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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 13955

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Special Issue Editor

Dr. Ivan Bratchenko

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Guest Editor

Department of Laser and Biotechnical Systems, Samara National Research University, Samara 443086, Russia
Interests: translational biophotonics; classification; Raman spectroscopy; optical biopsy; liquid biopsy; statistical analysis; statistical models stability

Special Issue Information

Dear Colleagues,

Raman spectroscopy (RS) can provide an information on the chemical composition of tested samples at the molecular level and help to track even the smallest changes in the chemical composition of tested tissues and biofluids. At the same time, the complexity and multicollinearity of Raman spectral from biological samples makes the extraction of molecular composition a non-trivial task. A solution of this problem provides an opportunity to create reliable approaches for precise composition determination and the further diagnosis of diseases based on spectral comparison estimations between healthy subjects and patients. Currently, advanced machine learning techniques pave the way for RS to overcome the described problems and become a routinely used approach in clinical practice. This Special Issue of the International Journal of Molecular Sciences focuses on the molecular origin of Raman spectra and RS applications in molecular medicine. In order to demonstrate the specific origination of Raman bands utilized in medical analysis, RS studies should be complemented with chemical analysis approaches that are already utilized in biochemical practice. Translational studies of RS to real clinical fields are welcomed and may include the application of different RS modalities and different approaches of advanced machine learning techniques for the determination of the presence and severity of human diseases. Potential topics of the Special Issue may include (but are not limited to) conventional RS, surface-enhanced RS, stimulated RS, coherent RS, and other RS applications, as well as machine learning methods such as principal component analysis, projection on latent structures, neural networks, decision trees, support vector machines, multivariate curve resolution, and their numeral modifications.

Dr. Ivan Bratchenko
Guest Editor

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Keywords

Raman spectroscopy
machine learning
disease
molecular diagnostics
optical biopsy
liquid biopsy

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Published Papers (7 papers)

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Research

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14 pages, 1928 KiB

Open AccessArticle

Unlocking Preclinical Alzheimer’s: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics

by Eneko Lopez, Jaione Etxebarria-Elezgarai, Maite García-Sebastián, Miren Altuna, Mirian Ecay-Torres, Ainara Estanga, Mikel Tainta, Carolina López, Pablo Martínez-Lage, Jose Manuel Amigo and Andreas Seifert

Int. J. Mol. Sci. 2024, 25(9), 4737; https://doi.org/10.3390/ijms25094737 - 26 Apr 2024

Cited by 1 | Viewed by 1511

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer’s encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer’s still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer’s diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer’s, which is particularly surprising because of Raman spectroscopy’s high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer’s disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods. Full article

(This article belongs to the Special Issue Raman Spectroscopy and Machine Learning in Human Disease)

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27 pages, 5049 KiB

Open AccessArticle

Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques

by Bogdan Adrian Buhas, Valentin Toma, Jean-Baptiste Beauval, Iulia Andras, Răzvan Couți, Lucia Ana-Maria Muntean, Radu-Tudor Coman, Teodor Andrei Maghiar, Rareș-Ionuț Știufiuc, Constantin Mihai Lucaciu and Nicolae Crisan

Int. J. Mol. Sci. 2024, 25(7), 3891; https://doi.org/10.3390/ijms25073891 - 31 Mar 2024

Cited by 2 | Viewed by 1788

Abstract

The advent of Surface-Enhanced Raman Scattering (SERS) has enabled the exploration and detection of small molecules, particularly in biological fluids such as serum, blood plasma, urine, saliva, and tears. SERS has been proposed as a simple diagnostic technique for various diseases, including cancer. Renal cell carcinoma (RCC) ranks as the sixth most commonly diagnosed cancer in men and is often asymptomatic, with detection occurring incidentally. The onset of symptoms typically aligns with advanced disease, aggressive histology, and unfavorable prognosis, and therefore new methods for an early diagnosis are needed. In this study, we investigated the utility of label-free SERS in urine, coupled with two multivariate analysis approaches: Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM), to discriminate between 50 RCC patients and 44 healthy donors. Employing LDA-PCA, we achieved a discrimination accuracy of 100% using 13 principal components, and an 88% accuracy in discriminating between different RCC stages. The SVM approach yielded a training accuracy of 100%, a validation accuracy of 99% for discriminating between RCC and controls, and an 80% accuracy for discriminating between stages. The comparative analysis of raw and normalized SERS spectral data shows that while raw data disclose relative concentration variations in urine metabolites between the two classes, the normalization of spectral data significantly improves the accuracy of discrimination. Moreover, the selection of principal components with markedly distinct scores between the two classes serves to alleviate overfitting risks and reduces the number of components employed for discrimination. We obtained the accuracy of the discrimination between the RCC patients cases and healthy donors of 90% for three PCs and a linear discrimination function, and a 88% accuracy of discrimination between stages using six PCs, mitigating practically the risk of overfitting and increasing the robustness of our analysis. Our findings underscore the potential of label-free SERS of urine in conjunction with chemometrics for non-invasive and early RCC detection. Full article

