Dataset Information

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

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 ?m) 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.

SUBMITTER: Maeda Y

PROVIDER: S-EPMC5378366 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Maeda Yoshiaki Y Dobashi Hironori H Sugiyama Yui Y Saeki Tatsuya T Lim Tae-Kyu TK Harada Manabu M Matsunaga Tadashi T Yoshino Tomoko T Tanaka Tsuyoshi T

PloS one 20170403 4

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 (diam ...[more]

PMID: 28369067

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Project description:5.3 million American couples of reproductive age (9%) are affected by infertility, among which male factors account for up to 50% of cases, which necessitates the identification of parameters defining sperm quality, including sperm count and motility. In vitro fertilization (IVF) with or without intra cytoplasmic sperm injection (ICSI) has become the most widely used assisted reproductive technology (ART) in modern clinical practice to overcome male infertility challenges. One of the obstacles of IVF and ICSI lies in identifying and isolating the most motile and presumably healthiest sperm from semen samples that have low sperm counts (oligozoospermia) and/or low sperm motility (oligospermaesthenia). Microfluidic systems have shown potential to sort sperm with flow systems. However, the small field of view (FOV) of conventional microscopes commonly used to image sperm motion presents challenges in tracking a large number of sperm cells simultaneously. To address this challenge, we have integrated a lensless charge-coupled device (CCD) with a microfluidic chip to enable wide FOV and automatic recording as the sperm move inside a microfluidic channel. The integrated system enables the sorting and tracking of a population of sperm that have been placed in a microfluidic channel. This channel can be monitored in both horizontal and vertical configuration similar to a swim-up column method used clinically. Sperm motilities can be quantified by tracing the shadow paths for individual sperm. Moreover, as the sperm are sorted by swimming from the inlet towards the outlet of a microfluidic channel, motile sperm that reach the outlet can be extracted from the channel at the end of the process. This technology can lead to methods to evaluate each sperm individually in terms of motility response in a wide field of view, which could prove especially useful, when working with oligozoospermic or oligospermaesthenic samples, in which the most motile sperm need to be isolated from a pool of small number of sperm.

Dataset Information

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

Publications

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

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