Rapid Antibiotic Susceptibility Testing of UropathogenicE. coliby Tracking Submicron Scale Motion of Single Bacterial Cells

ACS Sensors ◽  
2017 ◽  
Vol 2 (8) ◽  
pp. 1231-1239 ◽  
Author(s):  
Karan Syal ◽  
Simon Shen ◽  
Yunze Yang ◽  
Shaopeng Wang ◽  
Shelley E. Haydel ◽  
...  
Keyword(s):  
Antibiotic Susceptibility ◽  
Susceptibility Testing ◽  
Bacterial Cells ◽  
Antibiotic Susceptibility Testing ◽  
Submicron Scale ◽  
Scale Motion

scholarly journals Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Chi-Sing Ho ◽  
Neal Jean ◽  
Catherine A. Hogan ◽  
Lena Blackmon ◽  
Stefanie S. Jeffrey ◽  
...  
Keyword(s):  
Deep Learning ◽  
Antibiotic Susceptibility ◽  
Pathogenic Bacteria ◽  
Susceptibility Testing ◽  
Bacterial Species ◽  
Raman Signal ◽  
Bacterial Cells ◽  
Learning Approaches ◽  
Antibiotic Susceptibility Testing ◽  
Label Free

Abstract Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%. Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum.

scholarly journals Rapid antibiotic susceptibility testing and species identification for mixed infections

2021 ◽  
Author(s):  
Vinodh Kandavalli ◽  
Praneeth Karempudi ◽  
Jimmy Larsson ◽  
Johan Elf
Keyword(s):  
Species Identification ◽  
Antibiotic Susceptibility ◽  
Susceptibility Testing ◽  
Mixed Infections ◽  
Single Cell Level ◽  
Bacterial Cells ◽  
Antibiotic Susceptibility Testing ◽  
Cell Level ◽  
Proof Of Principle

Antimicrobial resistance is an increasing problem globally. Rapid antibiotic susceptibility testing (AST) is urgently needed in the clinic to enable personalized prescription in high-resistance environments and limit the use of broad-spectrum drugs. Previously we have described a 30 min AST method based on imaging of individual bacterial cells. However, current phenotypic AST methods do not include species identification (ID), leaving time-consuming plating or culturing as the only available option when ID is needed to make the sensitivity call. Here we describe a method to perform phenotypic AST at the single-cell level in a microfluidic chip that allows subsequent genotyping by in situ FISH. By stratifying the phenotypic AST response on the species of individual cells, it is possible to determine the susceptibility profile for each species in a mixed infection sample in 1.5 h. In this proof-of-principle study, we demonstrate the operation with four antibiotics and a mixed sample with four species.

scholarly journals Rapid antibiotic susceptibility testing and species identification for mixed infections

2021 ◽  
Author(s):  
Vinodh Kandavalli ◽  
Praneeth Karempudi ◽  
Jimmy Larsson ◽  
Johan Elf
Keyword(s):  
Species Identification ◽  
Antibiotic Susceptibility ◽  
Susceptibility Testing ◽  
Mixed Infections ◽  
Single Cell Level ◽  
Bacterial Cells ◽  
Antibiotic Susceptibility Testing ◽  
Cell Level ◽  
Proof Of Principle

Abstract Antimicrobial resistance is an increasing problem globally. Rapid antibiotic susceptibility testing (AST) is urgently needed in the clinic to enable personalized prescription in high-resistance environments and limit the use of broad-spectrum drugs. Previously we have described a 30 min AST method based on imaging of individual bacterial cells. However, current phenotypic AST methods do not include species identification (ID), leaving time-consuming plating or culturing as the only available option when ID is needed to make the sensitivity call. Here we describe a method to perform phenotypic AST at the single-cell level in a microfluidic chip that allows subsequent genotyping by in situ FISH. By stratifying the phenotypic AST response on the species of individual cells, it is possible to determine the susceptibility profile for each species in a mixed infection sample in 1.5 h. In this proof-of-principle study, we demonstrate the operation with four antibiotics and a mixed sample with four species.

scholarly journals Fast Antibiotic Susceptibility Testing via Raman Microspectrometry on Single Bacteria: An MRSA Case Study

