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Umar Hussain M, Kainat K, Nawaz H, Irfan Majeed M, Akhtar N, Alshammari A, Albekairi NA, Fatima R, Amber A, Bano A, Shabbir I, Tahira M, Pallares RM. SERS characterization of biochemical changes associated with biodesulfurization of dibenzothiophene using Gordonia sp. HS126-4N. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124534. [PMID: 38878718 DOI: 10.1016/j.saa.2024.124534] [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: 12/14/2023] [Revised: 05/08/2024] [Accepted: 05/24/2024] [Indexed: 07/08/2024]
Abstract
In this study, Gordonia sp. HS126-4N was employed for dibenzothiophene (DBT) biodesulfurization, tracked over 9 days using SERS. During the initial lag phase, no significant spectral changes were observed, but after 48 h, elevated metabolic activity was evident. At 72 h, maximal bacterial population correlated with peak spectrum variance, followed by stable spectral patterns. Despite 2-hydroxybiphenyl (2-HBP) induced enzyme suppression, DBT biodesulfurization persisted. PCA and PLS-DA analysis of the SERS spectra revealed distinctive features linked to both bacteria and DBT, showcasing successful desulfurization and bacterial growth stimulation. PLS-DA achieved a specificity of 95.5 %, sensitivity of 94.3 %, and AUC of 74 %, indicating excellent classification of bacteria exposed to DBT. SERS effectively tracked DBT biodesulfurization and bacterial metabolic changes, offering insights into biodesulfurization mechanisms and bacterial development phases. This study highlights SERS' utility in biodesulfurization research, including its use in promising advancements in the field.
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Affiliation(s)
- Muhammad Umar Hussain
- Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS), Faisalabad 38000, Pakistan
| | - Kiran Kainat
- 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.
| | - Nasrin Akhtar
- Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS), Faisalabad 38000, Pakistan.
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Post Box 2455, Riyadh 11451, Saudi Arabia
| | - Norah A Albekairi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Post Box 2455, Riyadh 11451, Saudi Arabia
| | - Rida Fatima
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Arooj Amber
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Aqsa Bano
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Ifra Shabbir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Maryam Tahira
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Roger M Pallares
- Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen 52074, Germany
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Bitra VS, Verma S, Rao BT. TinyML-Raman: A novel IoT based field-deployable spectra analysis for accurate identification of pharmaceuticals and trace dye-pesticide mixtures from facile SERS method. Anal Chim Acta 2024; 1322:343063. [PMID: 39182990 DOI: 10.1016/j.aca.2024.343063] [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: 04/11/2024] [Revised: 06/21/2024] [Accepted: 08/05/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Upcoming inexpensive, compact Internet of Things (IoT) microcontrollers i.e., tiny-machine learning (TinyML) takes the ML driven Raman spectroscopy one step ahead for realization of more affordable and highly compact field deployable instruments. Further, lack of large spectral datasets and need for numerous specialized SERS substrates impede the development of various ML-based surface enhanced Raman spectroscopy (SERS) applications. The aim is to introduce TinyML analysis on wide range of spectra classes using customized dataset obtained with low-cost SERS. In this regard, it is vital to establish an optimum ML model and efficient data handling methodology for low memory TinyML units. RESULTS We introduce a novel TinyML methodology for accurate classification of large spectra classes with smartphone assistance for data communication and results visualization. To generate large customized spectral dataset, we present a facile, micro-drop SERS using Au colloids and reusable grooved Al substrates. The results demonstrated that memory efficient 8-bit data quantization based convolutional neural network is effective for accurate identification of 22 different spectra classes of trace dye-pesticide mixtures and pharmaceuticals. In this novel quantized data analysis on significantly varied intensity and complex variation spectra classes i.e., many individual, binary-mixtures and some with varied compositions, data normalization is shown to be powerful for improving ML classification accuracy from about 55 % to >99.5 %. Its robustness is demonstrated using inter-instrument driven data variations such as spectral shifts, high noise, and abscissa-flip, with five-fold cross validation of model performance. In addition, on-site quantification of analyte through spectral intensity is also demonstrated. SIGNIFICANCE This study opens up a new approach of ML analysis towards realization of next generation field deployable analytical instruments maintaining data privacy. It presents a detailed procedure of quantized spectral data analysis and its implementation in TinyML, attractive for various users and instrument manufacturers. The presented innovative computer-free ML analysis can be employed in all types of spectrometers, meeting the common goal of Raman spectroscopy i.e., accurate identification of complex spectra classes.
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Affiliation(s)
- Venkat Suprabath Bitra
- International Institute of Information Technology Bangalore, Electronic City, Bengaluru, Karnataka, 560100, India.
| | - Shweta Verma
- Laser Materials Processing Division, Raja Ramanna Centre for Advanced Technology, Indore, Madhya Pradesh, 452013, India
| | - B Tirumala Rao
- Laser Materials Processing Division, Raja Ramanna Centre for Advanced Technology, Indore, Madhya Pradesh, 452013, India.
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Mohammadi S, Kharrazi S, Mazlomi M, Amani A, Tavoosidana G. Investigation of Melphalan interaction as an alkylating agent with nucleotides by using surface enhanced Raman spectroscopy (SERS). SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 317:124359. [PMID: 38704996 DOI: 10.1016/j.saa.2024.124359] [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: 08/18/2023] [Revised: 03/07/2024] [Accepted: 04/26/2024] [Indexed: 05/07/2024]
Abstract
SERS (Surface Enhanced Raman Spectroscopy) is a new Raman spectroscopy which relies on Surface Plasmon Resonance (SPR) of metal nanoparticles. We have applied colloidal silver and gold nanoparticles as amplifier agents to enhance nucleotide Raman signals. It is observed that without these enhancing agents, it is impossible to investigate nucleotide spectrum due to weak Raman signals. Interaction mechanism of Melphalan, an anticancer drug with four nucleotides (Adenine, Cytosine, Guanine, Thymine) was investigated using SERS to detect and identify changes due to alkylating process in Raman spectra. After incubating Melphalan drug with nucleotides for 24 h at 37 °C, some changes occurred in SERS spectrum and interpretation of SERS spectra revealed the influence of the alkyl substitution on peaks and Raman shifts. After incubation of Melphalan with each nucleotide, intensity of relevant SERS signals assigned to Amid III group of Cytosine and Amid I of Thymine decreased significantly, confirming alkylating taking place. In this study, we also investigated the effect of nanoparticles type on nucleotide spectrum. We could not obtain useful information in the cases of guanine nucleotide. The SERS spectrum of Cytosine as an example of nucleotides in aqueous solution compared to solid state and results demonstrated that in solid state better signals were obtained than in liquid state.
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Affiliation(s)
- Simah Mohammadi
- Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Sharmin Kharrazi
- Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Mohammadali Mazlomi
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Amani
- Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran; Natural Products and Medicinal Plants research Center, North Khorasan University of Medical Sciences, Bojnurd, Iran
| | - Gholamreza Tavoosidana
- Department of Molecular Medicine, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Contessa CR, Moreira EC, Moraes CC, de Medeiros Burkert JF. Production and SERS characterization of bacteriocin-like inhibitory substances by latilactobacillus sakei in whey permeate powder: exploring natural antibacterial potential. Bioprocess Biosyst Eng 2024:10.1007/s00449-024-03065-6. [PMID: 39014172 DOI: 10.1007/s00449-024-03065-6] [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: 01/16/2024] [Accepted: 07/10/2024] [Indexed: 07/18/2024]
Abstract
Bacteriocins are antimicrobial compounds that have awakened interest across several industries due to their effectiveness. However, their large-scale production often becomes unfeasible on an industrial scale, primarily because of high process costs. Addressing this challenge, this work analyzes the potential of using low-cost whey permeate powder, without any supplementation, to produce bacteriocin-like inhibitory substances (BLIS) through the fermentation of Latilactobacillus sakei. For this purpose, different concentrations of whey permeate powder (55.15 gL-1, 41.3 gL-1 and 27.5 gL-1) were used. The ability of L. sakei to produce BLIS was evaluated, as well as the potential of crude cell-free supernatant to act as a preservative. Raman spectroscopy and surface-enhanced Raman scattering (SERS) provided detailed insights into the composition and changes occurring during fermentation. SERS, in particular, enhanced peak definition significantly, allowing for the identification of key components, such as lactose, proteins, and phenylalanine, which are crucial in understanding the fermentation process and BLIS characteristics. The results revealed that the concentration of 55.15 gL-1 of whey permeate powder, in flasks without agitation and a culture temperature of 32.5 °C, presented the highest biological activity of BLIS, reaching 99% of inhibition of Escherichia coli and Staphylococcus aureus with minimum inhibitory concentration of 36-45%, respectively. BLIS production began within 60 h of cultivation and was associated with class II bacteriocins. The results demonstrate a promising approach for producing BLIS in an economical and environmentally sustainable manner, with potential implications for various industries.
