1
|
Feenstra L, van der Stel SD, Da Silva Guimaraes M, Dashtbozorg B, Ruers TJM. Point Projection Mapping System for Tracking, Registering, Labeling, and Validating Optical Tissue Measurements. J Imaging 2024; 10:37. [PMID: 38392085 PMCID: PMC10890146 DOI: 10.3390/jimaging10020037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/23/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024] Open
Abstract
The validation of newly developed optical tissue-sensing techniques for tumor detection during cancer surgery requires an accurate correlation with the histological results. Additionally, such an accurate correlation facilitates precise data labeling for developing high-performance machine learning tissue-classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with the histopathology results is proposed and validated on a case study. The proposed framework provides a more-robust and accurate method for the tracking and validation of optical tissue-sensing techniques, which saves time and resources compared to the available conventional techniques.
Collapse
Affiliation(s)
- Lianne Feenstra
- Image-Guided Surgery, Department of Surgical Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Stefan D van der Stel
- Image-Guided Surgery, Department of Surgical Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Marcos Da Silva Guimaraes
- Department of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Behdad Dashtbozorg
- Image-Guided Surgery, Department of Surgical Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Theo J M Ruers
- Image-Guided Surgery, Department of Surgical Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| |
Collapse
|
2
|
Ni H, Dessai CP, Lin H, Wang W, Chen S, Yuan Y, Ge X, Ao J, Vild N, Cheng JX. High-content stimulated Raman histology of human breast cancer. Theranostics 2024; 14:1361-1370. [PMID: 38389847 PMCID: PMC10879861 DOI: 10.7150/thno.90336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/17/2023] [Indexed: 02/24/2024] Open
Abstract
Histological examination is crucial for cancer diagnosis, however, the labor-intensive sample preparation involved in the histology impedes the speed of diagnosis. Recently developed two-color stimulated Raman histology could bypass the complex tissue processing to generates result close to hematoxylin and eosin staining, which is one of the golden standards in cancer histology. Yet, the underlying chemical features are not revealed in two-color stimulated Raman histology, compromising the effectiveness of prognostic stratification. Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues. Methods: By utilizing both hyperspectral SRS imaging in the C-H vibration window and sparsity-penalized unmixing of overlapped spectral profiles, HC-SRH enabled high-content chemical mapping of saturated lipids, unsaturated lipids, cellular protein, extracellular matrix (ECM), and water. Spectral selective sampling was further implemented to boost the speed of HC-SRH. To show the potential for clinical use, HC-SRH using a compact fiber laser-based stimulated Raman microscope was demonstrated. Harnessing the wide and rapid tuning capability of the fiber laser, both C-H and fingerprint vibration windows were accessed. Results: HC-SRH successfully mapped unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue. With these five chemical maps, HC-SRH provided distinct contrast for tissue components including duct, stroma, fat cell, necrosis, and vessel. With selective spectral sampling, the speed of HC-SRH was improved by one order of magnitude. The fiber-laser-based HC-SRH produced the same image quality in the C-H window as the state-of-the-art solid laser. In the fingerprint window, nucleic acid and solid-state ester contrast was demonstrated. Conclusions: HC-SRH provides both morphological and chemical information of tissue in a label-free manner. The chemical information detected is beyond the reach of traditional hematoxylin and eosin staining and heralds the potential of HC-SRH for biomarker discovery.
Collapse
Affiliation(s)
- Hongli Ni
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | | | - Haonan Lin
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Wei Wang
- Hologic Inc., 250 campus drive, Marlborough, MA 01752, USA
| | - Shaoxiong Chen
- Indiana University School of Medicine 340 West 10th Street, Fairbanks Hall, Suite 6200, IN 46202, USA
| | - Yuhao Yuan
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Xiaowei Ge
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Jianpeng Ao
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Nolan Vild
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Ji-Xin Cheng
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, MA 02215, USA
| |
Collapse
|
3
|
Jadhav PA, Hole A, Sivaprasad M, Viswanath K, Sahay M, Sahay R, Bhanuprakash Reddy G, Murali Krishna C. Raman spectroscopy analysis of plasma of diabetes patients with and without retinopathy, nephropathy, and neuropathy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123337. [PMID: 37703793 DOI: 10.1016/j.saa.2023.123337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 07/17/2023] [Accepted: 08/31/2023] [Indexed: 09/15/2023]
Abstract
Diabetes is now one of the major public health challenges, globally. Prolonged diabetes leads to various diabetic microvascular complications (DMCs) like retinopathy, nephropathy, and neuropathy. Multiple factors are likely to be involved in predisposing diabetic individuals to complications. Early detection or diagnosis is essential in developing strategies to reduce the risk factors and management costs of these diabetic complications. In this study, we employed Raman Spectroscopy (RS) to analyse the plasma samples of diabetes patients without and with DMCs along with the plasma samples of healthy subjects. Spectral comparisons revealed decrease in protein content in Diabetes group and further subsequent decrease in proteins in DMC groups when compared with control group, which corroborates with the fact that there exists increased secretion of proteins in urine and corresponding decreased protein content in their blood in case of diabetic individuals. Among all study groups, it was noted that 75% of control spectra show correct classification, while spectral misclassification is high amongst the subjects with Diabetes and DMCs. Interestingly, very few Diabetes and DMC plasma spectra are misclassified as control spectra. Findings demonstrate that 70% of the Diabetes subjects without complications can be correctly identified from diabetes with complications. Further, investigations could also attempt to explore the use of serum instead of plasma to reduce the spectral misclassifications as one of the abundant constituents namely clotting factors could be avoided. The outcome of RS study may be imminent for the early detection or diagnosis of DMCs.
Collapse
Affiliation(s)
- Priyanka A Jadhav
- Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Kharghar, Navi Mumbai, India; Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, India
| | - Arti Hole
- Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Kharghar, Navi Mumbai, India
| | - M Sivaprasad
- Department of Biochemistry, ICMR-National Institute of Nutrition, Hyderabad, India
| | - K Viswanath
- Pushpagiri Vitreo Retina Institute, Hyderabad, India
| | - Manisha Sahay
- Osmania Medical College and General Hospital, Hyderabad, India
| | - Rakesh Sahay
- Osmania Medical College and General Hospital, Hyderabad, India
| | - G Bhanuprakash Reddy
- Department of Biochemistry, ICMR-National Institute of Nutrition, Hyderabad, India.
| | - C Murali Krishna
- Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Kharghar, Navi Mumbai, India; Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, India.
| |
Collapse
|
4
|
Wang X, Chen C, Chen C, Zuo E, Han S, Yang J, Yan Z, Lv X, Hou J, Jia Z. Novel SERS biosensor for rapid detection of breast cancer based on Ag 2O-Ag-PSi nanochips. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123226. [PMID: 37567026 DOI: 10.1016/j.saa.2023.123226] [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: 05/19/2023] [Revised: 07/11/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
Ag2O-Ag-PSi (porous silicon) surface-enhanced Raman scattering (SERS) chip was successfully synthesized by electrochemical corrosion, in situ reduction and heat treatment technology. The influence of different heat treatment temperature on SERS performance of the chip is studied. The results show that the chip treated at 300 °C has the best SERS performance. The chip was composed of Ag2O-Ag nano core shell with a diameter of 40-60 nm and porous silicon substrate. Then, the optimized chip was used to perform SERS test on serum samples from 30 healthy volunteers and 30 early breast cancer patients, and the baseline was corrected by LabSpec6 software. Finally, the data were analyzed by principal component analysis combined with t-distributed Stochastic Neighbor Embedding (PCA-t-SNE). The results showed that the accuracy of the improved substrate combined with multivariate statistical method was 98%. The shelf life of the chips exceeded six months due to the presence of the Ag2O shell. This study provides a basis for developing a low-cost rapid and sensitive early screening technology for breast cancer.
Collapse
Affiliation(s)
- Xuehua Wang
- College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Enguang Zuo
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Shibin Han
- College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China
| | - Jie Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Ziwei Yan
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Junwei Hou
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing at Karamay, Karamay 834000, China.
| | - Zhenhong Jia
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| |
Collapse
|
5
|
Sheikh E, Agrawal K, Roy S, Burk D, Donnarumma F, Ko YH, Guttula PK, Biswal NC, Shukla HD, Gartia MR. Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug. Adv Healthc Mater 2023; 12:e2301815. [PMID: 37706285 PMCID: PMC10842640 DOI: 10.1002/adhm.202301815] [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: 07/03/2023] [Indexed: 09/15/2023]
Abstract
Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.
Collapse
Affiliation(s)
- Elnaz Sheikh
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Kirti Agrawal
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Sanjit Roy
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - David Burk
- Department of Cell Biology and Bioimaging, Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA
| | - Fabrizio Donnarumma
- Department of Chemistry, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Young H Ko
- NewG Lab Pharma, 701 East Pratt Street, Columbus Center, Baltimore, MD, 21202, USA
| | - Praveen Kumar Guttula
- Sprott Center for Stem Cell Research, Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON, K1H 8L6, Canada
- Department of Biochemistry, Microbiology, and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, K1H 8M5, Canada
| | - Nrusingh C Biswal
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Hem D Shukla
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Manas Ranjan Gartia
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
Tanwar S, Ghaemi B, Raj P, Singh A, Wu L, Yuan Y, Arifin DR, McMahon MT, Bulte JWM, Barman I. A Smart Intracellular Self-Assembling Bioorthogonal Raman Active Nanoprobe for Targeted Tumor Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304164. [PMID: 37715297 DOI: 10.1002/advs.202304164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/21/2023] [Indexed: 09/17/2023]
Abstract
Inspired by the principle of in situ self-assembly, the development of enzyme-activated molecular nanoprobes can have a profound impact on targeted tumor detection. However, despite their intrinsic promise, obtaining an optical readout of enzyme activity with high specificity in native milieu has proven to be challenging. Here, a fundamentally new class of Raman-active self-assembling bioorthogonal enzyme recognition (nanoSABER) probes for targeted tumor imaging is reported. This class of Raman probes presents narrow spectral bands reflecting their vibrational fingerprints and offers an attractive solution for optical imaging at different bio-organization levels. The optical beacon harnesses an enzyme-responsive peptide sequence, unique tumor-penetrating properties, and vibrational tags with stretching frequencies in the cell-silent Raman window. The design of nanoSABER is tailored and engineered to transform into a supramolecular structure exhibiting distinct vibrational signatures in presence of target enzyme, creating a direct causality between enzyme activity and Raman signal. Through the integration of substrate-specific for tumor-associated enzyme legumain, unique capabilities of nanoSABER for imaging enzyme activity at molecular, cellular, and tissue levels in combination with machine learning models are shown. These results demonstrate that the nanoSABER probe may serve as a versatile platform for Raman-based recognition of tumor aggressiveness, drug accumulation, and therapeutic response.
