results have been reported, probably due to lack of standardization of assays with methodological and analytical differences. We used machine-learning and a cross-validation-based statical workflow to explore and analyze the potential impact of key biological factors, such as age, sex, apolipoproteinE (APOE) genotype (the major genetic risk factor for late-onset AD) on Cyt. A set of Cyt was selected based on previous literature, and we investigated any potential association in a pooled cohort of cognitively healthy, mild cognitive impairment (MCI), and AD-like dementia patients. We also performed explorative analyses to extrapolate preliminary clinical insights. We found a robust sex effect on IL12 and an APOE-related difference in IL10, with the latter being also related to the presence of advanced cognitive decline. IL1β was the variable most significantly associated with MCI-to-dementia conversion over a 2.5 year-clinical follow-up. Albeit preliminary, our data support further clinical research to understand whether plasma Cyt may represent reliable and non-invasive tools serving the investigation of neuroimmune and inflammatory dynamics in AD and to foster biomarker-guided pathway-based therapeutic approaches, within the precision medicine development framework.
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Affiliation(s)
- A Galgani
- Neurology Unit, Pisa University Hospital, Pisa, Italy
| | - A Vergallo
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - N Campese
- Neurology Unit, Pisa University Hospital, Pisa, Italy
| | - F Lombardo
- U.O.C. "Risonanza Magnetica Specialistica e Neuroradiologia", Fondazione "G. Monasterio"- National Research Council/Tuscany Region, Pisa, Italy
| | - N Pavese
- Clinical Ageing Research Unit, Newcastle University, Newcastle upon Tyne, UK.,Institute of clinical Medicine, PET Centre, Aarhus University
| | - L Petrozzi
- Neurology Unit, Pisa University Hospital, Pisa, Italy
| | - A LoGerfo
- Neurology Unit, Pisa University Hospital, Pisa, Italy
| | - M Franzini
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - D Cecchetti
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | | | | | - G Siciliano
- Neurology Unit, Pisa University Hospital, Pisa, Italy
| | - G Tognoni
- Neurology Unit, Pisa University Hospital, Pisa, Italy
| | - F Baldacci
- Neurology Unit, Pisa University Hospital, Pisa, Italy
| | - S Lista
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France.,Memory Resources and Research Center (CMRR), Neurology Department, Gui de Chauliac University Hospital, Montpellier, France
| | - H Hampel
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - F Fornai
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy.,IRCCS Neuromed, Pozzilli, Italy
| | - F S Giorgi
- Neurology Unit, Pisa University Hospital, Pisa, Italy.,Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
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40
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The Activated AMPK/mTORC2 Signaling Pathway Associated with Oxidative Stress in Seminal Plasma Contributes to Idiopathic Asthenozoospermia. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:4240490. [PMID: 35720189 PMCID: PMC9200551 DOI: 10.1155/2022/4240490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 04/09/2022] [Accepted: 05/16/2022] [Indexed: 11/17/2022]
Abstract
Asthenozoospermia is a common form of abnormal sperm quality in idiopathic male infertility. While most sperm-mediated causes have been investigated in detail, the significance of seminal plasma has been neglected. Herein, we aimed to investigate the possible pathogenic factors leading to decreased sperm motility based on seminal plasma. Semen was collected from normo- (NOR, n = 70), idiopathic oligo- (OLI, n = 57), and idiopathic asthenozoospermic (AST, n = 53) patients. Using attenuated total reflection-Fourier transform infrared coupled with chemometrics, distinct differences in the biochemical compositions of nucleic acids, protein structure (amides I, II, and III), lipids, and carbohydrates in seminal plasma of AST were observed when compared to NOR and OLI. Compared with NOR and OLI, the levels of peptide aggregation, protein phosphorylation, unsaturated fatty acid, and lipid to protein ratio were significantly increased in AST; however, the level of lipid saturation was significantly decreased in seminal plasma of AST. Compared with NOR, the levels of ROS, MDA, 8-iso-prostaglandin F2α (8-isoPGF2α), and the ratio of phospho-AMPKα/AMPKα1 were significantly increased in AST; however, the levels of SOD, glutathione S-transferase (GSTs), protein carbonyl derivative (PC), and the ratio of phospho-Rictor/Rictor were significantly decreased in seminal plasma of AST. Changes of the AMPK/mTORC2 signaling in the seminal microenvironment possibly induce abnormal glucose and lipid metabolism, which impairs energy production. Oxidative stress potentially damages seminal plasma lipids and proteins, which in turn leads to impaired sperm structure and function. These findings provide evidence that the changes in seminal plasma compositions, oxidative stress, and activation of the AMPK/mTORC2 signaling contribute to the development of asthenozoospermia.