ACS Omega ◽  
2021 ◽  
Author(s):  
Armelle Novelli Rousseau ◽  
Nicolas Faure ◽  
Fabian Rol ◽  
Zohreh Sedaghat ◽  
Joël Le Galudec ◽  
...  
Keyword(s):  
Antibiotic Susceptibility ◽  
Susceptibility Testing ◽  
Antibiotic Susceptibility Testing ◽  
Raman Microspectrometry

scholarly journals Longitudinal Characterization and Transmission Dynamics of Antibiotic-Resistant Organisms in an ICU (LOCATE AROs)

2020 ◽  
Vol 41 (S1) ◽  
pp. s42-s43
Author(s):  
Kimberley Sukhum ◽  
Candice Cass ◽  
Meghan Wallace ◽  
Caitlin Johnson ◽  
Steven Sax ◽  
...  
Keyword(s):  
Antibiotic Susceptibility ◽  
Environmental Samples ◽  
Susceptibility Testing ◽  
Marrow Transplant ◽  
Transmission Dynamics ◽  
Hospital Environment ◽  
Antibiotic Susceptibility Testing ◽  
Antibiotic Resistant ◽  
Clonal Group ◽  
Communal Areas

Background: Healthcare-associated infections caused by antibiotic-resistant organisms (AROs) are a major cause of significant morbidity and mortality. To create and optimize infection prevention strategies, it is crucial to delineate the role of the environment and clinical infections. Methods: Over a 14-month period, we collected environmental samples, patient feces, and patient bloodstream infection (BSI) isolates in a newly built bone marrow transplant (BMT) intensive care unit (ICU). Samples were collected from 13 high-touch areas in the patient room and 4 communal areas. Samples were collected from the old BMT ICU, in the new BMT ICU before patients moved in, and for 1 year after patients moved in. Selective microbiologic culture was used to isolate AROs, and whole-genome sequencing (WGS) was used to determine clonality. Antibiotic susceptibility testing was performed using Kirby-Bauer disk diffusion assays. Using linear mixed modeling, we compared ARO recovery across time and sample area. Results: AROs were collected and cultured from environmental samples, patient feces, and BSI isolates (Fig. 1a). AROs were found both before and after a patient entered the ICU (Fig. 1b). Sink drains had significantly more AROs recovered per sample than any other surface area (P < .001) (Fig. 1c). The most common ARO isolates were Pseudomonas aeruginosa and Stenotrophomonas maltophila (Fig. 1d). The new BMT ICU had fewer AROs recovered per sample than the old BMT ICU (P < .001) and no increase in AROs recovered over the first year of opening (P > .05). Furthermore, there was no difference before versus after patients moved into the hospital (P > .05). Antibiotic susceptibility testing reveal that P. aeruginosa isolates recovered from the old ICU were resistant to more antibiotics than isolates recovered from the new ICU (Fig. 2a). ANI and clonal analyses of P. aeruginosa revealed a large cluster of clonal isolates (34 of 76) (Fig. 2b). This clonal group included isolates found before patients moved into the BMT ICU and patient blood isolates. Furthermore, this clonal group was initially found in only 1 room in the BMT ICU, and over 26 weeks, it was found in sink drains in all 6 rooms sampled (Fig. 2b). Conclusions: AROs are present before patients move into a new BMT ICU, and sink drains act as a reservoir for AROs over time. Furthermore, sink-drain P. aeruginosa isolates are clonally related to isolates found in patient BSIs. Overall, these results provide insight into ARO transmission dynamics in the hospital environment.Funding: Research reported in this publication was supported by the Washington University Institute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.Disclosures: None

scholarly journals Reproducibility of Control Strains for Antibiotic Susceptibility Testing

1976 ◽  
Vol 10 (3) ◽  
pp. 436-440 ◽  
Author(s):  
M. B. Coyle ◽  
M. F. Lampe ◽  
C. L. Aitken ◽  
P. Feigl ◽  
J. C. Sherris
Keyword(s):  
Antibiotic Susceptibility ◽  
Susceptibility Testing ◽  
Antibiotic Susceptibility Testing
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