Automatic counting of FISH-labeled microbes by an LED illuminated detecting apparatus

Nishimura, Masahiko; Shimakita, Tomonori; Matsuzaki, Takuhiro; Tashiro, Yoshikazu; Kogure, Kazuhiro

doi:10.1111/j.1444-2906.2008.01537.x

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…In Nishimura et al. ( 2008 ), BP is used for the quantification of eukaryotic and prokaryotic cells. The cells are stained and captured using a CCD camera that can capture photons emitted from bacteria or yeast cells.…”

Section: Other Microorganism Counting Methodsmentioning

confidence: 99%

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Zhang

Rahaman

et al. 2021

Artif Intell Rev

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Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

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Section: Other Microorganism Counting Methodsmentioning

confidence: 99%

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Zhang

Rahaman

et al. 2021

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…The criteria for microscopic visual counting differ among investigators (21); therefore conventional fluorescence microscopy is not suitable when one must count bacteria in many samples. Automated processes such as laser scanning cytometry (24), automated microscopic counting (9,22) or image analysis (23) might save time and labor for counting and improve the reproducibility of TSA-CTC-FISH. …”

Section: Discussionmentioning

confidence: 99%

Rapid Detection of Starved Escherichia coli with Respiratory Activity in Potable Water by Signal-Amplified in situ Hybridization Following Formazan Reduction

2009

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The aim of this study was to develop a rapid method for the specific detection of respiring Escherichia coli (an indicator of fecal contamination) in potable water. Fluorescence in situ hybridization (FISH) with a rRNA-targeted oligonucleotide probe was used to detect E. coli cells and bacterial respiratory activity was estimated using 5-cyano-2,3-ditolyl tetrazolium chloride (CTC). Fluorescent signals from hybridized cells were increased by optimized tyramide signal amplification (TSA). Respiring E. coli in potable ground water with low rRNA content were enumerated within 8 hours using signal-amplified in situ hybridization following formazan reduction (TSA-CTC-FISH), whereas these starved E. coli cells could not be detected by conventional FISH (FISH without signal amplification) which generated weak fluorescence. TSA-CTC-FISH can be used for simultaneous identification in situ based on phylogenetic information and the activity of individual bacterial cells in potable water. This method would be useful in the rapid monitoring of harmful or fecal indicator bacteria in potable water.Key words: potable water, microbial monitoring, fecal indicator, respiratory activity, fluorescence in situ hybridization A continuous supply of microbiologically wholesome potable water is essential for life. While most potable water has been chlorinated for bacterial sterilization, non-chlorinated and dechlorinated waters have also been generally utilized for consumption, such as natural mineral water and tap water filtered through a household water purifier. The numbers of bacterial cells in non-chlorinated water sometimes increase after an environmental change (e.g. after bottling) and storage (19). Therefore, the abundance of bacteria in potable water should be routinely monitored to maintain microbiological quality control, and methods of monitoring harmful or fecal indicator bacteria in potable water are required for microbiological quality assurance. Several methods are currently applied to detect viable bacteria in potable water, and the most popular and simple techniques include culture-based ones. However, the required incubation times can be quite lengthy and the numbers of bacteria can be underestimated because most viable bacteria in oligotrophic environments are difficult to culture under conventional conditions. Therefore, more rapid, sensitive and reliable methods are desirable. Fluorescence staining can quickly detect viable bacteria without the need for culture. For example, carboxyfluorescein diacetate (CFDA) can detect esterase-active bacteria (6, 33) and the dimeric cyanine dye, BOBO-3, can be used to evaluate bacterial viability based on membrane integrity (3, 16). The redox dye, 5-cyano-2,3-ditolyl tetrazolium chloride (CTC), is widely used to detect respiring bacteria (7) in freshwater such as groundwater (26) and drinking water (28).However, CTC staining alone cannot reveal phylogenetic information regarding targeted bacteria and several species of harmful bacteria must be detected to ensure the quality of p...

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“…However, it is important to distinguish viable microbial cells from total microbial cells to understand the microbial process of contamination. For this purpose, further investigation for the detection of viable microbial cells using 6-carboxyfluorescein diacetate (Dive et al, 1988) and/or specific bacteria using fluorescent in situ hybridization staining (Nishimura et al, 2008) is required for practical monitoring and is in progress. ponents was greatly reduced by this treatment.…”

Section: Detection Of Coliforms In the Soymilkmentioning

confidence: 99%

“…Bioplorer is an optical device originally designed to enumerate bacteria in cosmetics, toiletries, and food (Arai et al, 2006;Masakiyo et al, 2010;Nishimura et al, 2006;Nishimura et al, 2008;Shimakita et al, 2006;Shimakita et al, 2007). It is composed of a charge-coupled device camera, an optical unit, a light-emitting diode (LED) light source and a driving stage.…”

Section: Introductionmentioning

confidence: 99%

Rapid Enumeration of Microbial Cells in Solubilized Soymilk Using an Automatic Cell Counting System with LED Illumination

Tsumura

Tsuboi

2012

FSTR

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A fluorescent filter method using the Bioplorer apparatus and a conventional standard plate count method were compared for microbial cell enumeration in soymilk. Soymilk samples were solubilized for filtration. The microbial cell count obtained using the Bioplorer method showed good correlation with that obtained by the standard plate count method. The results of this study showed that the Bioplorer method is a rapid and efficient method for enumeration of bacterial cells in soymilk.

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Automatic counting of FISH-labeled microbes by an LED illuminated detecting apparatus

Cited by 7 publications

References 17 publications

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Rapid Detection of Starved Escherichia coli with Respiratory Activity in Potable Water by Signal-Amplified in situ Hybridization Following Formazan Reduction

Rapid Enumeration of Microbial Cells in Solubilized Soymilk Using an Automatic Cell Counting System with LED Illumination

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