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Dunnington EL, Wong BS, Fu D. Innovative Approaches for Drug Discovery: Quantifying Drug Distribution and Response with Raman Imaging. Anal Chem 2024; 96:7926-7944. [PMID: 38625100 PMCID: PMC11108735 DOI: 10.1021/acs.analchem.4c01413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Affiliation(s)
| | | | - Dan Fu
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
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2
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Chen C, Wang Y, Wu F, Hong W. Rapid Antifungal Susceptibility Testing Based on Single-Cell Metabolism Analysis Using Stimulated Raman Scattering Imaging. Anal Chem 2023; 95:15556-15565. [PMID: 37815933 DOI: 10.1021/acs.analchem.3c02243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Rapid antifungal susceptibility testing (AFST) is urgently needed in clinics to treat invasive fungal infections with the appropriate antifungal drugs and to slow the emergence of antifungal resistance. However, current AFST methods are time-consuming (24-48 h) due to the slow growth of fungal cells and the methods not being able to work directly for clinical samples. Here, we demonstrate rapid AFST by measuring the metabolism in single fungal cells using stimulated Raman scattering imaging and deuterium probing. Distinct metabolic responses were observed in Candida albicans to different classes of antifungal drugs: while the metabolism was inhibited by amphotericin B, it was stimulated by azoles (fluconazole and voriconazole) and micafungin. Accordingly, we propose metabolism change as a biomarker for rapid AFST. The results were obtained in 4 h with 100% categorical agreement with the gold standard broth microdilution test. In addition, a protocol was developed for direct AFST from positive blood cultures. This method overcomes the limitation of slow growth in conventional methods and has the potential for the rapid diagnosis of candidemia and other clinical fungal infections.
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Affiliation(s)
- Chen Chen
- School of Biological Science and Medical Engineering, Beihang University; Beijing 100083, China
| | - Yi Wang
- Department of Clinical Laboratory, Beijing Bo'ai Hospital, China Rehabilitation Research Center, Capital Medical University, Beijing 100068, China
| | - Fan Wu
- School of Biological Science and Medical Engineering, Beihang University; Beijing 100083, China
| | - Weili Hong
- School of Biological Science and Medical Engineering, Beihang University; Beijing 100083, China
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3
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Liao X, Deng R, Warriner K, Ding T. Antibiotic resistance mechanism and diagnosis of common foodborne pathogens based on genotypic and phenotypic biomarkers. Compr Rev Food Sci Food Saf 2023; 22:3212-3253. [PMID: 37222539 DOI: 10.1111/1541-4337.13181] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 04/22/2023] [Accepted: 05/06/2023] [Indexed: 05/25/2023]
Abstract
The emergence of antibiotic-resistant bacteria due to the overuse or inappropriate use of antibiotics has become a significant public health concern. The agri-food chain, which serves as a vital link between the environment, food, and human, contributes to the large-scale dissemination of antibiotic resistance, posing a concern to both food safety and human health. Identification and evaluation of antibiotic resistance of foodborne bacteria is a crucial priority to avoid antibiotic abuse and ensure food safety. However, the conventional approach for detecting antibiotic resistance heavily relies on culture-based methods, which are laborious and time-consuming. Therefore, there is an urgent need to develop accurate and rapid tools for diagnosing antibiotic resistance in foodborne pathogens. This review aims to provide an overview of the mechanisms of antibiotic resistance at both phenotypic and genetic levels, with a focus on identifying potential biomarkers for diagnosing antibiotic resistance in foodborne pathogens. Furthermore, an overview of advances in the strategies based on the potential biomarkers (antibiotic resistance genes, antibiotic resistance-associated mutations, antibiotic resistance phenotypes) for antibiotic resistance analysis of foodborne pathogens is systematically exhibited. This work aims to provide guidance for the advancement of efficient and accurate diagnostic techniques for antibiotic resistance analysis in the food industry.
