Data from: Measuring the biodiversity of microbial communities by flow cytometry

1. Measuring the microbial diversity in natural and engineered environments is important for ecosystem characterization, ecosystem monitoring and hypothesis testing. Although the conventional assessment through single marker gene surveys has resulted in major advances, the complete procedure remains slow (i.e., weeks to months), labour-intensive and susceptible to multiple sources of laboratory and data processing bias. Growing interest, in highly resolved, temporal surveys of microbial diversity, necessitates rapid, inexpensive and robust analytical platforms that require limited computational effort. 2. Here, we demonstrate that sensitive single-cell measurements of phenotypic attributes, obtained via flow cytometry, can provide fast (i.e., within minutes) first-line assessments of microbial diversity dynamics, without demanding extensive sample preparation and downstream data processing. We developed a data processing pipeline that fits bivariate kernel density functions to phenotypic parameter combinations of an entire microbial community, and concatenates them to a single one-dimensional phenotypic fingerprint. By calculating established diversity metrics from such phenotypic fingerprints, we construct an alternative interpretation of the microbial diversity that incorporates distinct phenotypic traits underlying cell-to-cell heterogeneity (i.e., morphology and nucleic acid content). 3. Based on a detailed longitudinal study of a highly dynamic microbial ecosystem, our approach delivered temporal alpha diversity profiles that strongly correlated with the reference diversity, as estimated by 16S rRNA amplicon sequencing. This strongly suggests that the distribution of a limited amount of phenotypic features within a microbial community already provides sufficient resolving power for the measurement of diversity dynamics at the species level. 4. We present a fast, robust analysis method for monitoring the microbial biodiversity of natural and engineered ecosystems that correlates well with the conventional marker gene surveys. Our work has both applied and fundamental implications that stretch from ecosystem monitoring and studies on microbial community dynamics, to supervised sampling strategies. Furthermore, our approach offers perspectives for the development of on-line and in situ monitoring systems for microbial ecosystems.

1. 在自然与人工构建环境中测定微生物多样性，对于生态系统表征、生态系统监测与假说验证均具有重要意义。尽管传统的单标记基因调查评估手段已取得重大进展，但完整流程仍耗时较长（需数周至数月）、劳动强度大，且易受实验室与数据处理环节多种偏倚因素的影响。当前学界对高分辨率微生物多样性时间序列调查的兴趣与日俱增，亟需快速、低成本、鲁棒性佳且计算需求有限的分析平台。
2. 本研究证实，通过流式细胞术（flow cytometry）获取的表型属性单细胞灵敏检测结果，可在数分钟内完成微生物多样性动态的初步评估，且无需复杂的样品制备与下游数据处理流程。我们开发了一套数据处理流程：将二元核密度函数（bivariate kernel density functions）拟合至整个微生物群落的表型参数组合，并将其整合为单一的一维表型指纹。通过从此类表型指纹中计算既定多样性指标，我们构建了一种全新的微生物多样性解读框架，该框架纳入了细胞间异质性背后的独特表型特征（即形态与核酸含量）。
3. 基于对高度动态的微生物生态系统开展的详细纵向研究，本方法生成的时间序列α多样性（alpha diversity）谱与通过16S rRNA扩增子测序（16S rRNA amplicon sequencing）得到的参考多样性显著相关。这有力表明，微生物群落内有限数量的表型特征分布，已足以提供物种水平多样性动态测定所需的分辨能力。
4. 本研究提出了一种快速、鲁棒的分析方法，可用于监测自然与人工构建环境的微生物生物多样性，其结果与传统标记基因调查结果具有良好的相关性。本研究兼具应用与基础研究价值，其影响范围涵盖生态系统监测、微生物群落动态研究，乃至指导采样策略的制定。此外，本方法为微生物生态系统在线与原位监测系统的开发提供了新思路。