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41
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Extraction of Reduced Infrared Biomarker Signatures for the Stratification of Patients Affected by Parkinson’s Disease: An Untargeted Metabolomic Approach. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10060229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes.
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42
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Xia BT, He Y, Guo Y, Huang JL, Tang XJ, Wang JR, Tan Y, Duan P. Multi- and transgenerational biochemical effects of low-dose exposure to bisphenol A and 4-nonylphenol on testicular interstitial (Leydig) cells. ENVIRONMENTAL TOXICOLOGY 2022; 37:1032-1046. [PMID: 35005817 DOI: 10.1002/tox.23462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/12/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
Bisphenol A (BPA) and 4-nonylphenol (NP) are well-known endocrine-disrupting chemicals (EDCs) that have been proven to affect Leydig cell (LC) functions and testosterone production, but whether BPA and NP have multi- and transgenerational biochemical effects on Leydig cells (LCs) is unknown. Fourier transform infrared (FTIR) spectroscopy is a powerful analytical technique that enables label-free and non-destructive analysis of the tissue specimen. Herein we employed FTIR coupled with chemometrics analysis to identify biomolecular changes in testicular interstitial (Leydig) cells of rats after chronic exposure to low doses of BPA and NP. Cluster segregations between exposed and control groups were observed based on the fingerprint region of 1800-900 cm-1 in all generations. The main biochemical alterations for segregation were amide I, amide II and nucleic acids. BPA and NP single and co-exposure induced significant differences in the ratio of amide I to amide II compared to the corresponding control group in all generations. BPA exposure resulted in remarkable changes of cellular gene transcription and DNA oxidative damage across all generations. Direct exposure to BPA, NP, and BPA&NP of F0 and F1 generations could significantly decrease lipid accumulation in LCs in the F2 and F3 generations. The overall findings revealed that single or co-exposure to BPA and NP at environmental concentrations affects the biochemical structures and properties of LCs.
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Affiliation(s)
- Bin-Tong Xia
- Postgraduate Training Basement of Jinzhou Medicinal University, Shiyan Renmin Hospital, Hubei University of Medicine, Shiyan, China
- Department of Urology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yan He
- Department of Obstetrics and Gynecology, Xiangyang No.1 People's Hospital, Jinzhou Medical University Union Training Base, Xiangyang, China
- Department of Obstetrics and Gynecology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yang Guo
- Department of Obstetrics and Gynecology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jiao-Long Huang
- Department of Obstetrics and Gynecology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xiao-Juan Tang
- College of Basic Medicine, Hubei University of Medicine, Shiyan, China
| | - Jian-Ru Wang
- Public Health and Management College, Hubei University of Medicine, Shiyan, China
| | - Yan Tan
- Department of Andrology, Renmin Hospital, Hubei University of Medicine, Shiyan, China
- Hubei Key Laboratory of Embryonic Stem Cell Research, Hubei University of Medicine, Shiyan, China
- Biomedical Engineering College, Hubei University of Medicine, Shiyan, China
| | - Peng Duan
- Department of Obstetrics and Gynecology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Hubei Key Laboratory of Embryonic Stem Cell Research, Hubei University of Medicine, Shiyan, China
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43
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Ami D, Mereghetti P, Natalello A. Contribution of Infrared Spectroscopy to the Understanding of Amyloid Protein Aggregation in Complex Systems. Front Mol Biosci 2022; 9:822852. [PMID: 35463965 PMCID: PMC9023755 DOI: 10.3389/fmolb.2022.822852] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Infrared (IR) spectroscopy is a label-free and non-invasive technique that probes the vibrational modes of molecules, thus providing a structure-specific spectrum. The development of infrared spectroscopic approaches that enable the collection of the IR spectrum from a selected sample area, from micro- to nano-scale lateral resolutions, allowed to extend their application to more complex biological systems, such as intact cells and tissues, thus exerting an enormous attraction in biology and medicine. Here, we will present recent works that illustrate in particular the applications of IR spectroscopy to the in situ characterization of the conformational properties of protein aggregates and to the investigation of the other biomolecules surrounding the amyloids. Moreover, we will discuss the potential of IR spectroscopy to the monitoring of cell perturbations induced by protein aggregates. The essential support of multivariate analyses to objectively pull out the significant and non-redundant information from the spectra of highly complex systems will be also outlined.
