1
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Evaluation of a Raman Chemometric Method for Detecting Protein Structural Conformational Changes in Solution. J Pharm Sci 2023; 112:573-586. [PMID: 36152698 DOI: 10.1016/j.xphs.2022.09.006] [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: 02/22/2022] [Revised: 09/07/2022] [Accepted: 09/07/2022] [Indexed: 01/18/2023]
Abstract
Raman scattering shows promise as a powerful routine tool, to determine both secondary and the smaller tertiary structural changes that precede aggregation in both solutions and solids. A method was developed utilizing principal component analysis (PCA) of Raman spectra for detection of small, but meaningful, pH induced changes in tertiary protein structure linked to aggregate formation using α-lactalbumin solutions as a model. The sample preparation and spectral parameters, were optimized for a bulk Raman probe. Analysis of large regions (600-1850 cm-1) yielded principal component (PC) scores useful for semi-quantitative comparison of protein conformation between formulations. PC loadings corresponded to specific structural peaks known to change with solution pH. PCA of circular dichroism (CD) spectra of dilute solutions yielded similar results. Sucrose is a common formulation excipient with a Raman spectrum that overlaps many protein peaks. With sucrose in the protein solution, the ability of PCA to discern protein structural changes from the Raman spectra was somewhat reduced. Analysis of a more limited spectral region (1530-1780 cm-1) with negligible sucrose spectral contribution improved the discrimination of protein conformational states. The new Raman method accurately distinguished differences in protein structure in concentrated solutions. The long-term goal is to explore Raman characterization as a routine monitoring tool of protein stability in both solution and solid states.
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2
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Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies. Cancers (Basel) 2021; 13:cancers13164196. [PMID: 34439355 PMCID: PMC8392399 DOI: 10.3390/cancers13164196] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/12/2021] [Accepted: 08/16/2021] [Indexed: 11/26/2022] Open
Abstract
Simple Summary Isocitrate dehydrogenase (IDH) mutation is one of the most important prognostic markers in glioma tumors. Raman spectroscopy (RS) is an optical technique with great potential in intraoperative molecular diagnosis and surgical guidance. We analyzed RS’s ability to detect the IDH mutation onto unprocessed glioma biopsies. A total of 2073 Raman spectra were extracted from 38 tumor specimens. From the 103 Raman shifts screened, we identified 52 shifts (related to lipids, collagen, DNA and cholesterol/phospholipids) with the highest performance in the distinction of the two groups. We described 18 shifts never used before for IDH detection with RS in fresh or frozen samples. We were able to distinguish between IDH-mutated and IDH-wild-type tumors with an accuracy and precision of 87%. RS showed optimal accuracy and precision in discriminating IDH-mutated glioma from IDH-wild-type tumors ex-vivo onto fresh surgical specimens. Abstract Isocitrate dehydrogenase (IDH) mutational status is pivotal in the management of gliomas. Patients with IDH-mutated (IDH-MUT) tumors have a better prognosis and benefit more from extended surgical resection than IDH wild-type (IDH-WT). Raman spectroscopy (RS) is a minimally invasive optical technique with great potential for intraoperative diagnosis. We evaluated the RS’s ability to characterize the IDH mutational status onto unprocessed glioma biopsies. We extracted 2073 Raman spectra from thirty-eight unprocessed samples. The classification performance was assessed using the eXtreme Gradient Boosted trees (XGB) and Support Vector Machine with Radial Basis Function kernel (RBF-SVM). Measured Raman spectra displayed differences between IDH-MUT and IDH-WT tumor tissue. From the 103 Raman shifts screened as input features, the cross-validation loop identified 52 shifts with the highest performance in the distinction of the two groups. Raman analysis showed differences in spectral features of lipids, collagen, DNA and cholesterol/phospholipids. We were able to distinguish between IDH-MUT and IDH-WT tumors with an accuracy and precision of 87%. RS is a valuable and accurate tool for characterizing the mutational status of IDH mutation in unprocessed glioma samples. This study improves RS knowledge for future personalized surgical strategy or in situ target therapies for glioma tumors.
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3
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Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples. Cancers (Basel) 2021; 13:cancers13051073. [PMID: 33802369 PMCID: PMC7959285 DOI: 10.3390/cancers13051073] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 02/17/2021] [Accepted: 02/25/2021] [Indexed: 12/14/2022] Open
Abstract
Identifying tumor cells infiltrating normal-appearing brain tissue is critical to achieve a total glioma resection. Raman spectroscopy (RS) is an optical technique with potential for real-time glioma detection. Most RS reports are based on formalin-fixed or frozen samples, with only a few studies deployed on fresh untreated tissue. We aimed to probe RS on untreated brain biopsies exploring novel Raman bands useful in distinguishing glioma and normal brain tissue. Sixty-three fresh tissue biopsies were analyzed within few minutes after resection. A total of 3450 spectra were collected, with 1377 labelled as Healthy and 2073 as Tumor. Machine learning methods were used to classify spectra compared to the histo-pathological standard. The algorithms extracted information from 60 different Raman peaks identified as the most representative among 135 peaks screened. We were able to distinguish between tumor and healthy brain tissue with accuracy and precision of 83% and 82%, respectively. We identified 19 new Raman shifts with known biological significance. Raman spectroscopy was effective and accurate in discriminating glioma tissue from healthy brain ex-vivo in fresh samples. This study added new spectroscopic data that can contribute to further develop Raman Spectroscopy as an intraoperative tool for in-vivo glioma detection.
