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Appl Environ Microbiol. 2010 Nov;76(22):7451-8. doi: 10.1128/AEM.01595-10. Epub 2010 Sep 24.

Impacts of inter- and intralaboratory variations on the reproducibility of microbial community analyses.

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  • 1Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Rijksstraatweg 6, NL-3631 AC Nieuwersluis, The Netherlands.


With the advent of molecular biological techniques, especially next-generation sequencing and metagenomics, the number of microbial biogeography studies is rapidly increasing. However, these studies involve the synthesis of data generated by different laboratories using different protocols, chemicals, etc., all with inherent biases. The aim of this study was to assess inter- as well as intralaboratory variations in microbial community composition when standardized protocols are applied to a single soil sample. Aliquots from a homogenized soil sample from a rice field in Italy were sent to five participating laboratories. DNA was extracted by two investigators per laboratory using an identical protocol. All DNA samples were sent to one laboratory to perform DNA quantification, quantitative PCR (QPCR), and microarray and denaturing gradient gel electrophoresis (DGGE) analyses of methanotrophic communities. Yields, as well as purity of DNA, were significantly different between laboratories but in some cases also between investigators within the same laboratory. The differences in yield and quality of the extracted DNA were reflected in QPCR, microarray, and DGGE analysis results. Diversity indices (Shannon-Wiener, evenness, and richness) differed significantly between laboratories. The observed differences have implications for every project in which microbial communities are compared in different habitats, even if assessed within the same laboratory. To be able to make sensible comparisons leading to valid conclusions, intralaboratory variation should be assessed. Standardization of DNA extraction protocols and possible use of internal standards in interlaboratory comparisons may help in rendering a "quantifiable" bias.

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