1
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Tewes TJ, Kerst M, Pavlov S, Huth MA, Hansen U, Bockmühl DP. Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms. Heliyon 2024; 10:e27824. [PMID: 38510034 PMCID: PMC10950671 DOI: 10.1016/j.heliyon.2024.e27824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 03/22/2024] Open
Abstract
In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%-88%. The six remaining species were correctly predicted by only 0%-49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.
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Affiliation(s)
- Thomas J. Tewes
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
| | - Mario Kerst
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
| | - Svyatoslav Pavlov
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
| | - Miriam A. Huth
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
| | - Ute Hansen
- Faculty of Communication and Environment, Rhine-Waal University of Applied Sciences, Friedrich-Heinrich-Allee, 47475, Kamp-Lintfort, Germany
| | - Dirk P. Bockmühl
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
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2
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Frempong SB, Salbreiter M, Mostafapour S, Pistiki A, Bocklitz TW, Rösch P, Popp J. Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms. Molecules 2024; 29:1077. [PMID: 38474589 DOI: 10.3390/molecules29051077] [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: 12/19/2023] [Revised: 02/13/2024] [Accepted: 02/18/2024] [Indexed: 03/14/2024] Open
Abstract
Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.
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Affiliation(s)
- Sandra Baaba Frempong
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Markus Salbreiter
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Sara Mostafapour
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Aikaterini Pistiki
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance-Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Thomas W Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance-Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Jürgen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance-Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany
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3
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Vaculík O, Bernatová S, Rebrošová K, Samek O, Šilhan L, Růžička F, Šerý M, Šiler M, Ježek J, Zemánek P. Rapid identification of pathogens in blood serum via Raman tweezers in combination with advanced processing methods. BIOMEDICAL OPTICS EXPRESS 2023; 14:6410-6421. [PMID: 38420303 PMCID: PMC10898560 DOI: 10.1364/boe.503628] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/06/2023] [Accepted: 10/21/2023] [Indexed: 03/02/2024]
Abstract
Pathogenic microbes contribute to several major global diseases that kill millions of people every year. Bloodstream infections caused by these microbes are associated with high morbidity and mortality rates, which are among the most common causes of hospitalizations. The search for the "Holy Grail" in clinical diagnostic microbiology, a reliable, accurate, low cost, real-time, and easy-to-use diagnostic method, is one of the essential issues in clinical practice. These very critical conditions can be met by Raman tweezers in combination with advanced analysis methods. Here, we present a proof-of-concept study based on Raman tweezers combined with spectral mixture analysis that allows for the identification of microbial strains directly from human blood serum without user intervention, thus eliminating the influence of a data analyst.
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Affiliation(s)
- Ondřej Vaculík
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, Brno, 61264, Czech Republic
| | - Silvie Bernatová
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, Brno, 61264, Czech Republic
| | - Katarína Rebrošová
- Department of Microbiology, Faculty of Medicine of Masaryk University and St. Anne's, University Hospital, Pekařská 53, Brno, 65691, Czech Republic
| | - Ota Samek
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, Brno, 61264, Czech Republic
| | - Lukáš Šilhan
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, Brno, 61264, Czech Republic
| | - Filip Růžička
- Department of Microbiology, Faculty of Medicine of Masaryk University and St. Anne's, University Hospital, Pekařská 53, Brno, 65691, Czech Republic
| | - Mojmír Šerý
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, Brno, 61264, Czech Republic
| | - Martin Šiler
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, Brno, 61264, Czech Republic
| | - Jan Ježek
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, Brno, 61264, Czech Republic
| | - Pavel Zemánek
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, Brno, 61264, Czech Republic
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Aubrechtová Dragounová K, Ryabchykov O, Steinbach D, Recla V, Lindig N, González Vázquez MJ, Foller S, Bauer M, Bocklitz TW, Popp J, Rödel J, Neugebauer U. Identification of bacteria in mixed infection from urinary tract of patient's samples using Raman analysis of dried droplets. Analyst 2023; 148:3806-3816. [PMID: 37463011 DOI: 10.1039/d3an00679d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Urinary tract infections (UTI) are among the most frequent nosocomial infections. A fast identification of the pathogen and assignment of Gram type could help to prescribe most suitable treatments. Raman spectroscopy holds high potential for fast and reliable bacterial pathogens identification. While most studies so far have focused on individual pathogens or artificial mixtures, this contribution aims to translate the analysis to primary urine samples from patients with suspected UTIs. For this, we have included 59 primary urine samples out of which 29 were diagnosed as mixed infections. For Raman analysis, we first trained two classification models based on principal component analysis - linear discriminant analysis (PCA-LDA) with more than 3500 Raman spectra of 85 clinical isolates from 23 species in order to (1) identify the Gram type of the bacteria and (2) assign family membership to one of the six most abundant bacterial families in urinary tract infections (Enterobacteriaceae, Morganellaceae, Pseudomonadaceae, Enterococcaceae, Staphylococcaceae and Streptococcaceae). The classification models were applied to artificial mixtures of Gram positive and Gram negative bacteria to correctly predict mixed infections with an accuracy of 75%. Raman scans of dried droplets did not yet yield optimal classification results on family level. When translating the method to primary urine samples, we observed a strong bias towards Gram negative bacteria, on family level towards Morganellaceae, which reduced prediction accuracy. Spectral differences were observed between isolates grown on standard growth medium and bacteria of the same strain when characterized directly from the patient. Thus, improvement of the classification accuracy is expected with a larger data base containing also bacteria measured directly from the urine sample.
