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Ni R, Ge K, Luo Y, Zhu T, Hu Z, Li M, Tao P, Chi J, Li G, Yuan H, Pang Q, Gao W, Zhang P, Zhu Y. Highly sensitive microfluidic sensor using integrated optical fiber and real-time single-cell Raman spectroscopy for diagnosis of pancreatic cancer. Biosens Bioelectron 2024; 264:116616. [PMID: 39137518 DOI: 10.1016/j.bios.2024.116616] [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: 04/21/2024] [Revised: 07/26/2024] [Accepted: 07/29/2024] [Indexed: 08/15/2024]
Abstract
Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer.
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Affiliation(s)
- Renhao Ni
- Health Science Center, Ningbo University, Ningbo, 315211, China
| | - Kaixin Ge
- Key Laboratory of Photoelectric Materials and Devices of Zhejiang Province, Ningbo University, Ningbo, 315211, China; Engineering Research Center for Advanced Infrared Photoelectric Materials and Devices of Zhejiang Province, Ningbo University, Ningbo, 315211, China
| | - Yang Luo
- Health Science Center, Ningbo University, Ningbo, 315211, China
| | - Tong Zhu
- Health Science Center, Ningbo University, Ningbo, 315211, China
| | - Zeming Hu
- Health Science Center, Ningbo University, Ningbo, 315211, China
| | - Min Li
- College of Information Science and Engineering, Ningbo University, Ningbo, 315211, China
| | - Pan Tao
- Key Laboratory of Photoelectric Materials and Devices of Zhejiang Province, Ningbo University, Ningbo, 315211, China; Engineering Research Center for Advanced Infrared Photoelectric Materials and Devices of Zhejiang Province, Ningbo University, Ningbo, 315211, China
| | - Jinyi Chi
- Health Science Center, Ningbo University, Ningbo, 315211, China
| | - Guanron Li
- Health Science Center, Ningbo University, Ningbo, 315211, China; The First Affiliated Hospital of Ningbo University, Ningbo, 315020, China
| | - Haojun Yuan
- College of Information Science and Engineering, Ningbo University, Ningbo, 315211, China
| | - Qian Pang
- Health Science Center, Ningbo University, Ningbo, 315211, China
| | - Wanlei Gao
- College of Information Science and Engineering, Ningbo University, Ningbo, 315211, China.
| | - Peiqing Zhang
- Key Laboratory of Photoelectric Materials and Devices of Zhejiang Province, Ningbo University, Ningbo, 315211, China; Engineering Research Center for Advanced Infrared Photoelectric Materials and Devices of Zhejiang Province, Ningbo University, Ningbo, 315211, China.
| | - Yabin Zhu
- Health Science Center, Ningbo University, Ningbo, 315211, China.
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Hajab H, Anwar A, Nawaz H, Majeed MI, Alwadie N, Shabbir S, Amber A, Jilani MI, Nargis HF, Zohaib M, Ismail S, Kamal A, Imran M. Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 311:124046. [PMID: 38364514 DOI: 10.1016/j.saa.2024.124046] [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: 12/04/2023] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.
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Affiliation(s)
- Hawa Hajab
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ayesha Anwar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan.