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Affiliation(s)
- Diletta Ami
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, Italy
- *Correspondence: Diletta Ami, ; Antonino Natalello,
| | | | - Antonino Natalello
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, Italy
- *Correspondence: Diletta Ami, ; Antonino Natalello,
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44
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Rapid Discrimination of Neuromyelitis Optica Spectrum Disorder and Multiple Sclerosis Using Machine Learning on Infrared Spectra of Sera. Int J Mol Sci 2022; 23:ijms23052791. [PMID: 35269934 PMCID: PMC8911153 DOI: 10.3390/ijms23052791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 12/13/2022] Open
Abstract
Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are both autoimmune inflammatory and demyelinating diseases of the central nervous system. NMOSD is a highly disabling disease and rapid introduction of the appropriate treatment at the acute phase is crucial to prevent sequelae. Specific criteria were established in 2015 and provide keys to distinguish NMOSD and MS. One of the most reliable criteria for NMOSD diagnosis is detection in patient’s serum of an antibody that attacks the water channel aquaporin-4 (AQP-4). Another target in NMOSD is myelin oligodendrocyte glycoprotein (MOG), delineating a new spectrum of diseases called MOG-associated diseases. Lastly, patients with NMOSD can be negative for both AQP-4 and MOG antibodies. At disease onset, NMOSD symptoms are very similar to MS symptoms from a clinical and radiological perspective. Thus, at first episode, given the urgency of starting the anti-inflammatory treatment, there is an unmet need to differentiate NMOSD subtypes from MS. Here, we used Fourier transform infrared spectroscopy in combination with a machine learning algorithm with the aim of distinguishing the infrared signatures of sera of a first episode of NMOSD from those of a first episode of relapsing-remitting MS, as well as from those of healthy subjects and patients with chronic inflammatory demyelinating polyneuropathy. Our results showed that NMOSD patients were distinguished from MS patients and healthy subjects with a sensitivity of 100% and a specificity of 100%. We also discuss the distinction between the different NMOSD serostatuses. The coupling of infrared spectroscopy of sera to machine learning is a promising cost-effective, rapid and reliable differential diagnosis tool capable of helping to gain valuable time in patients’ treatment.
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45
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Characterization and identification of microplastics using Raman spectroscopy coupled with multivariate analysis. Anal Chim Acta 2022; 1197:339519. [DOI: 10.1016/j.aca.2022.339519] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/02/2022] [Accepted: 01/17/2022] [Indexed: 11/21/2022]
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46
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Klamminger GG, Frauenknecht KBM, Mittelbronn M, Kleine Borgmann FB. From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives. FREE NEUROPATHOLOGY 2022; 3:19. [PMID: 37284145 PMCID: PMC10209863 DOI: 10.17879/freeneuropathology-2022-4210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/17/2022] [Indexed: 06/08/2023]
Abstract
In recent years, Raman spectroscopy has been more and more frequently applied to address research questions in neuroscience. As a non-destructive technique based on inelastic scattering of photons, it can be used for a wide spectrum of applications including neurooncological tumor diagnostics or analysis of misfolded protein aggregates involved in neurodegenerative diseases. Progress in the technical development of this method allows for an increasingly detailed analysis of biological samples and may therefore open new fields of applications. The goal of our review is to provide an introduction into Raman scattering, its practical usage and also commonly associated pitfalls. Furthermore, intraoperative assessment of tumor recurrence using Raman based histology images as well as the search for non-invasive ways of diagnosis in neurodegenerative diseases are discussed. Some of the applications mentioned here may serve as a basis and possibly set the course for a future use of the technique in clinical practice. Covering a broad range of content, this overview can serve not only as a quick and accessible reference tool but also provide more in-depth information on a specific subtopic of interest.
