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Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis

View ORCID ProfilePhilip W. Fowler, View ORCID ProfileAna Luíza Gibertoni Cruz, Sarah J. Hoosdally, Lisa Jarrett, View ORCID ProfileEmanuele Borroni, Matteo Chiacchiaretta, Priti Rathod, Timothy M. Walker, Esther Robinson, Timothy E. A. Peto, View ORCID ProfileDaniela Maria Cirillo, E. Grace Smith, View ORCID ProfileDerrick W. Crook
doi: https://doi.org/10.1101/229427
Philip W. Fowler
1Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, UK
2National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
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  • For correspondence: philip.fowler@ndm.ox.ac.uk
Ana Luíza Gibertoni Cruz
1Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, UK
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Sarah J. Hoosdally
1Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, UK
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Lisa Jarrett
3National Mycobacterial Reference Service, National Infection Service, Public Health Laboratory Birmingham, Heartlands Hospital, Bordesley Green, Birmingham, B9 5SS, UK
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Emanuele Borroni
4Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Matteo Chiacchiaretta
4Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Priti Rathod
3National Mycobacterial Reference Service, National Infection Service, Public Health Laboratory Birmingham, Heartlands Hospital, Bordesley Green, Birmingham, B9 5SS, UK
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Timothy M. Walker
1Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, UK
2National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
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Esther Robinson
3National Mycobacterial Reference Service, National Infection Service, Public Health Laboratory Birmingham, Heartlands Hospital, Bordesley Green, Birmingham, B9 5SS, UK
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Timothy E. A. Peto
1Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, UK
5NIHR Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, UK
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Daniela Maria Cirillo
4Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
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E. Grace Smith
3National Mycobacterial Reference Service, National Infection Service, Public Health Laboratory Birmingham, Heartlands Hospital, Bordesley Green, Birmingham, B9 5SS, UK
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Derrick W. Crook
1Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, UK
5NIHR Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, UK
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Abstract

M. tuberculosis grows slowly and is challenging to work with experimentally compared with many other bacteria. Although microtitre plates have the potential to enable high-throughput phenotypic testing of M. tuberculosis, they can be difficult to read and interpret. Here we present a software package, the Automated Mycobacterial Growth Detection Algorithm (AMyGDA), that measures how much M. tuberculosis is growing in each well of a 96-well microtitre plate. The plate used here has serial dilutions of 14 anti-tuberculosis drugs, thereby permitting the minimum inhibitory concentrations (MICs) to be elucidated. The two participating laboratories each inoculated ten 96-well plates with the standard H37Rv reference strain and, after two weeks incubation, measured the MICs for all 14 drugs on each plate and took a photograph. By analysing the images, we demonstrate that AMyGDA is reproducible, and that the MICs measured are comparable to those measured by a laboratory scientist. AMyGDA software will be used by the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) to measure the drug susceptibility profile of a large number (> 30,000) of samples of M. tuberculosis from patients over the next few years.

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Posted June 15, 2018.
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Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis
Philip W. Fowler, Ana Luíza Gibertoni Cruz, Sarah J. Hoosdally, Lisa Jarrett, Emanuele Borroni, Matteo Chiacchiaretta, Priti Rathod, Timothy M. Walker, Esther Robinson, Timothy E. A. Peto, Daniela Maria Cirillo, E. Grace Smith, Derrick W. Crook
bioRxiv 229427; doi: https://doi.org/10.1101/229427
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Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis
Philip W. Fowler, Ana Luíza Gibertoni Cruz, Sarah J. Hoosdally, Lisa Jarrett, Emanuele Borroni, Matteo Chiacchiaretta, Priti Rathod, Timothy M. Walker, Esther Robinson, Timothy E. A. Peto, Daniela Maria Cirillo, E. Grace Smith, Derrick W. Crook
bioRxiv 229427; doi: https://doi.org/10.1101/229427

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