| | - Najah Alwadie
- Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Sana Shabbir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Arooj Amber
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Hafiza Faiza Nargis
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Muhammad Zohaib
- Department of Zoology, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Sidra Ismail
- Medical College, Foundation University Islamabad, Pakistan
| | - Abida Kamal
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Muhammad Imran
- Department of Chemistry, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia
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Lee S, Jue M, Lee K, Paulson B, Oh J, Cho M, Kim JK. Early-stage diagnosis of bladder cancer using surface-enhanced Raman spectroscopy combined with machine learning algorithms in a rat model. Biosens Bioelectron 2024; 246:115915. [PMID: 38081101 DOI: 10.1016/j.bios.2023.115915] [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: 04/24/2023] [Revised: 07/24/2023] [Accepted: 12/04/2023] [Indexed: 12/30/2023]
Abstract
Early diagnosis and accurate assessment of tumor development facilitate early bladder cancer resection and initiation of drug therapy. This study enabled an early, accurate, label-free, noninvasive diagnosis of bladder tumors by analyzing nano-biomarkers in a single drop of urine through surface-enhanced Raman spectroscopy (SERS). In a standard N-butyl-N-4-hydroxybutyl nitrosamine-induced rat model of bladder cancer, cancer stage and polyp tumor development were monitored using a small endoscope with a diameter of 1.2 mm in a minimally invasive manner without the need to kill the rats. Samples were divided into cancer-free, early-stage, and polyp-form cancer. Training data were classified according to micro-cystoscopic 5-aminolevulinic acid fluorescence diagnosis, and specimens were postmortem verified through histopathological analysis. A drop of urine from each sample group was placed on an Au-coated zinc oxide nanoporous chip to filter nano-biomaterials and selectively enhance the Raman signals of nanoscale analytes via SERS. Principal component analysis was used to reduce the dimensionality of the collected Raman spectra, and partial least squares discriminant analysis was used to find diagnostic clusters based on the labeled samples. The combination of SERS and machine learning achieved an accuracy ≥99.6% in diagnosing both early- and polyp-stage bladder tumors. With an area under the receiver operating characteristic curve greater than 0.996, the accuracy of the diagnosis in the rat model suggests that SERS-based diagnostic methods are promising when coupled with machine learning. Low-cost, label-free, and noninvasive surface-enhanced Raman spectra are ideal for developing clinically relevant point-of-care diagnostic techniques.
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Affiliation(s)
- Sanghwa Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea
| | - Miyeon Jue
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea; Apollon, Inc., 68 Achasan-ro, Seongdong-gu, Seoul, 05505, Republic of Korea
| | - Kwanhee Lee
- Department of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea
| | - Bjorn Paulson
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea; Morgridge Institute for Research, Madison, WI, 53715, USA
| | - Jeongmin Oh
- Department of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea
| | - Minju Cho
- Department of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea
| | - Jun Ki Kim
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea; Department of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea.
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Robertson JL, Dervisis N, Rossmeisl J, Nightengale M, Fields D, Dedrick C, Ngo L, Issa AS, Guruli G, Orlando G, Senger RS. Cancer detection in dogs using rapid Raman molecular urinalysis. Front Vet Sci 2024; 11:1328058. [PMID: 38384948 PMCID: PMC10879274 DOI: 10.3389/fvets.2024.1328058] [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: 10/26/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction The presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor. Methods Urine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix®Toolbox software. Results and discussion The procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.
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Affiliation(s)
- John L. Robertson
- Department of Biomedical Engineering and Mechanics, College of Engineering, Virginia Tech, Blacksburg, VA, United States
- Rametrix Technologies Inc., Blacksburg, VA, United States
| | - Nikolas Dervisis
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - John Rossmeisl
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - Marlie Nightengale
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - Daniel Fields
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - Cameron Dedrick
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - Lacey Ngo
- Department of Biomedical Engineering and Mechanics, College of Engineering, Virginia Tech, Blacksburg, VA, United States
| | - Amr Sayed Issa
- Rametrix Technologies Inc., Blacksburg, VA, United States
| | - Georgi Guruli
- Department of Surgery, VCU Health, Richmond, VA, United States
| | - Giuseppe Orlando
- Department of General Surgery, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Ryan S. Senger
- Rametrix Technologies Inc., Blacksburg, VA, United States
- Department of Biological Systems Engineering, College of Agriculture & Life Sciences and College of Engineering, Virginia Tech, Blacksburg, VA, United States
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Wang K, Chen J, Martiniuk J, Ma X, Li Q, Measday V, Lu X. Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks. Appl Environ Microbiol 2023; 89:e0167323. [PMID: 38038459 PMCID: PMC10734496 DOI: 10.1128/aem.01673-23] [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: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 12/02/2023] Open
Abstract
IMPORTANCE The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.