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Affiliation(s)
- Gilbert Georg Klamminger
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
| | - Katrin B M Frauenknecht
- National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
| | - Michel Mittelbronn
- National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
- Luxembourg Centre of Systems Biomedicine (LCSB), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Felix B Kleine Borgmann
- National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
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47
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Ami D, Duse A, Mereghetti P, Cozza F, Ambrosio F, Ponzini E, Grandori R, Lunetta C, Tavazzi S, Pezzoli F, Natalello A. Tear-Based Vibrational Spectroscopy Applied to Amyotrophic Lateral Sclerosis. Anal Chem 2021; 93:16995-17002. [PMID: 34905686 PMCID: PMC8717331 DOI: 10.1021/acs.analchem.1c02546] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
![]()
Biofluid analysis
by optical spectroscopy techniques is attracting
considerable interest due to its potential to revolutionize diagnostics
and precision medicine, particularly for neurodegenerative diseases.
However, the lack of effective biomarkers combined with the unaccomplished
identification of convenient biofluids has drastically hampered optical
advancements in clinical diagnosis and monitoring of neurodegenerative
disorders. Here, we show that vibrational spectroscopy applied to
human tears opens a new route, offering a non-invasive, label-free
identification of a devastating disease such as amyotrophic lateral
sclerosis (ALS). Our proposed approach has been validated using two
widespread techniques, namely, Fourier transform infrared (FTIR) and
Raman microspectroscopies. In conjunction with multivariate analysis,
this vibrational approach made it possible to discriminate between
tears from ALS patients and healthy controls (HCs) with high specificity
(∼97% and ∼100% for FTIR and Raman spectroscopy, respectively)
and sensitivity (∼88% and ∼100% for FTIR and Raman spectroscopy,
respectively). Additionally, the investigation of tears allowed us
to disclose ALS spectroscopic markers related to protein and lipid
alterations, as well as to a reduction of the phenylalanine level,
in comparison with HCs. Our findings show that vibrational spectroscopy
is a new potential ALS diagnostic approach and indicate that tears
are a reliable and non-invasive source of ALS biomarkers.
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Affiliation(s)
- Diletta Ami
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Alessandro Duse
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125 Milano, Italy.,COMiB Research Centre in Optics and Optometry, Via R. Cozzi 55, 20125 Milano, Italy
| | | | - Federica Cozza
- COMiB Research Centre in Optics and Optometry, Via R. Cozzi 55, 20125 Milano, Italy.,NEuroMuscular Omnicentre (NEMO), Serena Onlus Foundation, Piazza Ospedale Maggiore 3, 20162 Milano, Italy
| | - Francesca Ambrosio
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Erika Ponzini
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125 Milano, Italy.,COMiB Research Centre in Optics and Optometry, Via R. Cozzi 55, 20125 Milano, Italy
| | - Rita Grandori
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Christian Lunetta
- NEuroMuscular Omnicentre (NEMO), Serena Onlus Foundation, Piazza Ospedale Maggiore 3, 20162 Milano, Italy.,NEMO Lab, Piazza Ospedale Maggiore 3, 20162 Milano, Italy
| | - Silvia Tavazzi
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125 Milano, Italy.,COMiB Research Centre in Optics and Optometry, Via R. Cozzi 55, 20125 Milano, Italy
| | - Fabio Pezzoli
- Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125 Milano, Italy
| | - Antonino Natalello
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
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48
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Mazza C, Gaydou V, Eymard JC, Birembaut P, Untereiner V, Côté JF, Brocheriou I, Coeffic D, Villena P, Larré S, Vuiblet V, Piot O. Identification of Neoadjuvant Chemotherapy Response in Muscle-Invasive Bladder Cancer by Fourier-Transform Infrared Micro-Imaging. Cancers (Basel) 2021; 14:cancers14010021. [PMID: 35008184 PMCID: PMC8750189 DOI: 10.3390/cancers14010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/09/2021] [Accepted: 12/17/2021] [Indexed: 11/24/2022] Open
