Abstract
Detection of sulfur-oxidizing bacteria has largely been dependent on targeted gene sequencing technology or traditional cell cultivation, which usually takes from days to months to carry out. This clearly does not meet the requirements of analysis for time-sensitive samples and/or complicated environmental samples. Since energy-dispersive X-ray spectrometry (EDS) can be used to simultaneously detect multiple elements in a sample, including sulfur, with minimal sample treatment, this technology was applied to detect sulfur-oxidizing bacteria using their high sulfur content within the cell. This article describes the application of scanning electron microscopy imaging coupled with EDS mapping for quick detection of sulfur oxidizers in contaminated environmental water samples, with minimal sample handling. Scanning electron microscopy imaging revealed the existence of dense granules within the bacterial cells, while EDS identified large amounts of sulfur within them. EDS mapping localized the sulfur to these granules. Subsequent 16S rRNA gene sequencing showed that the bacteria detected in our samples belonged to the genus Chromatium, which are sulfur oxidizers. Thus, EDS mapping made it possible to identify sulfur oxidizers in environmental samples based on localized sulfur within their cells, within a short time (within 24 h of sampling). This technique has wide ranging applications for detection of sulfur bacteria in environmental water samples.
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Supported by the Basic Scientific Fund for National Public Research Institutes of China (Nos. GY02-2011T10, 2015P07), the Qingdao Talent Program (No. 13-CX-20), the National Natural Science Foundation of China (Nos. 31100567, 41176061), and the National Natural Science Foundation for Creative Groups (No. 41521064)
An erratum to this article is available at http://dx.doi.org/10.1007/s00343-017-7466-6.
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Sun, C., Jiang, F., Gao, W. et al. Scanning electron microscopy coupled with energy-dispersive X-ray spectrometry for quick detection of sulfur-oxidizing bacteria in environmental water samples. Chin. J. Ocean. Limnol. 35, 185–191 (2017). https://doi.org/10.1007/s00343-016-5175-1
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DOI: https://doi.org/10.1007/s00343-016-5175-1