Next-generation sequencing (NGS) has instigated the research on the role of the microbiome in health and disease. The compositional nature of such microbiome datasets makes it however challenging to identify those microbial taxa that are truly associated with an intervention or health outcome. Quantitative microbiome profiling overcomes the compositional structure of microbiome sequencing data by integrating absolute quantification of microbial abundances into the NGS data. Both cell-based methods (e.g., flow cytometry) and molecular methods (qPCR) have been used to determine the absolute microbial abundances, but to what extent different quantification methods generate similar quantitative microbiome profiles has so far not been explored. Here we compared relative microbiome profiling (without incorporation of microbial quantification) to three variations of quantitative microbiome profiling: (1) microbial cell counting using flow cytometry (QMP), (2) counting of microbial cells using flow cytometry combined with Propidium Monoazide pre-treatment of fecal samples before metagenomics DNA isolation in order to only profile the microbial composition of intact cells (QMP-PMA), and (3) molecular based quantification of the microbial load using qPCR targeting the 16S rRNA gene. Although qPCR and flow cytometry both resulted in accurate and strongly correlated results when quantifying the bacterial abundance of a mock community of bacterial cells, the two methods resulted in highly divergent quantitative microbial profiles when analyzing the microbial composition of fecal samples from 16 healthy volunteers. These differences could not be attributed to the presence of free extracellular prokaryotic DNA in the fecal samples as sample pre-treatment with Propidium Monoazide did not improve the concordance between qPCR-based and flow cytometry-based QMP. Also lack of precision of qPCR was ruled out as a major cause of the disconcordant findings, since quantification of the fecal microbial load by the highly sensitive digital droplet PCR correlated strongly with qPCR. In conclusion, quantitative microbiome profiling is an elegant approach to bypass the compositional nature of microbiome NGS data, however it is important to realize that technical sources of variability may introduce substantial additional bias depending on the quantification method being used.

下一代测序（NGS）促进了微生物组在健康和疾病中的作用研究。然而，此类微生物组数据集的组成性质使其难以鉴定与干预或健康结果真正相关的微生物分类群。通过将微生物丰度的绝对定量整合到NGS数据中，定量微生物组分析克服了微生物组测序数据的组成结构。基于细胞的方法（例如流式细胞仪）和分子方法（qPCR）都已用于确定绝对微生物丰度，但是迄今为止，尚未探索到何种程度的不同定量方法产生相似的定量微生物组谱。在这里，我们将相对微生物组图谱（不包括微生物定量分析）与定量微生物组图谱的三个变化进行了比较：（1）使用流式细胞仪（QMP）进行微生物细胞计数，（2）使用流式细胞仪结合单叠氮化丙锭前体对微生物细胞计数在宏基因组学DNA分离之前对粪便样品进行处理，以便仅分析完整细胞的微生物组成（QMP-PMA），以及（3）使用针对16S rRNA基因的qPCR对微生物负荷进行基于分子的定量。尽管定量定量模拟细菌细胞群落的细菌丰度时，qPCR和流式细胞仪均可得出准确且高度相关的结果，当分析来自16位健康志愿者的粪便样品的微生物组成时，这两种方法导致了高度不同的定量微生物谱。这些差异不能归因于粪便样品中游离细胞外原核DNA的存在，因为用单叠氮化丙锭进行样品预处理不能改善基于qPCR和基于流式细胞仪的QMP之间的一致性。还排除了qPCR精度不足的原因，这是不一致发现的主要原因，因为通过高度敏感的数字液滴PCR定量粪便微生物负荷与qPCR密切相关。总之，定量微生物组分析是一种绕过微生物组NGS数据的组成性质的绝妙方法，