Abstract
Simple Summary Assessing the tumor response to chemotherapy is a paramount predictive step to improve patient care. Infrared spectroscopy probes the chemical composition of samples, and in combination with statistical multivariate processing, presents the capacity to highlight subtle molecular alterations associated with malignancy characteristics. Microscopic infrared imaging of tissue samples reveals spectral heterogeneity within histological structures, providing a new approach to characterize tumoral heterogeneity. We have taken advantage of the analytical capabilities of mid-infrared spectral imaging to implement a classification model to predict the response of a tumor to chemotherapy. Our development was demonstrated in muscle-invasive bladder cancer (MIBC) by comparing samples from responders and non-responders to neoadjuvant chemotherapy. Abstract Background: Neoadjuvant chemotherapy (NAC) improves survival in responder patients. However, for non-responders, the treatment represents an ineffective exposure to chemotherapy and its potential adverse events. Predicting the response to treatment is a major issue in the therapeutic management of patients, particularly for patients with muscle-invasive bladder cancer. Methods: Tissue samples of trans-urethral resection of bladder tumor collected at the diagnosis time, were analyzed by mid-infrared imaging. A sequence of spectral data processing was implemented for automatic recognition of informative pixels and scoring each pixel according to a continuous scale (from 0 to 10) associated with the response to NAC. The ground truth status of the responder or non-responder was based on histopathological examination of the samples. Results: Although the TMA spots of tumors appeared histologically homogeneous, the infrared approach highlighted spectral heterogeneity. Both the quantification of this heterogeneity and the scoring of the NAC response at the pixel level were used to construct sensitivity and specificity maps from which decision criteria can be extracted to classify cancerous samples. Conclusions: This proof-of-concept appears as the first to evaluate the potential of the mid-infrared approach for the prediction of response to neoadjuvant chemotherapy in MIBC tissues.
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Affiliation(s)
- Camille Mazza
- Jean Godinot Institute, 51100 Reims, France; (C.M.); (J.-C.E.)
| | - Vincent Gaydou
- BioSpecT (Translational BioSpectroscopy) EA 7506, SFR Santé, Université de Reims Champagne-Ardenne, 51100 Reims, France; (V.G.); (S.L.)
| | | | - Philippe Birembaut
- Department of Biopathology, University Hospital of Reims, 51100 Reims, France;
| | - Valérie Untereiner
- Cellular and Tissular Imaging Platform (PICT), Université de Reims Champagne-Ardenne, 51100 Reims, France;
| | - Jean-François Côté
- Department of Biopathology, Hôpital de la Pitié-Salpêtrière, APHP, 51100 Paris, France; (J.-F.C.); (I.B.)
| | - Isabelle Brocheriou
- Department of Biopathology, Hôpital de la Pitié-Salpêtrière, APHP, 51100 Paris, France; (J.-F.C.); (I.B.)
| | - David Coeffic
- Polyclinique Courlancy, 51100 Reims, France; (D.C.); (P.V.)
| | | | - Stéphane Larré
- BioSpecT (Translational BioSpectroscopy) EA 7506, SFR Santé, Université de Reims Champagne-Ardenne, 51100 Reims, France; (V.G.); (S.L.)
- Department of Urology, University Hospital of Reims, 51100 Reims, France
| | - Vincent Vuiblet
- BioSpecT (Translational BioSpectroscopy) EA 7506, SFR Santé, Université de Reims Champagne-Ardenne, 51100 Reims, France; (V.G.); (S.L.)
- Department of Biopathology, University Hospital of Reims, 51100 Reims, France;
- Correspondence: (V.V.); (O.P.)
| | - Olivier Piot
- BioSpecT (Translational BioSpectroscopy) EA 7506, SFR Santé, Université de Reims Champagne-Ardenne, 51100 Reims, France; (V.G.); (S.L.)
- Cellular and Tissular Imaging Platform (PICT), Université de Reims Champagne-Ardenne, 51100 Reims, France;
- Correspondence: (V.V.); (O.P.)
