Visible resonance raman spectroscopy in human brain tissues - Zhou, Yan; Liu, Cheng-hui; Zhu, Ke; Wu, Binlin; Yu, Xinguang; Alfano, Robert R. (Neurophotonics and Biomedical Spectroscopy, 2019)
This chapter first reviews the research and applications of nonresonance and resonance Raman spectroscopy for analysis of human brain normal and abnormal tissues. Next, special emphasis is made on our recent achievements of visible resonance Raman (VRR) technique in primary human brain tumor disease investigation and diagnosis. Visible resonance Raman (VRR) spectroscopy technique uses excitation of visible light (532 nm) to evaluate the resonant and nonresonant molecular vibrational modes in biological tissues. The VRR signal intensities are enhanced by two to three orders of magnitude for faster use in medical applications in quasi real time. VRR opens up a new stainless “molecular optics based histopathology” diagnosis approach. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Brain cancer margin assessment; Glioblastoma multiforme (GBM); Glioblastoma multiforme (gbm); Gliomas; Meninges; Optical biopsy; Visible resonance Raman (VRR) spectroscopy; Visible resonance raman (vrr) spectroscopy; Wavelength 532 nm; Wavelength 532nm; dept.phy

Quantitative characterization for identification of brain cancer surgical margin using resonance Raman spectroscopy - Zhou, Yan; Liu, Chenghui; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhang, Chunyuan; Alfano, R R (OSA - The Optical Society, 2017)
A new criterion was developed to characterize brain tissue using resonance Raman spectroscopy, by which, negative margins of cancer can be differentiated from normal tissues. This method may help a surgeon better decide surgical margins. © OSA 2017.
Keywords: Brain cancer; Brain tissue; Diseases; Normal tissue; Quantitative characterization; Raman spectroscopy; Resonance Raman spectroscopy; Surgery; Tissue; dept.phy

Optical Molecular Signatures of IDH Genotype for Classification of Human Glioma Using Visible Resonance Raman Spectroscopy - Zhang, Liang; Zhou, Yan; Wu, Binlin; Zhang, Shengjia; Zhu, Ke; Liu, C.; Alfano, Robert; Yu, Xinguang (Journal of Biophotonics, 2026)
Keywords: Biomarker; Gene; Genotype; Glioma; IDH mutation; IDH1; Isocitrate dehydrogenase; Mutation; Raman spectroscopy; Resonance Raman spectroscopy; dept.phy; glioma; optical molecular biopsy; visible resonance Raman spectroscopy

Combined spatial frequency spectroscopy analysis with visible resonance Raman for optical biopsy of human brain metastases of lung cancers - Zhou, Yan; Liu, Cheng-Hui; Pu, Yang; Wu, Binlin; Nguyen, Thien An; Cheng, Gangge; Zhou, Lixin; Zhu, Ke; Chen, Jun; Li, Qingbo; Alfano, Robert R. (Journal of Innovative Optical Health Sciences, 2019)
The purpose of this study is to examine optical spatial frequency spectroscopy analysis (SFSA) combined with visible resonance Raman (VRR) spectroscopic method, for the first time, to discriminate human brain metastases of lung cancers adenocarcinoma (ADC) and squamous cell carcinoma (SCC) from normal tissues. A total of 31 label-free micrographic images of three types of brain tissues were obtained using a confocal micro-Raman spectroscopic system. VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532[Formula: see text]nm from the same sites of the tissues. Using SFSA method, the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian function fitting. The standard deviations, [Formula: see text] calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine (SVM) classifier. The key VRR biomolecular fingerprints of carotenoids, tryptophan, amide II, lipids and proteins (methylene/methyl groups) were also analyzed using SVM classifier. All three types of brain tissues were identified with high accuracy in the two approaches with high correlation. The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues, which are on-site, real-time and label-free and may improve the accuracy of brain biopsy.
Keywords: Amides; Amino acids; Article; Biopsy; Brain; Diseases; Histology; Human brain; Image analysis; Metastatic lung cancer; Pathology; Raman spectrometry; Resonance; Resonance Raman; Spatial frequency; Spatial frequency spectroscopy analysis (SFSA); Spatial variables measurement; Spectroscopic analysis; Spectrum analysis; Support vector machines; Tissue; accuracy; amide; brain metastasis; brain tissue; cancer classification; carbene; carotenoid; controlled study; dept.phy; diagnostic test accuracy study; excitation; fatty acid; high density lipoprotein; histopathology; human; human brain; human tissue; lipid; low density lipoprotein; lung adenocarcinoma; metastatic lung cancer; methyl group; molecular imaging; peer-reviewed; photomicrograph image; sensitivity and specificity; spatial frequency spectroscopy analysis; spectroscopy; squamous cell lung carcinoma; support vector machine; support vector machine (SVM); tryptophan; visible resonance Raman (VRR); visible resonance Raman spectroscopy