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4
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González-Viveros N, Castro-Ramos J, Gómez-Gil P, Cerecedo-Núñez HH. Characterization of glycated hemoglobin based on Raman spectroscopy and artificial neural networks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 247:119077. [PMID: 33137627 DOI: 10.1016/j.saa.2020.119077] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 10/01/2020] [Accepted: 10/09/2020] [Indexed: 06/11/2023]
Abstract
The World Health Organization has declared the glycated hemoglobin (HbA1c) as a gold standard biomarker for diabetes diagnosis; this has led to relevant research on the spectral behavior and characterization of HbA1c. This paper presents an analysis of Raman peaks of commercial lyophilized HbA1c, diluted in distilled water, using concentrations of 4.76% and 9.09%, as well as pure powder (100% concentration). Vibrational Raman peak positions of HbA1c powder were found at 1578, 1571, 1536, 1436, 1311, 1308, 1230, 1222, 1114, 1106, 969, 799 and 665 cm-1; these values are consistent with results reported in other works. Besides, a nonlinear regression model based on a Feed-Forward Neural Network (FFNN) was built to quantify percentages of HbA1c for unknown concentrations. Using the Raman spectra as independent variables, the regression provided a Root Mean Square Error in Cross-Validation (RMSECV) of 0.08% ± 0.04. We also include a detailed molecular assignment of the average spectra of lyophilized powder of HbA1c.
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Affiliation(s)
- N González-Viveros
- National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Santa María Tonantzintla, San Andrés Cholula, C.P. 72840 Puebla, México.
| | - J Castro-Ramos
- National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Santa María Tonantzintla, San Andrés Cholula, C.P. 72840 Puebla, México
| | - P Gómez-Gil
- National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Santa María Tonantzintla, San Andrés Cholula, C.P. 72840 Puebla, México
| | - H H Cerecedo-Núñez
- Faculty of Physics, Veracruzan University, Zona Universitaria, C.P. 91090 Xalapa, Veracruz, México
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5
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Vardaki MZ, Kourkoumelis N. Tissue Phantoms for Biomedical Applications in Raman Spectroscopy: A Review. Biomed Eng Comput Biol 2020; 11:1179597220948100. [PMID: 32884391 PMCID: PMC7440735 DOI: 10.1177/1179597220948100] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 07/16/2020] [Indexed: 12/26/2022] Open
Abstract
Raman spectroscopy is a group of analytical techniques, currently applied in several research fields, including clinical diagnostics. Tissue-mimicking optical phantoms have been established as an essential intermediate stage for medical applications with their employment from spectroscopic techniques to be constantly growing. This review outlines the types of tissue phantoms currently employed in different biomedical applications of Raman spectroscopy, focusing on their composition and optical properties. It is therefore an attempt to present an informed range of options for potential use to the researchers.
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Affiliation(s)
- Martha Z Vardaki
- Department of Medical Physics, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Nikolaos Kourkoumelis
- Department of Medical Physics, School of Health Sciences, University of Ioannina, Ioannina, Greece
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6
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Kang JW, Park YS, Chang H, Lee W, Singh SP, Choi W, Galindo LH, Dasari RR, Nam SH, Park J, So PTC. Direct observation of glucose fingerprint using in vivo Raman spectroscopy. SCIENCE ADVANCES 2020; 6:eaay5206. [PMID: 32042901 PMCID: PMC6981082 DOI: 10.1126/sciadv.aay5206] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Accepted: 11/20/2019] [Indexed: 05/03/2023]
Abstract
Noninvasive blood glucose monitoring has been a long-standing dream in diabetes management. The use of Raman spectroscopy, with its molecular specificity, has been investigated in this regard over the past decade. Previous studies reported on glucose sensing based on indirect evidence such as statistical correlation to the reference glucose concentration. However, these claims fail to demonstrate glucose Raman peaks, which has raised questions regarding the effectiveness of Raman spectroscopy for glucose sensing. Here, we demonstrate the first direct observation of glucose Raman peaks from in vivo skin. The signal intensities varied proportional to the reference glucose concentrations in three live swine glucose clamping experiments. Tracking spectral intensity based on linearity enabled accurate prospective prediction in within-subject and intersubject models. Our direct demonstration of glucose signal may quiet the long debate about whether glucose Raman spectra can be measured in vivo in transcutaneous glucose sensing.
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Affiliation(s)
- Jeon Woong Kang
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yun Sang Park
- Mobile Healthcare Laboratory, Device and System Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Hojun Chang
- Mobile Healthcare Laboratory, Device and System Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Woochang Lee
- Mobile Healthcare Laboratory, Device and System Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Surya Pratap Singh
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Wonjun Choi
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Luis H. Galindo
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ramachandra R. Dasari
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sung Hyun Nam
- Mobile Healthcare Laboratory, Device and System Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
- Corresponding author. (S.H.N.); (P.T.C.S.)
| | - Jongae Park
- Mobile Healthcare Laboratory, Device and System Research Center, Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Peter T. C. So
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Corresponding author. (S.H.N.); (P.T.C.S.)