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Affiliation(s)
- Kaidi Wang
- Food, Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Food Science and Agricultural Chemistry, Faculty of Agricultural and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Jing Chen
- Food, Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jay Martiniuk
- Wine Research Centre, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Xiangyun Ma
- School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Qifeng Li
- School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Vivien Measday
- Wine Research Centre, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Xiaonan Lu
- Food, Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Food Science and Agricultural Chemistry, Faculty of Agricultural and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada
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Wu Y, Wang Y, He C, Wang Y, Ma J, Lin Y, Zhou L, Xu S, Ye Y, Yin W, Ye J, Lu J. Precise diagnosis of breast phyllodes tumors using Raman spectroscopy: Biochemical fingerprint, tumor metabolism and possible mechanism. Anal Chim Acta 2023; 1283:341897. [PMID: 37977771 DOI: 10.1016/j.aca.2023.341897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/31/2023] [Accepted: 10/09/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Breast fibroadenomas and phyllodes tumors are both fibroepithelial tumors with comparable histological characteristics. However, rapid and precise differential diagnosis is a tough point in clinical pathology. Given the tendency of phyllodes tumors to recur, the difficulty in differential diagnosis with fibroadenomas leads to the difficulty in optimal management for these patients. METHOD In this study, we used Raman spectroscopy to differentiate phyllodes tumors from breast fibroadenomas based on the biochemical and metabolic composition and develop a classification model. The model was validated by 5-fold cross-validation in the training set and tested in an independent test set. The potential metabolic differences between the two types of tumors observed in Raman spectroscopy were confirmed by targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS). RESULTS A total of 204 patients with formalin-fixed paraffin-embedded (FFPE) tissue samples, including 100 fibroadenomas and 104 phyllodes tumors were recruited from April 2014 to August 2021. All patients were randomly divided into the training cohort (n = 153) and the test cohort (n = 51). The Raman classification model could differentiate phyllodes tumor versus fibroadenoma with cross-validation accuracy, sensitivity, precision, and area under curve (AUC) of 85.58 % ± 1.77 %, 83.82 % ± 1.01 %, 87.65 % ± 4.22 %, and 93.18 % ± 1.98 %, respectively. When tested in the independent test set, it performed well with the test accuracy, sensitivity, specificity, and AUC of 83.50 %, 86.54 %, 80.39 %, and 90.71 %. Furthermore, the AUC was significantly higher for the Raman model than that for ultrasound (P = 0.0017) and frozen section diagnosis (P < 0.0001). When it came to much more difficult diagnosis between fibroadenoma and benign or small-size phyllodes tumor for pathological examination, the Raman model was capable of differentiating with AUC up to 97.45 % and 95.61 %, respectively. On the other hand, targeted metabolomic analysis, based on fresh-frozen tissue samples, confirmed the differential metabolites (including thymine, dihydrothymine, trans-4-hydroxy-l-proline, etc.) identified from Raman spectra between phyllodes tumor and fibroadenoma. SIGNIFICANCE AND NOVELTY In this study, we obtained the molecular information map of breast phyllodes tumors provided by Raman spectroscopy for the first time. We identified a novel Raman fingerprint signature with the potential to precisely characterize and distinguish phyllodes tumors from fibroadenoma as a quick and accurate diagnostic tool. Raman spectroscopy is expected to further guide the precise diagnosis and optimal treatment of breast fibroepithelial tumors in the future.