Human brain meningioma tumor detection using visible resonance Raman spectroscopy - Zhang, Liang; Zhou, Yan; Zhang, Shengjia; Wu, Binlin; Zhu, Ke; Liu, Cheng-hui; Yu, Xinguang; Alfano, Robert R. (SPIE, 2021)
Keywords: Brain; Brain neoplasms; Detection and diagnosis; Diagnosis; Healthy tissues; Margin status; Remote control; Resonance Raman; Resonance Raman spectroscopy; Surgery; Tumor boundary; Tumor detection; Tumors; dept.phy

Intraoperative detection of human meningioma using a handheld visible resonance Raman analyzer. - Zhang, Liang; Zhou, Yan; Wu, Binlin; Zhang, Shengjia; Zhu, Ke; Liu, Cheng-hui; Yu, Xinguang; Alfano, Robert R. (Lasers in medical science, 2021)
Keywords: dept.phy; peer-reviewed

A handheld visible resonance raman analyzer used in intraoperative detection of human glioma - Zhang, Liang; Zhou, Yan; Wu, Binlin; Zhang, Shengjia; Zhu, Ke; Liu, Cheng-Hui; Yu, Xinguang; Alfano, Robert R. (Cancers, 2023)
There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.
Keywords: dept.phy; glioma; human brain; intraoperative; optical biopsy; optical molecular pathology; peer-reviewed; portable optical-fiber-probe Raman analyzer; visible resonance Raman (VRR)

Diagnosis of glioma brain cancer using visible resonance Raman spectroscopy - Zhou, Yan; Wu, Binlin; Liu, Chenghui; Yu, Xinguang; Cheng, Gangge; Wang, Kai; Zhang, Chunyuan; Shi, Lingyan; Alfano, R R (OSA, 2018)
Resonance Raman spectroscopy using 532nm excitation was used to distinguish normal brain tissue from different grades of glioma tissues. Principal component analysis was used to analyze the spectral data and achieved high accuracy. © 2018 The Author(s).
Keywords: Brain cancer; Brain tissue; Different grades; Glioma tissues; High-accuracy; Principal component analysis; Raman spectroscopy; Resonance Raman spectroscopy; Spectral data; Tumors; dept.phy

Glioma tumors classified using visible resonance raman spectroscopy and machine learning - Wu, Binlin; Zhou, Yan; Zhang, Liang; Zhang, Shengjia; Yu, Xinguang; Wang, Eric; Zhu, Ke; Liu, Cheng-hui; Alfano, Robert R. (The Optical Society, 2020)
Machine learning algorithms were used to classify and analyze spectral data collected by visible resonance Raman spectroscopy to distinguish normal human brain tissue and glioma tumor tissues at different grades and show promising results. © OSA 2020 © 2020 The Author(s)
Keywords: dept.phy; ⛔ No DOI found

Human blood biomarkers for glioma and meningioma detection by a portable visible resonance Raman analyzer - Zhang, Liang; Zhou, Yan; Zhang, Shengjia; Wu, Binlin; Zhu, Ke; Zhang, Chunyuan; Liu, Cheng-hui; Yu, Xinguang; Alfano, Robert R. (SPIE, 2021)
Recent reports pointed out that the application of optical spectroscopy in the field of liquid biopsy has aroused great interest among researchers and demonstrated the potential of its clinical application. We report a preliminary investigation on the visible resonance Raman (VRR) spectra of human brain blood liquid collected from the scalp and around the meningeal tumor during surgery and a set of venous blood samples from healthy people and glioma grade III patients using a portable VRR-LRRTM, HR800, HR-Evolution and WITec300 Raman systems in vivo and ex vivo. The biochemical fingerprints and molecular biomarkers were found. These findings indicate that if VRR spectroscopy technology is combined with polymerase chain reaction (PCR) or genetic molecular biomarker methods (VRR-PCR), it will greatly increase the possibility for its clinical application. © 2021 SPIE.
Keywords: Biomarkers; Biopsy; Blood; Clinical application; Healthy people; Molecular biomarker; Optical spectroscopy; Polymerase chain reaction; Raman systems; Resonance Raman; Spectroscopy; Spectroscopy technology; Tumors; Venous bloods; dept.phy

