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

Date Published: April 3, 2017

Publisher: Public Library of Science

Author(s): Yoshiaki Maeda, Hironori Dobashi, Yui Sugiyama, Tatsuya Saeki, Tae-kyu Lim, Manabu Harada, Tadashi Matsunaga, Tomoko Yoshino, Tsuyoshi Tanaka, Bok-Luel Lee.

http://doi.org/10.1371/journal.pone.0174723

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.

Partial Text

Identification of microbial species is routinely performed in a wide range of industries, including production of beverages, foods, cosmetics, and pharmaceuticals. It is also of great importance in clinical diagnosis. A number of methods based on phenotypic and genotypic analyses have been proposed for microbial identification at different classification levels (e.g., family, genus, species, and strain). A typical phenotypic analysis is the comprehensive profiling of biochemical metabolic pathways for which several tool kits enabling rapid identification are commercially available (e.g., API series [1], BIOLOG [2], and VITEK 2 [3]). Mass spectrometry-based phenotypic analysis has been increasingly used for microbial identification, where whole microbial cells are directly subjected to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) [4]. On the other hand, genotypic analyses identify target microbes on the basis of their genomic sequences. Sequencing of 16S (for prokaryotes) or 18S (for eukaryotes) ribosomal DNA (rDNA) is the most common method for estimation of microbial species. Other methods such as genome hybridization and ribotyping can also accurately identify microbes at species/strain levels. Nevertheless, the phenotypic and genotypic methods mentioned above still require expensive reagents (e.g., polymerase, restriction enzymes, fluorophores, and chromophores), high expertise, and long assay duration.

The lensless imaging system with a 2D image sensor successfully visualized microbial colonies and their growth in a wide field of view. The size of colonies visualized by lensless imaging was proportional with that according to microscopy, while the lensless imaging overestimated the colony size approximately by 14.5% as compared to microscopy (S3 Fig). The overestimation of colony size by the lensless imaging could be caused by the effect of light diffraction from the outer edge of the colonies that are placed 1490 μm above the CMOS image sensor (Fig 1).

We developed a lensless imaging system to examine microbial colonies. Prokaryotic and eukaryotic microbial colonies were successfully examined by means of the system, and each microorganism showed different colony patterns, which could represent the 3D structure of each colony. It is also possible to analyze colony growth over time. Furthermore, a number of quantitative parameters could be extracted from the lensless images of microbial colonies. Such parameters, referred to as colony fingerprints, helped us to discriminate microbial species. By taking advantage of a number of useful features of the wide observation area, easy handling, and small and inexpensive set-ups, lensless imaging could become a powerful tool of microorganism research.

 

Source:

http://doi.org/10.1371/journal.pone.0174723

 

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