Colony fingerprint for discrimination of microbial species based on lensless imaging of microcolonies.

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Bacterial Typing Techniques/*methods ; Candida albicans/*classification ; Escherichia coli/*classification ; Mycological Typing Techniques/*methods ; Pseudomonas aeruginosa/*classification ; Salmonella enterica/*classification ; Staphylococcus aureus/*classification; Cluster Analysis ; Image Processing, Computer-Assisted
    • Abstract:
      Detection and identification of microbial species are crucial in a wide range of industries, including production of beverages, foods, cosmetics, and pharmaceuticals. Traditionally, colony formation and its morphological analysis (e.g., size, shape, and color) with a naked eye have been employed for this purpose. However, such a conventional method is time consuming, labor intensive, and not very reproducible. To overcome these problems, we propose a novel method that detects microcolonies (diameter 10-500 μm) using a lensless imaging system. When comparing colony images of five microorganisms from different genera (Escherichia coli, Salmonella enterica, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans), the images showed obvious different features. Being closely related species, St. aureus and St. epidermidis resembled each other, but the imaging analysis could extract substantial information (colony fingerprints) including the morphological and physiological features, and linear discriminant analysis of the colony fingerprints distinguished these two species with 100% of accuracy. Because this system may offer many advantages such as high-throughput testing, lower costs, more compact equipment, and ease of automation, it holds promise for microbial detection and identification in various academic and industrial areas.
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    • Publication Date:
      Date Created: 20170404 Date Completed: 20170907 Latest Revision: 20190208
    • Publication Date:
      20221213
    • Accession Number:
      PMC5378366
    • Accession Number:
      10.1371/journal.pone.0174723
    • Accession Number:
      28369067