Flow cytometric monitoring of bacterioplankton phenotypic diversity predicts high population‐specific feeding rates by invasive dreissenid mussels

Abstract

Species invasion is an important disturbance to ecosystems worldwide, yet knowledge about the impacts of invasive species on bacterial communities remains sparse. Using a novel approach, we simultaneously detected phenotypic and derived taxonomic change in a natural bacterioplankton community when subjected to feeding pressure by quagga mussels, a widespread aquatic invasive species. We detected a significant decrease in diversity within 1 h of feeding and a total diversity loss of 11.6 ± 4.1% after 3 h. This loss of microbial diversity was caused by the selective removal of high nucleic acid populations (29 ± 5% after 3 h). We were able to track the community diversity at high temporal resolution by calculating phenotypic diversity estimates from flow cytometry (FCM) data of minute amounts of sample. Through parallel FCM and 16S rRNA gene amplicon sequencing analysis of environments spanning a broad diversity range, we showed that the two approaches resulted in highly correlated diversity measures and captured the same seasonal and lake-specific patterns in community composition. Based on our results, we predict that selective feeding by invasive dreissenid mussels directly impacts the microbial component of the carbon cycle, as it may drive bacterioplankton communities toward less diverse and potentially less productive states.

Neutral mechanisms and niche differentiation in steady‐state insular microbial communities revealed by single cell analysis

“…Observations of the dynamics of species-rich systems are challenging. Available studies have either used sequencing approaches (Faust et al, 2015;Shen et al, 2018) or flow cytometry (Props et al, 2016(Props et al, , 2017(Props et al, and 2018Koch and Müller, 2018), but the use of these technologies to measure dynamic community behaviour is still rather rare. Next-generation sequencing platforms provided by, e.g., Illumina, PacBio and Nanopore (Goodwin et al, 2016) are currently the most up-to-date and commonly used methods to resolve microbial community composition and function.…”

Learning Single‐Cell Distances from Cytometry Data

“…In all cases, cells were classified according to their phylogeny using k ‐NN classification based on the Euclidean distance metric or the Mahalanobis distance metric, as determined by DMLMJ. Experiments were carried out using two different settings, on the raw data and on data for which each variable was transformed by f ( x ) = asinh( x ), a transformation that is used as a preprocessing step for the analysis of microbial cytometry data . Next, the communities for S = 10 were retained, which are called the target communities. For each community, microbial populations were randomly removed one by one, until only two remained.…”

Stochasticity in microbiology: managing unpredictability to reach the Sustainable Development Goals

“…Such a fingerprint and its evolution in function of time can be obtained on the genetic level through 16S rRNA gene amplicon sequencing (Pilloni et al , 2012) or even simpler/older techniques, such as terminal restriction fragment length polymorphism (T‐RFLP) (Pilloni et al , 2012; Camarinha‐Silva et al , 2014; De Vrieze et al , 2018), automated ribosomal intergenic spacer analysis (ARISA) (Gobet et al , 2014; van Dorst et al , 2014) or denaturing gradient gel electrophoresis (DGGE) (Marzorati et al , 2008). Fingerprinting can also be carried out at the phenotypic level through flow cytometry (De Roy et al , 2012; Props et al , 2018) and at the metabolic level through MALDI‐TOF MS (Sala‐Comorera et al , 2016; Sandrin and Demirev, 2018). A key aspect in this approach concerns knowledge of the contribution of deterministic processes, which can be monitored and controlled, to the microbial community dynamics.…”