Resonance raman imaging for detecting and monitoring molecular pathological changes in human brain tumors related to Warburg effect - Zhou, Yan; Liu, Cheng-hui; Zhu, Ke; Zhang, Chunyuan; Yang, Yang; Yu, Xinguang; Hu, Hailong; Cheng, Gangge; Wu, Binlin; Shi, Lingyan; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
The goal of the research is to determine the prognostic molecular pathological changes in components and composition, for human brain glioma gradings in comparison with normal tissues in three-dimensional Raman imaging profiles by visible Resonance Raman (VRR) imaging. VRR images from twenty-five specimens including three healthy tissues, one normal control, and twenty-one glioma tissues of grades II, II-III and III-IV with histology examination were measured and investigated using WITec300R confocal micro Raman imaging system with laser excitation of 532nm. Two-dimensional RR spectral mappings performed in 20μm x 20μm generated 400 images which integrated the intensity of the specific biochemical bonds as the third dimension. The three-dimension (3D) map demonstrated the spatial distributions of three selected sets of RR spectra of molecular biomarkers, and revealed significant differences in the spectra between normal and glioma tissues of different grades due to the composition changes in key molimageecules. These RR molecular spectral fingerprints have displayed: a clear enhancement of RR vibrational modes at 1129-1131cm-1 and 2934cm-1 which are supposed to be arising from lipoproteins; evident decreased RR vibrational modes at 1442cm-1 and 2854cm-1 which are from saturated fatty acids bonds in all-grades of glioma brain tissues compared with normal tissues; and the enhanced RR spectral modes of 1129 cm-1 and 2938cm-1 which suggest contribution from lactate. These findings may provide a novel proof for anaerobic glycolysis metabolic process in brain glioma cancer tissues that has been explained by Warburg effects.
Keywords: 2-d imaging; 2D imaging; Biopsy; Brain; Gliomas; Histology; Human Brain Tissues; Human brain; Laser excitation; Molecular imaging; Resonance; Resonance Raman; Saturated fatty acids; Tumors; Visible resonance Raman (VRR); Warburg effect; Warburg effects; dept.phy

Amyloid beta and tau protein metabolic changes detected by visible resonance Raman scattering microscopy in Alzheimer's disease mouse model - Shi, Lingyan; Zhou, Yan; Wu, Binlin; Zhu, Ke; Zhang, Shengjia; Boydston-White, Susie; Zhang, Lin; Pei, Zhe; Nguyen, Thien An; Liu, Cheng-Hui; Alfano, Robert R. (Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2023)
Alzheimer’s disease (AD) pathogenesis is widely believed to be associated with the production and deposition of the β-amyloid peptide (Aβ) and neurofibrillary tangles (NFTs) which are composed of a highly-phosphorylated form of the microtubule-associated protein tau. Based on the above hypothesis, there are currently no sufficiently effective technologies and drugs for early detection and treatment of AD. Even the most promising new drug Lecanemab that is based on an anti-amyloid monoclonal antibody therapy, has only partially slowed down the cognitive performance of patients with mild impairment caused by Alzheimer's disease. The main symptoms of AD brain tissue lesions in patients are the deposition of β-amyloid peptide and the hyperphosphorylation of tau protein, which aggregates the microtubule structure of neurons. Therefore, Aβ deposition and hyperphosphorylation of Tau are important pathological biomarkers of Alzheimer's disease. Therefore, the main targets of research for AD prevention, detection and pharmaceuticals are still Aβ and Tau protein. The aim of this study was to detect the changes of Aβ and Tau proteins in the mouse brain tissue with AD and control samples using Visible Resonance Raman (VRR) spectroscopic technology. An attempt was made to develop criteria for the detection of early AD lesions by optical spectroscopy technology. The VRR spectra of AD, the control mouse brain tissues, and Aβ and Tau proteins were recorded and analyzed. The AD and the control mouse brain tissue samples were selected from the thalamus, frontal lobe cortex and hippocampus brain areas. VRR technology with high spatial resolution and the resonance-enhanced features of certain protein molecules is first used in this study to detect and characterize the changes of Aβ and Tau proteins in AD mouse brain model. The optical spectroscopy biomarkers of AD and Control brain tissue were identified in fingerprint and the high-wavenumber regions. The Raman spectra of the secondary structure of protein in amide (I-II-III-B-A) are detected and analyzed. The results indicate that the intensity of Amide I decreased at the 1666 cm-1 corresponding to the β-sheet structure, and the intensity of the amide III bands (1220- 1320 cm-1) increased in all AD brain tissues. It was also observed that the Raman peaks of 1448 and 980 cm-1 related to the abundance of proline, serine, and threonine at tau phosphorylation sites were significantly enhanced in the frontal lobe cortex and hippocampus of AD brain tissues. The intensity ratio biomarker of high phosphorylation in the high wavenumber range from 2898 to 2932 cm-1 increased in all AD brain tissues. Changes of protein secondary conformation and abnormally phosphorylated tau or tauopathies were observed. In summary, VRR is a sensitive tool for characterizing protein structural changes and monitoring the tau phosphorylation. It may potentially be used for early detection of AD.
Keywords: dept.phy