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Affiliation(s)
- Camila Ramão Contessa
- Engineering and Science of Food Graduate Program, College of Chemistry and Food Engineering, Laboratory Bioprocess Engineering, Federal University of Rio Grande, PO Box 474, Rio Grande, RS, 96203-900, Brazil.
| | - Eduardo Ceretta Moreira
- Science and Engineering of Materials Graduate Program, Spectroscopy Laboratory, Federal University of Pampa, PO Box 1650, Bagé, RS, 96413170, Brazil
| | - Caroline Costa Moraes
- Science and Engineering of Materials Graduate Program, Laboratory of Microbiology and Food Toxicology, Federal University of Pampa, PO Box 1650, Bagé, RS, 96413170, Brazil
| | - Janaína Fernandes de Medeiros Burkert
- Engineering and Science of Food Graduate Program, College of Chemistry and Food Engineering, Laboratory Bioprocess Engineering, Federal University of Rio Grande, PO Box 474, Rio Grande, RS, 96203-900, Brazil
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Stoia D, De Sio L, Petronella F, Focsan M. Recent advances towards point-of-care devices for fungal detection: Emphasizing the role of plasmonic nanomaterials in current and future technologies. Biosens Bioelectron 2024; 255:116243. [PMID: 38547645 DOI: 10.1016/j.bios.2024.116243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 04/15/2024]
Abstract
Fungal infections are a significant global health problem, particularly affecting individuals with weakened immune systems. Moreover, as uncontrolled antibiotic and immunosuppressant use increases continuously, fungal infections have seen a dramatic increase, with some strains developing antibiotic resistance. Traditional approaches to identifying fungal strains often rely on morphological characteristics, thus owning limitations, such as struggles in identifying several strains or distinguishing between fungal strains with similar morphologies. This review explores the multifaceted impact of fungi infections on individuals, healthcare providers, and society, highlighting the often-underestimated economic burden and healthcare implications of these infections. In light of the serious constraints of traditional fungal identification methods, this review discusses the potential of plasmonic nanoparticle-based biosensors for fungal infection identification. These biosensors can enable rapid and precise fungal pathogen detection by exploiting several readout approaches, including various spectroscopic techniques, colorimetric and electrochemical assays, as well as lateral-flow immunoassay methods. Moreover, we report the remarkable impact of plasmonic Lab on a Chip technology and microfluidic devices, as they recently emerged as a class of advanced biosensors. Finally, we provide an overview of smartphone-based Point-of-Care devices and the associated technologies developed for detecting and identifying fungal pathogens.
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Affiliation(s)
- Daria Stoia
- Biomolecular Physics Department, Faculty of Physics, Babes-Bolyai University, 1 M. Kogalniceanu Street, 400084, Cluj-Napoca, Romania; Nanobiophotonics and Laser Microspectroscopy Centre, Interdisciplinary Research Institute on Bio-Nano-Sciences, Babes-Bolyai University, 42 Treboniu Laurian Street, 400271, Cluj-Napoca, Romania
| | - Luciano De Sio
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Corso della Repubblica 79, 04100, Latina, Italy
| | - Francesca Petronella
- National Research Council of Italy, Institute of Crystallography CNR-IC, Area della Ricerca Roma 1 Strada Provinciale 35d, n. 9, 00010, Montelibretti (RM), Italy.
| | - Monica Focsan
- Biomolecular Physics Department, Faculty of Physics, Babes-Bolyai University, 1 M. Kogalniceanu Street, 400084, Cluj-Napoca, Romania; Nanobiophotonics and Laser Microspectroscopy Centre, Interdisciplinary Research Institute on Bio-Nano-Sciences, Babes-Bolyai University, 42 Treboniu Laurian Street, 400271, Cluj-Napoca, Romania.
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Srivastava S, Sandhu N, Liu J, Xie YH. AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids. Bioengineering (Basel) 2024; 11:482. [PMID: 38790349 PMCID: PMC11117800 DOI: 10.3390/bioengineering11050482] [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: 03/03/2024] [Revised: 05/05/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for elucidating the molecular makeup of materials. It possesses the unique characteristics of single-molecule sensitivity and extremely high specificity. However, the true potential of SERS, particularly in capturing the biochemical content of particles, remains underexplored. In this study, we harnessed transformer neural networks to interpret SERS spectra, aiming to discern the amino acid profiles within proteins. By training the network on the SERS profiles of 20 amino acids of human proteins, we explore the feasibility of predicting the predominant proteins within the µL-scale detection volume of SERS. Our results highlight a consistent alignment between the model's predictions and the protein's known amino acid compositions, deepening our understanding of the inherent information contained within SERS spectra. For instance, the model achieved low root mean square error (RMSE) scores and minimal deviation in the prediction of amino acid compositions for proteins such as Bovine Serum Albumin (BSA), ACE2 protein, and CD63 antigen. This novel methodology offers a robust avenue not only for protein analytics but also sets a precedent for the broader realm of spectral analyses across diverse material categories. It represents a solid step forward to establishing SERS-based proteomics.
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Affiliation(s)
| | | | | | - Ya-Hong Xie
- Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA; (S.S.); (N.S.); (J.L.)
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Tahseen H, Ul Huda N, Nawaz H, Majeed MI, Alwadie N, Rashid N, Aslam MA, Zafar N, Asghar M, Anwar A, Ashraf A, Umer R. Surface-enhanced Raman spectroscopy for comparison of biochemical profile of bacteriophage sensitive and resistant methicillin-resistant Staphylococcus aureus (MRSA) strains. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 310:123968. [PMID: 38330510 DOI: 10.1016/j.saa.2024.123968] [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: 07/05/2023] [Revised: 01/10/2024] [Accepted: 01/24/2024] [Indexed: 02/10/2024]
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is gram positive bacteria and leading cause of a wide variety of diseases. It is a common cause of hospitalized and community-acquired infections. Development of increasing antibiotic-resistance by methicillin-resistant S. aureus (MRSA) strains demand to develop alternate novel therapies. Bacteriophages are now widely used as antibacterial therapies against antibiotic-resistant gram-positive pathogens. So, there is an urgent need to find fast detection techniques to point out phage susceptible and resistant strains of methicillin-resistant S. aureus (MRSA) bacteria. Samples of two separate strains of bacteria, S. aureus, in form of pellets and supernatant, were used for this purpose. Strain-I was resistant to phage, while the other (strain-II) was sensitive. Surface Enhanced Raman Spectroscopy (SERS) has detected significant biochemical changes in these bacterial strains of pellets and supernatants in the form of SERS spectral features. The protein portion of these two types of strains of methicillin-resistant S. aureus (MRSA) in their relevant pellets and supernatants is major distinguishing biomolecule as shown by their representative SERS spectral features. In addition, multivariate data analysis techniques such as principal component analysis (PCA) and a partial least squares-discriminant analysis (PLS-DA) were found to be helpful in identifying and characterizing various strains of S. aureus which are sensitive and resistant to bacteriophage with 100% specificity, 100% accuracy, and 99.8% sensitivity in case of SERS spectral data sets of bacterial cell pellets. Moreover, in case of supernatant samples, the results of PLS-DA model including 95.5% specificity, 96% sensitivity, and 96.5% accuracy are obtained.