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Affiliation(s)
- Yifan Wu
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Yaohui Wang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
| | - Chang He
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Yan Wang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Jiayi Ma
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Yanping Lin
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Liheng Zhou
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Shuguang Xu
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Yumei Ye
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Wenjin Yin
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
| | - Jian Ye
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
| | - Jingsong Lu
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
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Amber A, Nawaz H, Bhatti HN, Mushtaq Z. Surface-enhanced Raman spectroscopy for the characterization of different anatomical subtypes of oral cavity cancer. Photodiagnosis Photodyn Ther 2023:103607. [PMID: 37220841 DOI: 10.1016/j.pdpdt.2023.103607] [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: 03/27/2023] [Revised: 05/04/2023] [Accepted: 05/09/2023] [Indexed: 05/25/2023]
Abstract
BACKGROUND The prognosis for oral cancer patients is still very poor worldwide. Early detection and treatment therapy remain the key issue to be addressed for improved patient survival. The characteristic Raman spectral features associated with the biochemical changes in the blood serum samples can be used for the diagnosis of diseases, particularly for oral cancer. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for non-invasive and early detection of oral cancer by analyzing molecular changes in body fluids. OBJECTIVES To detect oral cavity anatomical subsites (buccal mucosa, cheek, hard palate, lips, mandible, maxilla, tongue and tonsillar region) cancers by using blood serum samples, SERS with principal component analysis is used. MATERIAL AND METHOD SERS is employed with silver nanoparticles for the analysis and detection of oral cancer serum samples by comparing with healthy serum samples. SERS spectra are recorded by Raman instrument and preprocessed using the statistical tool. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) are used to discriminate between oral cancer serum samples and control serum samples. RESULTS Some major SERS peaks are observed at 1136 cm-1 (Phospholipids) and 1006 cm-1 (Phenylalanine) remain higher in intensities for oral cancer spectra as compared to healthy spectra. The peak at 1241 cm-1 (amide III) is observed only in oral cancer serum samples while absent in healthy serum samples. Higher protein and DNA contents were detected in SERS mean spectra of oral cancer. Moreover, PCA is used to identify the biochemical differences in the form of SERS features which is used to differentiate between oral cancer and healthy blood serum samples, while PLS-DA is used to build differentiation model of oral cancer serum samples and healthy control serum samples. PLS-DA provides successful differentiation with 94% specificity and 95.5% sensitivity. CONCLUSIONS SERS can be used for the diagnosis of oral cancer and to identify metabolic changes that occur during disease development.
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Affiliation(s)
- Arooj Amber
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, (38000), Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, (38000), Pakistan.
| | - Haq Nawaz Bhatti
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, (38000), Pakistan
| | - Zahid Mushtaq
- Department of Biochemistry, University of Agriculture Faisalabad, Faisalabad, (38000), Pakistan
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Kang Z, Li Y, Liu J, Chen C, Wu W, Chen C, Lv X, Liang F. H-CNN combined with tissue Raman spectroscopy for cervical cancer detection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 291:122339. [PMID: 36641920 DOI: 10.1016/j.saa.2023.122339] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/24/2022] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
Cervical cancer is one of the most common cancers with a long latent period and slow onset process. Early and accurate identification of the stage of cervical cancer can significantly improve the cure rate and patient survival time. In this study, we collected 699 Raman spectral data of tissue sections from 233 different patients. We analyzed and compared the characteristics and differences of the mean Raman spectra of the seven tissues and pointed out the main differences in the biochemical composition of the seven tissues. In this study, 1D hierarchical convolutional neural network (H-CNN) is proposed by integrating the prior knowledge of hierarchical classification relations with the research of deep learning in Raman spectroscopy. H-CNN is based on CNN and is added with three network branches. Hierarchical classification is performed from coarse to fine for tissue samples of cervicitis, Low-grade Squamous Cell Carcinoma, High-grade Squamous Cell Carcinoma, Well Differentiated Squamous Cell Carcinoma, Moderately Differentiated Squamous Cell Carcinoma, Poorly Differentiated Squamous Cell Carcinoma and cervical adenocarcinoma. To evaluate the recognition performance of H-CNN, we compared it with traditional methods such as Bayesian classifier (NB), decision tree classifier (DT), support vector machine classifier (SVM) and CNN. The experimental results show that H-CNN can accurately identify different classes of tissue sections and has apparent advantages in several aspects such as recognition accuracy, stability and sensitivity compared with the other four traditional recognition methods. The classification Macro-Accuracy of H-CNN can reach 94.91%, Macro-Recall can reach 95.31%, Macro-F1 can reach 95.23%, and Macro-AUC can reach 97.35%. The hierarchical classification method proposed in this study can diagnose patients more accurately. This could lay the foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer.
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Affiliation(s)
- Zhenping Kang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Yizhe Li
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Jie Liu
- Department of Gynecology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, 830054, China.; Xinjiang Key Laboratory of Medical Animal Model Research, Urumqi, 830054, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of signal detection and processing, Xinjiang University, Urumqi 830046, China.
| | - Wei Wu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Department of Gynecology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China; Key Laboratory of signal detection and processing, Xinjiang University, Urumqi 830046, China
| | - Fei Liang
- Xinjiang Karamay Central Hospital, Karamay 834099, China
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Zhang B, Zhang Z, Gao B, Zhang F, Tian L, Zeng H, Wang S. Raman microspectroscopy based TNM staging and grading of breast cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121937. [PMID: 36201869 DOI: 10.1016/j.saa.2022.121937] [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: 08/13/2022] [Revised: 09/23/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, β-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.