Invited Article: Molecular biomarkers characterization for human brain glioma grading using visible resonance Raman spectroscopy - Zhou, Yan; Liu, Cheng-hui; Wu, Binlin; Zhang, Chunyuan; Yu, Xinguang; Cheng, Gangge; Chen, Hong; Li, Shenglin; Liang, Qijun; Zhang, Mingqian; Zhu, Ke; Shi, Lingyan; et al. (APL Photonics, 2018)
The accurate identification of the human brain tumor boundary and the complete resection of the tumor are two essential factors for the removal of the glioma tumor in brain surgery. We present a visible resonance Raman (VRR) spectroscopy technique for differentiating the brain tumor margin and glioma grading. Eighty-seven VRR spectra from twenty-one human brain specimens of four types of brain tissues, including the control, glioma grade II, III, and IV tissues, were observed. This study focuses on observing the characteristics of new biomarkers and their changes in the four types of brain tissue. We found that two new RR peaks at 1129 cm-1 and 1338 cm-1 associated with molecular vibrational bonds in four types of brain tissues are significantly different in peak intensities of VRR spectra. These two resonance enhanced peaks may arise from lactic acid/phosphatidic acid and adenosine triphosphate (ATP)/nicotinamide adenine dinucleotide, respectively. We found that lactic acid and ATP concentrations vary with glioma gratings. The higher the grade of malignancy, the more the increase in lactic acid and ATP concentrations. These two RR peaks may be considered as new molecular biomarkers and used to evaluate glioma grades and identify the margin of gliomas from the control tissues. The metabolic process of lactic acid and ATP in glioma cells based on the VRR spectral changes may reveal the Warburg hypothesis. © 2018 Author(s).
Keywords: Adenosinetriphosphate; Biomarkers; Brain; Control tissues; Grading; Histology; Human brain tumors; Lactic acid; Metabolic process; Molecular biomarker; Resonance; Resonance Raman; Resonance Raman spectroscopy; Resonance enhanced; Spectral change; Tumors; dept.phy; peer-reviewed

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy. - Zhou, Yan; Liu, Cheng-Hui; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhu, Ke; Wang, Kai; Zhang, Chunyuan; Zhao, Mingyue; Zong, Rui; Zhang, Lin; Shi, Lingyan; et al. (Journal of biomedical optics, 2019)
Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.
Keywords: *Biomarkers, Tumor/me [Metabolism]; *Brain Neoplasms/me [Metabolism]; *Brain Neoplasms/pa [Pathology]; *Glioma/me [Metabolism]; *Glioma/pa [Pathology]; *Spectrum Analysis, Raman/mt [Methods]; Amides; Amino acids; Biomarkers; Biomarkers, Tumor; Biomarkers, Tumor/ch [Chemistry]; Biopsy; Brain; Brain Neoplasms; Brain Neoplasms/su [Surgery]; Brain/me [Metabolism]; Brain/pa [Pathology]; Brain/su [Surgery]; Cancer grades; Cancer margins; Carotenoids; Carotenoids/me [Metabolism]; Diseases; Glioblastoma; Glioblastomas; Glioma; Glioma/su [Surgery]; Gliomas; Grading; Histology; Histopathology; Humans; Label free; Label-free; Lipid Metabolism; Margins of Excision; Neoplasm Grading; Nerve Tissue Proteins; Nerve Tissue Proteins/ch [Chemistry]; Nerve Tissue Proteins/me [Metabolism]; Optical Phenomena; Optical biopsies; Optical biopsy; Pigments; Principal Component Analysis; Principal component analysis; Protein Structure, Secondary; Raman spectrometry; Resonance; Resonance Raman; Spectrum Analysis, Raman; Support Vector Machine; Support vector machines; Surgery; Tryptophan; Tryptophan/me [Metabolism]; Tumors; brain; brain tumor; cancer grading; carotenoid; chemistry; dept.phy; glioma; human; light related phenomena; lipid metabolism; metabolism; nerve protein; pathology; peer-reviewed; principal component analysis; procedures; protein secondary structure; support vector machine; surgical margin; tryptophan; tumor marker

