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Biosensors
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Raman Spectroscopy for Clinics
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Raman Spectroscopy for Clinics

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Optical and Photonic Biosensors".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 16778

Special Issue Editor

Dr. Rishikesh Pandey

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: Raman Spectroscopy; Biophotonics; label-free imaging

Special Issue Information

Dear Colleagues,

Raman spectroscopy has emerged as a non-invasive tool to investigate clinical biospecimens due to its unprecedented ability to probe water containing biological samples with exquisite molecular specificity. The advancements in instrumentation, miniaturization, and fiber-probe-based implementation have made it suitable for clinical application. The clinical usage has been further aided by the rapid progress in artificial intelligence and machine learning methods that enable the extraction of near inappreciable and subtle diagnostic differences from complex biological systems that are beyond human interpretability.

This Special Issue intends to compile recent innovations and advancements in the Raman spectroscopy field that unlock new prospects for clinical translation.

Dr. Rishikesh Pandey
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Raman spectroscopy
  • Raman microscopy
  • clinical diagnostic
  • label-free sensing
  • vibrational spectroscopy
  • surface-enhanced Raman spectroscopy
  • stimulated Raman

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Published Papers (5 papers)

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Research

Jump to: Review, Other

14 pages, 3007 KiB  
Article
SERS-Tags: Selective Immobilization and Detection of Bacteria by Strain-Specific Antibodies and Surface-Enhanced Raman Scattering
by Markéta Benešová, Silvie Bernatová, Filip Mika, Zuzana Pokorná, Jan Ježek, Martin Šiler, Ota Samek, Filip Růžička, Katarina Rebrošová, Pavel Zemánek and Zdeněk Pilát
Biosensors 2023, 13(2), 182; https://doi.org/10.3390/bios13020182 - 24 Jan 2023
Cited by 10 | Viewed by 3114
Abstract
Efficient separation and sensitive identification of pathogenic bacterial strains is essential for a prosperous modern society, with direct applications in medical diagnostics, drug discovery, biodefense, and food safety. We developed a fast and reliable method for antibody-based selective immobilization of bacteria from suspension [...] Read more.
Efficient separation and sensitive identification of pathogenic bacterial strains is essential for a prosperous modern society, with direct applications in medical diagnostics, drug discovery, biodefense, and food safety. We developed a fast and reliable method for antibody-based selective immobilization of bacteria from suspension onto a gold-plated glass surface, followed by detection using strain-specific antibodies linked to gold nanoparticles decorated with a reporter molecule. The reporter molecules are subsequently detected by surface-enhanced Raman spectroscopy (SERS). Such a multi-functionalized nanoparticle is called a SERS-tag. The presented procedure uses widely accessible and cheap materials for manufacturing and functionalization of the nanoparticles and the immobilization surfaces. Here, we exemplify the use of the produced SERS-tags for sensitive single-cell detection of opportunistic pathogen Escherichia coli, and we demonstrate the selectivity of our method using two other bacterial strains, Staphylococcus aureus and Serratia marcescens, as negative controls. We believe that the described approach has a potential to inspire the development of novel medical diagnostic tools for rapid identification of bacterial pathogens. Full article
(This article belongs to the Special Issue Raman Spectroscopy for Clinics)
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11 pages, 2873 KiB  
Article
Silver Nanostar-Based SERS for the Discrimination of Clinically Relevant Acinetobacter baumannii and Klebsiella pneumoniae Species and Clones
by Miguel Peixoto de Almeida, Carla Rodrigues, Ângela Novais, Filipa Grosso, Nicolae Leopold, Luísa Peixe, Ricardo Franco and Eulália Pereira
Biosensors 2023, 13(2), 149; https://doi.org/10.3390/bios13020149 - 17 Jan 2023
Cited by 2 | Viewed by 3082
Abstract
The development of rapid, reliable, and low-cost methods that enable discrimination among clinically relevant bacteria is crucial, with emphasis on those listed as WHO Global Priority 1 Critical Pathogens, such as carbapenem-resistant Acinetobacter baumannii and carbapenem-resistant or ESBL-producing Klebsiella pneumoniae. To address [...] Read more.
