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Balmages I, Liepins J, Zolins S, Bliznuks D, Broks R, Lihacova I, Lihachev A. Tools for classification of growing/non-growing bacterial colonies using laser speckle imaging. Front Microbiol 2023; 14:1279667. [PMID: 37928664 PMCID: PMC10623326 DOI: 10.3389/fmicb.2023.1279667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023] Open
Abstract
Prior research has indicated the feasibility of assessing growth-associated activity in bacterial colonies through the application of laser speckle imaging techniques. A subpixel correlation method was employed to identify variations in sequential laser speckle images, thereby facilitating the visualization of specific zones indicative of microbial growth within the colony. Such differentiation between active (growing) and inactive (non-growing) bacterial colonies holds considerable implications for medical applications, like bacterial response to certain drugs or antibiotics. The present study substantiates the capability of laser speckle imaging to categorize bacterial colonies as growing or non-growing, a parameter which nonvisible in colonies when observed under white light illumination.
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Affiliation(s)
- Ilya Balmages
- Faculty of Computer Science and Information Technology, Riga Technical University, Riga, Latvia
- Institute of Atomic Physics and Spectroscopy, University of Latvia, Riga, Latvia
| | - Janis Liepins
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | | | - Dmitrijs Bliznuks
- Faculty of Computer Science and Information Technology, Riga Technical University, Riga, Latvia
| | - Renars Broks
- Faculty of Computer Science and Information Technology, Riga Technical University, Riga, Latvia
- Department of Biology and Microbiology, Riga Stradins University, Riga, Latvia
| | - Ilze Lihacova
- Institute of Atomic Physics and Spectroscopy, University of Latvia, Riga, Latvia
| | - Alexey Lihachev
- Institute of Atomic Physics and Spectroscopy, University of Latvia, Riga, Latvia
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Doh IJ, Zuniga DVS, Shin S, Pruitt RE, Rajwa B, Robinson JP, Bae E. Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns. SENSORS (BASEL, SWITZERLAND) 2023; 23:3485. [PMID: 37050545 PMCID: PMC10098818 DOI: 10.3390/s23073485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/16/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.
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Affiliation(s)
- Iyll-Joon Doh
- Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | | | - Sungho Shin
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
| | - Robert E. Pruitt
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA
| | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - J. Paul Robinson
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Euiwon Bae
- Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
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Paquin P, Durmort C, Paulus C, Vernet T, Marcoux PR, Morales S. Spatio-temporal based deep learning for rapid detection and identification of bacterial colonies through lens-free microscopy time-lapses. PLOS DIGITAL HEALTH 2022; 1:e0000122. [PMID: 36812631 PMCID: PMC9931332 DOI: 10.1371/journal.pdig.0000122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 09/09/2022] [Indexed: 06/18/2023]
Abstract
Detection and identification of pathogenic bacteria isolated from biological samples (blood, urine, sputum, etc.) are crucial steps in accelerated clinical diagnosis. However, accurate and rapid identification remain difficult to achieve due to the challenge of having to analyse complex and large samples. Current solutions (mass spectrometry, automated biochemical testing, etc.) propose a trade-off between time and accuracy, achieving satisfactory results at the expense of time-consuming processes, which can also be intrusive, destructive and costly. Moreover, those techniques tend to require an overnight subculture on solid agar medium delaying bacteria identification by 12-48 hours, thus preventing rapid prescription of appropriate treatment as it hinders antibiotic susceptibility testing. In this study, lens-free imaging is presented as a possible solution to achieve a quick and accurate wide range, non-destructive, label-free pathogenic bacteria detection and identification in real-time using micro colonies (10-500 μm) kinetic growth pattern combined with a two-stage deep learning architecture. Bacterial colonies growth time-lapses were acquired thanks to a live-cell lens-free imaging system and a thin-layer agar media made of 20 μl BHI (Brain Heart Infusion) to train our deep learning networks. Our architecture proposal achieved interesting results on a dataset constituted of seven different pathogenic bacteria-Staphylococcus aureus (S. aureus), Enterococcus faecium (E. faecium), Enterococcus faecalis (E. faecalis), Staphylococcus epidermidis (S. epidermidis), Streptococcus pneumoniae R6 (S. pneumoniae), Streptococcus pyogenes (S. pyogenes), Lactococcus Lactis (L. Lactis). At T = 8h, our detection network reached an average 96.0% detection rate while our classification network precision and sensitivity averaged around 93.1% and 94.0% respectively, both were tested on 1908 colonies. Our classification network even obtained a perfect score for E. faecalis (60 colonies) and very high score for S. epidermidis at 99.7% (647 colonies). Our method achieved those results thanks to a novel technique coupling convolutional and recurrent neural networks together to extract spatio-temporal patterns from unreconstructed lens-free microscopy time-lapses.