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7
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Hossain MN, Igne B, Anderson CA, Drennen JK. Influence of moisture variation on the performance of Raman spectroscopy in quantitative pharmaceutical analyses. J Pharm Biomed Anal 2019; 164:528-535. [DOI: 10.1016/j.jpba.2018.10.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/12/2018] [Accepted: 10/12/2018] [Indexed: 10/28/2022]
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8
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Gómez-Laserna O, Cardiano P, Diez-Garcia M, Prieto-Taboada N, Kortazar L, Olazabal MÁ, Madariaga JM. Multi-analytical methodology to diagnose the environmental impact suffered by building materials in coastal areas. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:4371-4386. [PMID: 29181758 DOI: 10.1007/s11356-017-0798-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 11/17/2017] [Indexed: 06/07/2023]
Abstract
This work is focused on the development of an innovative multi-analytical methodology to estimate the impact suffered by building materials in coastal environments. With the aim of improving the in situ spectroscopic assessment, which is often based on XRF and Raman spectrometers, diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy was implemented in the diagnosis study. In this way, the additional benefits from DRIFT were compared to the usual in situ analyses of building materials, which often have interferences from fluorescence and reststrahlen effects. The studies were extended to the laboratory scale by μ-X-ray fluorescence (μ-XRF) cross-section mapping and ion chromatography (IC), and the IC quantitative data were employed to develop thermodynamic models using the ECOS-RUNSALT program, with the aim of rationalizing the behavior of soluble salts with variations in the temperature and the relative humidity (RH). The multi-analytical methodology allowed identification of the most significant weathering agents and classification of the severity of degradation according to the salt content. The suitability of a DRIFT portable device to analyze these types of matrices was verified. Although the Kramers-Kronig algorithm correction proved to be inadequate to decrease the expected spectral distortions, the assignment was successfully performed based on the secondary bands and intensification of the overtones and decreased the time needed for in situ data collection. In addition, the pollutants' distribution in the samples and the possible presence of dangerous compounds, which were not detected during the in situ analysis campaigns, provided valuable information to clarify weathering phenomena.
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Affiliation(s)
- Olivia Gómez-Laserna
- Department of Analytical Chemistry, University of the Basque Country (EHU/UPV), Barrio Sarriena s/n, E-48080, Leioa, Bilbao, Spain.
| | - Paola Cardiano
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale F. Stagno d'Alcontres 31, I-98166, Messina, Italy
| | - Marta Diez-Garcia
- Department of Analytical Chemistry, University of the Basque Country (EHU/UPV), Barrio Sarriena s/n, E-48080, Leioa, Bilbao, Spain
| | - Nagore Prieto-Taboada
- Department of Analytical Chemistry, University of the Basque Country (EHU/UPV), Barrio Sarriena s/n, E-48080, Leioa, Bilbao, Spain
| | - Leire Kortazar
- Department of Analytical Chemistry, University of the Basque Country (EHU/UPV), Barrio Sarriena s/n, E-48080, Leioa, Bilbao, Spain
| | - María Ángeles Olazabal
- Department of Analytical Chemistry, University of the Basque Country (EHU/UPV), Barrio Sarriena s/n, E-48080, Leioa, Bilbao, Spain
| | - Juan Manuel Madariaga
- Department of Analytical Chemistry, University of the Basque Country (EHU/UPV), Barrio Sarriena s/n, E-48080, Leioa, Bilbao, Spain
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9
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Pandey R, Paidi SK, Valdez TA, Zhang C, Spegazzini N, Dasari RR, Barman I. Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy. Acc Chem Res 2017; 50:264-272. [PMID: 28071894 DOI: 10.1021/acs.accounts.6b00472] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The successful development of a noninvasive blood glucose sensor that can operate reliably over sustained periods of time has been a much sought after but elusive goal in diabetes management. Since diabetes has no well-established cure, control of elevated glucose levels is critical for avoiding severe secondary health complications in multiple organs including the retina, kidney and vasculature. While fingerstick testing continues to be the mainstay of blood glucose detection, advances in electrochemical sensing-based minimally invasive approaches have opened the door for alternate methods that would considerably improve the quality of life for people with diabetes. In the quest for better sensing approaches, optical technologies have surfaced as attractive candidates as researchers have sought to exploit the endogenous contrast of glucose, notably its absorption, scattering, and polarization properties. Vibrational spectroscopy, especially spontaneous Raman scattering, has exhibited substantial promise due to its exquisite molecular specificity and minimal interference of water in the spectral profiles acquired from the blood-tissue matrix. Yet, it has hitherto been challenging to leverage the Raman scattering signatures of glucose for prediction in all but the most basic studies and under the least demanding conditions. In this Account, we discuss the newly developed array of methodologies that address the key challenges in measuring blood glucose accurately using Raman spectroscopy and unlock new prospects for translation to sustained noninvasive measurements in people with diabetes. Owing to the weak intensity of spontaneous Raman scattering, recent research has focused on enhancement of signals from the blood constituents by designing novel excitation-collection geometries and tissue modulation methods while our attempts have led to the incorporation of nonimaging optical elements. Additionally, invoking mass transfer modeling into chemometric algorithms has not only addressed the physiological lag between the actual blood glucose and the measured interstitial fluid glucose values but also offered a powerful tool for predictive measurements of hypoglycemia. This framework has recently been extended to provide longitudinal tracking of glucose concentration without necessitating extensive a priori concentration information. These findings are advanced by the results of recent glucose tolerance studies in human subjects, which also hint at the need for designing nonlinear calibration models that can account for subject-to-subject variations in skin heterogeneity and hematocrit levels. Together, the emerging evidence underscores the promise of a blood withdrawal-free optical platform-featuring a combination of high-throughput Raman spectroscopic instrumentation and data analysis of subtle variations in spectral expression-for diabetes screening in the clinic and, ultimately, for personalized monitoring.
