Biomedical Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.
Sci Rep. 2018 Jun 14;8(1):9095. doi: 10.1038/s41598-018-27314-3.
Workflows for microbiome community profiling by high-throughput sequencing are prone to sample mix-ups and cross-contamination due to the complexity of the procedures and large number of samples typically analyzed in parallel. We employed synthetic 16S rRNA gene spike-in controls to establish a method for tracking of sample identity and detection of cross-contamination in microbiome community profiling assays based on 16S rRNA gene amplicon sequencing (16S-seq). Results demonstrated that combinatorial sample tracking mixes (STMs) can be reliably resolved by Illumina sequencing and faithfully represent their sample of origin. In a single-blinded experiment, addition of STMs at low levels was shown to be sufficient to unambiguously identify and resolve swapped samples. Using artificial admixtures of individually SMT-tagged samples, we further established the ability to detect and quantify cross-contamination down to a level of approximately 1%. The utility of our technique was underscored through detection of an unplanned case of cross-contamination that occurred during this study. By enabling detection of sample mix-ups and cross-contamination throughout 16S-seq workflows, the present technique thus assures provenance of sequence data on a per-sample basis. The method can be readily implemented in standard 16S-seq workflows and its routine application is expected to enhance the reliability of 16S-seq data.
由于高通量测序的微生物组群落分析流程复杂，且通常需要同时分析大量样本，因此容易发生样品混淆和交叉污染的问题。我们使用合成 16S rRNA 基因 Spike-in 对照物，建立了一种基于 16S rRNA 基因扩增子测序（16S-seq）的方法，用于跟踪样品身份和检测微生物组群落分析检测中的交叉污染。结果表明，组合样品跟踪混合体（STM）可以通过 Illumina 测序可靠地区分，并真实反映其原始样品。在一项单盲实验中，添加低水平的 STM 足以明确识别和区分交换的样品。使用单独 SMT 标记样品的人工混合物，我们进一步建立了检测和量化交叉污染的能力，最低可达约 1%的水平。通过检测本研究中发生的一起意外的交叉污染案例，突出了我们技术的实用性。通过在整个 16S-seq 工作流程中检测样品混淆和交叉污染，本技术可确保每个样品的序列数据来源可追溯。该方法可以轻松地整合到标准的 16S-seq 工作流程中，其常规应用有望提高 16S-seq 数据的可靠性。