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Affiliation(s)
- Hira Tahseen
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Noor Ul Huda
- 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.
| | - Najah Alwadie
- Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan
| | - Muhammad Aamir Aslam
- Institute of Microbiology, Faculty of Veterinary, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Nishat Zafar
- Institute of Microbiology, Faculty of Veterinary, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Maria Asghar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Ayesha Anwar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Ayesha Ashraf
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Rabiea Umer
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
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8
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Bao H, Hackshaw KV, Castellvi SDL, Wu Y, Gonzalez CM, Nuguri SM, Yao S, Goetzman CM, Schultz ZD, Yu L, Aziz R, Osuna-Diaz MM, Sebastian KR, Giusti MM, Rodriguez-Saona L. Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics. Biomedicines 2024; 12:133. [PMID: 38255238 PMCID: PMC10813180 DOI: 10.3390/biomedicines12010133] [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: 12/21/2023] [Revised: 12/28/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.
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Affiliation(s)
- Haona Bao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Kevin V. Hackshaw
- Department of Internal Medicine, Division of Rheumatology, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - Silvia de Lamo Castellvi
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
- Departament d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain
| | - Yalan Wu
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Celeste Matos Gonzalez
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Shreya Madhav Nuguri
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Siyu Yao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
- Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, Nanjing 210009, China
| | - Chelsea M. Goetzman
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA; (C.M.G.); (Z.D.S.)
- Savannah River National Laboratory, Jackson, SC 29831, USA
| | - Zachary D. Schultz
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA; (C.M.G.); (Z.D.S.)
| | - Lianbo Yu
- Center of Biostatistics and Bioinformatics, The Ohio State University, Columbus, OH 43210, USA;
| | - Rija Aziz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Michelle M. Osuna-Diaz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Katherine R. Sebastian
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Monica M. Giusti
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Luis Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
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Ju Y, Neumann O, Bajomo M, Zhao Y, Nordlander P, Halas NJ, Patel A. Identifying Surface-Enhanced Raman Spectra with a Raman Library Using Machine Learning. ACS NANO 2023; 17:21251-21261. [PMID: 37910670 DOI: 10.1021/acsnano.3c05510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Since its discovery, surface-enhanced Raman spectroscopy (SERS) has shown outstanding promise of identifying trace amounts of unknown molecules in rapid, portable formats. However, the many different types of nanoparticles or nanostructured metallic SERS substrates created over the past few decades show substantial variability in the SERS spectra they provide. These inconsistencies have even raised speculation that substrate-specific SERS spectral libraries must be compiled for practical use of this type of spectroscopy. Here, we report a machine learning (ML) algorithm that can identify chemicals by matching their SERS spectra to those of a standard Raman spectral library. We use an approach analogous to facial recognition that utilizes feature extraction in the presence of multiple nuisance variables for spectral recognition. The key element is a metric we call "Characteristic Peak Similarity" (CaPSim) that focuses on the characteristic peaks in the SERS spectra. It has the flexibility to accommodate substrate-specific variability when quantifying the degree of similarity to a Raman spectrum. Analysis shows that CaPSim substantially outperforms existing spectral matching algorithms in terms of accuracy. This ML-based approach could greatly facilitate the spectroscopic identification of molecules in fieldable SERS applications.
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Affiliation(s)
| | | | | | - Yiping Zhao
- Department of Physics and Astronomy, University of Georgia, Athens, Georgia 30602, United States
| | | | | | - Ankit Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, United States
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Wang W, Vikesland PJ. Metabolite-Mediated Bacterial Antibiotic Resistance Revealed by Surface-Enhanced Raman Spectroscopy. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:13375-13383. [PMID: 37624741 DOI: 10.1021/acs.est.3c04001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
A prompt on-site, real-time method to detect bacterial antibiotic resistance is crucial for controlling the spread of resistance. Herein, we report the use of surface-enhanced Raman spectroscopy (SERS) for the monitoring of bioactive metabolites produced by ampicillin-resistant Pseudomonas aeruginosa strains and identification of mechanisms underlying antibiotic resistance. The results indicate that the blue-green pigment pyocyanin (PYO) dominates the metabolite signals and is significantly enhanced upon exposure to subminimal inhibitory concentrations of ampicillin. PYO accumulates during exponential growth and subsequently either diffuses into the culture medium or is consumed in response to nutrient deprivation. The SERS spectra further reveal that the production of some intermediate substances such as polysaccharides and amino acids is minimally impacted and that nutrient consumption remains consistent. Moreover, the intensity changes and peak shifts observed in the SERS spectra of non-PYO-producing ampicillin-susceptible Escherichia coli demonstrate that exogenously added PYO enhances E. coli tolerance to ampicillin to some extent. These results indicate that PYO mediates antibiotic resistance not only in the parent species but also in cocultured bacterial strains. The metabolic SERS signal provides new insight regarding antibiotic resistance with promising applications for both environmental monitoring and rapid clinical detection.
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Affiliation(s)
- Wei Wang
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, United States
| | - Peter J Vikesland
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, United States
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11
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Kotsifaki DG, Rajiv Singh R, Nic Chormaic S, Truong VG. Asymmetric split-ring plasmonic nanostructures for the optical sensing of Escherichia coli. BIOMEDICAL OPTICS EXPRESS 2023; 14:4875-4887. [PMID: 37791281 PMCID: PMC10545205 DOI: 10.1364/boe.497820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 10/05/2023]
Abstract
Strategies for in-liquid micro-organism detection are crucial for the clinical and pharmaceutical industries. While Raman spectroscopy is a promising label-free technique for micro-organism detection, it remains challenging due to the weak bacterial Raman signals. In this work, we exploit the unique electromagnetic properties of metamaterials to identify bacterial components in liquid using an array of Fano-resonant metamolecules. This Fano-enhanced Raman scattering (FERS) platform is designed to exhibit a Fano resonance close to the protein amide group fingerprint around 6030 nm. Raman signatures of Escherichia coli were recorded at several locations on the metamaterial under off-resonance laser excitation at 530 nm, where the photodamage effect is minimized. As the sizes of the Escherichia coli are comparable to the micro-gaps i.e, 0.41 µm, of the metamaterials, its local immobilisation leads to an increase in the Raman sensitivity. We also observed that the time-dependent FERS signal related to bacterial amide peaks increased during the bacteria's mid-exponential phase while it decreased during the stationary phase. This work provides a new set of opportunities for developing ultrasensitive FERS platforms suitable for large-scale applications and could be particularly useful for diagnostics and environmental studies at off-resonance excitation.