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Affiliation(s)
- Paul Paquin
- Univ. Grenoble Alpes, CEA, LETI, DTBS, LSIV, 38054 Grenoble, France
| | - Claire Durmort
- Univ. Grenoble Alpes, CNRS, CEA, IBS, 38054 Grenoble, France
| | - Caroline Paulus
- Univ. Grenoble Alpes, CEA, LETI, DTBS, LSIV, 38054 Grenoble, France
| | - Thierry Vernet
- Univ. Grenoble Alpes, CNRS, CEA, IBS, 38054 Grenoble, France
| | | | - Sophie Morales
- Univ. Grenoble Alpes, CEA, LETI, DTBS, LSIV, 38054 Grenoble, France
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Doh IJ, Dowden B, Patsekin V, Rajwa B, Robinson JP, Bae E. Development of a Smartphone-Integrated Reflective Scatterometer for Bacterial Identification. SENSORS (BASEL, SWITZERLAND) 2022; 22:2646. [PMID: 35408260 PMCID: PMC9003293 DOI: 10.3390/s22072646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/21/2022] [Accepted: 03/27/2022] [Indexed: 06/14/2023]
Abstract
We present a smartphone-based bacterial colony phenotyping instrument using a reflective elastic light scattering (ELS) pattern and the resolving power of the new instrument. The reflectance-type device can acquire ELS patterns of colonies on highly opaque media as well as optically dense colonies. The novel instrument was built using a smartphone interface and a 532 nm diode laser, and these essential optical components made it a cost-effective and portable device. When a coherent and collimated light source illuminated a bacterial colony, a reflective ELS pattern was created on the screen and captured by the smartphone camera. The collected patterns whose shapes were determined by the colony morphology were then processed and analyzed to extract distinctive features for bacterial identification. For validation purposes, the reflective ELS patterns of five bacteria grown on opaque growth media were measured with the proposed instrument and utilized for the classification. Cross-validation was performed to evaluate the classification, and the result showed an accuracy above 94% for differentiating colonies of E. coli, K. pneumoniae, L. innocua, S. enteritidis, and S. aureus.
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Affiliation(s)
- Iyll-Joon Doh
- Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA;
| | - Brianna Dowden
- Basic Medical Science, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA; (B.D.); (V.P.); (J.P.R.)
| | - Valery Patsekin
- Basic Medical Science, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA; (B.D.); (V.P.); (J.P.R.)
| | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA;
| | - J. Paul Robinson
- Basic Medical Science, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA; (B.D.); (V.P.); (J.P.R.)
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Euiwon Bae
- Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA;
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On SL, Miller WG, Yee E, Sturgis J, Patsekin V, Lindsay JA, Robinson JP. Identification of colonies of cultured shellfish-associated Arcobacter species by Elastic Light Scatter Analysis. CURRENT RESEARCH IN MICROBIAL SCIENCES 2021; 2:100033. [PMID: 34841324 PMCID: PMC8610310 DOI: 10.1016/j.crmicr.2021.100033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/12/2021] [Accepted: 04/12/2021] [Indexed: 11/16/2022] Open
Abstract
An increasing number of Arcobacter species (including several regarded as emerging human foodborne pathogens) have been isolated from shellfish, an important food commodity. A method to distinguish these species and render viable isolates for further analysis would benefit epidemiological and ecological studies. We describe a method based on Elastic Light Scatter analysis (ELSA) for the detection and discrimination of eleven shellfish-associated Arcobacter species. Although substantive differences in the growth rates of some taxa were seen, ELSA was able to differentiate all the species studied, apart from some strains of A. butzleri and A. cryaerophilus, which were nonetheless distinguished from all other species examined. ELSA appears to be a promising new approach for the detection and identification of Arcobacter species in shellfish and may also be applicable for studies in other foods and matrices.
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Affiliation(s)
- Stephen L.W. On
- Department of Wine, Food & Molecular Biosciences, Lincoln University, New Zealand
| | - William G. Miller
- Produce Safety and Microbiology Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Albany, CA, USA
| | - Emma Yee
- Produce Safety and Microbiology Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Albany, CA, USA
| | - Jennifer Sturgis
- School of Mechanical Engineering, Purdue University, W. Lafayette, USA
| | - Valery Patsekin
- Department of Basic Medical Science, Purdue University, W. Lafayette, USA
| | | | - J. Paul Robinson
- School of Mechanical Engineering, Purdue University, W. Lafayette, USA
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