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Affiliation(s)
- Rishikesh Pandey
- Connecticut
Children’s Innovation Center, University of Connecticut Health, Farmington, Connecticut 06032, United States
| | - Santosh Kumar Paidi
- Department
of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Tulio A. Valdez
- Connecticut
Children’s Innovation Center, University of Connecticut Health, Farmington, Connecticut 06032, United States
- Otolaryngology,
Head and Neck Surgery, Connecticut Children’s Medical Center, 282 Washington
St, Hartford, Connecticut 06106, United States
| | - Chi Zhang
- Department
of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Nicolas Spegazzini
- Laser
Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Ramachandra Rao Dasari
- Laser
Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Ishan Barman
- Department
of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department
of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, United States
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10
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Method for Removing Spectral Contaminants to Improve Analysis of Raman Imaging Data. Sci Rep 2017; 7:39891. [PMID: 28054587 PMCID: PMC5215229 DOI: 10.1038/srep39891] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 11/29/2016] [Indexed: 11/26/2022] Open
Abstract
The spectral contaminants are inevitable during micro-Raman measurements. A key challenge is how to remove them from the original imaging data, since they can distort further results of data analysis. Here, we propose a method named “automatic pre-processing method for Raman imaging data set (APRI)”, which includes the adaptive iteratively reweighted penalized least-squares (airPLS) algorithm and the principal component analysis (PCA). It eliminates the baseline drifts and cosmic spikes by using the spectral features themselves. The utility of APRI is illustrated by removing the spectral contaminants from a Raman imaging data set of a wood sample. In addition, APRI is computationally efficient, conceptually simple and potential to be extended to other methods of spectroscopy, such as infrared (IR), nuclear magnetic resonance (NMR), X-Ray Diffraction (XRD). With the help of our approach, a typical spectral analysis can be performed by a non-specialist user to obtain useful information from a spectroscopic imaging data set.
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11
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Paidi SK, Siddhanta S, Strouse R, McGivney JB, Larkin C, Barman I. Rapid Identification of Biotherapeutics with Label-Free Raman Spectroscopy. Anal Chem 2016; 88:4361-8. [DOI: 10.1021/acs.analchem.5b04794] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Santosh Kumar Paidi
- Department
of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Soumik Siddhanta
- Department
of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Robert Strouse
- Bioprocess
Development, MedImmune LLC, Gaithersburg, Maryland 20878, United States
| | - James B McGivney
- Bioprocess
Development, MedImmune LLC, Gaithersburg, Maryland 20878, United States
| | - Christopher Larkin
- Bioprocess
Development, MedImmune LLC, Gaithersburg, Maryland 20878, United States
| | - Ishan Barman
- Department
of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department
of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, United States
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12
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Butler HJ, Ashton L, Bird B, Cinque G, Curtis K, Dorney J, Esmonde-White K, Fullwood NJ, Gardner B, Martin-Hirsch PL, Walsh MJ, McAinsh MR, Stone N, Martin FL. Using Raman spectroscopy to characterize biological materials. Nat Protoc 2016; 11:664-87. [PMID: 26963630 DOI: 10.1038/nprot.2016.036] [Citation(s) in RCA: 619] [Impact Index Per Article: 77.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.