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Affiliation(s)
- Domna G. Kotsifaki
- Light-Matter Interactions for Quantum Technologies Unit, Okinawa Institute of Science and Technology Graduate University, Onna 904-0495 Okinawa, Japan
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316 Jiangsu Province, China
| | - Ranjan Rajiv Singh
- Information Processing Biology Unit, Okinawa Institute of Science and Technology Graduate University, Onna 904-0495 Okinawa, Japan
| | - Síle Nic Chormaic
- Light-Matter Interactions for Quantum Technologies Unit, Okinawa Institute of Science and Technology Graduate University, Onna 904-0495 Okinawa, Japan
| | - Viet Giang Truong
- Light-Matter Interactions for Quantum Technologies Unit, Okinawa Institute of Science and Technology Graduate University, Onna 904-0495 Okinawa, Japan
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12
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Jin L, Yang J, You G, Ge C, Cao Y, Shen S, Wang D, Hui Q. A characteristic bacterial SERS marker for direct identification of Salmonella in real samples assisted by a high-performance SERS chip and a selective culture medium. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 301:122941. [PMID: 37302194 DOI: 10.1016/j.saa.2023.122941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/19/2023] [Accepted: 05/27/2023] [Indexed: 06/13/2023]
Abstract
Salmonella should be absent in pharmaceutical preparations and foods according to the regulations. However, up to now, rapid and convenient identification of Salmonella is still full of challenge. Herein, we reported a label-free surface-enhanced Raman scattering (SERS) method for direct identification of Salmonella spiked in drug samples based on a characteristic bacterial SERS marker assisted by a high-performance SERS chip and a selective culture medium. The SERS chip being fabricated through in situ growth of bimetallic Au-Ag nanocomposites on silicon wafer within 2 h, featured a high SERS activity (EF > 107), good uniformity and batch-to-batch consistency (RSD < 10 %), and satisfactory chemical stability. The directly-visualized SERS marker at 1222 cm-1 originated from bacterial metabolite hypoxanthine was robust and exclusive for discrimination of Salmonella with other bacterial species. Moreover, the method was successfully used for direct discrimination of Salmonella in mixed pathogens by using a selective culture medium, and could identify Salmonella contaminant at ∼1 CFU spiked level in a real sample (Wenxin granule, a botanical drug) after 12 h of enrichment. The combined results showed that developed SERS method is practical and reliable, and could be a promising alternative for rapid identification of Salmonella contamination in pharmaceutical and foods industries.
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Affiliation(s)
- Lei Jin
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325000, China.
| | - Jinmei Yang
- School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325001, China
| | - Guohui You
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaojie Ge
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China
| | - Yanrong Cao
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China
| | - Siyuan Shen
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China
| | - Danyan Wang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China
| | - Qi Hui
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
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13
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Safir F, Vu N, Tadesse LF, Firouzi K, Banaei N, Jeffrey SS, Saleh AAE, Khuri-Yakub B(P, Dionne JA. Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood. NANO LETTERS 2023; 23:2065-2073. [PMID: 36856600 PMCID: PMC10037319 DOI: 10.1021/acs.nanolett.2c03015] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 02/19/2023] [Indexed: 06/18/2023]
Abstract
Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.
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Affiliation(s)
- Fareeha Safir
- *Department
of Mechanical Engineering, Stanford University, Stanford, California 94305, United States
| | - Nhat Vu
- Pumpkinseed
Technologies, Inc., Palo Alto, California 94306, United States
| | - Loza F. Tadesse
- Department
of Bioengineering, Stanford University School
of Medicine and School of Engineering, Stanford, California 94305, United States
| | - Kamyar Firouzi
- E.
L. Ginzton Laboratory, Stanford University, Stanford, California 94305, United States
| | - Niaz Banaei
- Department
of Pathology, Stanford University School
of Medicine, Stanford, 94305 California, United
States
- Clinical
Microbiology Laboratory, Stanford Health Care, Palo Alto, California 94304, United States
- Department
of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California 94305, United States
| | - Stefanie S. Jeffrey
- Department
of Surgery, Stanford University School of
Medicine, Stanford, California 94305, United States
| | - Amr. A. E. Saleh
- Department
of Engineering Mathematics and Physics, Cairo University, Cairo 12613, Egypt
- Department
of Materials Science and Engineering, Stanford
University, Stanford, California 94305, United States
| | - Butrus (Pierre)
T. Khuri-Yakub
- E.
L. Ginzton Laboratory, Stanford University, Stanford, California 94305, United States
- Department
of Electrical Engineering, Stanford University, Stanford, California 94305, United States
| | - Jennifer A. Dionne
- Department
of Materials Science and Engineering, Stanford
University, Stanford, California 94305, United States
- Department
of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California 94035, United States
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14
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Haq AU, Majeed MI, Nawaz H, Rashid N, Javed MR, Raza A, Shakeel M, Zahra ST, Meraj L, Perveen A, Murtaza S, Khaliq S. Surface-enhanced Raman spectroscopy for monitoring antibacterial activity of imidazole derivative (1-benzyl-3-(sec‑butyl)-1H-imidazole-3-ium bromide) against Bacillus subtilis and Escherichia coli. Photodiagnosis Photodyn Ther 2023; 42:103533. [DOI: 10.1016/j.pdpdt.2023.103533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/17/2023] [Accepted: 03/21/2023] [Indexed: 04/05/2023]
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15
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Wang W, Rahman A, Kang S, Vikesland PJ. Investigation of the Influence of Stress on Label-Free Bacterial Surface-Enhanced Raman Spectra. Anal Chem 2023; 95:3675-3683. [PMID: 36757218 DOI: 10.1021/acs.analchem.2c04636] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Label-free surface-enhanced Raman spectroscopy (SERS) has been proposed as a promising bacterial detection technique. However, the quality of the collected bacterial spectra can be affected by the time between sample acquisition and the SERS measurement. This study evaluated how storage stress stimuli influence the label-free SERS spectra of Pseudomonas syringae samples stored in phosphate buffered saline. The results indicate that when faced with nutrient limitations and changes in osmatic pressure, samples at room temperature (25 °C) exhibit more significant spectral changes than those stored at cold temperature (4 °C). At higher temperatures, bacterial communities secrete extracellular biomolecules that induce programmed cell death and result in increases in the supernatant SERS signals. Surviving cells consume cellular components to support their metabolism, thus leading to measurable declines in cell SERS intensity. Two-dimensional correlation spectroscopy analysis suggests that cellular component signatures decline sequentially in the following order: proteins, nucleic acids, and lipids. Extracellular nucleic acids, proteins, and carbohydrates are secreted in turn. After subtracting the SERS changes resulting from storage, we evaluated bacterial response to viral infection. P. syringae SERS profile changes enable accurate bacteriophage Phi6 quantification over the range of 104-1010 PFU/mL. The results indicate that storage conditions impact bacterial label-free SERS signals and that such influences need to be accounted for and if possible avoided when detecting bacteria or evaluating bacterial response to stress stimuli.
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Affiliation(s)
- Wei Wang
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.,Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Asifur Rahman
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.,Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Seju Kang
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.,Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Peter J Vikesland
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.,Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Virginia Tech, Blacksburg, Virginia 24061, United States
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16
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Daniel F, Kesterson D, Lei K, Hord C, Patel A, Kaffenes A, Congivaram H, Prakash S. Application of Microfluidics for Bacterial Identification. Pharmaceuticals (Basel) 2022; 15:ph15121531. [PMID: 36558982 PMCID: PMC9781190 DOI: 10.3390/ph15121531] [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: 10/23/2022] [Revised: 11/29/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Bacterial infections continue to pose serious public health challenges. Though anti-bacterial therapeutics are effective remedies for treating these infections, the emergence of antibiotic resistance has imposed new challenges to treatment. Often, there is a delay in prescribing antibiotics at initial symptom presentation as it can be challenging to clinically differentiate bacterial infections from other organisms (e.g., viruses) causing infection. Moreover, bacterial infections can arise from food, water, or other sources. These challenges have demonstrated the need for rapid identification of bacteria in liquids, food, clinical spaces, and other environments. Conventional methods of bacterial identification rely on culture-based approaches which require long processing times and higher pathogen concentration thresholds. In the past few years, microfluidic devices paired with various bacterial identification methods have garnered attention for addressing the limitations of conventional methods and demonstrating feasibility for rapid bacterial identification with lower biomass thresholds. However, such culture-free methods often require integration of multiple steps from sample preparation to measurement. Research interest in using microfluidic methods for bacterial identification is growing; therefore, this review article is a summary of current advancements in this field with a focus on comparing the efficacy of polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), and emerging spectroscopic methods.