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Affiliation(s)
- Kateřina Aubrechtová Dragounová
- Department of Anaesthesiology and Intensive Care Medicine and Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany.
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), a member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Oleg Ryabchykov
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), a member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
- Biophotonics Diagnostics GmbH, Am Wiesenbach 30, 07751 Jena, Germany
| | - Daniel Steinbach
- Department of Urology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Vincent Recla
- Institute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Nora Lindig
- Institute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - María José González Vázquez
- Department of Anaesthesiology and Intensive Care Medicine and Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany.
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), a member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Susan Foller
- Department of Urology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Michael Bauer
- Department of Anaesthesiology and Intensive Care Medicine and Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany.
| | - Thomas W Bocklitz
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), a member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe School of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Institute of Computer Science, Faculty of Mathematics, Physics & Computer Science, University Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), a member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe School of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
| | - Jürgen Rödel
- Institute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Ute Neugebauer
- Department of Anaesthesiology and Intensive Care Medicine and Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany.
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), a member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe School of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
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5
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Rebrosova K, Bernatová S, Šiler M, Mašek J, Samek O, Ježek J, Kizovsky M, Holá V, Zemanek P, Růžička F. Rapid Identification of Pathogens Causing Bloodstream Infections by Raman Spectroscopy and Raman Tweezers. Microbiol Spectr 2023; 11:e0002823. [PMID: 37078868 PMCID: PMC10269886 DOI: 10.1128/spectrum.00028-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023] Open
Abstract
The search for the "Holy Grail" in clinical diagnostic microbiology-a reliable, accurate, low-cost, real-time, easy-to-use method-has brought up several methods with the potential to meet these criteria. One is Raman spectroscopy, an optical, nondestructive method based on the inelastic scattering of monochromatic light. The current study focuses on the possible use of Raman spectroscopy for identifying microbes causing severe, often life-threatening bloodstream infections. We included 305 microbial strains of 28 species acting as causative agents of bloodstream infections. Raman spectroscopy identified the strains from grown colonies, with 2.8% and 7% incorrectly identified strains using the support vector machine algorithm based on centered and uncentred principal-component analyses, respectively. We combined Raman spectroscopy with optical tweezers to speed up the process and captured and analyzed microbes directly from spiked human serum. The pilot study suggests that it is possible to capture individual microbial cells from human serum and characterize them by Raman spectroscopy with notable differences among different species. IMPORTANCE Bloodstream infections are among the most common causes of hospitalizations and are often life-threatening. To establish an effective therapy for a patient, the timely identification of the causative agent and characterization of its antimicrobial susceptibility and resistance profiles are essential. Therefore, our multidisciplinary team of microbiologists and physicists presents a method that reliably, rapidly, and inexpensively identifies pathogens causing bloodstream infections-Raman spectroscopy. We believe that it might become a valuable diagnostic tool in the future. Combined with optical trapping, it offers a new approach where the microorganisms are individually trapped in a noncontact way by optical tweezers and investigated by Raman spectroscopy directly in a liquid sample. Together with the automatic processing of measured Raman spectra and comparison with a database of microorganisms, it makes the whole identification process almost real time.