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Affiliation(s)
- Holly J Butler
- Lancaster Environment Centre, Lancaster University, Lancaster, UK.,Centre for Global Eco-Innovation, Lancaster Environment Centre, Lancaster University, Lancaster, UK
| | - Lorna Ashton
- Department of Chemistry, Lancaster University, Lancaster, UK
| | | | - Gianfelice Cinque
- Diamond Light Source, Harwell Science and Innovation Campus, Chilton, Oxfordshire, UK
| | - Kelly Curtis
- Department of Biomedical Physics, Physics and Astronomy, University of Exeter, Exeter, UK
| | - Jennifer Dorney
- Department of Biomedical Physics, Physics and Astronomy, University of Exeter, Exeter, UK
| | - Karen Esmonde-White
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Nigel J Fullwood
- Department of Biomedical and Life Sciences, School of Health and Medicine, Lancaster University, Lancaster, UK
| | - Benjamin Gardner
- Department of Biomedical Physics, Physics and Astronomy, University of Exeter, Exeter, UK
| | - Pierre L Martin-Hirsch
- Lancaster Environment Centre, Lancaster University, Lancaster, UK.,School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, UK
| | - Michael J Walsh
- Department of Pathology, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Martin R McAinsh
- Lancaster Environment Centre, Lancaster University, Lancaster, UK
| | - Nicholas Stone
- Biophotonics Research Unit, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK
| | - Francis L Martin
- Lancaster Environment Centre, Lancaster University, Lancaster, UK
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13
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Optical investigation of osteoarthritic human cartilage (ICRS grade) by confocal Raman spectroscopy: a pilot study. Anal Bioanal Chem 2015; 407:8067-77. [PMID: 26319282 DOI: 10.1007/s00216-015-8979-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Revised: 08/12/2015] [Accepted: 08/13/2015] [Indexed: 02/07/2023]
Abstract
Biomolecular changes in the cartilage matrix during the early stage of osteoarthritis may be detected by Raman spectroscopy. The objective of this investigation was to determine vibrational spectral differences among different grades (grades I, II, and III) of osteoarthritis in human osteoarthritic cartilage, which was classified according to the International Cartilage Repair Society (ICRS) grading system. Degenerative articular cartilage samples were collected during total joint replacement surgery and were classified according to the ICRS grading system for osteoarthritis. Twelve cartilage sections (4 sections of each ICRS grades I, II, and III) were selected for Raman spectroscopic analysis. Safranin-O/Fast green was used for histological staining and assignment of the Osteoarthritis Research Society International (OARSI) grade. Multivariate principal component analysis (PCA) was used for data analysis. Spectral analysis indicates that the content of disordered coil collagen increases significantly during the early progression of osteoarthritis. However, the increase was not statistically significant during later stages of the disease. A decrease in the content of proteoglycan was observed only during advanced stages of osteoarthritis. Our investigation shows that Raman spectroscopy can classify the different stage of osteoarthritic cartilage and can provide details on biochemical changes. This proof-of-concept study encourages further investigation of fresh cartilage on a larger population using fiber-based miniaturized Raman probe for the development of in vivo Raman arthroscopy as a potential diagnostic tool for osteoarthritis.
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14
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Pandey R, Paidi SK, Kang JW, Spegazzini N, Dasari RR, Valdez TA, Barman I. Discerning the differential molecular pathology of proliferative middle ear lesions using Raman spectroscopy. Sci Rep 2015; 5:13305. [PMID: 26289566 PMCID: PMC4542608 DOI: 10.1038/srep13305] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Accepted: 07/13/2015] [Indexed: 11/10/2022] Open
Abstract
Despite its widespread prevalence, middle ear pathology, especially the development of proliferative lesions, remains largely unexplored and poorly understood. Diagnostic evaluation is still predicated upon a high index of clinical suspicion on otoscopic examination of gross morphologic features. We report the first technique that has the potential to non-invasively identify two key lesions, namely cholesteatoma and myringosclerosis, by providing real-time information of differentially expressed molecules. In addition to revealing signatures consistent with the known pathobiology of these lesions, our observations provide the first evidence of the presence of carbonate- and silicate-substitutions in the calcium phosphate plaques found in myringosclerosis. Collectively, these results demonstrate the potential of Raman spectroscopy to not only provide new understanding of the etiology of these conditions by defining objective molecular markers but also aid in margin assessment to improve surgical outcome.
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Affiliation(s)
- Rishikesh Pandey
- Laser Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
| | - Santosh Kumar Paidi
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Jeon Woong Kang
- Laser Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
| | - Nicolas Spegazzini
- Laser Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
| | - Ramachandra Rao Dasari
- Laser Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
| | - Tulio Alberto Valdez
- Otolaryngology, Head and Neck Surgery, University of Connecticut, 263 Farmington Ave, Farmington, Connecticut, 06030, USA.,Otolaryngology, Head and Neck Surgery, Connecticut Children's Medical Center, 282 Washington St, Hartford, Connecticut, 06106, USA
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.,Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, USA
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15
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Desroches J, Jermyn M, Mok K, Lemieux-Leduc C, Mercier J, St-Arnaud K, Urmey K, Guiot MC, Marple E, Petrecca K, Leblond F. Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification. BIOMEDICAL OPTICS EXPRESS 2015; 6. [PMID: 26203368 PMCID: PMC4505696 DOI: 10.1364/boe.6.002380] [Citation(s) in RCA: 91] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
A detailed characterization study is presented of a Raman spectroscopy system designed to maximize the volume of resected cancer tissue in glioma surgery based on in vivo molecular tissue characterization. It consists of a hand-held probe system measuring spectrally resolved inelastically scattered light interacting with tissue, designed and optimized for in vivo measurements. Factors such as linearity of the signal with integration time and laser power, and their impact on signal to noise ratio, are studied leading to optimal data acquisition parameters. The impact of ambient light sources in the operating room is assessed and recommendations made for optimal operating conditions. In vivo Raman spectra of normal brain, cancer and necrotic tissue were measured in 10 patients, demonstrating that real-time inelastic scattering measurements can distinguish necrosis from vital tissue (including tumor and normal brain tissue) with an accuracy of 87%, a sensitivity of 84% and a specificity of 89%.