The development of rapid, reliable, and low-cost methods that enable discrimination among clinically relevant bacteria is crucial, with emphasis on those listed as WHO Global Priority 1 Critical Pathogens, such as carbapenem-resistant Acinetobacter baumannii and carbapenem-resistant or ESBL-producing Klebsiella pneumoniae. To address this problem, we developed and validated a protocol of surface-enhanced Raman spectroscopy (SERS) with silver nanostars for the discrimination of A. baumannii and K. pneumoniae species, and their globally disseminated and clinically relevant antibiotic resistant clones. Isolates were characterized by mixing bacterial colonies with silver nanostars, followed by deposition on filter paper for SERS spectrum acquisition. Spectral data were processed with unsupervised and supervised multivariate data analysis methods, including principal component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), respectively. Our proposed SERS procedure using silver nanostars adsorbed to the bacteria, followed by multivariate data analysis, enabled differentiation between and within species. This pilot study demonstrates the potential of SERS for the rapid discrimination of clinically relevant A. baumannii and K. pneumoniae species and clones, displaying several advantages such as the ease of silver nanostars synthesis and the possible use of a handheld spectrometer, which makes this approach ideal for point-of-care applications. Full article
(This article belongs to the Special Issue Raman Spectroscopy for Clinics)
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11 pages, 3189 KiB  
Article
Unraveling the Secrets of Colistin Resistance with Label-Free Raman Spectroscopy
by Dimple Saikia, Priyanka Jadhav, Arti R. Hole, Chilakapati Murali Krishna and Surya P. Singh
Biosensors 2022, 12(9), 749; https://doi.org/10.3390/bios12090749 - 11 Sep 2022
Cited by 6 | Viewed by 2891
Abstract
The rise in number of infections from multidrug-resistant (MDR) Gram-negative microbes has led to an increase in the use of a variety of ‘polymyxins’ such as colistin. Even though colistin is known to cause minor nephro- and neuro-toxicity, it is still considered as [...] Read more.
The rise in number of infections from multidrug-resistant (MDR) Gram-negative microbes has led to an increase in the use of a variety of ‘polymyxins’ such as colistin. Even though colistin is known to cause minor nephro- and neuro-toxicity, it is still considered as last resort antibiotic for treating MDR infections. In this study, we have applied Raman spectroscopy to understand the differences among colistin sensitive and resistant bacterial strains at community level. We have successfully generated colistin resistant clones and verified the presence of resistance-causing MCR-1 plasmid. A unique spectral profile associated with specific drug concentration has been obtained. Successful delineation between resistant and sensitive cells has also been achieved via principal component analysis. Overall findings support the prospective utility of Raman spectroscopy in identifying anti-microbial resistance. Full article
(This article belongs to the Special Issue Raman Spectroscopy for Clinics)
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Review

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26 pages, 5044 KiB  
Review
Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications
by Jeewan C. Ranasinghe, Ziyang Wang and Shengxi Huang
Biosensors 2023, 13(1), 27; https://doi.org/10.3390/bios13010027 - 26 Dec 2022
Cited by 15 | Viewed by 3381
Abstract
Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have [...] Read more.
Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs. Full article
(This article belongs to the Special Issue Raman Spectroscopy for Clinics)
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Other

Jump to: Research, Review

16 pages, 1666 KiB  
Perspective
Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era
by Csaba Voros, David Bauer, Ede Migh, Istvan Grexa, Attila Gergely Végh, Balázs Szalontai, Gastone Castellani, Tivadar Danka, Saso Dzeroski, Krisztian Koos, Filippo Piccinini and Peter Horvath
Biosensors 2023, 13(2), 187; https://doi.org/10.3390/bios13020187 - 26 Jan 2023
Cited by 3 | Viewed by 3080
Abstract
Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow [...] Read more.
Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps. Full article
(This article belongs to the Special Issue Raman Spectroscopy for Clinics)
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