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Affiliation(s)
- Katarina Rebrosova
- Department of Microbiology, Faculty of Medicine of Masaryk University, St. Anne’s University Hospital, Brno, Czech Republic
| | - Silvie Bernatová
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Martin Šiler
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Jan Mašek
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czech Republic
- Department of Plant Developmental Genetics, Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic
| | - Ota Samek
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Jan Ježek
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Martin Kizovsky
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Veronika Holá
- Department of Microbiology, Faculty of Medicine of Masaryk University, St. Anne’s University Hospital, Brno, Czech Republic
| | - Pavel Zemanek
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Filip Růžička
- Department of Microbiology, Faculty of Medicine of Masaryk University, St. Anne’s University Hospital, Brno, Czech Republic
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6
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Kalpana S, Lin WY, Wang YC, Fu Y, Lakshmi A, Wang HY. Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective. Diagnostics (Basel) 2023; 13:1014. [PMID: 36980322 PMCID: PMC10047325 DOI: 10.3390/diagnostics13061014] [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: 02/07/2023] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/11/2023] Open
Abstract
Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".
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Affiliation(s)
- Sriram Kalpana
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 333423, Taiwan
| | | | - Yu-Chiang Wang
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Yiwen Fu
- Department of Medicine, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA 95051, USA
| | - Amrutha Lakshmi
- Department of Biochemistry, University of Madras, Guindy Campus, Chennai 600025, India
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 333423, Taiwan
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Zhao B, Zhai H, Shao H, Bi K, Zhu L. Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107295. [PMID: 36706562 PMCID: PMC9711896 DOI: 10.1016/j.cmpb.2022.107295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/10/2022] [Accepted: 11/29/2022] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.
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Affiliation(s)
- Bingqiang Zhao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Honglin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China.
| | - Haiping Shao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Kexin Bi
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Ling Zhu
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
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de Almeida MP, Rodrigues C, Novais Â, Grosso F, Leopold N, Peixe L, Franco R, Pereira E. Silver Nanostar-Based SERS for the Discrimination of Clinically Relevant Acinetobacter baumannii and Klebsiella pneumoniae Species and Clones. BIOSENSORS 2023; 13:149. [PMID: 36831915 PMCID: PMC9953856 DOI: 10.3390/bios13020149] [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: 12/20/2022] [Revised: 01/12/2023] [Accepted: 01/14/2023] [Indexed: 06/18/2023]
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 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.
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Affiliation(s)
- Miguel Peixoto de Almeida
- LAQV/REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
| | - Carla Rodrigues
- UCIBIO—Applied Molecular Biosciences Unit, Department of Biological Sciences, Laboratory of Microbiology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Ângela Novais
- UCIBIO—Applied Molecular Biosciences Unit, Department of Biological Sciences, Laboratory of Microbiology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- 4TOXRUN, Toxicology Research Unit, University Institute of Health Sciences, CESPU (IUCS-CESPU), 4585-116 Gandra, Portugal
| | - Filipa Grosso
- UCIBIO—Applied Molecular Biosciences Unit, Department of Biological Sciences, Laboratory of Microbiology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Nicolae Leopold
- Faculty of Physics, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Luísa Peixe
- UCIBIO—Applied Molecular Biosciences Unit, Department of Biological Sciences, Laboratory of Microbiology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Associate Laboratory, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Ricardo Franco
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, School of Science and Technology, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal
- UCIBIO—Applied Molecular Biosciences Unit, Departamento de Química, School of Science and Technology, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal
| | - Eulália Pereira
- LAQV/REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
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Pistiki A, Salbreiter M, Sultan S, Rösch P, Popp J. Application of Raman spectroscopy in the hospital environment. TRANSLATIONAL BIOPHOTONICS 2022. [DOI: 10.1002/tbio.202200011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Aikaterini Pistiki
- Leibniz‐Institute of Photonic Technology Member of the Leibniz Research Alliance–Leibniz Health Technologies Jena Germany
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
| | - Markus Salbreiter
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
- Institute of Physical Chemistry and Abbe Center of Photonics Friedrich Schiller University Jena Germany
| | - Salwa Sultan
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
- Institute of Physical Chemistry and Abbe Center of Photonics Friedrich Schiller University Jena Germany
| | - Petra Rösch
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
- Institute of Physical Chemistry and Abbe Center of Photonics Friedrich Schiller University Jena Germany
| | - Jürgen Popp
- Leibniz‐Institute of Photonic Technology Member of the Leibniz Research Alliance–Leibniz Health Technologies Jena Germany
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
- Institute of Physical Chemistry and Abbe Center of Photonics Friedrich Schiller University Jena Germany
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10
