Absolute quantitation of microbes using 16S rRNA gene metabarcoding: A rapid normalization of relative abundances by quantitative PCR targeting a 16S rRNA gene spike‐in standard

• Main Authors: Olivier Zemb, Caroline S. Achard, Jerome Hamelin, Marie‐Léa De Almeida, Béatrice Gabinaud, Laurent Cauquil, Lisanne M.G. Verschuren, Jean‐Jacques Godon
• Format: Article
• Language: English
• Published: Wiley 2020-03-01
• Series: MicrobiologyOpen
• Subjects: 16S rRNA gene; absolute count data; metabarcoding; microbiome; normalization; spike‐in
• Online Access: https://doi.org/10.1002/mbo3.977

Abstract Metabarcoding of the 16S rRNA gene is commonly used to characterize microbial communities, by estimating the relative abundance of microbes. Here, we present a method to retrieve the concentrations of the 16S rRNA gene per gram of any environmental sample using a synthetic standard in minuscule amounts (100 ppm to 1% of the 16S rRNA sequences) that is added to the sample before DNA extraction and quantified by two quantitative polymerase chain reaction (qPCR) reactions. This allows normalizing by the initial microbial density, taking into account the DNA recovery yield. We quantified the internal standard and the total load of 16S rRNA genes by qPCR. The qPCR for the latter uses the exact same primers as those used for Illumina sequencing of the V3‐V4 hypervariable regions of the 16S rRNA gene to increase accuracy. We are able to calculate the absolute concentration of the species per gram of sample, taking into account the DNA recovery yield. This is crucial for an accurate estimate as the yield varied between 40% and 84%. This method avoids sacrificing a high proportion of the sequencing effort to quantify the internal standard. If sacrificing a part of the sequencing effort to the internal standard is acceptable, we however recommend that the internal standard accounts for 30% of the environmental 16S rRNA genes to avoid the PCR bias associated with rare phylotypes. The method proposed here was tested on a feces sample but can be applied more broadly on any environmental sample. This method offers a real improvement of metabarcoding of microbial communities since it makes the method quantitative with limited efforts.