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

• Published in: PloS one Vol. 12; no. 4; p. e0174723
• Main Authors: Maeda, Yoshiaki, Dobashi, Hironori, Sugiyama, Yui, Saeki, Tatsuya, Lim, Tae-Kyu, Harada, Manabu, Matsunaga, Tadashi, Yoshino, Tomoko, Tanaka, Tsuyoshi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.04.2017; Public Library of Science (PLoS)
• Subjects: Accuracy; Automation; Bacterial Typing Techniques - methods; Beverages; Biology and Life Sciences; Biotechnology; Candida albicans; Candida albicans - classification; Classification; Cluster Analysis; Cosmetics; Discriminant analysis; Discrimination; Drugs; E coli; Engineering; Engineering and Technology; Equipment costs; Escherichia coli; Escherichia coli - classification; Feature extraction; Fingerprinting; Fingerprints; Food; Fourier transforms; Growth; Identification; Image Processing, Computer-Assisted; Imaging; Industrial areas; Lasers; Life sciences; Light; Listeria; Mass spectrometry; Medicine and Health Sciences; Methods; Microbial colonies; Microbial contamination; Microorganisms; Microscopy; Motility; Mycological Typing Techniques - methods; Observations; Physical Sciences; Principal components analysis; Pseudomonas aeruginosa; Pseudomonas aeruginosa - classification; Research and Analysis Methods; Ribosomal DNA; RNA polymerase; Salmonella; Salmonella enterica - classification; Salmonella Typhimurium; Scientific imaging; Semiconductors; Staphylococcus aureus; Staphylococcus aureus - classification; Staphylococcus epidermidis
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0174723

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.