Collapse
Affiliation(s)
- Swati Tanwar
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Behnaz Ghaemi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Piyush Raj
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Aruna Singh
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Inc., Baltimore, MD, 21205, USA
| | - Lintong Wu
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Yue Yuan
- Department of Chemistry, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, China
| | - Dian R Arifin
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Michael T McMahon
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Inc., Baltimore, MD, 21205, USA
| | - Jeff W M Bulte
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Inc., Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Oncology, Johns Hopkins University, Baltimore, MD, 21231, USA
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Oncology, Johns Hopkins University, Baltimore, MD, 21231, USA
| |
Collapse
|
8
|
Hermanns S, Dammeier S, Neugebauer A, Enderle MD. [Methods, applications, and future perspectives of intraoperative tissue identification]. PATHOLOGIE (HEIDELBERG, GERMANY) 2023; 44:183-187. [PMID: 37966557 DOI: 10.1007/s00292-023-01257-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/19/2023] [Indexed: 11/16/2023]
Abstract
Over the last century, there has been a steady development of new technologies for intraoperative tissue identification and differentiation. The applications are varied, with the core purpose being to identify target structures while preserving adjacent tissue and thereby follow a general paradigm of minimally invasive medicine. Particularly in oncology, a further asset of these technologies is the identification or classification of neoplastic tissue to support and improve therapy, for example, in breast cancer surgery.Many technologies under consideration make use of the different physical characteristics of treated tissues, such as induced fluorescence, optical coherence, and electrical impedance.Recent developments are focusing on moving from ex vivo to in situ and from asynchronous to real-time assistance of the clinicians, for example, by means of optical emission spectroscopy. Refinements of existing and the creation of new methods will include AI tools to make them more powerful while reducing the inter-operator variability in operative interventions. This talk addresses several aspects of the usage and suitability of these technologies for intraoperative, therapy-supporting application.
Collapse
Affiliation(s)
- Sanja Hermanns
- Erbe Elektromedizin GmbH, Waldhörnlestr. 17, 72072, Tübingen, Deutschland
| | - Sascha Dammeier
- Erbe Elektromedizin GmbH, Waldhörnlestr. 17, 72072, Tübingen, Deutschland
| | | | - Markus D Enderle
- Erbe Elektromedizin GmbH, Waldhörnlestr. 17, 72072, Tübingen, Deutschland.
| |
Collapse
|
9
|
Rainu SK, Ramachandran RG, Parameswaran S, Krishnakumar S, Singh N. Advancements in Intraoperative Near-Infrared Fluorescence Imaging for Accurate Tumor Resection: A Promising Technique for Improved Surgical Outcomes and Patient Survival. ACS Biomater Sci Eng 2023; 9:5504-5526. [PMID: 37661342 DOI: 10.1021/acsbiomaterials.3c00828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Clear surgical margins for solid tumor resection are essential for preventing cancer recurrence and improving overall patient survival. Complete resection of tumors is often limited by a surgeon's ability to accurately locate malignant tissues and differentiate them from healthy tissue. Therefore, techniques or imaging modalities are required that would ease the identification and resection of tumors by real-time intraoperative visualization of tumors. Although conventional imaging techniques such as positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), or radiography play an essential role in preoperative diagnostics, these cannot be utilized in intraoperative tumor detection due to their large size, high cost, long imaging time, and lack of cancer specificity. The inception of several imaging techniques has paved the way to intraoperative tumor margin detection with a high degree of sensitivity and specificity. Particularly, molecular imaging using near-infrared fluorescence (NIRF) based nanoprobes provides superior imaging quality due to high signal-to-noise ratio, deep penetration to tissues, and low autofluorescence, enabling accurate tumor resection and improved survival rates. In this review, we discuss the recent developments in imaging technologies, specifically focusing on NIRF nanoprobes that aid in highly specific intraoperative surgeries with real-time recognition of tumor margins.
Collapse
Affiliation(s)
- Simran Kaur Rainu
- Center for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Remya Girija Ramachandran
- L&T Ocular Pathology Department, Vision Research Foundation, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Chennai 600006, India
| | - Sowmya Parameswaran
- L&T Ocular Pathology Department, Vision Research Foundation, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Chennai 600006, India
| | - Subramanian Krishnakumar
- L&T Ocular Pathology Department, Vision Research Foundation, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Chennai 600006, India
| | - Neetu Singh
- Center for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
- Biomedical Engineering Unit, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| |
Collapse
|
10
|
Mokari A, Guo S, Bocklitz T. Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning. Molecules 2023; 28:6886. [PMID: 37836728 PMCID: PMC10574384 DOI: 10.3390/molecules28196886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/13/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.
Collapse
Affiliation(s)
- Azadeh Mokari
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
- Institute of Physical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany
| | - Shuxia Guo
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
- Institute of Physical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany
- Institute of Computer Science, Faculty of Mathematics, Physics & Computer Science, University Bayreuth, Universitaet sstraße 30, 95447 Bayreuth, Germany
| |
Collapse
|
11
|
Haskell J, Hubbard T, Murray C, Gardner B, Ives C, Ferguson D, Stone N. High wavenumber Raman spectroscopy for intraoperative assessment of breast tumour margins. Analyst 2023; 148:4373-4385. [PMID: 37594446 DOI: 10.1039/d3an00574g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Optimal oncological results and patient outcomes are achieved in surgery for early breast cancer with breast conserving surgery (BCS) where this is appropriate. A limitation of BCS occurs when cancer is present at, or close, to the resection margin - termed a 'positive' margin - and re-excision is recommended to reduce recurrence rate. This is occurs in 17% of BCS in the UK and there is therefore a critical need for a way to assess margin status intraoperatively to ensure complete excision with adequate margins at the first operation. This study presents the potential of high wavenumber (HWN) Raman spectroscopy to address this. Freshly excised specimens from thirty patients undergoing surgery for breast cancer were measured using a surface Raman probe, and a multivariate classification model to predict normal versus tumour was developed from the data. This model achieved 77.1% sensitivity and 90.8% specificity following leave one patient out cross validation, with the defining features being differences in water content and lipid versus protein content. This demonstrates the feasibility of HWN Raman spectroscopy to facilitate future intraoperative margin assessment at specific locations. Clinical utility of the approach will require further research.
Collapse
Affiliation(s)
- Jennifer Haskell
- Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, Exeter, Devon, UK.
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, Devon, UK
| | - Thomas Hubbard
- Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, Exeter, Devon, UK.
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, Devon, UK
| | - Claire Murray
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, Devon, UK
| | - Benjamin Gardner
- Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, Exeter, Devon, UK.
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, Devon, UK
| | - Charlotte Ives
- Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, Exeter, Devon, UK.
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, Devon, UK
| | - Douglas Ferguson
- Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, Exeter, Devon, UK.
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, Devon, UK
| | - Nick Stone
- Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, Exeter, Devon, UK.
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, Devon, UK
| |
Collapse
|
12
|
Oshima Y, Haruki T, Koizumi K, Yonezawa S, Taketani A, Kadowaki M, Saito S. Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy. Int J Mol Sci 2023; 24:12170. [PMID: 37569541 PMCID: PMC10418989 DOI: 10.3390/ijms241512170] [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: 07/07/2023] [Revised: 07/23/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.
Collapse
Affiliation(s)
- Yusuke Oshima
- Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Medicine, Oita University, Yufu 879-5593, Japan
| | - Takayuki Haruki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Sustainable Design, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-8555, Japan
| | - Shota Yonezawa
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Akinori Taketani
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| |
Collapse
|
13
|
Sharaha U, Hania D, Lapidot I, Salman A, Huleihel M. Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning. Cells 2023; 12:1909. [PMID: 37508572 PMCID: PMC10378363 DOI: 10.3390/cells12141909] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/06/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.
Collapse
Affiliation(s)
- Uraib Sharaha
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
- Department of Biology, Science and Technology College, Hebron University, Hebron P760, Palestine
| | - Daniel Hania
- Department of Green Engineering, SCE-Shamoon College of Engineering, Beer-Sheva 84100, Israel
| | - Itshak Lapidot
- Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv 69107, Israel
- Laboratoire Informatique d'Avignon (LIA), Avignon Université, 339 Chemin des Meinajaries, 84000 Avignon, France
| | - Ahmad Salman
- Department of Physics, SCE-Shamoon College of Engineering, Beer-Sheva 84100, Israel
| | - Mahmoud Huleihel
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| |
Collapse
|
14
|
Ehsan U, Nawaz H, Irfan Majeed M, Rashid N, Ali Z, Zulfiqar A, Tariq A, Shahbaz M, Meraj L, Naheed I, Sadaf N. Surface-enhanced Raman spectroscopy of centrifuged blood serum samples of diabetic type II patients by using 50KDa filter devices. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 293:122457. [PMID: 36764165 DOI: 10.1016/j.saa.2023.122457] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Blood serum contains essential biochemical information which are used for early disease diagnosis. Blood serum consisted of higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF). The disease biomarkers are lower molecular weight fraction proteins, and their contribution to disease diagnosis is suppressed due to higher molecular weight fraction proteins. To diagnose diabetes in early stages are difficult because of the presence of huge amount of these HMWF. In the current study, surface-enhanced Raman spectroscopy (SERS) are employed to diagnose diabetes after centrifugation of serum samples using Amicon ultra filter devices of 50 kDa which produced two fractions of whole blood serum of filtrate, low molecular weight fraction, and residue, high molecular weight fraction. Furthermore SERS is employed to study the LMW fractions of healthy and diseased samples. Some prominent SERS bands are observed at 725 cm-1, 842 cm-1, 1025 cm-1, 959 cm-1, and 1447 cm-1 due to small molecular weight proteins, and these biomarkers helped to diagnose the disease early stage. Moreover, chemometric techniques such as principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are employed to check the potential of surface-enhanced Raman spectroscopy for the differentiation and classifications of the blood serum samples. SERS can be employed for the early diagnosis and screening of biochemical changes during type II diabetes.