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Affiliation(s)
- Fraser Daniel
- Department of Mechanical and Aerospace Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Delaney Kesterson
- Center for Life Sciences Education, The Ohio State University, Columbus, OH 43210, USA
| | - Kevin Lei
- Department of Chemical and Biomolecular Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Catherine Hord
- Center for Life Sciences Education, The Ohio State University, Columbus, OH 43210, USA
| | - Aarti Patel
- Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Anastasia Kaffenes
- Department of Neuroscience, College of Arts and Sciences and College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Harrshavasan Congivaram
- School of Health and Rehabilitation Sciences, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Shaurya Prakash
- Department of Mechanical and Aerospace Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
- Correspondence:
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17
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Mushtaq A, Nawaz H, Irfan Majeed M, Rashid N, Tahir M, Zaman Nawaz M, Shahzad K, Dastgir G, Zaki Abdul Bari R, Ul Haq A, Saleem M, Akhtar F. Surface-enhanced Raman spectroscopy (SERS) for monitoring colistin-resistant and susceptible E. coli strains. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 278:121315. [PMID: 35576839 DOI: 10.1016/j.saa.2022.121315] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/21/2022] [Accepted: 04/24/2022] [Indexed: 06/15/2023]
Abstract
The emergence of drug-resistant bacteria is a precarious global health concern. In this study, surface-enhanced Raman spectroscopy (SERS) is used to characterize colistin-resistant and susceptible E. coli strains based on their distinguished SERS spectral features for the development of rapid and cost-effective detection and differentiation methods. For this purpose, three colistin-resistant and three colistin susceptible E. coli strains were analyzed by comparing their SERS spectral signatures. Moreover, multivariate data analysis techniques including Principal component analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were used to examine the SERS spectral data of colistin-resistant and susceptible strains. PCA technique was employed for differentiating colistin susceptible and resistant E.coli strains due to alteration in biochemical compositions of the bacterial cell. PLS-DA is employed on SERS spectral data sets for discrimination of these resistant and susceptible E. coli strains with 100% specificity, 100% accuracy, 99.8% sensitivity, and 86% area under receiver operating characteristics (AUROC) curve.
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Affiliation(s)
- Aqsa Mushtaq
- 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.
| | - Muhammad Tahir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Zaman Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Kashif Shahzad
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Ghulam Dastgir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Rana Zaki Abdul Bari
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Anwar Ul Haq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Mudassar Saleem
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Farwa Akhtar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
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18
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Wang W, Rahman A, Huang Q, Vikesland PJ. Surface-enhanced Raman spectroscopy enabled evaluation of bacterial inactivation. WATER RESEARCH 2022; 220:118668. [PMID: 35689895 DOI: 10.1016/j.watres.2022.118668] [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: 02/28/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
An improved understanding of bacterial inactivation mechanisms will provide useful insights for infectious disease control and prevention. We evaluated bacterial response to several inactivation methods using surface-enhanced Raman spectroscopy (SERS). The results indicate that changes in the SERS signal are highly related to cellular disruption and that cellular changes arising after cell inactivation cannot be ignored. The membrane integrity of heat and the combination of UV254 and free chlorine (UV254/chlorine) treated Pseudomonas syringae (P. syringae) cells were severely disrupted, leading to significantly increased peak intensities. Conversely, ethanol treated bacteria exhibited intact cell morphologies and the SERS spectra remained virtually unchanged. On the basis of time dependent SERS signals, we extracted dominant SERS patterns. Peaks related to nucleic acids accounted for the main changes observed during heat, UV254, and UV254/chlorine treatment, likely due to their outward diffusion from the cell cytoplasm. For free chlorine treated P. syringae, carbohydrates and proteins on the cell membrane were denatured or lost, resulting in a decrease in related peak intensities. The nucleobases were likely oxidized when treated with UV254 and chlorine, thus leading to shifts in the related peaks. The generality of the method was verified using two additional bacterial strains: Escherichia coli and Bacillus subtilis as well as in different water matrices. The results suggest that SERS spectral analysis is a promising means to examine bacterial stress response at the molecular level and has applicability in diverse environmental implementations.
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Affiliation(s)
- Wei Wang
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, USA
| | - Asifur Rahman
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, USA
| | - Qishen Huang
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, USA
| | - Peter J Vikesland
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, USA.
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19
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Allen DM, Einarsson GG, Tunney MM, Bell SEJ. Characterization of Bacteria Using Surface-Enhanced Raman Spectroscopy (SERS): Influence of Microbiological Factors on the SERS Spectra. Anal Chem 2022; 94:9327-9335. [PMID: 35713672 PMCID: PMC9260712 DOI: 10.1021/acs.analchem.2c00817] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
SERS is currently being explored as a rapid method for identification of bacteria but variation in the experimental procedures has resulted in considerable variation in the spectra reported for a range of bacterial species. Here, we show that mixing bacteria with a conventional citrate-reduced silver colloid (CRSC) and drying the resulting suspension yield highly reproducible spectra. These signals were due to intracellular components released when the structure of the bacteria was disrupted during sample preparation. This reproducibility allowed us to examine the effects of variables that do not arise in SERS of simple solutions but are relevant in studies of bacteria. These included growth phase and biological variation, which occurred when the same bacterial isolates were cultured under nominally identical conditions on different days. It was found that even under optimal standardized conditions the effect of differences in experimental parameters such as growth phase was very large in some bacterial species but insignificant in others. This suggests that it is important to avoid drawing general conclusions about bacterial SERS based on studies using small numbers of samples. Similarly, discrimination between bacterial species was straightforward when a small number of isolates with distinct spectral features were investigated; however, this became more challenging when more bacterial species were included, as this increased the possibility of finding different species of bacteria with similar spectra. These observations are important because they clearly delineate the challenges that will need to be addressed if SERS is to be used for clinical applications.
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Affiliation(s)
- Danielle M Allen
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast, Northern Ireland BT9 7BL, UK
| | - Gisli G Einarsson
- Centre for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, 97 Lisburn Road, Belfast, Northern Ireland BT9 7BL, UK
| | - Michael M Tunney
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast, Northern Ireland BT9 7BL, UK
| | - Steven E J Bell
- School of Chemistry and Chemical Engineering, Queen's University Belfast, University Road, Belfast, Northern Ireland BT7 1NN, UK
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20
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Park J, Lee S, Lee H, Han S, Kang TH, Kim D, Kang T, Choi I. Colloidal Multiscale Assembly via Photothermally Driven Convective Flow for Sensitive In-Solution Plasmonic Detections. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2201075. [PMID: 35570749 DOI: 10.1002/smll.202201075] [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: 02/18/2022] [Revised: 04/29/2022] [Indexed: 06/15/2023]
Abstract
The assembly of metal nanoparticles and targets to be detected in a small light probe volume is essential for achieving sensitive in-solution surface-enhanced Raman spectroscopy (SERS). Such assemblies generally require either chemical linkers or templates to overcome the random diffusion of the colloids unless the aqueous sample is dried. Here, a facile method is reported to produce 3D multiscale assemblies of various colloids ranging from molecules and nanoparticles to microparticles for sensitive in-solution SERS detection without chemical linkers and templates by exploiting photothermally driven convective flow. The simulations suggest that colloids sub 100 nm in diameter can be assembled by photothermally driven convective flow regardless of density; the assembly of larger colloids up to several micrometers by convective flow is significant only if their density is close to that of water. Consistent with the simulation results, the authors confirm that the photothermally driven convective flow is mainly responsible for the observed coassembly of plasmonic gold nanorods with either smaller molecules or larger microparticles. It is further found that the coassembly with the plasmonic nanoantennae leads to dramatic Raman enhancements of molecules, microplastics, and microbes by up to fivefold of magnitude compared to those measured in solution without the coassembly.