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Affiliation(s)
- Joannie Desroches
- Dept. of Engineering Physics, Polytechnique Montreal, CP 6079, Succ. Centre-Ville, Montreal, QC, H3C 3A7, Canada
| | - Michael Jermyn
- Dept. of Engineering Physics, Polytechnique Montreal, CP 6079, Succ. Centre-Ville, Montreal, QC, H3C 3A7, Canada
- Brain Tumour Research Centre, Montreal Neurological Institute and Hospital, Dept. of Neurology and Neurosurgery, McGill University, 3801 University St., Montreal, QC, H3A 2B4, Canada
| | - Kelvin Mok
- Neuronavigation Unit, Montreal Neurological Institute and Hospital, McGill University, 3801 University St., Montreal, QC, H3A 2B4, Canada
| | - Cédric Lemieux-Leduc
- Dept. of Engineering Physics, Polytechnique Montreal, CP 6079, Succ. Centre-Ville, Montreal, QC, H3C 3A7, Canada
| | - Jeanne Mercier
- Dept. of Engineering Physics, Polytechnique Montreal, CP 6079, Succ. Centre-Ville, Montreal, QC, H3C 3A7, Canada
| | - Karl St-Arnaud
- Dept. of Engineering Physics, Polytechnique Montreal, CP 6079, Succ. Centre-Ville, Montreal, QC, H3C 3A7, Canada
| | - Kirk Urmey
- EMVision LLC, 1471 F Road, Loxahatchee, Florida, 33470, USA
| | - Marie-Christine Guiot
- Division of Neuropathology, Department of Pathology, McGill University, 3801 University St., Montreal, QC, H3A 2B4, Canada
| | - Eric Marple
- EMVision LLC, 1471 F Road, Loxahatchee, Florida, 33470, USA
| | - Kevin Petrecca
- Brain Tumour Research Centre, Montreal Neurological Institute and Hospital, Dept. of Neurology and Neurosurgery, McGill University, 3801 University St., Montreal, QC, H3A 2B4, Canada
| | - Frédéric Leblond
- Dept. of Engineering Physics, Polytechnique Montreal, CP 6079, Succ. Centre-Ville, Montreal, QC, H3C 3A7, Canada
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16
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Abstract
Raman spectroscopy is a fundamental form of molecular spectroscopy that is widely used to investigate structures and properties of molecules using their vibrational transitions. It relies on inelastic scattering of monochromatic laser light irradiating the specimen. After appropriate filtering the scattered light is dispersed onto a detector to determine the shift from the excitation wavelength, which appears in the form of characteristic spectral patterns. The technique can investigate biological samples and provide real-time diagnosis of diseases. However, despite its intrinsic advantages of specificity and minimal perturbation, the Raman scattered light is typically very weak and limits applications of Raman spectroscopy due to measurement (im)precision, driven by inherent noise in the acquired spectra. In this article, we review the principal noise sources that impact quantitative biological Raman spectroscopy. Further, we discuss how such noise effects can be reduced by innovative changes in the constructed Raman system and appropriate signal processing methods.
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17
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Myakalwar AK, Dingari NC, Dasari RR, Barman I, Gundawar MK. Non-gated laser induced breakdown spectroscopy provides a powerful segmentation tool on concomitant treatment of characteristic and continuum emission. PLoS One 2014; 9:e103546. [PMID: 25084522 PMCID: PMC4118875 DOI: 10.1371/journal.pone.0103546] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2014] [Accepted: 06/30/2014] [Indexed: 11/19/2022] Open
Abstract
We demonstrate the application of non-gated laser induced breakdown spectroscopy (LIBS) for characterization and classification of organic materials with similar chemical composition. While use of such a system introduces substantive continuum background in the spectral dataset, we show that appropriate treatment of the continuum and characteristic emission results in accurate discrimination of pharmaceutical formulations of similar stoichiometry. Specifically, our results suggest that near-perfect classification can be obtained by employing suitable multivariate analysis on the acquired spectra, without prior removal of the continuum background. Indeed, we conjecture that pre-processing in the form of background removal may introduce spurious features in the signal. Our findings in this report significantly advance the prior results in time-integrated LIBS application and suggest the possibility of a portable, non-gated LIBS system as a process analytical tool, given its simple instrumentation needs, real-time capability and lack of sample preparation requirements.