Collapse
Affiliation(s)
- Usama Ehsan
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan.
| | - Zain Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Anam Zulfiqar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Ayesha Tariq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Shahbaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Lubna Meraj
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Iqra Naheed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Nimra Sadaf
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| |
Collapse
|
15
|
Murali VP, Karunakaran V, Murali M, Lekshmi A, Kottarathil S, Deepika S, Saritha VN, Ramya AN, Raghu KG, Sujathan K, Maiti KK. A clinically feasible diagnostic spectro-histology built on SERS-nanotags for multiplex detection and grading of breast cancer biomarkers. Biosens Bioelectron 2023; 227:115177. [PMID: 36871528 DOI: 10.1016/j.bios.2023.115177] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/14/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023]
Abstract
Simultaneous detection of multiple biomarkers is always an obstacle in immunohistochemical (IHC) analysis. Herein, a straightforward spectroscopy-driven histopathologic approach has emerged as a paradigm of Raman-label (RL) nanoparticle probes for multiplex recognition of pertinent biomarkers in heterogeneous breast cancer. The nanoprobes are constructed by sequential incorporation of signature RL and target specific antibodies on gold nanoparticles, which are coined as Raman-Label surface enhanced Raman scattering (RL-SERS)-nanotags to evaluate simultaneous recognition of clinically relevant breast cancer biomarkers i.e., estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor2 (HER2). As a foot-step assessment, breast cancer cell lines having varied expression levels of the triple biomarkers are investigated. Subsequently, the optimized detection strategy using RL-SERS-nanotags is subjected to clinically confirmed, retrospective formalin-fixed paraffin embedded (FFPE) breast cancer tissue samples to fish out the quick response of singleplex, duplex as well as triplex biomarkers in a single tissue specimen by adopting a ratiometric signature RL-SERS analysis which enabled to minimize the false negative and positive results. Significantly, sensitivity and specificity of 95% and 92% for singleplex, 88% and 85% for duplex, and 75% and 67% for triplex biomarker has been achieved by assessing specific Raman fingerprints of the respective SERS-tags. Furthermore, a semi-quantitative evaluation of HER2 grading between 4+/2+/1+ tissue samples was also achieved by the Raman intensity profiling of the SERS-tag, which is fully in agreement with the expensive fluorescent in situ hybridization analysis. Additionally, the practical diagnostic applicability of RL-SERS-tags has been achieved by large area SERS imaging of areas covering 0.5-5 mm2 within 45 min. These findings unveil an accurate, inexpensive and multiplex diagnostic modality envisaging large-scale multi-centric clinical validation.
Collapse
Affiliation(s)
- Vishnu Priya Murali
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India
| | - Varsha Karunakaran
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Madhukrishnan Murali
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Asha Lekshmi
- Regional Cancer Centre (RCC), Division of Cancer Research, Thiruvananthapuram, 695011, Kerala, India
| | - Shamna Kottarathil
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India
| | - Selvakumar Deepika
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India
| | - Valliamma N Saritha
- Regional Cancer Centre (RCC), Division of Cancer Research, Thiruvananthapuram, 695011, Kerala, India
| | - Adukkadan N Ramya
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Kozhiparambil G Raghu
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Agro-Processing and Technology Division (APTD), Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Kunjuraman Sujathan
- Regional Cancer Centre (RCC), Division of Cancer Research, Thiruvananthapuram, 695011, Kerala, India.
| | - Kaustabh Kumar Maiti
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
| |
Collapse
|
16
|
Kazemzadeh M, Martinez-Calderon M, Otupiri R, Artuyants A, Lowe MM, Ning X, Reategui E, Schultz ZD, Xu W, Blenkiron C, Chamley LW, Broderick NGR, Hisey CL. Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533481. [PMID: 36993759 PMCID: PMC10055277 DOI: 10.1101/2023.03.20.533481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.
Collapse
|
17
|
Corden C, Boitor R, Dusanjh PK, Harwood A, Mukherjee A, Gomez D, Notingher I. Autofluorescence-Raman Spectroscopy for Ex Vivo Mapping Colorectal Liver Metastases and Liver Tissue. J Surg Res 2023; 288:10-20. [PMID: 36940563 DOI: 10.1016/j.jss.2023.02.014] [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: 07/22/2022] [Revised: 01/15/2023] [Accepted: 02/17/2023] [Indexed: 03/23/2023]
Abstract
INTRODUCTION Identifying colorectal liver metastases (CRLM) during liver resection could assist in achieving clear surgical margins, which is an important prognostic variable for both disease-free and overall survival. The aim of this study was to investigate the effect of auto-fluorescence (AF) and Raman spectroscopy for ex vivo label-free discrimination of CRLMs from normal liver tissue. Secondary aims include exploring options for multimodal AF-Raman integration with respect to diagnosis accuracy and imaging speed on human liver tissue and CRLM. METHODS Liver samples were obtained from patients undergoing liver surgery for CRLM who provided informed consent (15 patients were recruited). AF and Raman spectroscopy was performed on CRLM and normal liver tissue samples and then compared to histology. RESULTS AF emission spectra demonstrated that the 671 nm and 775/785 nm excitation wavelengths provided the highest contrast, as normal liver tissue elicited on average around eight-fold higher AF intensity compared to CRLM. The use of the 785 nm wavelength had the advantage of enabling Raman spectroscopy measurements from CRLM regions, allowing discrimination of CRLM from regions of normal liver tissue eliciting unusual low AF intensity, preventing misclassification. Proof-of-concept experiments using small pieces of CRLM samples covered by large normal liver tissue demonstrated the feasibility of a dual-modality AF-Raman for detection of positive margins within few minutes. CONCLUSIONS AF imaging and Raman spectroscopy can discriminate CRLM from normal liver tissue in an ex vivo setting. These results suggest the potential for developing integrated multimodal AF-Raman imaging techniques for intraoperative assessment of surgical margins.
Collapse
Affiliation(s)
- Christopher Corden
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Radu Boitor
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Palminder Kaur Dusanjh
- Histopathology Department, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK
| | - Andrew Harwood
- Histopathology Department, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK
| | - Abhik Mukherjee
- Histopathology Department, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK; School of Medicine, University of Nottingham, Nottingham, UK
| | - Dhanwant Gomez
- Department of Hepatobiliary and Pancreatic Surgery, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK
| | - Ioan Notingher
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
| |
Collapse
|
18
|
Xu FX, Rathbone EG, Fu D. Simultaneous Dual-Band Hyperspectral Stimulated Raman Scattering Microscopy with Femtosecond Optical Parametric Oscillators. J Phys Chem B 2023; 127:2187-2197. [PMID: 36883604 PMCID: PMC10144064 DOI: 10.1021/acs.jpcb.2c09105] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Stimulated Raman scattering (SRS) microscopy is a label-free quantitative optical technique for imaging molecular distributions in cells and tissues by probing their intrinsic vibrational frequencies. Despite its usefulness, existing SRS imaging techniques have limited spectral coverage due to either a wavelength tuning constraint or narrow spectral bandwidth. High-wavenumber SRS imaging is commonly used to map lipid and protein distribution in biological cells and visualize cell morphology. However, to detect small molecules or Raman tags, imaging in the fingerprint region or "silent" region, respectively, is often required. For many applications, it is desirable to collect SRS images in two Raman spectral regions simultaneously for visualizing the distribution of specific molecules in cellular compartments or providing accurate ratiometric analysis. In this work, we present an SRS microscopy system using three beams generated by a femtosecond oscillator to acquire hyperspectral SRS image stacks in two arbitrary vibrational frequency bands, between 650-3280 cm-1, simultaneously. We demonstrate potential biomedical applications of the system in investigating fatty acid metabolism, cellular drug uptake and accumulation, and lipid unsaturation level in tissues. We also show that the dual-band hyperspectral SRS imaging system can be adapted for the broadband fingerprint region hyperspectral imaging (1100-1800 cm-1) by simply adding a modulator.
Collapse
Affiliation(s)
- Fiona Xi Xu
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Emily G Rathbone
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Dan Fu
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| |
Collapse
|
19
|
Parlatan U, Ozen MO, Kecoglu I, Koyuncu B, Torun H, Khalafkhany D, Loc I, Ogut MG, Inci F, Akin D, Solaroglu I, Ozoren N, Unlu MB, Demirci U. Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2205519. [PMID: 36642804 DOI: 10.1002/smll.202205519] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.
Collapse
Affiliation(s)
- Ugur Parlatan
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| | - Mehmet Ozgun Ozen
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| | - Ibrahim Kecoglu
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
| | - Batuhan Koyuncu
- Department of Computer Engineering, Bogazici University, Istanbul, 34342, Turkey
| | - Hulya Torun
- Koc University Graduate School of Sciences and Engineering, Istanbul, 34450, Turkey
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, 34450, Turkey
| | - Davod Khalafkhany
- Department of Molecular Biology and Genetics, Center for Life Sciences and Technologies, Apoptosis and Cancer Immunology Laboratory (AKiL), Bogazici University, Istanbul, 34342, Turkey
| | - Irem Loc
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
| | - Mehmet Giray Ogut
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| | - Fatih Inci
- UNAM-National Nanotechnology Research Center, Bilkent University, Ankara, 06800, Turkey
- Institute of Materials Science and Nanotechnology, Bilkent University, Ankara, 06800, Turkey
| | - Demir Akin
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| | - Ihsan Solaroglu
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, 34450, Turkey
- School of Medicine, Koc University, Istanbul, 34450, Turkey
| | - Nesrin Ozoren
- Department of Molecular Biology and Genetics, Center for Life Sciences and Technologies, Apoptosis and Cancer Immunology Laboratory (AKiL), Bogazici University, Istanbul, 34342, Turkey
| | - Mehmet Burcin Unlu
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
- Faculty of Engineering, Hokkaido University, North-13 West-8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan
- Global Center for Biomedical Science and Engineering Quantum Medical Science and Engineering (GI-CoRE Cooperating Hub), Faculty of Medicine, Hokkaido University, Sapporo, 060-8638, Japan
| | - Utkan Demirci
- Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA
| |
Collapse
|
20
|
David S, Tran T, Dallaire F, Sheehy G, Azzi F, Trudel D, Tremblay F, Omeroglu A, Leblond F, Meterissian S. In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:036009. [PMID: 37009577 PMCID: PMC10062385 DOI: 10.1117/1.jbo.28.3.036009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
SIGNIFICANCE As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing in situ detection of cancer could reduce re-excision procedure rates and improve patient survival. AIM Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue ex-vivo. The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer. APPROACH The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 ex-vivo measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis. RESULTS Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around 940 cm - 1 and the symmetric ring breathing at 1004 cm - 1 associated with phenylalanine. CONCLUSIONS Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.