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Affiliation(s)
- Junhee Park
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Seungki Lee
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Hyunjoo Lee
- Department of Mechanical Engineering, Sogang University, Seoul, 04107, Republic of Korea
| | - Seungyeon Han
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Tae Ho Kang
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Dongchoul Kim
- Department of Mechanical Engineering, Sogang University, Seoul, 04107, Republic of Korea
| | - Taewook Kang
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, 04107, Republic of Korea
| | - Inhee Choi
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
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21
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Agyekum AA, Kutsanedzie FYH, Mintah BK, Annavaram V, Braimah AO. Rapid Detection and Prediction of Norfloxacin in Fish Using Bimetallic Au@Ag Nano-Based SERS Sensor Coupled Multivariate Calibration. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02297-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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22
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Separation-free bacterial identification in arbitrary media via deep neural network-based SERS analysis. Biosens Bioelectron 2022; 202:113991. [DOI: 10.1016/j.bios.2022.113991] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 01/04/2022] [Accepted: 01/07/2022] [Indexed: 11/22/2022]
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23
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Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study. Microbiol Spectr 2022; 10:e0240921. [PMID: 35107359 PMCID: PMC8809336 DOI: 10.1128/spectrum.02409-21] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phenotypic resistance. Thus, fast and accurate methods for the analysis of bacterial antibiotic resistance are in high demand for clinical applications. In this pilot study, we isolated 7 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains and 8 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from clinical samples. Surface-enhanced Raman spectroscopy (SERS) as a label-free and noninvasive method was employed for discriminating CSKP strains from CRKP strains through computational analysis. Eight supervised machine learning algorithms were applied for sample analysis. According to the results, all supervised machine learning methods could successfully predict carbapenem sensitivity and resistance in K. pneumoniae, with a convolutional neural network (CNN) algorithm on top of all other methods. Taken together, this pilot study confirmed the application potentials of surface-enhanced Raman spectroscopy in fast and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. IMPORTANCE With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.
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24
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Sportelli MC, Kranz C, Mizaikoff B, Cioffi N. Recent advances on the spectroscopic characterization of microbial biofilms: A critical review. Anal Chim Acta 2022; 1195:339433. [DOI: 10.1016/j.aca.2022.339433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 12/30/2021] [Accepted: 01/02/2022] [Indexed: 02/07/2023]
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25
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Cialla-May D, Krafft C, Rösch P, Deckert-Gaudig T, Frosch T, Jahn IJ, Pahlow S, Stiebing C, Meyer-Zedler T, Bocklitz T, Schie I, Deckert V, Popp J. Raman Spectroscopy and Imaging in Bioanalytics. Anal Chem 2021; 94:86-119. [PMID: 34920669 DOI: 10.1021/acs.analchem.1c03235] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Dana Cialla-May
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,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
| | - Christoph Krafft
- 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
| | - Tanja Deckert-Gaudig
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Torsten Frosch
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Izabella J Jahn
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Susanne Pahlow
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,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
| | - Clara Stiebing
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Tobias Meyer-Zedler
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Thomas Bocklitz
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Iwan Schie
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Ernst-Abbe-Hochschule Jena, University of Applied Sciences, Department of Biomedical Engineering and Biotechnology, Carl-Zeiss-Promenade 2, 07745 Jena, Germany
| | - Volker Deckert
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,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
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26
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Almohammed S, K. Orhan O, Daly S, O’Regan DD, Rodriguez BJ, Casey E, Rice JH. Electric Field Tunability of Photoluminescence from a Hybrid Peptide-Plasmonic Metal Microfabricated Chip. JACS AU 2021; 1:1987-1995. [PMID: 35574042 PMCID: PMC8611722 DOI: 10.1021/jacsau.1c00323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Indexed: 06/14/2023]
Abstract
Enhancement of fluorescence through the application of plasmonic metal nanostructures has gained substantial research attention due to the widespread use of fluorescence-based measurements and devices. Using a microfabricated plasmonic silver nanoparticle-organic semiconductor platform, we show experimentally the enhancement of fluorescence intensity achieved through electro-optical synergy. Fluorophores located sufficiently near silver nanoparticles are combined with diphenylalanine nanotubes (FFNTs) and subjected to a DC electric field. It is proposed that the enhancement of the fluorescence signal arises from the application of the electric field along the length of the FFNTs, which stimulates the pairing of low-energy electrons in the FFNTs with the silver nanoparticles, enabling charge transport across the metal-semiconductor template that enhances the electromagnetic field of the plasmonic nanoparticles. Many-body perturbation theory calculations indicate that, furthermore, the charging of silver may enhance its plasmonic performance intrinsically at particular wavelengths, through band-structure effects. These studies demonstrate for the first time that field-activated plasmonic hybrid platforms can improve fluorescence-based detection beyond using plasmonic nanoparticles alone. In order to widen the use of this hybrid platform, we have applied it to enhance fluorescence from bovine serum albumin and Pseudomonas fluorescens. Significant enhancement in fluorescence intensity was observed from both. The results obtained can provide a reference to be used in the development of biochemical sensors based on surface-enhanced fluorescence.
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Affiliation(s)
- Sawsan Almohammed
- School
of Physics, University College Dublin, Belfield, Dublin D04 V1W8, Ireland
- Conway
Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin D04 V1W8, Ireland
| | - Okan K. Orhan
- School
of Physics, AMBER, and CRANN Institute, Trinity College Dublin, The University of Dublin, Dublin D02 PN40, Ireland
| | - Sorcha Daly
- School
of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin D04 V1W8, Ireland
| | - David D. O’Regan
- School
of Physics, AMBER, and CRANN Institute, Trinity College Dublin, The University of Dublin, Dublin D02 PN40, Ireland
| | - Brian J. Rodriguez
- School
of Physics, University College Dublin, Belfield, Dublin D04 V1W8, Ireland
- Conway
Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin D04 V1W8, Ireland
| | - Eoin Casey
- School
of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin D04 V1W8, Ireland
| | - James H. Rice
- School
of Physics, University College Dublin, Belfield, Dublin D04 V1W8, Ireland
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Kashif M, Majeed MI, Nawaz H, Rashid N, Abubakar M, Ahmad S, Ali S, Hyat H, Bashir S, Batool F, Akbar S, Anwar MA. Surface-enhanced Raman spectroscopy for identification of food processing bacteria. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 261:119989. [PMID: 34087771 DOI: 10.1016/j.saa.2021.119989] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/16/2021] [Accepted: 05/19/2021] [Indexed: 06/12/2023]
Abstract
Food processing bacteria play important role in providing flavors, ingredients and other beneficial characteristics to the food but at the same time some bacteria are responsible for food spoilage. Therefore, quick and reliable identification of these food processing bacteria is very necessary for the differentiation between different species which may help in the development of more useful food processing methodologies. In this study, analysis of different bacterial species (Lactobacillus fermentum, Fructobacillus fructosus, Pediococcus pentosaceus and Halalkalicoccus jeotgali) was performed with our in-house developed Ag NPs-based surface-enhanced Raman spectroscopy (SERS) method. The SERS spectral data was analyzed by multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Bacterial species were differentiated on the basis of SERS spectral features and potential of SERS was compared with the Raman spectroscopy (RS). SERS along with PCA and PLS-DA was found to be an efficient technique for identification and differentiation of food processing bacterial species. Differentiation with accuracy of 99.5% and sensitivity of 99.7% was depicted by PLS-DA model using leave one out cross validation.