(This article belongs to the Special Issue Raman Spectroscopy and Machine Learning in Human Disease)

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14 pages, 5011 KiB

Open AccessArticle

Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis

by Maria Anthi Kouri, Maria Karnachoriti, Ellas Spyratou, Spyros Orfanoudakis, Dimitris Kalatzis, Athanassios G. Kontos, Ioannis Seimenis, Efstathios P. Efstathopoulos, Alexandra Tsaroucha and Maria Lambropoulou

Int. J. Mol. Sci. 2023, 24(23), 16582; https://doi.org/10.3390/ijms242316582 - 21 Nov 2023

Viewed by 1553

Abstract

Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal component analysis (PCA) was performed to facilitate discrimination between malignant and normal tissues and to highlight their biochemical differences using loading plots. A transfer learning model based on a one-dimensional convolutional neural network (1D-CNN) was employed for the Raman spectra data to assess the classification accuracy of Raman spectra in live animals. The 1D-CNN model yielded an 89.9% accuracy and 91.4% precision in tissue classification. Our results contribute to the field of Raman spectroscopy in cancer diagnosis, highlighting its promising role within clinical applications. Full article

(This article belongs to the Special Issue Raman Spectroscopy and Machine Learning in Human Disease)

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17 pages, 2010 KiB

Open AccessArticle

Atopic Dermatitis: Molecular Alterations between Lesional and Non-Lesional Skin Determined Noninvasively by In Vivo Confocal Raman Microspectroscopy

by Michael Zolotas, Johannes Schleusener, Jürgen Lademann, Martina C. Meinke, Georgios Kokolakis and Maxim E. Darvin

Int. J. Mol. Sci. 2023, 24(19), 14636; https://doi.org/10.3390/ijms241914636 - 27 Sep 2023

Viewed by 1566

Abstract

Atopic dermatitis (AD)/atopic eczema is a chronic relapsing inflammatory skin disease affecting nearly 14% of the adult population. An important pathogenetic pillar in AD is the disrupted skin barrier function (SBF). The atopic stratum corneum (SC) has been examined using several methods, including Raman microspectroscopy, yet so far, there is no depth-dependent analysis over the entire SC thickness. Therefore, we recruited 21 AD patients (9 female, 12 male) and compared the lesional (LAS) with non-lesional atopic skin (nLAS) in vivo with confocal Raman microspectroscopy. Our results demonstrated decreased total intercellular lipid and carotenoid concentrations, as well as a shift towards decreased orthorhombic lateral lipid organisation in LAS. Further, we observed a lower concentration of natural moisturising factor (NMF) and a trend towards increased strongly bound and decreased weakly bound water in LAS. Finally, LAS showed an altered secondary and tertiary keratin structure, demonstrating a more folded keratin state than nLAS. The obtained results are discussed in comparison with healthy skin and yield detailed insights into the atopic SC structure. LAS clearly shows molecular alterations at certain SC depths compared with nLAS which imply a reduced SBF. A thorough understanding of these alterations provides useful information on the aetiology of AD and for the development/control of targeted topical therapies. Full article

(This article belongs to the Special Issue Raman Spectroscopy and Machine Learning in Human Disease)

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16 pages, 2340 KiB

Open AccessArticle

Label-Free Human Disease Characterization through Circulating Cell-Free DNA Analysis Using Raman Spectroscopy

by Vassilis M. Papadakis, Christina Cheimonidi, Maria Panagopoulou, Makrina Karaglani, Paraskevi Apalaki, Klytaimnistra Katsara, George Kenanakis, Theodosis Theodosiou, Theodoros C. Constantinidis, Kalliopi Stratigi and Ekaterini Chatzaki

Int. J. Mol. Sci. 2023, 24(15), 12384; https://doi.org/10.3390/ijms241512384 - 3 Aug 2023