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Affiliation(s)
- Ashwin Kumar Myakalwar
- Advanced Centre of Research in High Energy Materials (ACRHEM), University of Hyderabad, Gachibowli, Hyderabad, Andhra Pradesh, India
| | - Narahara Chari Dingari
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ramachandra Rao Dasari
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Manoj Kumar Gundawar
- Advanced Centre of Research in High Energy Materials (ACRHEM), University of Hyderabad, Gachibowli, Hyderabad, Andhra Pradesh, India
- * E-mail:
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18
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Tan KM, Barman I, Dingari NC, Singh GP, Chia TF, Tok WL. Toward the Development of Raman Spectroscopy as a Nonperturbative Online Monitoring Tool for Gasoline Adulteration. Anal Chem 2013; 85:1846-51. [DOI: 10.1021/ac3032349] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Khay M. Tan
- Einst Technology Pte. Ltd., 1092 Lower Delta Road, Tiong Bahru Industrial
Estate, #04-01, 169203, Singapore
| | - Ishan Barman
- G. R. Harrison Spectroscopy
Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Narahara C. Dingari
- G. R. Harrison Spectroscopy
Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Gajendra P. Singh
- Problight Diagnostics, Ltd., Dundee, Scotland, DD5 1PX, United Kingdom
| | - Tet F. Chia
- Mindwaves LLP, 51 Bukit Batok Crescent, Unity Centre, #08-01, 658077, Singapore
| | - Wee L. Tok
- Einst Technology Pte. Ltd., 1092 Lower Delta Road, Tiong Bahru Industrial
Estate, #04-01, 169203, Singapore
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19
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Duraipandian S, Zheng W, Ng J, Low JJH, Ilancheran A, Huang Z. Non-invasive analysis of hormonal variations and effect of postmenopausal Vagifem treatment on women using in vivo high wavenumber confocal Raman spectroscopy. Analyst 2013; 138:4120-8. [DOI: 10.1039/c3an00526g] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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20
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Barman I, Dingari NC, Singh GP, Kumar R, Lang S, Nabi G. Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis. Anal Bioanal Chem 2012; 404:3091-9. [DOI: 10.1007/s00216-012-6424-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2012] [Revised: 08/25/2012] [Accepted: 09/13/2012] [Indexed: 11/29/2022]
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21
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Barman I, Dingari NC, Singh GP, Soares JS, Dasari RR, Smulko JM. Investigation of noise-induced instabilities in quantitative biological spectroscopy and its implications for noninvasive glucose monitoring. Anal Chem 2012; 84:8149-56. [PMID: 22950485 DOI: 10.1021/ac301200n] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Over the past decade, optical spectroscopy has been employed in combination with multivariate chemometric models to investigate a wide variety of diseases and pathological conditions, primarily due to its excellent chemical specificity and lack of sample preparation requirements. Despite promising results in several proof-of-concept studies, its translation to the clinical setting has often been hindered by inadequate accuracy of the conventional spectroscopic models. To address this issue and the possibility of curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture spectra (due to fluctuations in sample and environmental conditions), support vector machine-based least-squares nonlinear regression (LS-SVR) has been recently proposed. In this paper, we investigate the robustness of this methodology to noise-induced instabilities and present an analytical formula for estimating modeling precision as a function of measurement noise and model parameters. This formalism can be readily used to evaluate uncertainty in information extracted from spectroscopic measurements, particularly important for rapid-acquisition biomedical applications. Subsequently, using field data (Raman spectra) acquired from a glucose clamping study on an animal model subject, we perform the first systematic investigation of the relative effect of additive interference components (namely, noise in prediction spectra, calibration spectra, and calibration concentrations) on the prediction error of nonlinear spectroscopic models. Our results show that the LS-SVR method gives more accurate results and is substantially more robust to additive noise when compared with conventional regression methods such as partial least-squares regression (PLS), when careful selection of the LS-SVR model parameters are performed. We anticipate that these results will be useful for uncertainty estimation in similar biomedical applications where the precision of measurements and its response to noise in the data set is as important, if not more so, than the generic accuracy level.
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Affiliation(s)
- Ishan Barman
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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22
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Dingari NC, Horowitz GL, Kang JW, Dasari RR, Barman I. Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation. PLoS One 2012; 7:e32406. [PMID: 22393405 PMCID: PMC3290592 DOI: 10.1371/journal.pone.0032406] [Citation(s) in RCA: 89] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2011] [Accepted: 01/30/2012] [Indexed: 01/15/2023] Open
Abstract
We present the first demonstration of glycated albumin detection and quantification using Raman spectroscopy without the addition of reagents. Glycated albumin is an important marker for monitoring the long-term glycemic history of diabetics, especially as its concentrations, in contrast to glycated hemoglobin levels, are unaffected by changes in erythrocyte life times. Clinically, glycated albumin concentrations show a strong correlation with the development of serious diabetes complications including nephropathy and retinopathy. In this article, we propose and evaluate the efficacy of Raman spectroscopy for determination of this important analyte. By utilizing the pre-concentration obtained through drop-coating deposition, we show that glycation of albumin leads to subtle, but consistent, changes in vibrational features, which with the help of multivariate classification techniques can be used to discriminate glycated albumin from the unglycated variant with 100% accuracy. Moreover, we demonstrate that the calibration model developed on the glycated albumin spectral dataset shows high predictive power, even at substantially lower concentrations than those typically encountered in clinical practice. In fact, the limit of detection for glycated albumin measurements is calculated to be approximately four times lower than its minimum physiological concentration. Importantly, in relation to the existing detection methods for glycated albumin, the proposed method is also completely reagent-free, requires barely any sample preparation and has the potential for simultaneous determination of glycated hemoglobin levels as well. Given these key advantages, we believe that the proposed approach can provide a uniquely powerful tool for quantification of glycation status of proteins in biopharmaceutical development as well as for glycemic marker determination in routine clinical diagnostics in the future.