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Affiliation(s)
- Muhammad Kashif
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | | | - Haq Nawaz
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan.
| | - Nosheen Rashid
- Department of Physics, University of Agriculture, Faisalabad, Pakistan
| | - Muhammad Abubakar
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | - Shamsheer Ahmad
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | - Saqib Ali
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | - Hamza Hyat
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | - Saba Bashir
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | - Fatima Batool
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | - Saba Akbar
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | - Munir Ahmad Anwar
- Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan
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28
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Wang W, Kang S, Vikesland PJ. Surface-Enhanced Raman Spectroscopy of Bacterial Metabolites for Bacterial Growth Monitoring and Diagnosis of Viral Infection. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:9119-9128. [PMID: 34133126 DOI: 10.1021/acs.est.1c02552] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Bacterial metabolites are intermediate products of bacterial metabolism and their production reflects metabolic activity. Herein, we report the use of surface-enhanced Raman spectroscopy (SERS) for detection of both volatile and nonvolatile metabolites and the application of this approach for bacterial growth quantification and diagnosis of viral infection. The time-dependent SERS signal of the volatile metabolite dimethyl disulfide in the headspace above bacteria growing on an agar plate was detected and quantified. In addition, SERS signals arising from the plate reflected nutrient consumption and production of nonvolatile metabolites. The measurement of metabolite accumulation can be used for bacterial quantification. In the presence of bacteriophage virus, bacterial metabolism is suppressed, and the relative decrease in SERS intensity reflects the initial virus concentration. Using multivariate analysis, we detect viral infection with a prediction accuracy of 93%. Our SERS-based approach for metabolite production monitoring provides new insights toward viral infection diagnosis.
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Affiliation(s)
- Wei Wang
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, United States
| | - Seju Kang
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, United States
| | - Peter J Vikesland
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, United States
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Witkowska E, Łasica AM, Niciński K, Potempa J, Kamińska A. In Search of Spectroscopic Signatures of Periodontitis: A SERS-Based Magnetomicrofluidic Sensor for Detection of Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans. ACS Sens 2021; 6:1621-1635. [PMID: 33792284 PMCID: PMC8155661 DOI: 10.1021/acssensors.1c00166] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
![]()
Recently, Porphyromonas gingivalis, the keystone pathogen implicated
in the development of gum disease
(periodontitis), was detected in the brains of Alzheimer’s
disease patients, opening up a fascinating possibility that it is
also involved in the pathobiology of this neurodegenerative illness.
To verify this hypothesis, an unbiased, specific, and sensitive method
to detect this pathogen in biological specimens is needed. To this end, our interdisciplinary
studies demonstrate that P. gingivalis can be easily identified by surface-enhanced Raman scattering (SERS).
Moreover, based on SERS measurements, P. gingivalis can be distinguished from another common periodontal pathogen, Aggregatibacter actinomycetemcomitans, and also from
ubiquitous oral Streptococcus spp.
The results were confirmed by principal component analysis (PCA).
Furthermore, we have shown that different P. gingivalis and A. actinomycetemcomitans strains
can easily adsorb to silver-coated magnetic nanoparticles (Fe2O3@AgNPs). Thus, it is possible to magnetically
separate investigated bacteria from other components of a specimen
using the microfluidic chip. To obtain additional enhancement of the
Raman signal, the NPs adsorbed to bacterial cells were magnetically
attracted to the Si/Ag SERS platform. Afterward, the SERS spectra
could be recorded. Such a time-saving procedure can be very helpful
in rapid medical diagnostics and thus in starting the appropriate
pharmacological therapy to prevent the development of periodontitis
and associated comorbidities, e.g., Alzheimerʼs disease.
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Affiliation(s)
- Evelin Witkowska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
| | - Anna M. Łasica
- Department of Bacterial Genetics, Institute of Microbiology, Faculty of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland
| | - Krzysztof Niciński
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
| | - Jan Potempa
- Department of Microbiology, Faculty of Biochemistry, Biophysics, and Biotechnology, Jagiellonian University, Gronostajowa 7, 30-387 Krakow, Poland
- Oral Immunology and Infectious Diseases, University of Louisville School of Dentistry, 501 S. Preston Street, Louisville, Kentucky 40202, United States
| | - Agnieszka Kamińska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
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30
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Bashir S, Nawaz H, Majeed MI, Mohsin M, Abdullah S, Ali S, Rashid N, Kashif M, Batool F, Abubakar M, Ahmad S, Abdulraheem A. Rapid and sensitive discrimination among carbapenem resistant and susceptible E. coli strains using Surface Enhanced Raman Spectroscopy combined with chemometric tools. Photodiagnosis Photodyn Ther 2021; 34:102280. [PMID: 33823284 DOI: 10.1016/j.pdpdt.2021.102280] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/08/2021] [Accepted: 03/29/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Raman spectroscopy is a powerful technique for the robust, reliable and rapid detection and discrimination of bacteria. OBJECTIVES To develop a rapid and sensitive technique based on surface-enhanced Raman spectroscopy (SERS) with multivariate data analysis tools for discrimination among carbapenem resistant and susceptible E. coli strains. METHODS SERS was employed to differentiate different strains of carbapenem resistant and susceptible E. coli by using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, four strains of carbapenem resistant and three strains of carbapenem susceptible E. coli were analyzed by comparing their SERS spectral signatures. Furthermore, multivariate data analysis techniques including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were performed over the spectral range of 400-1800 cm-1 (fingerprint region) for the identification and differentiation of different E. coli strains. RESULTS The SERS spectral features associated with resistant development against carbapenem antibiotics were separated by comparing each spectrum of susceptible strains with each resistant strain. PCA and HCA were found effective for the qualitative differentiation of all the strains analysed. PLS-DA successfully discriminated the carbapenem resistant and susceptible E. coli pellets on the strain level with 99.8 % sensitivity, 100 % specificity, 100 % accuracy and 86 % area under receiver operating characteristic (AUROC) curve. CONCLUSION SERS can be employed for the rapid discrimination among carbapenem resistant and susceptible strains of E. coil.
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Affiliation(s)
- Saba Bashir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan.
| | - Mashkoor Mohsin
- Institute of Microbiology, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan.
| | - Sabahat Abdullah
- Institute of Microbiology, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan
| | - Saqib Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan
| | - Nosheen Rashid
- Department of Chemistry, University of Central Punjab, Faisalabad Campus, Faisalabad, Pakistan
| | - Muhammad Kashif
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan
| | - Fatima Batool
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan
| | - Muhammad Abubakar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan
| | - Shamsheer Ahmad
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan
| | - Aliza Abdulraheem
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan
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31
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Dina NE, Gherman AMR, Colniță A, Marconi D, Sârbu C. Fuzzy characterization and classification of bacteria species detected at single-cell level by surface-enhanced Raman scattering. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 247:119149. [PMID: 33188974 DOI: 10.1016/j.saa.2020.119149] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/08/2020] [Accepted: 10/26/2020] [Indexed: 06/11/2023]
Abstract
Advanced chemometric methods, such as fuzzy c-means, a semi-supervised clustering method, and fuzzy linear discriminant analysis (FLDA), a new robust supervised classification method in combination with principal component analysis (PCA), namely PCA-FLDA, have been successfully applied for characterization and classification of bacterial species detected at single-cell level by surface-enhanced Raman scattering (SERS) spectroscopy. SERS spectra of three species (S. aureus, E. faecalis and P. aeruginosa) were recorded in an original fashion, using in situ laser induced silver spot as metallic substrate. The detection process of bacteria was isolated inside a hermetically sealed in-house built microfluidic device, connected to a syringe pump for injecting the analytes and a portable Raman spectrometer as detection tool. The obtained results (fuzzy partitions) and spectra of the prototypes (robust fuzzy spectra mean corresponding to each fuzzy partition) clearly demonstrated the efficiency and information power of the advanced fuzzy methods in bacteria characterization and classification based on SERS spectra, and allowed a rationale assigning to a specific group. Also, this powerful detection and classification methodology generates the premises for future investigations of Raman and other spectroscopic data obtained for various samples.
