Buffer contamination and cross-contamination. De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation

Introduction: Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties. Methods: We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks. Results: Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell number. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome composition. Conclusions: Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis.

引言：微生物组扩增子测序数据会受到细菌DNA提取、污染、序列错误以及嵌合体（chimera）等多种依赖实验方案的偏倚影响，进而阻碍临床微生物组应用的开展。尤为关键的是，提取偏倚是基于测序的微生物组分析中的主要混杂因素，目前尚无有效的校正方法。本研究提出基于细菌形态学特征，利用模拟群落对照（mock community control）通过计算手段校正提取偏倚。

方法：我们对比了3组具有均匀或交错组成的细胞模拟群落的稀释梯度系列。我们采用8种不同的提取方案（2种缓冲液、2种提取试剂盒、2种裂解条件），对上述模拟群落样本以及额外的皮肤微生物组样本进行DNA提取。将提取得到的DNA与对应的DNA模拟群落一同进行测序，测序靶点为V1-V3区16S核糖体RNA（16S rRNA）基因。

结果：微生物组组成在不同提取试剂盒与裂解条件之间存在显著差异，但在不同缓冲液之间无显著差异。无论采用何种提取方案，嵌合体（chimera）的形成比例均会随着输入细胞数的增加而升高。污染物主要来源于缓冲液，且在低输入量样本中可观察到较为严重的交叉污染。通过对比细胞模拟群落与对应DNA模拟群落的微生物组组成，可揭示分类群特异性的依赖实验方案的提取偏倚。值得注意的是，每个物种的提取偏倚可通过细菌细胞形态学特征进行预测。基于形态学的提取偏倚计算校正方法应用于不同模拟群落样本（即使样本包含不同分类群）时，可显著优化最终得到的群落组成。将该校正方法应用于皮肤样本时，同样可对微生物组组成产生显著影响。

结论：本研究结果表明，更高的DNA浓度会提升聚合酶链式反应（PCR，polymerase chain reaction）扩增过程中嵌合体的形成比例。此外，本研究证实，借助合适的阳性对照，基于细菌形态学特征的提取偏倚计算校正方法具备可行性，这为克服微生物组分析中依赖实验方案的偏倚迈出了重要一步。