Cited by 1 | Viewed by 2450

Abstract

Circulating cell-free DNA (ccfDNA) is a liquid biopsy biomaterial attracting significant attention for the implementation of precision medicine diagnostics. Deeper knowledge related to its structure and biology would enable the development of such applications. In this study, we employed Raman spectroscopy to unravel the biomolecular profile of human ccfDNA in health and disease. We established reference Raman spectra of ccfDNA samples from healthy males and females with different conditions, including cancer and diabetes, extracting information about their chemical composition. Comparative observations showed a distinct spectral pattern in ccfDNA from breast cancer patients taking neoadjuvant therapy. Raman analysis of ccfDNA from healthy, prediabetic, and diabetic males uncovered some differences in their biomolecular fingerprints. We also studied ccfDNA released from human benign and cancer cell lines and compared it to their respective gDNA, confirming it mirrors its cellular origin. Overall, we explored for the first time Raman spectroscopy in the study of ccfDNA and provided spectra of samples from different sources. Our findings introduce Raman spectroscopy as a new approach to implementing liquid biopsy diagnostics worthy of further elaboration. Full article

(This article belongs to the Special Issue Raman Spectroscopy and Machine Learning in Human Disease)

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Review

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23 pages, 1460 KiB

Open AccessReview

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

by Yulia Khristoforova, Lyudmila Bratchenko and Ivan Bratchenko

Int. J. Mol. Sci. 2023, 24(21), 15605; https://doi.org/10.3390/ijms242115605 - 26 Oct 2023

Cited by 8 | Viewed by 2392

Abstract

Raman spectroscopy is a widely developing approach for noninvasive analysis that can provide information on chemical composition and molecular structure. High chemical specificity calls for developing different medical diagnostic applications based on Raman spectroscopy. This review focuses on the Raman-based techniques used in medical diagnostics and provides an overview of such techniques, possible areas of their application, and current limitations. We have reviewed recent studies proposing conventional Raman spectroscopy and surface-enhanced Raman spectroscopy for rapid measuring of specific biomarkers of such diseases as cardiovascular disease, cancer, neurogenerative disease, and coronavirus disease (COVID-19). As a result, we have discovered several most promising Raman-based applications to identify affected persons by detecting some significant spectral features. We have analyzed these approaches in terms of their potentially diagnostic power and highlighted the remaining challenges and limitations preventing their translation into clinical settings. Full article

(This article belongs to the Special Issue Raman Spectroscopy and Machine Learning in Human Disease)

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16 pages, 961 KiB

Open AccessReview

Raman Spectroscopy as a Potential Adjunct of Thyroid Nodule Evaluation: A Systematic Review

by Monika Kujdowicz, Dominika Januś, Anna Taczanowska-Niemczuk, Marek W. Lankosz and Dariusz Adamek

Int. J. Mol. Sci. 2023, 24(20), 15131; https://doi.org/10.3390/ijms242015131 - 13 Oct 2023

Cited by 4 | Viewed by 1807

Abstract

The incidence of thyroid nodules (TNs) is estimated at 36.5% and 23% in females and males, respectively. A single thyroid nodule is usually detected during ultrasound assessment in patients with symptoms of thyroid dysfunction or neck mass. TNs are classified as benign tumours (non-malignant hyperplasia), benign neoplasms (e.g., adenoma, a non-invasive follicular tumour with papillary nuclear features) or malignant carcinomas (follicular cell-derived or C-cell derived). The differential diagnosis is based on fine-needle aspiration biopsies and cytological assessment (which is burdened with the bias of subjectivity). Raman spectroscopy (RS) is a laser-based, semiquantitative technique which shows for oscillations of many chemical groups in one label-free measurement. RS, through the assessment of chemical content, gives insight into tissue state which, in turn, allows for the differentiation of disease on the basis of spectral characteristics. The purpose of this study was to report if RS could be useful in the differential diagnosis of TN. The Web of Science, PubMed, and Scopus were searched from the beginning of the databases up to the end of June 2023. Two investigators independently screened key data using the terms “Raman spectroscopy” and “thyroid”. From the 4046 records found initially, we identified 19 studies addressing the differential diagnosis of TNs applying the RS technique. The lasers used included 532, 633, 785, 830, and 1064 nm lines. The thyroid RS investigations were performed at the cellular and/or tissue level, as well as in serum samples. The accuracy of papillary thyroid carcinoma detection is approx. 90%. Furthermore, medullary, and follicular thyroid carcinoma can be detected with up to 100% accuracy. These results might be biased with low numbers of cases in some research and overfitting of models as well as the reference method. The main biochemical changes one can observe in malignancies are as follows: increase of protein, amino acids (like phenylalanine, tyrosine, and tryptophan), and nucleic acid content in comparison with non-malignant TNs. Herein, we present a review of the literature on the application of RS in the differential diagnosis of TNs. This technique seems to have powerful application potential in thyroid tumour diagnosis. Full article

(This article belongs to the Special Issue Raman Spectroscopy and Machine Learning in Human Disease)

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