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Affiliation(s)
- Nicoleta Elena Dina
- Department of Molecular and Biomolecular Physics, National Institute for Research and Development of Isotopic and Molecular Technologies, 400293 Cluj-Napoca, Romania.
| | - Ana Maria Raluca Gherman
- Department of Molecular and Biomolecular Physics, National Institute for Research and Development of Isotopic and Molecular Technologies, 400293 Cluj-Napoca, Romania; Faculty of Physics, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Alia Colniță
- Department of Molecular and Biomolecular Physics, National Institute for Research and Development of Isotopic and Molecular Technologies, 400293 Cluj-Napoca, Romania.
| | - Daniel Marconi
- Department of Molecular and Biomolecular Physics, National Institute for Research and Development of Isotopic and Molecular Technologies, 400293 Cluj-Napoca, Romania
| | - Costel Sârbu
- Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, 400028 Cluj-Napoca, Romania
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Combined negative dielectrophoresis with a flexible SERS platform as a novel strategy for rapid detection and identification of bacteria. Anal Bioanal Chem 2021; 413:2007-2020. [PMID: 33507352 DOI: 10.1007/s00216-021-03169-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 12/25/2020] [Accepted: 01/07/2021] [Indexed: 10/22/2022]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a vibrational method successfully applied in analytical chemistry, molecular biology and medical diagnostics. In this article, we demonstrate the combination of the negative dielectrophoretic (nDEP) phenomenon and a flexible surface-enhanced Raman platform for quick isolation (3 min), concentration and label-free identification of bacteria. The platform ensures a strong enhancement factor, high stability and reproducibility for the SERS response of analyzed samples. By introducing radial dielectrophoretic forces directed at the SERS platform, we can efficiently execute bacterial cell separation, concentration and deposition onto the SERS-active surface, which simultaneously works as a counter electrode and thus enables such hybrid DEP-SERS device vibration-based detection. Additionally, we show the ability of our DEP-SERS system to perform rapid, cultivation-free, direct detection of bacteria in urine and apple juice samples. The device provides new opportunities for the detection of pathogens.
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33
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Andrei CC, Moraillon A, Larquet E, Potara M, Astilean S, Jakab E, Bouckaert J, Rosselle L, Skandrani N, Boukherroub R, Ozanam F, Szunerits S, Gouget-Laemmel AC. SERS characterization of aggregated and isolated bacteria deposited on silver-based substrates. Anal Bioanal Chem 2021; 413:1417-1428. [PMID: 33388848 DOI: 10.1007/s00216-020-03106-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/27/2020] [Accepted: 12/01/2020] [Indexed: 12/11/2022]
Abstract
Surface-enhanced Raman scattering (SERS), based on the enhancement of the Raman signal of molecules positioned within a few nanometres from a structured metal surface, is ideally suited to provide bacterial-specific molecular fingerprints which can be used for analytical purposes. However, for some complex structures such as bacteria, the generation of reproducible SERS spectra is still a challenging task. Among the various factors influencing the SERS variability (such as the nature of SERS-active substrate, Raman parameters and bacterial specificity), we demonstrate in this study that the environment of Gram-positive and Gram-negative bacteria deposited on ultra-thin silver films also impacts the origin of the SERS spectra. In the case of densely packed bacteria, the obtained SERS signatures were either characteristic of the secretion of adenosine triphosphate for Staphylococcus aureus (S. aureus) or the cell wall and the pili/flagella for Escherichia coli (E. coli), allowing for an easy discrimination between the various strains. In the case of isolated bacteria, SERS mapping together with principal component analysis revealed some variabilities of the spectra as a function of the bacteria environment and the bactericidal effect of the silver. However, the variability does not preclude the SERS signatures of various E. coli strains to be discriminated.
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Affiliation(s)
- Cristina-Cassiana Andrei
- Laboratoire de Physique de la Matière Condensée, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120, Palaiseau, France
| | - Anne Moraillon
- Laboratoire de Physique de la Matière Condensée, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120, Palaiseau, France
| | - Eric Larquet
- Laboratoire de Physique de la Matière Condensée, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120, Palaiseau, France
| | - Monica Potara
- Nanobiophotonics and Laser Microspectroscopy Center, Interdisciplinary Research Institute in Bio-Nano-Sciences, Babes-Bolyai University, T. Laurian Str. 42, 400271, Cluj-Napoca, Romania
| | - Simion Astilean
- Nanobiophotonics and Laser Microspectroscopy Center, Interdisciplinary Research Institute in Bio-Nano-Sciences, Babes-Bolyai University, T. Laurian Str. 42, 400271, Cluj-Napoca, Romania.,Department of Biomolecular Physics, Faculty of Physics, Babes-Bolyai University, M Kogalniceanu Str. 1, 400084, Cluj-Napoca, Romania
| | - Endre Jakab
- Hungarian Department of Biology and Ecology, Faculty of Biology and Geology, Babes-Bolyai University, Clinicilor 5-7, 400006, Cluj-Napoca, Romania.,Molecular Biology Center, Interdisciplinary Research Institute in Bio-Nano-Sciences, Babes-Bolyai University, T. Laurian Str. 42, 400271, Cluj-Napoca, Romania
| | - Julie Bouckaert
- Unité de Glycobiologie Structurale et Fonctionnelle (UGSF), UMR 8576 of the CNRS, University of Lille, 50 avenue de Halley, 59658, Villeneuve-d'Ascq, France
| | - Léa Rosselle
- TissueAegis SAS, 14E rue Pierre de Coubertin, 21000, Dijon, France.,University of Lille, CNRS, Centrale Lille, University of Polytechnique Hauts-de-France, UMR 8520 - IEMN, F-59000, Lille, France
| | - Nadia Skandrani
- TissueAegis SAS, 14E rue Pierre de Coubertin, 21000, Dijon, France
| | - Rabah Boukherroub
- University of Lille, CNRS, Centrale Lille, University of Polytechnique Hauts-de-France, UMR 8520 - IEMN, F-59000, Lille, France
| | - François Ozanam
- Laboratoire de Physique de la Matière Condensée, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120, Palaiseau, France
| | - Sabine Szunerits
- University of Lille, CNRS, Centrale Lille, University of Polytechnique Hauts-de-France, UMR 8520 - IEMN, F-59000, Lille, France.
| | - Anne Chantal Gouget-Laemmel
- Laboratoire de Physique de la Matière Condensée, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120, Palaiseau, France.
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Guo Z, Wang M, Barimah AO, Chen Q, Li H, Shi J, El-Seedi HR, Zou X. Label-free surface enhanced Raman scattering spectroscopy for discrimination and detection of dominant apple spoilage fungus. Int J Food Microbiol 2020; 338:108990. [PMID: 33267967 DOI: 10.1016/j.ijfoodmicro.2020.108990] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 11/05/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023]
Abstract
Fungal infection is one of the main causes of apple corruption. The main dominant spoilage fungi in causing apple spoilage are storage mainly include Penicillium Paecilomyces paecilomyces (P. paecilomyces), penicillium chrysanthemum (P. chrysogenum), expanded Penicillium expansum (P. expansum), Aspergillus niger (Asp. niger) and Alternaria. In this study, surface-enhanced Raman spectroscopy (SERS) based on gold nanorod (AuNRs) substrate method was developed to collect and examine the Raman fingerprints of dominant apple spoilage fungus spores. Standard normal variable (SNV) was used to pretreat the obtained spectra to improve signal-to-noise ratio. Principal component analysis (PCA) was applied to extract useful spectral information. Linear discriminant analysis (LDA) and non-linear pattern recognition methods including K nearest neighbor (KNN), Support vector machine (SVM) and back propagation artificial neural networks (BPANN) were used to identify fungal species. As the comparison of modeling results shown, the BPANN model established based on the characteristic spectra variables have achieved the satisfactory result with discrimination accuracy of 98.23%; while the PCA-LDA model built using principal component variables achieved the best distinguish result with discrimination accuracy of 98.31%. It was concluded that SERS has the potential to be an inexpensive, rapid and effective method to detect and identify fungal species.
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Affiliation(s)
- Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Mingming Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Alberta Osei Barimah
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Jiyong Shi
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Hesham R El-Seedi
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; Division of Pharmacognosy, Department of Medicinal Chemistry, Uppsala University, Box 574, SE-75 123 Uppsala, Sweden
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
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