Collapse
Affiliation(s)
- Sandryne David
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Trang Tran
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Feryel Azzi
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
| | - Francine Tremblay
- McGill University Health Center, Department of Surgery, Montreal, Quebec, Canada
| | - Atilla Omeroglu
- McGill University Health Center, Department of Pathology, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Sarkis Meterissian
- McGill University Health Center, Department of Surgery, Montreal, Quebec, Canada
| |
Collapse
|
21
|
Zhang X, Song X, Li W, Chen C, Wusiman M, Zhang L, Zhang J, Lu J, Lu C, Lv X. Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods. Sci Rep 2023; 13:3418. [PMID: 36854769 PMCID: PMC9974944 DOI: 10.1038/s41598-022-22204-1] [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: 07/04/2022] [Accepted: 10/11/2022] [Indexed: 03/02/2023] Open
Abstract
Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high mortality rate, such as renal failure and uremia. At present, the diagnosis of membranous nephropathy mainly relies on the clinical manifestations of patients and pathological examination of kidney biopsy, which are expensive, time-consuming, and have certain chance and other disadvantages. Therefore, there is an urgent need to find a rapid, accurate and non-invasive diagnostic technique for the diagnosis of membranous nephropathy. In this study, Raman spectra of serum and urine were combined with deep learning methods to diagnose membranous nephropathy. After baseline correction and smoothing of the data, Gaussian white noise of different decibels was added to the training set for data amplification, and the amplified data were imported into ResNet, AlexNet and GoogleNet models to obtain the evaluation results of the models for membranous nephropathy. The experimental results showed that the three deep learning models achieved an accuracy of 1 for the classification of serum data of patients with membranous nephropathy and control group, and the discrimination of urine data was above 0.85, among which AlexNet was the best classification model for both samples. The above experimental results illustrate the great potential of serum- and urine-based Raman spectroscopy combined with deep learning methods for rapid and accurate identification of patients with membranous nephropathy.
Collapse
Affiliation(s)
- Xueqin Zhang
- grid.410644.3People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001 China
| | - Xue Song
- grid.410644.3People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001 China
| | - Wenjing Li
- grid.413254.50000 0000 9544 7024College of Software, Xinjiang University, Urumqi, 830046 China
| | - Cheng Chen
- grid.413254.50000 0000 9544 7024College of Software, Xinjiang University, Urumqi, 830046 China
| | - Miriban Wusiman
- grid.13394.3c0000 0004 1799 3993Xinjiang Medical University, Urumqi, 830054 China
| | - Li Zhang
- grid.412631.3The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011 China
| | - Jiahui Zhang
- grid.13394.3c0000 0004 1799 3993Xinjiang Medical University, Urumqi, 830054 China
| | - Jinyu Lu
- grid.410644.3People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001 China
| | - Chen Lu
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046, China.
| |
Collapse
|
22
|
Kunitake JA, Sudilovsky D, Johnson LM, Loh HC, Choi S, Morris PG, Jochelson MS, Iyengar NM, Morrow M, Masic A, Fischbach C, Estroff LA. Biomineralogical signatures of breast microcalcifications. SCIENCE ADVANCES 2023; 9:eade3152. [PMID: 36812311 PMCID: PMC9946357 DOI: 10.1126/sciadv.ade3152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Microcalcifications, primarily biogenic apatite, occur in cancerous and benign breast pathologies and are key mammographic indicators. Outside the clinic, numerous microcalcification compositional metrics (e.g., carbonate and metal content) are linked to malignancy, yet microcalcification formation is dependent on microenvironmental conditions, which are notoriously heterogeneous in breast cancer. We interrogate multiscale heterogeneity in 93 calcifications from 21 breast cancer patients using an omics-inspired approach: For each microcalcification, we define a "biomineralogical signature" combining metrics derived from Raman microscopy and energy-dispersive spectroscopy. We observe that (i) calcifications cluster into physiologically relevant groups reflecting tissue type and local malignancy; (ii) carbonate content exhibits substantial intratumor heterogeneity; (iii) trace metals including zinc, iron, and aluminum are enhanced in malignant-localized calcifications; and (iv) the lipid-to-protein ratio within calcifications is lower in patients with poor composite outcome, suggesting that there is potential clinical value in expanding research on calcification diagnostic metrics to include "mineral-entrapped" organic matrix.
Collapse
Affiliation(s)
| | - Daniel Sudilovsky
- Department of Pathology and Laboratory Medicine, Cayuga Medical Center at Ithaca, Ithaca, NY 14850, USA
- Pathology Department, Kingman Regional Medical Center, Kingman, AZ 86409, USA
- Pathology Department, Western Arizona Medical Center, Bullhead City, AZ 86442, USA
- Pathology Department, Yuma Regional Medical Center, Yuma, AZ 85364, USA
| | - Lynn M. Johnson
- Cornell Statistical Consulting Unit, Cornell University, Ithaca, NY 14850, USA
| | - Hyun-Chae Loh
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Siyoung Choi
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850, USA
| | - Patrick G. Morris
- Medical Oncology Service, Beaumont Hospital, Dublin, Ireland
- Breast Medicine Service, Department of Medicine, Memorial Sloan Kettering Cancer Center/Evelyn H. Lauder Breast and Imaging Center, New York, NY 10065, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY 10021, USA
| | - Maxine S. Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center/Evelyn H. Lauder Breast and Imaging Center, New York, NY 10065, USA
| | - Neil M. Iyengar
- Department of Medicine, Weill Cornell Medical College, New York, NY 10021, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Monica Morrow
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Admir Masic
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Corresponding author. (L.A.E.); (C.F.); (A.M.)
| | - Claudia Fischbach
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850, USA
- Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY 14850, USA
- Corresponding author. (L.A.E.); (C.F.); (A.M.)
| | - Lara A. Estroff
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14850, USA
- Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY 14850, USA
- Corresponding author. (L.A.E.); (C.F.); (A.M.)
| |
Collapse
|
23
|
Han J, Ishigaki M, Takahashi Y, Watanabe H, Umebayashi Y. Analytical chemistry toward on-site diagnostics. ANAL SCI 2023; 39:133-137. [PMID: 36653697 DOI: 10.1007/s44211-023-00271-2] [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: 12/26/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023]
Abstract
Analytical Chemistry, through quantitative and/or qualitative analysis (identification), is a discipline that involves the development of methodologies and the exploration of new principles to obtain answers to given problems. In situ analysis techniques have attracted attention for its ability to elucidate phenomena occurring and to evaluate amount of a certain component in substances at real time and biological samples as applications of such analysis technology. Lots of techniques have been performed to understand the fundamental phenomena in varied fields such as X-ray, vibrational, and electrochemical impedance spectroscopies and also analytical reagents that enable to semi-quantitative analysis just observation. In fact, applying various in situ techniques in analytical chemistry expands to the medical diagnosis, which leads to be able to detect early diseases. Here, we describe some of previous researches in many fields such as electrochemical device for energy storage, biology, environment, and pathology and briefly introduce our recent challenges to analytical chemistry toward the on-site diagnosis.
Collapse
Affiliation(s)
- Jihae Han
- Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-No-Cho, Nishi-Ku, Niigata, Niigata, 950-2181, Japan
| | - Mika Ishigaki
- Institute of Agricultural and Life Sciences, Academic Assembly, Shimane University, 1060 Nishikawatsu, Matsue, Shimane, 690-8504, Japan
| | - Yukiko Takahashi
- Materials Science and Bioengineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Hikari Watanabe
- Department of Pure and Applied Chemistry, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan
| | - Yasuhiro Umebayashi
- Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-No-Cho, Nishi-Ku, Niigata, Niigata, 950-2181, Japan.
| |
Collapse
|
24
|
Differentiating Follicular Thyroid Carcinoma and Thyroid Adenoma by Using Near-Infrared Surface-Enhanced Raman Spectroscopy. Indian J Surg 2023. [DOI: 10.1007/s12262-023-03666-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
|
25
|
Yang Y, Liu Z, Huang J, Sun X, Ao J, Zheng B, Chen W, Shao Z, Hu H, Yang Y, Ji M. Histological diagnosis of unprocessed breast core-needle biopsy via stimulated Raman scattering microscopy and multi-instance learning. Theranostics 2023; 13:1342-1354. [PMID: 36923541 PMCID: PMC10008736 DOI: 10.7150/thno.81784] [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: 12/12/2022] [Accepted: 01/09/2023] [Indexed: 03/14/2023] Open
Abstract
Core-needle biopsy (CNB) plays a vital role in the initial diagnosis of breast cancer. However, the complex tissue processing and global shortage of pathologists have hindered traditional histopathology from timely diagnosis on fresh biopsies. In this work, we developed a full digital platform by integrating label-free stimulated Raman scattering (SRS) microscopy with weakly-supervised learning for rapid and automated cancer diagnosis on un-labelled breast CNB. Methods: We first compared the results of SRS imaging with standard hematoxylin and eosin (H&E) staining on adjacent frozen tissue sections. Then fresh unprocessed biopsy tissues were imaged by SRS to reveal diagnostic histoarchitectures. Next, weakly-supervised learning, i.e., the multi-instance learning (MIL) model was conducted to evaluate the ability to differentiate between benign and malignant cases, and compared with the performance of supervised learning model. Finally, gradient-weighted class activation mapping (Grad-CAM) and semantic segmentation were performed to spatially resolve benign/malignant areas with high efficiency. Results: We verified the ability of SRS in revealing essential histological hallmarks of breast cancer in both thin frozen sections and fresh unprocessed biopsy, generating histoarchitectures well correlated with H&E staining. Moreover, we demonstrated that weakly-supervised MIL model could achieve superior classification performance to supervised learnings, reaching diagnostic accuracy of 95% on 61 biopsy specimens. Furthermore, Grad-CAM allowed the trained MIL model to visualize the histological heterogeneity within the CNB. Conclusion: Our results indicate that MIL-assisted SRS microscopy provides rapid and accurate diagnosis on histologically heterogeneous breast CNB, and could potentially help the subsequent management of patients.