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Affiliation(s)
- Baoping Zhang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Zhanqin Zhang
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Bingran Gao
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Furong Zhang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Lu Tian
- Department of Physics, Northwest University, Xi'an, Shaanxi 710127, China
| | - Haishan Zeng
- Imaging Unit - Integrative Oncology Department, BC Cancer Research Center, Vancouver, BC V5Z 1L3, Canada
| | - Shuang Wang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China.
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10
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Milligan K, Van Nest SJ, Deng X, Ali-Adeeb R, Shreeves P, Punch S, Costie N, Pavey N, Crook JM, Berman DM, Brolo AG, Lum JJ, Andrews JL, Jirasek A. Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer. JOURNAL OF BIOPHOTONICS 2022; 15:e202200121. [PMID: 35908273 DOI: 10.1002/jbio.202200121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/14/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.
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Affiliation(s)
- Kirsty Milligan
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Samantha J Van Nest
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
- Department of Radiation Oncology, Weill Cornell Medicine, New York, New York, USA
| | - Xinchen Deng
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Phillip Shreeves
- Department of Mathematics and Statistics, University of British Columbia, Kelowna, Canada
| | - Samantha Punch
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Nathalie Costie
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Nils Pavey
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Juanita M Crook
- Sindi Ahluwalia Hawkins Centre for the Southern Interior, BC Cancer, Kelowna, Canada
- Department of Radiation Oncology, University of British Columbia, Kelowna, Canada
| | - David M Berman
- Department of Pathology and Molecular Medicine, Queens University, Kingston, Canada
| | | | - Julian J Lum
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Mathematics and Statistics, University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, University of British Columbia, Kelowna, Canada
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11
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Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects. Br J Cancer 2022; 126:1125-1139. [PMID: 34893761 PMCID: PMC8661339 DOI: 10.1038/s41416-021-01659-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/11/2021] [Accepted: 11/25/2021] [Indexed: 12/26/2022] Open
Abstract
Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.
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12
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Senger RS, Sayed Issa A, Agnor B, Talty J, Hollis A, Robertson JL. Disease-Associated Multimolecular Signature in the Urine of Patients with Lyme Disease Detected Using Raman Spectroscopy and Chemometrics. APPLIED SPECTROSCOPY 2022; 76:284-299. [PMID: 35102746 DOI: 10.1177/00037028211061769] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A urine-based screening technique for Lyme disease (LD) was developed in this research. The screen is based on Raman spectroscopy, iterative smoothing-splines with root error adjustment (ISREA) spectral baselining, and chemometric analysis using Rametrix software. Raman spectra of urine from 30 patients with positive serologic tests (including the US Centers for Disease Control [CDC] two-tier standard) for LD were compared against subsets of our database of urine spectra from 235 healthy human volunteers, 362 end-stage kidney disease (ESKD) patients, and 17 patients with active or remissive bladder cancer (BCA). We found statistical differences (p < 0.001) between urine scans of healthy volunteers and LD-positive patients. We also found a unique LD molecular signature in urine involving 112 Raman shifts (31 major Raman shifts) with significant differences from urine of healthy individuals. We were able to distinguish the LD molecular signature as statistically different (p < 0.001) from the molecular signatures of ESKD and BCA. When comparing LD-positive patients against healthy volunteers, the Rametrix-based urine screen performed with 86.7% for overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively. When considering patients with ESKD and BCA in the LD-negative group, these values were 88.7% (accuracy), 83.3% (sensitivity), 91.0% (specificity), 80.7% (PPV), and 92.4% (NPV). Additional advantages to the Raman-based urine screen include that it is rapid (minutes per analysis), is minimally invasive, requires no chemical labeling, uses a low-profile, off-the-shelf spectrometer, and is inexpensive relative to other available LD tests.
