Heyse Jasmine, Schattenberg Florian, Rubbens Peter, Müller Susann, Waegeman Willem, Boon Nico, Props Ruben
Center for Microbial Ecology and Technology (CMET), Department of Biochemical and Microbial Technology, Ghent University, Ghent, Belgium.
Department of Environmental Microbiology, Helmholtz Centre for Environmental Researchgrid.7492.8-UFZ, Leipzig, Germany.
mSystems. 2021 Oct 26;6(5):e0055121. doi: 10.1128/mSystems.00551-21. Epub 2021 Sep 21.
Microbiome management research and applications rely on temporally resolved measurements of community composition. Current technologies to assess community composition make use of either cultivation or sequencing of genomic material, which can become time-consuming and/or laborious in case high-throughput measurements are required. Here, using data from a shrimp hatchery as an economically relevant case study, we combined 16S rRNA gene amplicon sequencing and flow cytometry data to develop a computational workflow that allows the prediction of taxon abundances based on flow cytometry measurements. The first stage of our pipeline consists of a classifier to predict the presence or absence of the taxon of interest, with yielded an average accuracy of 88.13% ± 4.78% across the top 50 operational taxonomic units (OTUs) of our data set. In the second stage, this classifier was combined with a regression model to predict the relative abundances of the taxon of interest, which yielded an average of 0.35 ± 0.24 across the top 50 OTUs of our data set. Application of the models to flow cytometry time series data showed that the generated models can predict the temporal dynamics of a large fraction of the investigated taxa. Using cell sorting, we validated that the model correctly associates taxa to regions in the cytometric fingerprint, where they are detected using 16S rRNA gene amplicon sequencing. Finally, we applied the approach of our pipeline to two other data sets of microbial ecosystems. This pipeline represents an addition to the expanding toolbox for flow cytometry-based monitoring of bacterial communities and complements the current plating- and marker gene-based methods. Monitoring of microbial community composition is crucial for both microbiome management research and applications. Existing technologies, such as plating and amplicon sequencing, can become laborious and expensive when high-throughput measurements are required. In recent years, flow cytometry-based measurements of community diversity have been shown to correlate well with those derived from 16S rRNA gene amplicon sequencing in several aquatic ecosystems, suggesting that there is a link between the taxonomic community composition and phenotypic properties as derived through flow cytometry. Here, we further integrated 16S rRNA gene amplicon sequencing and flow cytometry survey data in order to construct models that enable the prediction of both the presence and the abundances of individual bacterial taxa in mixed communities using flow cytometric fingerprinting. The developed pipeline holds great potential to be integrated into routine monitoring schemes and early warning systems for biotechnological applications.
微生物群落管理研究与应用依赖于对群落组成的时间分辨测量。当前评估群落组成的技术要么利用基因组材料的培养，要么利用测序，在需要高通量测量的情况下，这可能会变得耗时且费力。在这里，我们以一个虾苗孵化场的数据作为具有经济相关性的案例研究，结合16S rRNA基因扩增子测序和流式细胞术数据，开发了一种计算工作流程，该流程能够基于流式细胞术测量预测分类单元的丰度。我们工作流程的第一阶段由一个分类器组成，用于预测目标分类单元的存在与否，在我们数据集的前50个操作分类单元（OTU）中，其平均准确率为88.13%±4.78%。在第二阶段，该分类器与一个回归模型相结合，以预测目标分类单元的相对丰度，在我们数据集的前50个OTU中，其平均值为0.35±0.24。将这些模型应用于流式细胞术时间序列数据表明，生成的模型可以预测大部分被研究分类单元的时间动态。通过细胞分选，我们验证了该模型能够正确地将分类单元与细胞计量指纹中的区域相关联，在这些区域中可以使用16S rRNA基因扩增子测序检测到它们。最后，我们将工作流程的方法应用于另外两个微生物生态系统数据集。该工作流程是基于流式细胞术监测细菌群落的不断扩展的工具库的补充，并且补充了当前基于平板培养和标记基因的方法。微生物群落组成的监测对于微生物群落管理研究与应用都至关重要。当需要高通量测量时，现有的技术，如平板培养和扩增子测序，可能会变得费力且昂贵。近年来，在几个水生生态系统中，基于流式细胞术的群落多样性测量已被证明与源自16S rRNA基因扩增子测序的测量结果具有良好的相关性，这表明通过流式细胞术得出的分类群落组成与表型特性之间存在联系。在这里，我们进一步整合了16S rRNA基因扩增子测序和流式细胞术调查数据，以便构建能够使用流式细胞术指纹预测混合群落中单个细菌分类单元的存在和丰度的模型。所开发的工作流程具有很大的潜力，可被整合到生物技术应用的常规监测方案和预警系统中。