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Saleem M, Majeed MI, Nawaz H, Iqbal MA, Hassan A, Rashid N, Tahir M, Raza A, ul Hassan HM, Sabir A, Ashfaq R, Sharif S. Surface-Enhanced Raman Spectroscopy for the Characterization of the Antibacterial Properties of Imidazole Derivatives against Bacillus subtilis with Principal Component Analysis and Partial Least Squares–Discriminant Analysis. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2047997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Mudassar Saleem
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Muhammad Adnan Iqbal
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ahmad Hassan
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad, Pakistan
| | - Muhammad Tahir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ali Raza
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Amina Sabir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Rayha Ashfaq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Sana Sharif
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
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11
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Rebrosova K, Samek O, Kizovsky M, Bernatova S, Hola V, Ruzicka F. Raman Spectroscopy—A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings. Front Cell Infect Microbiol 2022; 12:866463. [PMID: 35531343 PMCID: PMC9072635 DOI: 10.3389/fcimb.2022.866463] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/07/2022] [Indexed: 12/02/2022] Open
Abstract
Rapid and accurate identification of pathogens causing infections is one of the biggest challenges in medicine. Timely identification of causative agents and their antimicrobial resistance profile can significantly improve the management of infection, lower costs for healthcare, mitigate ever-growing antimicrobial resistance and in many cases, save lives. Raman spectroscopy was shown to be a useful—quick, non-invasive, and non-destructive —tool for identifying microbes from solid and liquid media. Modifications of Raman spectroscopy and/or pretreatment of samples allow single-cell analyses and identification of microbes from various samples. It was shown that those non-culture-based approaches could also detect antimicrobial resistance. Moreover, recent studies suggest that a combination of Raman spectroscopy with optical tweezers has the potential to identify microbes directly from human body fluids. This review aims to summarize recent advances in non-culture-based approaches of identification of microbes and their virulence factors, including antimicrobial resistance, using methods based on Raman spectroscopy in the context of possible use in the future point-of-care diagnostic process.
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Affiliation(s)
- Katarina Rebrosova
- Department of Microbiology, Faculty of Medicine of Masaryk University and St. Anne’s University Hospital, Brno, Czechia
| | - Ota Samek
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Martin Kizovsky
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Silvie Bernatova
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Veronika Hola
- Department of Microbiology, Faculty of Medicine of Masaryk University and St. Anne’s University Hospital, Brno, Czechia
- *Correspondence: Veronika Hola,
| | - Filip Ruzicka
- Department of Microbiology, Faculty of Medicine of Masaryk University and St. Anne’s University Hospital, Brno, Czechia
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12
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Autostereoscopic-Raman Spectrometry-Based Three-Dimensional Metrology System for Measurements, Tracking and Identification in a Volume. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Three-dimensional compound measurement within a volume of interest is of great importance in industrial manufacturing and the biomedical field. However, there is no current method that can simultaneously perform spatial localization and 3D measurement in a non-scanning manner as well as the identification of material in a volume. In this paper, an Autostereoscopic-Raman Spectrometry-based (ARS) three-dimensional measurement system is proposed. The target object in a large depth range is initially positioned by the autostereoscopic 3D measurement method, and then the accurate position information is cross-checked and obtained by combining the spectral signal. Meanwhile, the spectral signal at the precise excitation position guided by the autostereoscopic signal also carries the material composition information. In order to verify the proposed ARS method, an associated measurement system was developed, and experimental studies of detecting various fibers of different depths in multi-layer glass structure were conducted. The spatial locations and dimensional information of multiple different targets can be measured in a volume, and their material can also be identified at the same time. The average error between the calculated position processed by the ARS system and the actual spatial position is within sub-micron levels, and the success rate of spectrum acquisition reaches 98%.
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13
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A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing. PHOTONICS 2022. [DOI: 10.3390/photonics9030133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Antimicrobial resistance (AMR) is a global medical threat that seriously endangers human health. Rapid bacterial identification and antimicrobial susceptibility testing (AST) are key interventions to combat the spread and emergence of AMR. Although current clinical bacterial identification and AST provide comprehensive information, they are labor-intensive, complex, inaccurate, and slow (requiring several days, depending on the growth of pathogenic bacteria). Recently, Raman-based identification and AST technologies have played an increasingly important role in fighting AMR. This review summarizes major Raman-based techniques for bacterial identification and AST, including spontaneous Raman scattering, surface-enhanced Raman scattering (SERS), and coherent Raman scattering (CRS) imaging. Then, we discuss recent developments in rapid identification and AST methods based on Raman technology. Finally, we highlight the major challenges and potential future efforts to improve clinical outcomes through rapid bacterial identification and AST.
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