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Affiliation(s)
- Xinyu Liao
- Department of Food Science and Nutrition, Zhejiang University, Hangzhou, Zhejiang, China
- School of Mechanical and Energy Engineering, NingboTech University, Ningbo, Zhejiang, China
- Future Food Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, Zhejiang, China
| | - Ruijie Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu, Sichuan, China
| | - Keith Warriner
- Department of Food Science, University of Guelph, Guelph, Ontario, Canada
| | - Tian Ding
- Department of Food Science and Nutrition, Zhejiang University, Hangzhou, Zhejiang, China
- Future Food Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, Zhejiang, China
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4
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Novikov A, Sayfutdinova A, Botchkova E, Kopitsyn D, Fakhrullin R. Antibiotic Susceptibility Testing with Raman Biosensing. Antibiotics (Basel) 2022; 11:antibiotics11121812. [PMID: 36551469 PMCID: PMC9774239 DOI: 10.3390/antibiotics11121812] [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: 10/20/2022] [Revised: 11/29/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
Antibiotics guard us against bacterial infections and are among the most commonly used medicines. The immediate consequence of their large-scale production and prescription is the development of antibiotic resistance. Therefore, rapid detection of antibiotic susceptibility is required for efficient antimicrobial therapy. One of the promising methods for rapid antibiotic susceptibility testing is Raman spectroscopy. Raman spectroscopy combines fast and contactless acquisition of spectra with good selectivity towards bacterial cells. The antibiotic-induced changes in bacterial cell physiology are detected as distinct features in Raman spectra and can be associated with antibiotic susceptibility. Therefore, the Raman-based approach may be beneficial in designing therapy against multidrug-resistant infections. The surface-enhanced Raman spectroscopy (SERS) and resonance Raman spectroscopy (RRS) additionally provide excellent sensitivity. In this review, we present an analysis of the Raman spectroscopy-based optical biosensing approaches aimed at antibiotic susceptibility testing.
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Affiliation(s)
- Andrei Novikov
- Department of Physical and Colloid Chemistry, Gubkin University, 65/1 Leninsky Prospect, 119991 Moscow, Russia
- Correspondence: (A.N.); (R.F.)
| | - Adeliya Sayfutdinova
- Department of Physical and Colloid Chemistry, Gubkin University, 65/1 Leninsky Prospect, 119991 Moscow, Russia
| | - Ekaterina Botchkova
- Department of Physical and Colloid Chemistry, Gubkin University, 65/1 Leninsky Prospect, 119991 Moscow, Russia
| | - Dmitry Kopitsyn
- Department of Physical and Colloid Chemistry, Gubkin University, 65/1 Leninsky Prospect, 119991 Moscow, Russia
| | - Rawil Fakhrullin
- Department of Physical and Colloid Chemistry, Gubkin University, 65/1 Leninsky Prospect, 119991 Moscow, Russia
- Institute of Fundamental Medicine and Biology, Kazan Federal University, 420008 Kazan, Republic of Tatarstan, Russia
- Correspondence: (A.N.); (R.F.)
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5
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Zhang W, Sun H, He S, Chen X, Yao L, Zhou L, Wang Y, Wang P, Hong W. Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine. Front Microbiol 2022; 13:874966. [PMID: 36090077 PMCID: PMC9449455 DOI: 10.3389/fmicb.2022.874966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 08/05/2022] [Indexed: 11/23/2022] Open
Abstract
Rapid identification and antimicrobial susceptibility testing (AST) of bacteria are key interventions to curb the spread and emergence of antimicrobial resistance. The current gold standard identification and AST methods provide comprehensive diagnostic information but often take 3 to 5 days. Here, a compound Raman microscopy (CRM), which integrates Raman spectroscopy and stimulated Raman scattering microscopy in one system, is presented and demonstrated for rapid identification and AST of pathogens in urine. We generated an extensive bacterial Raman spectral dataset and applied deep learning to identify common clinical bacterial pathogens. In addition, we employed stimulated Raman scattering microscopy to quantify bacterial metabolic activity to determine their antimicrobial susceptibility. For proof-of-concept, we demonstrated an integrated assay to diagnose urinary tract infection pathogens, S. aureus and E. coli. Notably, the CRM system has the unique ability to provide Gram-staining classification and AST results within ~3 h directly from urine samples and shows great potential for clinical applications.