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Affiliation(s)
- Ryan S Senger
- Department of Biological Systems Engineering, 1757Virginia Tech, Blacksburg, Virginia, USA
- DialySensors Inc., Blacksburg, Virginia, USA
| | | | - Ben Agnor
- Department of Biological Systems Engineering, 1757Virginia Tech, Blacksburg, Virginia, USA
| | - Janine Talty
- Neuromusculoskeletal Medicine & OMM, Roanoke, Virginia, USA
| | | | - John L Robertson
- DialySensors Inc., Blacksburg, Virginia, USA
- Department of Biomedical Engineering and Mechanics, 1757Virginia Tech, Blacksburg, Virginia, USA
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13
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Wang S, Li H, Ren Y, Yu F, Song D, Zhu L, Yu S, Jiang S, Zeng H. Studying the pathological and biochemical features in breast cancer progression by confocal Raman microspectral imaging of excised tissue samples. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B-BIOLOGY 2021; 222:112280. [PMID: 34375907 DOI: 10.1016/j.jphotobiol.2021.112280] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/29/2020] [Accepted: 08/02/2021] [Indexed: 11/19/2022]
Abstract
Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800 cm-1 and 2800 to 3000 cm-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.
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Affiliation(s)
- Shuang Wang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China.
| | - Heping Li
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Yu Ren
- Department of breast surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Fan Yu
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Dongliang Song
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Lizhe Zhu
- Department of breast surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Shibo Yu
- Department of breast surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Siyuan Jiang
- Department of breast surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Haishan Zeng
- Imaging Unit - Integrative Oncology Department, BC Cancer Research Center, Vancouver, BC V5Z 1L3, Canada.
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14
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Gogone ICVP, Ferreira GH, Gava D, Schaefer R, de Paula-Lopes FF, Rocha RDA, de Barros FRO. Applicability of Raman spectroscopy on porcine parvovirus and porcine circovirus type 2 detection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 249:119336. [PMID: 33385972 DOI: 10.1016/j.saa.2020.119336] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 06/12/2023]
Abstract
Porcine parvovirus (PPV) is one of the major infectious causes of reproductive failure of swine. This disease is characterized by embryonic and fetal infection and death, responsible for important economic losses. PPV is also implicated as a trigger in the development of post-weaning multisystemic wasting syndrome (PMWS) caused by Porcine circovirus type 2 (PCV2). Their detection is PCR-based, which is quite sensitive and specific, but laborious, costly and time-demanding. Therefore, this study aimed to assess Raman spectroscopy (RS) as a diagnostic tool for PPV and PCV2 due to its label-free properties and unique ability to search and identify molecular fingerprints. Briefly, swine testis (ST) cells were inoculated with PPV or PCV2 and in vitro cultured (37 °C, 5% CO2) for four days. Fixed cells were then submitted to RS investigation using a 633 nm laser. A total of 225 spectra centered at 1300 cm-1 was obtained for each sample (5 spectra/cell; 15 cells/replicate; 3 replicates) of PPV-, PCV2-infected and uninfected (control) ST cells. Clear statistical discrimination between samples from both virus-infected cells was achieved with a Principal Component - Linear Discriminant Analysis (PCA-LDA) model, reaching sensitivity rates from 95.55% to 97.77%, respectively to PCV2- and PPV-infected cells. These results were then submitted to a Leave-One-Out (LOO) validation algorithm resulting in 99.97% of accuracy. Extensive band assignment was analyzed and compiled for better understanding of PPV and PCV2 virus-cell interaction, demonstrating that specific protein, lipids and DNA/RNA bands are the most important assignments related to discrimination of virus-infected from uninfected cells. In conclusion, these results represent promising bases for RS application on PCV2 and PPV detection for future diagnostic applications.
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Affiliation(s)
| | | | | | | | | | - Raquel de A Rocha
- Universidade Tecnológica Federal do Paraná, Dois Vizinhos, PR, Brazil
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15
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Arcobacter Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks. Appl Environ Microbiol 2020; 86:AEM.00924-20. [PMID: 32801186 DOI: 10.1128/aem.00924-20] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/05/2020] [Indexed: 12/23/2022] Open
Abstract
Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation.IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.
