Inserm UMR-S1135, Centre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), 91 bd. de l'Hôpital, 75013, Paris, France.
Sorbonne Universités, UPMC Univ Paris 06, CR7, Centre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), Hôpital Pitié-Salpêtrière, 83 bd. de l'Hôpital, 75013, Paris, France.
Sci Rep. 2021 May 24;11(1):10741. doi: 10.1038/s41598-021-90226-2.
High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (10, 10, 10, 10 and 10 microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 10 cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 10 bacteria per sample for robust and reproducible microbiota analysis.
高通量系统发育 16S rRNA 基因分析使我们能够深入研究微生物群落的复杂性,并了解健康和疾病状态下宿主与微生物群的相互作用。该分析包括样本采集和储存、基因组 DNA 提取、16S rRNA 基因扩增、高通量扩增子测序和生物信息学分析。低生物量微生物群样本(例如活检、组织拭子和灌洗液)越来越受到关注,但针对低生物量样本的最佳标准化分析方法仍有待开发。在这里,我们使用三种不同的 DNA 提取方案、三种不同的机械裂解系列和两种不同的 PCR 方案,测试了进行 16S rRNA 基因分析所需的较低细菌浓度。采用模拟微生物群落标准和来自两名健康供体粪便的低生物量样本(10、10、10、10 和 10 个微生物),采用配对末端 Illumina MiSeq 技术评估了 16S rRNA 基因分析的最佳样本处理方法。在我们的研究中测试的三种 DNA 提取方案在代表微生物群落组成方面表现相似,但与珠吸收和化学沉淀相比,硅烷柱提取的产率更好。此外,增加机械裂解时间和重复次数可以改善细菌组成的代表性。使微生物群落组成得到适当代表的最具影响力的因素仍然是样本生物量。事实上,所有测试方案中,细菌密度低于 10 个细胞时,基于聚类分析会导致样本失去身份。最后,我们使用基因组 DNA 标准排除了 DNA 提取偏差,结果表明半巢式 PCR 方案比经典 PCR 更好地代表了微生物群落组成。基于我们的结果,起始材料浓度是一个重要的限制因素,这突出表明需要针对低生物量样本调整方案。我们的研究表明,延长机械裂解、硅烷膜 DNA 分离和半巢式 PCR 方案的使用可以改善低生物量样本的分析。使用改进的方案,我们报告了每个样本 10 个细菌的下限,用于稳健和可重复的微生物组分析。