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Affiliation(s)
- Weifeng Zhang
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hongyi Sun
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Shipei He
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Xun Chen
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- School of Engineering Medicine, Beihang University, Beijing, China
| | - Lin Yao
- Department of Urology, Peking University First Hospital, Beijing, China
- Lin Yao,
| | - Liqun Zhou
- Department of Urology, Peking University First Hospital, Beijing, China
| | - Yi Wang
- Department of Clinical Laboratory, China Rehabilitation Research Center, Capital Medical University, Beijing, China
| | - Pu Wang
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- *Correspondence: Pu Wang,
| | - Weili Hong
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Weili Hong,
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Zhang W, Chen X, Zhang J, Chen X, Zhou L, Wang P, Hong W. Rapid antimicrobial susceptibility testing for mixed bacterial infection in urine by AI-stimulated Raman scattering metabolic imaging. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2022. [DOI: 10.1016/j.medntd.2022.100132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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7
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A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing. PHOTONICS 2022. [DOI: 10.3390/photonics9030133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Antimicrobial resistance (AMR) is a global medical threat that seriously endangers human health. Rapid bacterial identification and antimicrobial susceptibility testing (AST) are key interventions to combat the spread and emergence of AMR. Although current clinical bacterial identification and AST provide comprehensive information, they are labor-intensive, complex, inaccurate, and slow (requiring several days, depending on the growth of pathogenic bacteria). Recently, Raman-based identification and AST technologies have played an increasingly important role in fighting AMR. This review summarizes major Raman-based techniques for bacterial identification and AST, including spontaneous Raman scattering, surface-enhanced Raman scattering (SERS), and coherent Raman scattering (CRS) imaging. Then, we discuss recent developments in rapid identification and AST methods based on Raman technology. Finally, we highlight the major challenges and potential future efforts to improve clinical outcomes through rapid bacterial identification and AST.
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Sun B, Kang X, Yue S, Lan L, Li R, Chen C, Zhang W, He S, Zhang C, Fan Y, Wang P, Zheng G, Hong W. A rapid procedure for bacterial identification and antimicrobial susceptibility testing directly from positive blood cultures. Analyst 2021; 147:147-154. [PMID: 34860216 DOI: 10.1039/d1an01210j] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
There is an urgent need to develop a rapid procedure that can rapidly identify and obtain antimicrobial susceptibility testing (AST) results directly from positive blood cultures. Here, we report a semi-automatic bacterial diagnosis procedure, which includes (1) a bacterial concentration process to isolate bacteria from a positive blood culture bottle (PBCB), (2) an identification process using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), and (3) a rapid AST process based on stimulated Raman scattering imaging of deuterium oxide (D2O) incorporation in bacteria. A total of 105 samples were tested for bacterial identification, and a bacterial identification accuracy of 92.3% was achieved. AST takes about 2.5 h after identification. This semi-automatic procedure only takes 3.5 h, which is demonstrated to be the fastest process to obtain identification and AST results starting from PBCBs.
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Affiliation(s)
- Bo Sun
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China. .,Laboratory Diagnosis Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Xixiong Kang
- Laboratory Diagnosis Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Shuhua Yue
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
| | - Lu Lan
- Vibronix Inc., West Lafayette, IN, USA
| | - Rui Li
- Vibronix Inc., West Lafayette, IN, USA
| | - Chen Chen
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
| | - Weifeng Zhang
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
| | - Shipei He
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
| | - Chenxi Zhang
- Laboratory Diagnosis Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Yubo Fan
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China. .,School of Medical Science and Engineering, Beihang University, Beijing, China
| | - Pu Wang
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
| | - Guanghui Zheng
- Laboratory Diagnosis Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Weili Hong
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
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