Collapse
Affiliation(s)
- Yifan Yang
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| | - Zhijie Liu
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| | - Jing Huang
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| | - Xiangjie Sun
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Jianpeng Ao
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| | - Bin Zheng
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Otolaryngology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Wanyuan Chen
- Cancer Center, Department of Pathology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhiming Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Hao Hu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032 China
| | - Yinlong Yang
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Minbiao Ji
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| |
Collapse
|
26
|
Optical spectroscopy and chemometrics in intraoperative tumor margin assessment. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
|
27
|
Simmons L, Feng L, Fatemi-Ardekani A, Noseworthy MD. The Role of Calcium in Non-Invasively Imaging Breast Cancer: An Overview of Current and Modern Imaging Techniques. Crit Rev Biomed Eng 2023; 51:43-62. [PMID: 37602447 DOI: 10.1615/critrevbiomedeng.2023047683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
The landscape of breast cancer diagnostics has significantly evolved over the past decade. With these changes, it is possible to provide a comprehensive assessment of both benign and malignant breast calcifications. The biochemistry of breast cancer and calcifications are thoroughly examined to describe the potential to characterize better different calcium salts composed of calcium carbonate, calcium oxalate, or calcium hydroxyapatite and their associated prognostic implications. Conventional mammographic imaging techniques are compared to available ones, including breast tomosynthesis and contrast-enhanced mammography. Additional methods in computed tomography and magnetic resonance imaging are discussed. The concept of using magnetic resonance imaging particularly magnetic susceptibility to characterize the biochemical characteristics of calcifications is described. As we know magnetic resonance imaging is safe and there is no ionization radiation. Experimental findings through magnetic resonance susceptibility imaging techniques are discussed to illustrate the potential for integrating this technique to provide a quantitative assessment of magnetic susceptibility. Under the right magnetic resonance imaging conditions, a distinct phase variability was isolated amongst different types of calcium salts.
Collapse
Affiliation(s)
- Lyndsay Simmons
- Medical Physics and Applied Radiation Sciences, McMaster University, Hamilton, ON, Canada; Mohawk College, Institute for Applied Health Sciences, Hamilton, ON, Canada; Imaging Research Centre, St. Joseph's Healthcare Hamilton, 50 Charlton Ave. E., Hamilton, ON, Canada
| | - Lisa Feng
- Medical Physics and Applied Radiation Sciences, McMaster University, Hamilton, ON, Canada
| | - Ali Fatemi-Ardekani
- Medical Physics, Merit Health, Southeast Cancer Network; Department of Physics, Jackson State University
| | - Michael D Noseworthy
- Medical Physics and Applied Radiation Sciences, McMaster University, Hamilton, ON, Canada; Imaging Research Centre, St. Joseph's Healthcare Hamilton, 50 Charlton Ave. E., Hamilton, ON, Canada; Department of Electrical and Computer Engineering, McMaster University, 280 Main Street W., Hamilton, ON, Canada; School of Biomedical Engineering, McMaster University, Hamilton ON, Canada; Department of Radiology, McMaster University, 1280 Main St. W., Hamilton, ON, Canada
| |
Collapse
|
28
|
Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity. PLoS One 2022; 17:e0279739. [PMID: 36584158 PMCID: PMC9803148 DOI: 10.1371/journal.pone.0279739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/14/2022] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE In this work, we explore and develop a method that uses Raman spectroscopy to measure and differentiate radiation induced toxicity in murine lungs with the goal of setting the foundation for a predictive disease model. METHODS Analysis of Raman tissue data is achieved through a combination of techniques. We first distinguish between tissue measurements and air pockets in the lung by using group and basis restricted non-negative matrix factorization. We then analyze the tissue spectra using sparse multinomial logistic regression to discriminate between fibrotic gradings. Model validation is achieved by splitting the data into a training set containing 70% of the data and a test set with the remaining 30%; classification accuracy is used as the performance metric. We also explore several other potential classification tasks wherein the response considered is the grade of pneumonitis and fibrosis sickness. RESULTS A classification accuracy of 91.6% is achieved on the test set of fibrotic gradings, illustrating the ability of Raman measurements to detect differing levels of fibrotic disease among the murine lungs. It is also shown via further modeling that coarser consideration of fibrotic grading via binning (ie. 'Low', 'Medium', 'High') does not degrade performance. Finally, we consider preliminary models for pneumonitis discrimination using the same methodologies.
Collapse
|
29
|
Vibrational spectroscopy for decoding cancer microbiota interactions: Current evidence and future perspective. Semin Cancer Biol 2022; 86:743-752. [PMID: 34273519 DOI: 10.1016/j.semcancer.2021.07.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/08/2021] [Accepted: 07/09/2021] [Indexed: 01/27/2023]
Abstract
The role of human microbiota in cancer initiation and progression is recognized in recent years. In order to investigate the interactions between cancer cells and microbes, a systematic analysis using various emerging techniques is required. Owing to the label-free, non-invasive and molecular fingerprinting characteristics, vibrational spectroscopy is uniquely suited to decode and understand the relationship and interactions between cancer and the microbiota at the molecular level. In this review, we first provide a quick overview of the fundamentals of vibrational spectroscopic techniques, namely Raman and infrared spectroscopy. Next, we discuss the emerging evidence underscoring utilities of these spectroscopic techniques to study cancer or microbes separately, and share our perspective on how vibrational spectroscopy can be employed at the intersection of the two fields. Finally, we envision the potential opportunities in exploiting vibrational spectroscopy not only in basic cancer-microbiome research but also in its clinical translation, and discuss the challenges in the bench to bedside translation.
Collapse
|
30
|
Barik AK, M SP, Lukose J, Upadhya R, Pai MV, Kartha VB, Chidangil S. In vivo spectroscopy: optical fiber probes for clinical applications. Expert Rev Med Devices 2022; 19:657-675. [PMID: 36175393 DOI: 10.1080/17434440.2022.2130046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Fiber optic probe based in-vivo spectroscopy techniques are fast and highly objective methods for intraoperative diagnoses and minimally invasive surgical interventions for all procedures where endoscopic observations are carried out for cancers of different types. The Raman spectral features provide molecular fingerprint-type information and can reveal the subjects' pathological state in label-free manner, making endoscopy multiplexed fiber optic probe-based devices with the potential for translation from bench to bedside for routine applications. AREAS COVERED This review provides a general overview of different fiber-optic probes for in-vivo measurements with emphasis on Raman spectroscopy for biomedical application. Various aspects such as fiber-optic probe, radiation source, detector, and spectrometer for extracting optimum spectral features have also been discussed. EXPERT OPINION : Optical spectroscopy-based fiber probe systems with "Chip-on-Tip" technology, combined with machine learning, can in the near future, become a complimentary diagnostic tool to magnetic resonance imaging (MRI), computed tomography (CT) scan, ultrasound, etc. Hyperspectral imaging and fluorescence-based devices are in the advanced stage of technology readiness level (TRL), and with advances in lasers and miniature spectroscopy systems, probe-based Raman devices are also coming up.
Collapse
Affiliation(s)
- Ajaya Kumar Barik
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| | - Sanoop Pavithran M
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| | - Jijo Lukose
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| | - Rekha Upadhya
- Department of Obstetrics and Gynaecology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education -576104, Manipal, India
| | - Muralidhar V Pai
- Department of Obstetrics and Gynaecology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education -576104, Manipal, India
| | - V B Kartha
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| | - Santhosh Chidangil
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| |
Collapse
|
31
|
Kazemzadeh M, Martinez-Calderon M, Xu W, Chamley LW, Hisey CL, Broderick NGR. Cascaded Deep Convolutional Neural Networks as Improved Methods of Preprocessing Raman Spectroscopy Data. Anal Chem 2022; 94:12907-12918. [PMID: 36067379 DOI: 10.1021/acs.analchem.2c03082] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Machine learning has had a significant impact on the value of spectroscopic characterization tools, particularly in biomedical applications, due to its ability to detect latent patterns within complex spectral data. However, it often requires extensive data preprocessing, including baseline correction and denoising, which can lead to an unintentional bias during classification. To address this, we developed two deep learning methods capable of fully preprocessing raw Raman spectroscopy data without any human input. First, cascaded deep convolutional neural networks (CNN) based on either ResNet or U-Net architectures were trained on randomly generated spectra with augmented defects. Then, they were tested using simulated Raman spectra, surface-enhanced Raman spectroscopy (SERS) imaging of chemical species, low resolution Raman spectra of human bladder cancer tissue, and finally, classification of SERS spectra from human placental extracellular vesicles (EVs). Both approaches resulted in faster training and complete spectral preprocessing in a single step, with more speed, defect tolerance, and classification accuracy compared to conventional methods. These findings indicate that cascaded CNN preprocessing is ideal for biomedical Raman spectroscopy applications in which large numbers of heterogeneous spectra with diverse defects need to be automatically, rapidly, and reproducibly preprocessed.
Collapse
Affiliation(s)
- Mohammadrahim Kazemzadeh
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland1010, New Zealand.,Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin9054, New Zealand
| | | | - Weiliang Xu
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland1010, New Zealand
| | - Lawrence W Chamley
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland1023, New Zealand.,Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland1023, New Zealand
| | - Colin L Hisey
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland1023, New Zealand.,Hub for Extracellular Vesicle Investigations, University of Auckland, Auckland1023, New Zealand.,Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio43210, United States
| | - Neil G R Broderick
- Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin9054, New Zealand.,Department of Physics, University of Auckland, Auckland1061, New Zealand
| |
Collapse
|
32
|
Jeng MJ, Sharma M, Lee CC, Lu YS, Tsai CL, Chang CH, Chen SW, Lin RM, Chang LB. Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury. J Clin Med 2022; 11:jcm11164829. [PMID: 36013069 PMCID: PMC9410447 DOI: 10.3390/jcm11164829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/08/2022] [Accepted: 08/14/2022] [Indexed: 11/17/2022] Open
Abstract
Acute kidney injury (AKI) is a common syndrome characterized by various etiologies and pathophysiologic processes that deteriorate kidney function. The aim of this study is to identify potential biomarkers in the urine of non-acute kidney injury (non-AKI) and AKI patients through Raman spectroscopy (RS) to predict the advancement in complications and kidney failure. Selected spectral regions containing prominent peaks of renal biomarkers were subjected to partial least squares linear discriminant analysis (PLS-LDA). This discriminant analysis classified the AKI patients from non-AKI subjects with a sensitivity and specificity of 97% and 100%, respectively. In this study, the RS measurements of urine specimens demonstrated that AKI had significantly higher nitrogenous compounds, porphyrin, tryptophan and neopterin when compared with non-AKI. This study’s specific spectral information can be used to design an in vivo RS approach for the detection of AKI diseases.