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16
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Fluorine-doped nickel cobalt oxide spinel as efficiently bifunctional catalyst for overall water splitting. Electrochim Acta 2019. [DOI: 10.1016/j.electacta.2019.01.012] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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17
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Liu M, Liu X, Huang Z, Tang X, Lin X, Xu Y, Chen G, Kwok HF, Lin Y, Feng S. Rapid discrimination of colon cancer cells with single base mutation in KRAS gene segment using laser tweezers Raman spectroscopy. JOURNAL OF BIOPHOTONICS 2019; 12:e201800332. [PMID: 30485680 DOI: 10.1002/jbio.201800332] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 10/27/2018] [Accepted: 11/21/2018] [Indexed: 05/10/2023]
Abstract
Laser tweezers Raman spectroscopy (LTRS) as a label-free and noninvasive technology has been proven to be an ideal tool for analysis of single living cells, which provides important fingerprint information without interference from surrounding environments. For the first time, LTRS system was successfully used to examine the colon cancer cells with single base mutation in KRAS gene segment, including DKS-8 (KRAS wild-type [WT]) and DLD-1 (KRAS mutant-type [MT]), HKE-3 (KRAS WT) and HCT-116 (KRAS MT). Spectra changes of some vital biomolecules due to the gene mutation state were sensitively recorded by our home-made LTRS system. As a result of the comparison between DKS-8 and DLD-1 cells, an index of 97.5% of correct classification was obtained by combining LTRS with principle component analysis coupled with linear discriminant analysis (PCA-LDA) statistical analysis, while an index of 97.0% of correct classification was achieved between HKE-3 and HCT-116 cells. Moreover, between WT cells (DKS-8 and HKE-3) vs MT cells (DLD-1 and HCT-116), the index of correct classification was 81.2%, which was quite encouraging. Our preliminary results showed that the LTRS system coupled with PCA-LDA analysis will have a great potential for further applications in the rapid and label-free detection of circulating tumor cells in liquid biopsy.
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Affiliation(s)
- Mengmeng Liu
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, China
| | - Xiujie Liu
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, China
| | - Zufang Huang
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, China
| | - Xiaoqiong Tang
- Fujian Normal University, College of Life Sciences, Fuzhou, China
| | - Xueliang Lin
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, China
| | - Yunchao Xu
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, China
| | - Guannan Chen
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, China
| | - Hang Fai Kwok
- Faculty of Health Sciences, Cancer Centre, University of Macau, Avenida de Universidade, Taipa, China
| | - Yao Lin
- Fujian Normal University, College of Life Sciences, Fuzhou, China
| | - Shangyuan Feng
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, China
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18
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Abstract
Despite significant effort, cancer still remains a leading cause of death worldwide. In order to reduce its burden, the development and improvement of noninvasive strategies for early detection and diagnosis of cancer are urgently needed. Raman spectroscopy, an optical technique that relies on inelastic light scattering arising from molecular vibrations, is one such strategy, as it can noninvasively probe cancerous markers using only endogenous contrast. In this review, spontaneous, coherent and surface enhanced Raman spectroscopies and imaging, as well as the fundamental principles governing the successful use of these techniques, are discussed. Methods for spectral data analysis are also highlighted. Utilization of the discussed Raman techniques for the detection and diagnosis of cancer in vitro, ex vivo and in vivo is described. The review concludes with a discussion of the future directions of Raman technologies, with particular emphasis on their clinical translation.
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Affiliation(s)
- Lauren A Austin
- Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, 149 13th Street, Charlestown, Massachusetts 02129, USA.
| | - Sam Osseiran
- Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, 149 13th Street, Charlestown, Massachusetts 02129, USA. and Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Avenue E25-519, Cambridge, Massachusetts 02139, USA
| | - Conor L Evans
- Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, 149 13th Street, Charlestown, Massachusetts 02129, USA.
