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.