Collapse
Affiliation(s)
- Ming-Jer Jeng
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
- Kidney Research Center, Department of Nephrology, Change Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan, Taoyuan 333, Taiwan
| | - Mukta Sharma
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Cheng-Chia Lee
- Kidney Research Center, Department of Nephrology, Change Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
| | - Yu-Sheng Lu
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Chia-Lung Tsai
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
- Kidney Research Center, Department of Nephrology, Change Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan, Taoyuan 333, Taiwan
- Correspondence:
| | - Chih-Hsiang Chang
- Kidney Research Center, Department of Nephrology, Change Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
| | - Shao-Wei Chen
- Department of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
| | - Ray-Ming Lin
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Liann-Be Chang
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan, Taoyuan 333, Taiwan
| |
Collapse
|
33
|
郝 哲, 岳 蜀, 周 利. [Application of Raman-based technologies in the detection of urological tumors]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2022; 54:779-784. [PMID: 35950408 PMCID: PMC9385527 DOI: 10.19723/j.issn.1671-167x.2022.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Indexed: 06/15/2023]
Abstract
Urinary system tumors affect a huge number of individuals, and are frequently recurrent and progressing following surgery, necessitating lifelong surveillance. As a result, early and precise diagnosis of urinary system cancers is important for prevention and therapy. Histopathology is now the golden stan-dard for the diagnosis, but it is invasive, time-consuming, and inconvenient for initial diagnosis and re-gular follow-up assessment. Endoscopy can directly witness the tumor's structure, but intrusive detection is likely to cause harm to the patient's organs, and it is apt to create other hazards in frequently examined patients. Imaging is a valuable non-invasive and quick assessment tool; however, it can be difficult to define the type of lesions and has limited sensitivity for early tumor detection. The conventional approaches for detecting tumors have their own set of limitations. Thus, detection methods that combine non-invasive detection, label-free detection, high sensitivity and high specificity are urgently needed to aid clinical diagnosis. Optical diagnostics and imaging are increasingly being employed in healthcare settings in a variety of sectors. Raman scattering can assess changes in molecular signatures in cancer cells or tissues based on the interaction with vibrational modes of common molecular bonds. Due to the advantages of label-free, strong chemical selectivity, and high sensitivity, Raman scattering, especially coherent Raman scattering microscopy imaging with high spatial resolution, has been widely used in biomedical research. And quantity studies have shown that it has a good application in the detection and diagnosis of bladder can-cer, renal clear cell carcinoma, prostate cancer, and other cancers. In this paper, several nonlinear imaging techniques based on Raman scattering technology are briefly described, including Raman spectroscopy, coherent anti-Stokes Raman scattering, stimulated Raman scattering, and surface-enhanced Raman spectroscopy. And we will discuss the application of these techniques for detecting urologic malignancy. Future research directions are predicted using the advantages and limitations of the aforesaid methodologies in the research. For clinical practice, Raman scattering technology is intended to enable more accurate, rapid, and non-invasive in early diagnosis, intraoperative margins, and pathological grading basis for clinical practice.
Collapse
Affiliation(s)
- 哲 郝
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
| | - 蜀华 岳
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
| | - 利群 周
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
| |
Collapse
|
34
|
Wang J, Zhang G. Side‐viewing handheld confocal Raman probe coupled with an off‐axis parabolic mirror for superficial epithelial Raman measurements of luminal organs. TRANSLATIONAL BIOPHOTONICS 2022. [DOI: 10.1002/tbio.202200010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Jianfeng Wang
- Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics Beijing Institute of Technology Beijing China
- Institute of Engineering Medicine, Beijing Institute of Technology Beijing China
| | - Guling Zhang
- College of Science Minzu University of China Beijing China
| |
Collapse
|
35
|
Paidi SK, Troncoso JR, Harper MG, Liu Z, Nguyen KG, Ravindranathan S, Rebello L, Lee DE, Ivers JD, Zaharoff DA, Rajaram N, Barman I. Raman spectroscopy reveals phenotype switches in breast cancer metastasis. Theranostics 2022; 12:5351-5363. [PMID: 35910801 PMCID: PMC9330538 DOI: 10.7150/thno.74002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022] Open
Abstract
The accurate analytical characterization of metastatic phenotype at primary tumor diagnosis and its evolution with time are critical for controlling metastatic progression of cancer. Here, we report a label-free optical strategy using Raman spectroscopy and machine learning to identify distinct metastatic phenotypes observed in tumors formed by isogenic murine breast cancer cell lines of progressively increasing metastatic propensities. Methods: We employed the 4T1 isogenic panel of murine breast cancer cells to grow tumors of varying metastatic potential and acquired label-free spectra using a fiber probe-based portable Raman spectroscopy system. We used MCR-ALS and random forests classifiers to identify putative spectral markers and predict metastatic phenotype of tumors based on their optical spectra. We also used tumors derived from 4T1 cells silenced for the expression of TWIST, FOXC2 and CXCR3 genes to assess their metastatic phenotype based on their Raman spectra. Results: The MCR-ALS spectral decomposition showed consistent differences in the contribution of components that resembled collagen and lipids between the non-metastatic 67NR tumors and the metastatic tumors formed by FARN, 4T07, and 4T1 cells. Our Raman spectra-based random forest analysis provided evidence that machine learning models built on spectral data can allow the accurate identification of metastatic phenotype of independent test tumors. By silencing genes critical for metastasis in highly metastatic cell lines, we showed that the random forest classifiers provided predictions consistent with the observed phenotypic switch of the resultant tumors towards lower metastatic potential. Furthermore, the spectral assessment of lipid and collagen content of these tumors was consistent with the observed phenotypic switch. Conclusion: Overall, our findings indicate that Raman spectroscopy may offer a novel strategy to evaluate metastatic risk during primary tumor biopsies in clinical patients.
Collapse
Affiliation(s)
- Santosh Kumar Paidi
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218
| | | | - Mason G. Harper
- University of Arkansas for Medical Sciences, Little Rock, AR, 72205
| | - Zhenhui Liu
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218
| | - Khue G. Nguyen
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, NC, 27695
| | | | - Lisa Rebello
- Cell and Molecular Biology Program, University of Arkansas, Fayetteville, AR 72701
| | - David E. Lee
- Department of Health, Human Performance, and Recreation, University of Arkansas, Fayetteville, AR, 72701
| | - Jesse D. Ivers
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR 72701
| | - David A. Zaharoff
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, NC, 27695
| | - Narasimhan Rajaram
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR 72701
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, 72205
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205
- Department of Oncology, Johns Hopkins University, Baltimore, MD, 21287
| |
Collapse
|
36
|
Melitto AS, Arias VEA, Shida JY, Gebrim LH, Silveira L. Diagnosing molecular subtypes of breast cancer by means of Raman spectroscopy. Lasers Surg Med Suppl 2022; 54:1143-1156. [PMID: 35789102 DOI: 10.1002/lsm.23580] [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: 10/21/2021] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Raman spectroscopy has been used to discriminate human breast cancer and its different tumor molecular subtypes (luminal A, luminal B, HER2, and triple-negative) from normal tissue in surgical specimens. MATERIALS AND METHODS Breast cancer and normal tissue samples from 31 patients were obtained by surgical resection and submitted for histopathology. Before anatomopathological processing, the samples had been submitted to Raman spectroscopy (830 nm, 25 mW excitation laser parameters). In total, 424 Raman spectra were obtained. Principal component analysis (PCA) was used in an exploratory analysis to unveil the compositional differences between the tumors and normal tissues. Discriminant models were developed to distinguish the different cancer subtypes by means of partial least squares (PLS) regression. RESULTS PCA vectors showed spectral features referred to the biochemical constitution of breast tissues, such as lipids, proteins, amino acids, and carotenoids, where lipids were decreased and proteins were increased in breast tumors. Despite the small spectral differences between the different subtypes of tumor and normal tissues, the discriminant model based on PLS was able to discriminate the spectra of the breast tumors from normal tissues with an accuracy of 97.3%, between luminal and nonluminal subtypes with an accuracy of 89.9%, between nontriple-negative and triple-negative with an accuracy of 94.7%, and each molecular subtype with an accuracy of 73.0%. CONCLUSION PCA could reveal the compositional difference between tumors and normal tissues, and PLS could discriminate the Raman spectra of breast tissues regarding the molecular subtypes of cancer, being a useful tool for cancer diagnosis.
Collapse
Affiliation(s)
| | - Victor E A Arias
- Biomedical Engineering Program, Universidade Anhembi Morumbi-UAM, São Paulo, SP, Brazil
| | - Jorge Y Shida
- Biomedical Engineering Program, Universidade Anhembi Morumbi-UAM, São Paulo, SP, Brazil
| | - Luiz H Gebrim
- Biomedical Engineering Program, Universidade Anhembi Morumbi-UAM, São Paulo, SP, Brazil
| | - Landulfo Silveira
- Mastology Department, CRSM-Hospital Pérola Byington, São Paulo, SP, Brazil.,Biomedical Engineering Institute, Center for Innovation, Technology and Education-CITÉ, São José dos Camp, SP, Brazil
| |
Collapse
|
37
|
Rani C, Tanwar M, Kandpal S, Ghosh T, Bansal L, Kumar R. Nonlinear Temperature-Dependent Phonon Decay in Heavily Doped Silicon: Predominant Interferon-Mediated Cold Phonon Annihilation. J Phys Chem Lett 2022; 13:5232-5239. [PMID: 35670640 DOI: 10.1021/acs.jpclett.2c01248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A nonlinear Fano interaction has been reported here which is manifest in terms of a parabolic temperature-dependent phonon decay process observable in terms of a Raman spectral parameter. Temperature-dependent Raman spectroscopic studies have been carried out on heavily and moderately doped crystalline silicon to investigate the behavior of anharmonic phonon decay in semiconductor systems where Fano interactions are present inherently. Systematic study reveals that in heavily doped systems an interferon-mediated decay route exists for cold phonons present at lower temperatures (<475 K) where Fano coupling is stronger and dominates over the typical multiple-phonon decay process. On the other hand, the anharmonic phonon decay remains the predominant process at higher temperatures irrespective of the doping level. Temperature-dependent phonon self-energy has been calculated using experimentally observed Raman line-shape parameters to validate the fact that the nonlinear decay of phonons through interferon mediation is a thermodynamically favorable process at low temperatures.