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19
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Jermyn M, Desroches J, Aubertin K, St-Arnaud K, Madore WJ, De Montigny E, Guiot MC, Trudel D, Wilson BC, Petrecca K, Leblond F. A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology. Phys Med Biol 2016; 61:R370-R400. [DOI: 10.1088/0031-9155/61/23/r370] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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20
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Bhattacharjee T, Khan A, Maru G, Ingle A, Krishna CM. A preliminary Raman spectroscopic study of urine: diagnosis of breast cancer in animal models. Analyst 2015; 140:456-66. [DOI: 10.1039/c4an01703j] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Analysis of urine by Raman spectroscopy (RS) as an alternative screening and diagnostic tool for breast cancer..
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Affiliation(s)
- T. Bhattacharjee
- Chilakapati Lab
- Advanced Center for Treatment Research and Education in Cancer (ACTREC)
- Tata Memorial Center (TMC)
- Navi-Mumbai
- India
| | - A. Khan
- Chilakapati Lab
- Advanced Center for Treatment Research and Education in Cancer (ACTREC)
- Tata Memorial Center (TMC)
- Navi-Mumbai
- India
| | - G. Maru
- Maru Lab
- ACTREC
- Navi-Mumbai
- India
| | - A. Ingle
- Laboratory Animal Facility
- ACTREC
- Navi-Mumbai
- India
| | - C. Murali Krishna
- Chilakapati Lab
- Advanced Center for Treatment Research and Education in Cancer (ACTREC)
- Tata Memorial Center (TMC)
- Navi-Mumbai
- India
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21
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Vyumvuhore R, Tfayli A, Piot O, Le Guillou M, Guichard N, Manfait M, Baillet-Guffroy A. Raman spectroscopy: in vivo quick response code of skin physiological status. JOURNAL OF BIOMEDICAL OPTICS 2014; 19:111603. [PMID: 24839943 DOI: 10.1117/1.jbo.19.11.111603] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Accepted: 02/05/2014] [Indexed: 06/03/2023]
Abstract
Dermatologists need to combine different clinically relevant characteristics for a better understanding of skin health. These characteristics are usually measured by different techniques, and some of them are highly time consuming. Therefore, a predicting model based on Raman spectroscopy and partial least square (PLS) regression was developed as a rapid multiparametric method. The Raman spectra collected from the five uppermost micrometers of 11 healthy volunteers were fitted to different skin characteristics measured by independent appropriate methods (transepidermal water loss, hydration, pH, relative amount of ceramides, fatty acids, and cholesterol). For each parameter, the obtained PLS model presented correlation coefficients higher than R2=0.9. This model enables us to obtain all the aforementioned parameters directly from the unique Raman signature. In addition to that, in-depth Raman analyses down to 20 μm showed different balances between partially bound water and unbound water with depth. In parallel, the increase of depth was followed by an unfolding process of the proteins. The combinations of all these information led to a multiparametric investigation, which better characterizes the skin status. Raman signal can thus be used as a quick response code (QR code). This could help dermatologic diagnosis of physiological variations and presents a possible extension to pathological characterization.
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Affiliation(s)
- Raoul Vyumvuhore
- Université Paris-Sud, Faculty of Pharmacy, Group of Analytical Chemistry of Paris-Sud (GCAPS), 51100 Chatenay-Malabry, France
| | - Ali Tfayli
- Université Paris-Sud, Faculty of Pharmacy, Group of Analytical Chemistry of Paris-Sud (GCAPS), 51100 Chatenay-Malabry, France
| | - Olivier Piot
- Université Reims Champagne Ardennes, CNRS FRE3481 MEDyC, Faculty of Pharmacy, MéDIAN-"Biophotonics and Technologies for Health", 51100 Reims, France
| | - Maud Le Guillou
- SILAB, Department of Research and Development, 19100 BP 213, Brive Cedex, France
| | - Nathalie Guichard
- SILAB, Department of Research and Development, 19100 BP 213, Brive Cedex, France
| | - Michel Manfait
- Université Reims Champagne Ardennes, CNRS FRE3481 MEDyC, Faculty of Pharmacy, MéDIAN-"Biophotonics and Technologies for Health", 51100 Reims, France
| | - Arlette Baillet-Guffroy
- Université Paris-Sud, Faculty of Pharmacy, Group of Analytical Chemistry of Paris-Sud (GCAPS), 51100 Chatenay-Malabry, France
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