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Kralova K, Kral M, Vrtelka O, Setnicka V. Comparative study of Raman spectroscopy techniques in blood plasma-based clinical diagnostics: A demonstration on Alzheimer's disease. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123392. [PMID: 37716043 DOI: 10.1016/j.saa.2023.123392] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/26/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023]
Abstract
Nowadays, there are still many diseases with limited or no reliable methods of early diagnosis. A popular approach in clinical diagnostic research is Raman spectroscopy, as a relatively simple, cost-effective, and high-throughput method for searching for disease-specific alterations in the composition of blood plasma. However, the high variability of the experimental designs, targeted diseases, or statistical processing in the individual studies makes it challenging to compare and compile the results to critically assess the applicability of Raman spectroscopy in real clinical practice. This study aimed to compare data from a single series of blood plasma samples of patients with Alzheimer's disease and non-demented elderly controls obtained by four different techniques/experimental setups - Raman spectroscopy with excitation at 532 and 785 nm, Raman optical activity, and surface-enhanced Raman scattering spectroscopy. The obtained results showed that the spectra from each Raman spectroscopy technique contain different information about biomolecules of blood plasma or their conformation and may, therefore, offer diverse points of view on underlying biochemical processes of the disease. The classification models based on the datasets generated by the three non-chiroptical variants of Raman spectroscopy exhibited comparable diagnostic performance, all reaching an accuracy close to or equal to 80%. Raman optical activity achieved only 60% classification accuracy, suggesting its limited applicability in the specific case of Alzheimer's disease diagnostics. The described differences in the outputs of the four utilized techniques/setups of Raman spectroscopy imply that their choice may crucially affect the acquired results and thus should be approached carefully concerning the specific purpose.
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Affiliation(s)
- Katerina Kralova
- Department of Analytical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Martin Kral
- Department of Physical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Ondrej Vrtelka
- Department of Analytical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Vladimir Setnicka
- Department of Analytical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic.
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Qian H, Shao X, Zhang H, Wang Y, Liu S, Pan J, Xue W. Diagnosis of urogenital cancer combining deep learning algorithms and surface-enhanced Raman spectroscopy based on small extracellular vesicles. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 281:121603. [PMID: 35868057 DOI: 10.1016/j.saa.2022.121603] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/02/2022] [Accepted: 07/04/2022] [Indexed: 06/15/2023]
Abstract
PURPOSE To identify and compare the capacities of serum and serum-derived small extracellular vesicles (EV) in diagnosis of common urogenital cancer combining Surface-enhanced Raman spectroscopy (SERS) and Convolutional Neural Networks (CNN). MATERIALS AND METHODS We collected serum samples from 32 patients with prostate cancer (PCa), 33 patients with renal cell cancer (RCC) and 30 patients with bladder cancer (BCa) as well as 35 healthy control (HC), which were thereafter used to enrich extracellular vesicles by ultracentrifuge. Label-free SERS was utilized to collect Raman spectra from serum and matched EV samples. We constructed CNN models to process SERS data for classification of malignant patients and healthy controls (HCs). RESULTS We collected 650 and 1206 spectra from serum and serum-derived EV, respectively. CNN models of EV spectra revealed high testing accuracies of 79.3%, 78.7% and 74.2% in diagnosis of PCa, RCC and BCa, respectively. In comparison, serum SERS-based CNN model had testing accuracies of 73.0%, 71.1%, 69.2% in PCa, RCC and BCa, respectively. Moreover, CNN models based on EV SERS data show significantly higher diagnostic capacities than matched serum CNN models with the area under curve (AUC) of 0.80, 0.88 and 0.74 in diagnosis of PCa, RCC and BCa, respectively. CONCLUSION Deep learning-based SERS analysis of EV has great potentials in diagnosis of urologic cancer outperforming serum SERS analysis, providing a novel tool in cancer screening.
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Affiliation(s)
- Hongyang Qian
- Department of Urology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xiaoguang Shao
- Department of Urology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Heng Zhang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, People's Republic of China
| | - Yan Wang
- Department of Urology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Shupeng Liu
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, People's Republic of China
| | - Jiahua Pan
- Department of Urology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
| | - Wei Xue
- Department of Urology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
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Qian H, Wang Y, Ma Z, Qian L, Shao X, Jin D, Cao M, Liu S, Chen H, Pan J, Xue W. Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy. Int J Nanomedicine 2022; 17:1635-1646. [PMID: 35411143 PMCID: PMC8994599 DOI: 10.2147/ijn.s354590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/14/2022] [Indexed: 01/01/2023] Open
Abstract
Objective To explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC). Patients and Methods SERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms. Results A total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm−1, 1328 cm−1 and 1455 cm−1. The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm−1, which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm−1 to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%. Conclusion We show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.
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Affiliation(s)
- Hongyang Qian
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Yiqiu Wang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Zehua Ma
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Lei Qian
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Xiaoguang Shao
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Di Jin
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Ming Cao
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Shupeng Liu
- Shanghai Institute for Advanced Communication and Data Science, Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, People’s Republic of China
| | - Haige Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Jiahua Pan
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
| | - Wei Xue
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China
- Correspondence: Wei Xue; Jiahua Pan, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 1630 Dongfang Road, Shanghai, 200127, People’s Republic of China, Tel +86 21 6838 3375, Email ;
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Grieve S, Puvvada N, Phinyomark A, Russell K, Murugesan A, Zed E, Hassan A, Legare JF, Kienesberger PC, Pulinilkunnil T, Reiman T, Scheme E, Brunt KR. Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy. Nanomedicine (Lond) 2021; 16:2175-2188. [PMID: 34547916 DOI: 10.2217/nnm-2021-0076] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.
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Affiliation(s)
- Stacy Grieve
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada.,IMPART investigator team, Canada
| | - Nagaprasad Puvvada
- Department of Pharmacology, Dalhousie University, Saint John, New Brunswick, Canada.,Department of Chemistry, Indrashil University, Gujarat, India
| | - Angkoon Phinyomark
- IMPART investigator team, Canada.,Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Kevin Russell
- Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| | - Alli Murugesan
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| | - Elizabeth Zed
- Department of Oncology, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Ansar Hassan
- IMPART investigator team, Canada.,Department of Cardiac Surgery, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Jean-Francois Legare
- IMPART investigator team, Canada.,Department of Cardiac Surgery, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Petra C Kienesberger
- IMPART investigator team, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada.,Department of Biochemistry & Molecular Biology, Dalhousie University, Saint John, New Brunswick, Canada
| | - Thomas Pulinilkunnil
- IMPART investigator team, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada.,Department of Biochemistry & Molecular Biology, Dalhousie University, Saint John, New Brunswick, Canada
| | - Tony Reiman
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada.,IMPART investigator team, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada.,Department of Oncology, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Erik Scheme
- IMPART investigator team, Canada.,Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| | - Keith R Brunt
- IMPART investigator team, Canada.,Department of Pharmacology, Dalhousie University, Saint John, New Brunswick, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
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