Collapse
Affiliation(s)
- Chanchal Rani
- Materials and Device Laboratory, Department of Physics, Indian Institute of Technology Indore, Simrol 453552, India
| | - Manushree Tanwar
- Materials and Device Laboratory, Department of Physics, Indian Institute of Technology Indore, Simrol 453552, India
| | - Suchita Kandpal
- Materials and Device Laboratory, Department of Physics, Indian Institute of Technology Indore, Simrol 453552, India
| | - Tanushree Ghosh
- Materials and Device Laboratory, Department of Physics, Indian Institute of Technology Indore, Simrol 453552, India
| | - Love Bansal
- Materials and Device Laboratory, Department of Physics, Indian Institute of Technology Indore, Simrol 453552, India
| | - Rajesh Kumar
- Materials and Device Laboratory, Department of Physics, Indian Institute of Technology Indore, Simrol 453552, India
| |
Collapse
|
38
|
Hassan M, Ali S, Saleem M, Sanaullah M, Fahad LG, Kim JY, Alquhayz H, Tahir SF. Diagnosis of dengue virus infection using spectroscopic images and deep learning. PeerJ Comput Sci 2022; 8:e985. [PMID: 35721412 PMCID: PMC9202626 DOI: 10.7717/peerj-cs.985] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/26/2022] [Indexed: 06/15/2023]
Abstract
Dengue virus (DENV) infection is one of the major health issues and a substantial epidemic infectious human disease. More than two billion humans are living in dengue susceptible regions with annual infection mortality rate is about 5%-20%. At initial stages, it is difficult to differentiate dengue virus symptoms with other similar diseases. The main objective of this research is to diagnose dengue virus infection in human blood sera for better treatment and rehabilitation process. A novel and robust approach is proposed based on Raman spectroscopy and deep learning. In this regard, the ResNet101 deep learning model is modified by exploiting transfer learning (TL) concept on Raman spectroscopic data of human blood sera. Sample size was selected using standard statistical tests. The proposed model is evaluated on 2,000 Raman spectra images in which 1,200 are DENV-infected of human blood sera samples, and 800 are healthy ones. It offers 96.0% accuracy on testing data for DENV infection diagnosis. Moreover, the developed approach demonstrated minimum improvement of 6.0% and 7.0% in terms of AUC and Kappa index respectively over the other state-of-the-art techniques. The developed model offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models. The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives.
Collapse
Affiliation(s)
- Mehdi Hassan
- Department of Computer Science, Air University, Islamabad, Pakistan
- Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, South Korea
| | - Safdar Ali
- Directorate of National Repository, Islamabad, Pakistan
| | - Muhammad Saleem
- Agriculture & Biophotonics Division, National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences (NILOP-C, PIEAS), Lehtrar Road, Nilore, Islamabad, Pakistan
| | - Muhammad Sanaullah
- Department of Computer Science, Bahaudian Zakaria University, Multan, Pakistan
| | - Labiba Gillani Fahad
- Department of Computer Science, National University of Computing and Emerging Sciences, FAST-NUCES, Islamabad, Pakistan
| | - Jin Young Kim
- Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, South Korea
| | - Hani Alquhayz
- Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, Saudi Arabia
| | - Syed Fahad Tahir
- Department of Computer Science, Air University, Islamabad, Pakistan
| |
Collapse
|
39
|
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: 43] [Impact Index Per Article: 21.5] [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.
Collapse
|
40
|
Kowalska AA, Czaplicka M, Nowicka AB, Chmielewska I, Kędra K, Szymborski T, Kamińska A. Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy. Biomedicines 2022; 10:biomedicines10050993. [PMID: 35625729 PMCID: PMC9138770 DOI: 10.3390/biomedicines10050993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/20/2022] [Accepted: 04/23/2022] [Indexed: 11/22/2022] Open
Abstract
We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.
Collapse
Affiliation(s)
- Aneta Aniela Kowalska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
- Correspondence: (A.A.K.); (A.K.)
| | - Marta Czaplicka
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
| | - Ariadna B. Nowicka
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
| | - Izabela Chmielewska
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, Jaczewskiego 8, 20-090 Lublin, Poland;
| | - Karolina Kędra
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
| | - Tomasz Szymborski
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
| | - Agnieszka Kamińska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
- Correspondence: (A.A.K.); (A.K.)
| |
Collapse
|
41
|
Parlatan U, Parlatan S, Sen K, Kecoglu I, Ulukan MO, Karakaya A, Erkanli K, Turkoglu H, Ugurlucan M, Unlu MB, Tanoren B. Atrial fibrillation designation with micro-Raman spectroscopy and scanning acoustic microscope. Sci Rep 2022; 12:6461. [PMID: 35440791 PMCID: PMC9018680 DOI: 10.1038/s41598-022-10380-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/31/2022] [Indexed: 11/09/2022] Open
Abstract
Atrial fibrillation (AF) is diagnosed with the electrocardiogram, which is the gold standard in clinics. However, sufficient arrhythmia monitoring takes a long time, and many of the tests are made in only a few seconds, which can lead arrhythmia to be missed. Here, we propose a combined method to detect the effects of AF on atrial tissue. We characterize tissues obtained from patients with or without AF by scanning acoustic microscopy (SAM) and by Raman spectroscopy (RS) to construct a mechano-chemical profile. We classify the Raman spectral measurements of the tissue samples with an unsupervised clustering method, k-means and compare their chemical properties. Besides, we utilize scanning acoustic microscopy to compare and determine differences in acoustic impedance maps of the groups. We compared the clinical outcomes with our findings using a neural network classification for Raman measurements and ANOVA for SAM measurements. Consequently, we show that the stiffness profiles of the tissues, corresponding to the patients with chronic AF, without AF or who experienced postoperative AF, are in agreement with the lipid-collagen profiles obtained by the Raman spectral characterization.
Collapse
Affiliation(s)
- Ugur Parlatan
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey.
| | - Seyma Parlatan
- Vocational School of Health Services, Istinye University, Istanbul, 34020, Turkey
| | - Kubra Sen
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
| | - Ibrahim Kecoglu
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
| | - Mustafa Ozer Ulukan
- Department of Cardiovascular Surgery, Istanbul Medipol University, Istanbul, 34214, Turkey
| | - Atalay Karakaya
- Department of Cardiovascular Surgery, Istanbul Medipol University, Istanbul, 34214, Turkey
| | - Korhan Erkanli
- Department of Cardiovascular Surgery, Istanbul Medipol University, Istanbul, 34214, Turkey
| | - Halil Turkoglu
- Department of Cardiovascular Surgery, Istanbul Medipol University, Istanbul, 34214, Turkey
| | - Murat Ugurlucan
- Department of Cardiovascular Surgery, Istanbul Medipol University, Istanbul, 34214, Turkey
| | - Mehmet Burcin Unlu
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey.,Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Sapporo, Japan
| | - Bukem Tanoren
- Department of Natural Sciences, Acıbadem University, Istanbul, 34684, Turkey
| |
Collapse
|
42
|
Gaba F, Tipping WJ, Salji M, Faulds K, Graham D, Leung HY. Raman Spectroscopy in Prostate Cancer: Techniques, Applications and Advancements. Cancers (Basel) 2022; 14:cancers14061535. [PMID: 35326686 PMCID: PMC8946151 DOI: 10.3390/cancers14061535] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 02/04/2023] Open
Abstract
Optical techniques are widely used tools in the visualisation of biological species within complex matrices, including biopsies, tissue resections and biofluids. Raman spectroscopy is an emerging analytical approach that probes the molecular signature of endogenous cellular biomolecules under biocompatible conditions with high spatial resolution. Applications of Raman spectroscopy in prostate cancer include biopsy analysis, assessment of surgical margins and monitoring of treatment efficacy. The advent of advanced Raman imaging techniques, such as stimulated Raman scattering, is creating opportunities for real-time in situ evaluation of prostate cancer. This review provides a focus on the recent preclinical and clinical achievements in implementing Raman-based techniques, highlighting remaining challenges for clinical applications. The research and clinical results achieved through in vivo and ex vivo Raman spectroscopy illustrate areas where these evolving technologies can be best translated into clinical practice.
Collapse
Affiliation(s)
- Fortis Gaba
- Department of Urology, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow G51 4TF, UK; (F.G.); (M.S.)
- School of Medicine, University of Glasgow, Glasgow G12 8QQ, UK
| | - William J. Tipping
- Department for Pure and Applied Chemistry, University of Strathclyde, Glasgow G1 1RD, UK; (W.J.T.); (K.F.); (D.G.)
| | - Mark Salji
- Department of Urology, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow G51 4TF, UK; (F.G.); (M.S.)
- Institute of Cancer Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G61 1QH, UK
- CRUK Beatson Institute, Bearsden, Glasgow G61 1BD, UK
| | - Karen Faulds
- Department for Pure and Applied Chemistry, University of Strathclyde, Glasgow G1 1RD, UK; (W.J.T.); (K.F.); (D.G.)
| | - Duncan Graham
- Department for Pure and Applied Chemistry, University of Strathclyde, Glasgow G1 1RD, UK; (W.J.T.); (K.F.); (D.G.)
| | - Hing Y. Leung
- Department of Urology, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow G51 4TF, UK; (F.G.); (M.S.)
- Institute of Cancer Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G61 1QH, UK
- CRUK Beatson Institute, Bearsden, Glasgow G61 1BD, UK
- Correspondence:
| |
Collapse
|
43
|
Redox state changes of mitochondrial cytochromes in brain and breast cancers by Raman spectroscopy and imaging. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2021.132134] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
44
|
Geldof F, Dashtbozorg B, Hendriks BHW, Sterenborg HJCM, Ruers TJM. Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy. Sci Rep 2022; 12:1698. [PMID: 35105926 PMCID: PMC8807816 DOI: 10.1038/s41598-022-05751-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/12/2022] [Indexed: 11/26/2022] Open
Abstract
During oncological surgery, it can be challenging to identify the tumor and establish adequate resection margins. This study proposes a new two-layer approach in which diffuse reflectance spectroscopy (DRS) is used to predict the top layer thickness and classify the layers in two-layered phantom and animal tissue. Using wavelet-based and peak-based DRS spectral features, the proposed method could predict the top layer thickness with an accuracy of up to 0.35 mm. In addition, the tissue types of the first and second layers were classified with an accuracy of 0.95 and 0.99. Distinguishing multiple tissue layers during spectral analyses results in a better understanding of more complex tissue structures encountered in surgical practice.
Collapse
Affiliation(s)
- Freija Geldof
- Department of Surgery, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.
| | - Behdad Dashtbozorg
- Department of Surgery, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Benno H W Hendriks
- Department of IGT and US Devices & Systems, Philips Research Laboratories, 5656 AE, Eindhoven, The Netherlands
- Department of BioMechanical Engineering, 3mE, Delft University of Technology, 2628 CD, Delft, The Netherlands
| | - Henricus J C M Sterenborg
- Department of Surgery, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, 1105 AZ, Amsterdam, The Netherlands
| | - Theo J M Ruers
- Department of Surgery, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
- Faculty of Science and Technology, University of Twente, 7522 NB, Enschede, The Netherlands
| |
Collapse
|
45
|
Who's Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy. Cancers (Basel) 2022; 14:cancers14020452. [PMID: 35053613 PMCID: PMC8773714 DOI: 10.3390/cancers14020452] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/05/2022] [Accepted: 01/12/2022] [Indexed: 12/14/2022] Open
Abstract
(1) Breast cancer is presently the leading cause of death in women worldwide. This study aims at identifying molecular biomarkers of cancer in human breast cancer cells, in order to differentiate highly aggressive triple-negative from non-triple-negative cancers, as well as distinct triple-negative subtypes, which is currently an unmet clinical need paramount for an improved patient care. (2) Raman and FTIR (Fourier transform infrared) microspectroscopy state-of-the-art techniques were applied, as highly sensitive, specific and non-invasive methods for probing heterogeneous biological samples such as human cells. (3) Particular biochemical features of malignancy were unveiled based on the cells' vibrational signature, upon principal component analysis of the data. This enabled discrimination between TNBC (triple-negative breast cancer) and non-TNBC, TNBC MSL (mesenchymal stem cell-like) and TNBC BL1 (basal-like 1) and TNBC BL1 highly metastatic and low-metastatic cell lines. This specific differentiation between distinct TNBC subtypes-mesenchymal from basal-like, and basal-like 1 with high-metastatic potential from basal-like 1 with low-metastatic potential-is a pioneer result, of potential high impact in cancer diagnosis and treatment.