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Ding M, Shu Q, Zhang N, Yan C, Niu H, Li X, Guan P, Hu X. Electrochemical Immunosensor for the Sensitive Detection of Alzheimer's Biomarker Amyloid‐β (1–42) Using the Heme‐amyloid‐β (1–42) Complex as the Signal Source. ELECTROANAL 2021. [DOI: 10.1002/elan.202100392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Minling Ding
- School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an 710072 P. R. China
| | - Qi Shu
- School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an 710072 P. R. China
| | - Nan Zhang
- School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an 710072 P. R. China
| | - Chaoren Yan
- School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an 710072 P. R. China
| | - Huizhe Niu
- School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an 710072 P. R. China
| | - Xiaoqian Li
- School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an 710072 P. R. China
| | - Ping Guan
- School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an 710072 P. R. China
| | - Xiaoling Hu
- School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xi'an 710072 P. R. China
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Wang J, Lundström SL, Seelow S, Rodin S, Meng Z, Astorga-Wells J, Jia Q, Zubarev RA. First Immunoassay for Measuring Isoaspartate in Human Serum Albumin. Molecules 2021; 26:molecules26216709. [PMID: 34771115 PMCID: PMC8587401 DOI: 10.3390/molecules26216709] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/08/2021] [Accepted: 10/19/2021] [Indexed: 01/01/2023] Open
Abstract
Isoaspartate (isoAsp) is a damaging amino acid residue formed in proteins mostly as a result of spontaneous deamidation of asparaginyl residues. An association has been found between isoAsp in human serum albumin (HSA) and Alzheimer’s disease (AD). Here we report on a novel monoclonal antibody (mAb) 1A3 with excellent specificity to isoAsp in the functionally important domain of HSA. Based on 1A3 mAb, an indirect enzyme-linked immunosorbent assay (ELISA) was developed, and the isoAsp occupancy in 100 healthy plasma samples was quantified for the first time, providing the average value of (0.74 ± 0.13)%. These results suggest potential of isoAsp measurements for supplementary AD diagnostics as well as for assessing the freshness of stored donor blood and its suitability for transfusion.
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Affiliation(s)
- Jijing Wang
- Department of Medical Biophysics and Biochemistry, Karolinska Institute, 171 77 Stockholm, Sweden; (J.W.); (S.L.L.); (S.S.); (S.R.); (Z.M.); (J.A.-W.); (Q.J.)
| | - Susanna L. Lundström
- Department of Medical Biophysics and Biochemistry, Karolinska Institute, 171 77 Stockholm, Sweden; (J.W.); (S.L.L.); (S.S.); (S.R.); (Z.M.); (J.A.-W.); (Q.J.)
| | - Sven Seelow
- Department of Medical Biophysics and Biochemistry, Karolinska Institute, 171 77 Stockholm, Sweden; (J.W.); (S.L.L.); (S.S.); (S.R.); (Z.M.); (J.A.-W.); (Q.J.)
| | - Sergey Rodin
- Department of Medical Biophysics and Biochemistry, Karolinska Institute, 171 77 Stockholm, Sweden; (J.W.); (S.L.L.); (S.S.); (S.R.); (Z.M.); (J.A.-W.); (Q.J.)
- Department of Surgical Sciences, Uppsala University, 752 36 Uppsala, Sweden
- Endocrinology Research Centre, 115478 Moscow, Russia
| | - Zhaowei Meng
- Department of Medical Biophysics and Biochemistry, Karolinska Institute, 171 77 Stockholm, Sweden; (J.W.); (S.L.L.); (S.S.); (S.R.); (Z.M.); (J.A.-W.); (Q.J.)
| | - Juan Astorga-Wells
- Department of Medical Biophysics and Biochemistry, Karolinska Institute, 171 77 Stockholm, Sweden; (J.W.); (S.L.L.); (S.S.); (S.R.); (Z.M.); (J.A.-W.); (Q.J.)
- HDXperts AB, 183 48 Danderyd, Sweden
| | - Qinyu Jia
- Department of Medical Biophysics and Biochemistry, Karolinska Institute, 171 77 Stockholm, Sweden; (J.W.); (S.L.L.); (S.S.); (S.R.); (Z.M.); (J.A.-W.); (Q.J.)
- HDXperts AB, 183 48 Danderyd, Sweden
| | - Roman A. Zubarev
- Department of Medical Biophysics and Biochemistry, Karolinska Institute, 171 77 Stockholm, Sweden; (J.W.); (S.L.L.); (S.S.); (S.R.); (Z.M.); (J.A.-W.); (Q.J.)
- Department of Pharmacological & Technological Chemistry, I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia
- The National Medical Research Center for Endocrinology, 115478 Moscow, Russia
- Correspondence:
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