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Affiliation(s)
- Narahara Chari Dingari
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Gary L. Horowitz
- Division of Clinical Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jeon Woong Kang
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ramachandra R. Dasari
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ishan Barman
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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23
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Dingari NC, Barman I, Kang JW, Kong CR, Dasari RR, Feld MS. Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy. JOURNAL OF BIOMEDICAL OPTICS 2011; 16:087009. [PMID: 21895336 PMCID: PMC3162621 DOI: 10.1117/1.3611006] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2011] [Revised: 05/19/2011] [Accepted: 06/22/2011] [Indexed: 05/14/2023]
Abstract
While Raman spectroscopy provides a powerful tool for noninvasive and real time diagnostics of biological samples, its translation to the clinical setting has been impeded by the lack of robustness of spectroscopic calibration models and the size and cumbersome nature of conventional laboratory Raman systems. Linear multivariate calibration models employing full spectrum analysis are often misled by spurious correlations, such as system drift and covariations among constituents. In addition, such calibration schemes are prone to overfitting, especially in the presence of external interferences that may create nonlinearities in the spectra-concentration relationship. To address both of these issues we incorporate residue error plot-based wavelength selection and nonlinear support vector regression (SVR). Wavelength selection is used to eliminate uninformative regions of the spectrum, while SVR is used to model the curved effects such as those created by tissue turbidity and temperature fluctuations. Using glucose detection in tissue phantoms as a representative example, we show that even a substantial reduction in the number of wavelengths analyzed using SVR lead to calibration models of equivalent prediction accuracy as linear full spectrum analysis. Further, with clinical datasets obtained from human subject studies, we also demonstrate the prospective applicability of the selected wavelength subsets without sacrificing prediction accuracy, which has extensive implications for calibration maintenance and transfer. Additionally, such wavelength selection could substantially reduce the collection time of serial Raman acquisition systems. Given the reduced footprint of serial Raman systems in relation to conventional dispersive Raman spectrometers, we anticipate that the incorporation of wavelength selection in such hardware designs will enhance the possibility of miniaturized clinical systems for disease diagnosis in the near future.
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Affiliation(s)
- Narahara Chari Dingari
- Massachusetts Institute of Technology, G. R. Harrison Spectroscopy Laboratory, Laser Biomedical Research Center, Cambridge, Massachusetts 02139, USA
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24
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Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements. Anal Bioanal Chem 2011; 400:2871-80. [PMID: 21509482 DOI: 10.1007/s00216-011-5004-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2011] [Revised: 04/05/2011] [Accepted: 04/08/2011] [Indexed: 10/18/2022]
Abstract
Although several in vivo blood glucose measurement studies have been performed by different research groups using near-infrared (NIR) absorption and Raman spectroscopic techniques, prospective prediction has proven to be a challenging problem. An important issue in this case is the demonstration of causality of glucose concentration to the spectral information, especially as the intrinsic glucose signal is smaller compared with that of the other analytes in the blood-tissue matrix. Furthermore, time-dependent physiological processes make the relation between glucose concentration and spectral data more complex. In this article, chance correlations in Raman spectroscopy-based calibration model for glucose measurements are investigated for both in vitro (physical tissue models) and in vivo (animal model and human subject) cases. Different spurious glucose concentration profiles are assigned to the Raman spectra acquired from physical tissue models, where the glucose concentration is intentionally held constant. Analogous concentration profiles, in addition to the true concentration profile, are also assigned to the datasets acquired from an animal model during a glucose clamping study as well as a human subject during an oral glucose tolerance test. We demonstrate that the spurious concentration profile-based calibration models are unable to provide prospective predictions, in contrast to those based on actual concentration profiles, especially for the physical tissue models. We also show that chance correlations incorporated by the calibration models are significantly less in Raman as compared to NIR absorption spectroscopy, even for the in vivo studies. Finally, our results suggest that the incorporation of chance correlations for in vivo cases can be largely attributed to the uncontrolled physiological sources of variations. Such uncontrolled physiological variations could either be intrinsic to the subject or stem from changes in the measurement conditions.
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25
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Barman I, Kong CR, Dingari NC, Dasari RR, Feld MS. Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose. Anal Chem 2010; 82:9719-26. [PMID: 21050004 PMCID: PMC3057474 DOI: 10.1021/ac101754n] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Sample-to-sample variability has proven to be a major challenge in achieving calibration transfer in quantitative biological Raman spectroscopy. Multiple morphological and optical parameters, such as tissue absorption and scattering, physiological glucose dynamics and skin heterogeneity, vary significantly in a human population introducing nonanalyte specific features into the calibration model. In this paper, we show that fluctuations of such parameters in human subjects introduce curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture Raman spectra. To account for these curved effects, we propose the use of support vector machines (SVM) as a nonlinear regression method over conventional linear regression techniques such as partial least-squares (PLS). Using transcutaneous blood glucose detection as an example, we demonstrate that application of SVM enables a significant improvement (at least 30%) in cross-validation accuracy over PLS when measurements from multiple human volunteers are employed in the calibration set. Furthermore, using physical tissue models with randomized analyte concentrations and varying turbidities, we show that the fluctuations in turbidity alone causes curved effects which can only be adequately modeled using nonlinear regression techniques. The enhanced levels of accuracy obtained with the SVM based calibration models opens up avenues for prospective prediction in humans and thus for clinical translation of the technology.
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Affiliation(s)
- Ishan Barman
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
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