Collapse
|
46
|
Zhang L, Li C, Peng D, Yi X, He S, Liu F, Zheng X, Huang WE, Zhao L, Huang X. Raman spectroscopy and machine learning for the classification of breast cancers. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 264:120300. [PMID: 34455388 DOI: 10.1016/j.saa.2021.120300] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/26/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
Breast cancer is a major health threat for women. The drug responses associated with different breast cancer subtypes have obvious effects on therapeutic outcomes; therefore, the accurate classification of breast cancer subtypes is critical. Breast cancer subtype classification has recently been examined using various methods, and Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the accurate and rapid classification of breast cancer subtypes currently requires a great deal of effort and experience with the processing and analysis of Raman spectra data. Here, we adopted Raman spectroscopy and machine learning techniques to simplify and accelerate the process used to distinguish normal from breast cancer cells and classify breast cancer subtypes. Raman spectra were obtained from cultured breast cancer cell lines, and the data were analyzed by two machine learning algorithms: principal component analysis (PCA)-discriminant function analysis (DFA) and PCA-support vector machine (SVM). The accuracies with which these two algorithms were able to distinguish normal breast cells from breast cancer cells were both greater than 97%, and the accuracies of breast cancer subtype classification for both algorithms were both greater than 92%. Moreover, our results showed evidence to support the use of characteristic Raman spectral features as cancer cell biomarkers, such as the intensity of intrinsic Raman bands, which increased in cancer cells. Raman spectroscopy combined with machine learning techniques provides a rapid method for breast cancer analysis able to reveal differences in intracellular compositions and molecular structures among subtypes.
Collapse
Affiliation(s)
- Lihao Zhang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Keling Road, Suzhou, Jiangsu Province, 215163, China
| | - Chengjian Li
- Department of Pharmacy, Shanghai Baoshan Luodian Hospital, Baoshan District, Shanghai, 201908, China; Luodian Clinical Drug Research Center, Institute for Translational Medicine Research, Shanghai University, Shanghai, 200444, China
| | - Di Peng
- Shanghai D-band Medical Instrument Co., Ltd, Huyi Highway, Jiading District, Shanghai, 201800, China
| | - Xiaofei Yi
- Shanghai D-band Medical Instrument Co., Ltd, Huyi Highway, Jiading District, Shanghai, 201800, China
| | - Shuai He
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Keling Road, Suzhou, Jiangsu Province, 215163, China
| | - Fengxiang Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Keling Road, Suzhou, Jiangsu Province, 215163, China
| | - Xiangtai Zheng
- Luodian Clinical Drug Research Center, Institute for Translational Medicine Research, Shanghai University, Shanghai, 200444, China
| | - Wei E Huang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
| | - Liang Zhao
- Department of Pharmacy, Shanghai Baoshan Luodian Hospital, Baoshan District, Shanghai, 201908, China; Luodian Clinical Drug Research Center, Institute for Translational Medicine Research, Shanghai University, Shanghai, 200444, China.
| | - Xia Huang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Keling Road, Suzhou, Jiangsu Province, 215163, China; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.
| |
Collapse
|
47
|
Sloan-Dennison S, Laing S, Graham D, Faulds K. From Raman to SESORRS: moving deeper into cancer detection and treatment monitoring. Chem Commun (Camb) 2021; 57:12436-12451. [PMID: 34734952 PMCID: PMC8609625 DOI: 10.1039/d1cc04805h] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Raman spectroscopy is a non-invasive technique that allows specific chemical information to be obtained from various types of sample. The detailed molecular information that is present in Raman spectra permits monitoring of biochemical changes that occur in diseases, such as cancer, and can be used for the early detection and diagnosis of the disease, for monitoring treatment, and to distinguish between cancerous and non-cancerous biological samples. Several techniques have been developed to enhance the capabilities of Raman spectroscopy by improving detection sensitivity, reducing imaging times and increasing the potential applicability for in vivo analysis. The different Raman techniques each have their own advantages that can accommodate the alternative detection formats, allowing the techniques to be applied in several ways for the detection and diagnosis of cancer. This feature article discusses the various forms of Raman spectroscopy, how they have been applied for cancer detection, and the adaptation of the techniques towards their use for in vivo cancer detection and in clinical diagnostics. Despite the advances in Raman spectroscopy, the clinical application of the technique is still limited and certain challenges must be overcome to enable clinical translation. We provide an outlook on the future of the techniques in this area and what we believe is required to allow the potential of Raman spectroscopy to be achieved for clinical cancer diagnostics.
Collapse
Affiliation(s)
- Sian Sloan-Dennison
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK.
| | - Stacey Laing
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK.
| | - Duncan Graham
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK.
| | - Karen Faulds
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK.
| |
Collapse
|
48
|
Penders J, Nagelkerke A, Cunnane EM, Pedersen SV, Pence IJ, Coombes RC, Stevens MM. Single Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers. ACS NANO 2021; 15:18192-18205. [PMID: 34735133 PMCID: PMC9286313 DOI: 10.1021/acsnano.1c07075] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysis─SPARTA─system through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (>95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.
Collapse
Affiliation(s)
- Jelle Penders
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - Anika Nagelkerke
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - Eoghan M. Cunnane
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - Simon V. Pedersen
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - Isaac J. Pence
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
| | - R. Charles Coombes
- Department
of Surgery and Cancer, Hammersmith Hospital, Imperial College, London W120HS, United Kingdom
| | - Molly M. Stevens
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
- Institute
of Biomedical Engineering, Imperial College
London, London SW7 2AZ, United Kingdom
- E-mail:
| |
Collapse
|
49
|
Raman spectroscopic analysis of skin as a diagnostic tool for Human African Trypanosomiasis. PLoS Pathog 2021; 17:e1010060. [PMID: 34780575 PMCID: PMC8629383 DOI: 10.1371/journal.ppat.1010060] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 11/29/2021] [Accepted: 10/23/2021] [Indexed: 02/08/2023] Open
Abstract
Human African Trypanosomiasis (HAT) has been responsible for several deadly epidemics throughout the 20th century, but a renewed commitment to disease control has significantly reduced new cases and motivated a target for the elimination of Trypanosoma brucei gambiense-HAT by 2030. However, the recent identification of latent human infections, and the detection of trypanosomes in extravascular tissues hidden from current diagnostic tools, such as the skin, has added new complexity to identifying infected individuals. New and improved diagnostic tests to detect Trypanosoma brucei infection by interrogating the skin are therefore needed. Recent advances have improved the cost, sensitivity and portability of Raman spectroscopy technology for non-invasive medical diagnostics, making it an attractive tool for gambiense-HAT detection. The aim of this work was to assess and develop a new non-invasive diagnostic method for T. brucei through Raman spectroscopy of the skin. Infections were performed in an established murine disease model using the animal-infective Trypanosoma brucei brucei subspecies. The skin of infected and matched control mice was scrutinized ex vivo using a confocal Raman microscope with 532 nm excitation and in situ at 785 nm excitation with a portable field-compatible instrument. Spectral evaluation and Principal Component Analysis confirmed discrimination of T. brucei-infected from uninfected tissue, and a characterisation of biochemical changes in lipids and proteins in parasite-infected skin indicated by prominent Raman peak intensities was performed. This study is the first to demonstrate the application of Raman spectroscopy for the detection of T. brucei by targeting the skin of the host. The technique has significant potential to discriminate between infected and non-infected tissue and could represent a unique, non-invasive diagnostic tool in the goal for elimination of gambiense-HAT as well as for Animal African Trypanosomiasis (AAT). Human African Trypanosomiasis (HAT), also known as sleeping sickness, is a disease caused by the parasite Trypanosoma brucei and has been responsible for the death of millions of people across Africa in the 20th century. It is also a major economic burden for countries endemic for trypanosomiasis, affecting livestock productivity in rural areas (Animal African Trypanosomiasis). A long-term international collaboration with the help of the World Health Organisation has resulted in the rate of human infection decreasing to less than 1000 new cases per year. However, the human disease continues to spread within remote villages. Current diagnosis is based on the detection of parasites in blood and serum samples, but this is challenging during chronic human infections with low or non-detectable parasitaemia. However, the recent discovery of extravascular skin-dwelling trypanosomes indicates that a reservoir of infection remains undetected, threatening the effort to eliminate the disease. In this study we have targeted the skin as a site for diagnosis using Raman spectroscopy and demonstrate that this method showed great promise in the laboratory, laying the foundation for field studies to examine its potential to strengthen current diagnostic strategies for detecting HAT cases.
Collapse
|
50
|
Tanwar S, Paidi SK, Prasad R, Pandey R, Barman I. Advancing Raman spectroscopy from research to clinic: Translational potential and challenges. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 260:119957. [PMID: 34082350 DOI: 10.1016/j.saa.2021.119957] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/08/2021] [Accepted: 05/10/2021] [Indexed: 05/18/2023]
Abstract
Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique due to its ability to provide molecule-specific information with ultrahigh sensitivity at near-physiological conditions. Despite exhibiting substantial potential, its translation from optical bench to clinical settings has been impacted by associated limitations. This perspective discusses recent clinical and biomedical applications of Raman spectroscopy and technological advancements that provide valuable insights and encouragement for resolving some of the most challenging hurdles.
Collapse
Affiliation(s)
- Swati Tanwar
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Santosh Kumar Paidi
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Ram Prasad
- Department of Botany, School of Life Sciences, Mahatma Gandhi Central University, Motihari, Bihar 845401, India
| | - Rishikesh Pandey
- CytoVeris Inc., Farmington, CT 06032, United States; Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, United States.
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States; The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University, School of Medicine, Baltimore, MD 21205, United States; Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, United States.
| |
Collapse
|