Visible resonance Raman spectroscopy detect key molecular biomarker vibrations to characterize for human brain gliomas - Zhou, Yan; Liu, Cheng-hui; Wu, Binlin; Yu, Xinguang; Gangge Cheng; Zhang, Chunyuan; Chen, Hong; Li, Shenglin; Zong, Rui; Liang, Qijun; Zhang, Ce; Zhang, Mingqian; et al. (Biomedical Imaging and Sensing Conference, 2018)
Visible resonant Raman (VRR) spectroscopy provides an effective way to enhance Raman signal from particular bonds associated with key molecules due to changes on molecular level. This paper reports on the VRR use for detection of human brain the control and gliomas of three grades. From the RR spectra additional two molecular vibrational biomarkers at 1129cm-1 and 1338cm-1, for the four types of brain tissues are significantly different in intensity. The new RR spectral peaks can be used as molecular biomarkers to evaluate glioma grades and identify the margin of gliomas from the controls. The metabolic process of glioma cells based on the RR spectral changes may reveal the Warburg hypothesis.
Keywords: 532nm; Biomarkers; Brain; Human brain tumors; Medical imaging; Raman probe; Raman spectroscopy; Resonance Raman; Tumors; Visible resonance Raman (VRR); confical Raman probe; dept.phy; gliomas; human brain tumor

Human glioma tumors detection by a portable visible resonance Raman analyzer with a hand-held optical fiber probe - Zhou, Yan; Zhang, Shengjia; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhu, Ke; Zhao, Mingyue; Zheng, Jichun; Zhang, Liang; Zhang, Mingqian; Liang, Qijun; Go, Yun Ii; et al. (Biomedical Vibrational Spectroscopy 2020: Advances in Research and Industry, 2020)
Based on Visible Resonance Raman (VRR) method, we have developed a novel label-free portable VRR LRR2000 Raman analyzer with a portable fiber-optic probe and used it for the classification of human gliomas ex vivo and for the analysis of changes in tumor chemical compositions in molecular level. The purpose of this study was to examine the performance of the LRR2000 Raman analyzer as an optical biopsy tool for detecting human brain tumors compared to the commercial laboratory HR800 and WITec300 micro confocal Raman spectroscopy instruments. As of 2018, a total 1,938 VRR spectra were collected using LRR2000, HR800 and WITec300 Raman system, ex vivo. Identification of the four grades of glioma tumors and control tissues was performed based on the characteristic native molecular fingerprints. LRR2000 demonstrated consistent diagnostic results with HR800 and WITec300 Raman systems. LRR2000 showed the advantages of high speed, convenience and low cost compared to the two confocal micro Raman systems. Using artificial intelligence (AI)-based analysis of part of the data, the cross-validated accuracy for identifying glioma tumors is ~90% compared with gold standard histopathology examination.
Keywords: Biopsy; Brain; Chemical analysis; Handhelds; Histology; Human brain; Industrial research; Molecular biology; Optical biopsies; Optical fibers; Optical molecular Pathology; Palmprint recognition; Principal component analysis; Principle component analysis; Probes; Resonance; Resonance Raman; Spectroscopy; Spectrum analysis; Support vector machines; Tissue engineering; Tumors; Vibrational spectroscopy; dept.phy; glioma tumor; handheld LRR2000 analyzer; human brain tissues; optical biopsy; principle component analysis(PCA); support vector machine (SVM); visible resonance Raman (VRR)