Resolution of natural microbial community dynamics by community fingerprinting, flow cytometry, and trend interpretation analysis

Single-Cell Techniques in Environmental Microbiology

Environmental microbiology has been an essential part of environmental research because it provides effective solutions to most pollutants. Hence, there is an interest in investigating microorganism behavior, such as observation, identification, isolation of pollutant degraders, and interactions between microbial species. To comprehensively understand cell heterogeneity, diverse approaches at the single-cell level are demanded. Thus far, the traditional bulk biological tools such as petri dishes are technically challenging for single cells, which could mask the heterogeneity. Single-cell technologies can reveal complex and rare cell populations by detecting heterogeneity among individual cells, which offers advantages of higher resolution, higher throughput, more accurate analysis, etc. Here, we overviewed several single-cell techniques on observation, isolation, and identification from aspects of methods and applications. Microscopic observation, sequencing identification, flow cytometric identification and isolation, Raman spectroscopy-based identification and isolation, and their applications are mainly discussed. Further development on multi-technique integrations at the single-cell level may highly advance the research progress of environmental microbiology, thereby giving more indication in the environmental microbial ecology.

Structure and functional capacity of a benzene-mineralizing, nitrate-reducing microbial community

AIMS

How benzene is metabolized by microbes under anoxic conditions is not fully understood. Here, we studied the degradation pathways in a benzene-mineralizing, nitrate-reducing enrichment culture.

METHODS AND RESULTS

Benzene mineralization was dependent on the presence of nitrate and correlated to the enrichment of a Peptococcaceae phylotype only distantly related to known anaerobic benzene degraders of this family. Its relative abundance decreased after benzene mineralization had terminated, while other abundant taxa-Ignavibacteriaceae, Rhodanobacteraceae and Brocadiaceae-slightly increased. Generally, the microbial community remained diverse despite the amendment of benzene as single organic carbon source, suggesting complex trophic interactions between different functional groups. A subunit of the putative anaerobic benzene carboxylase previously detected in Peptococcaceae was identified by metaproteomic analysis suggesting that benzene was activated by carboxylation. Detection of proteins involved in anaerobic ammonium oxidation (anammox) indicates that benzene mineralization was accompanied by anammox, facilitated by nitrite accumulation and the presence of ammonium in the growth medium.

CONCLUSIONS

The results suggest that benzene was activated by carboxylation and further assimilated by a novel Peptococcaceae phylotype.

SIGNIFICANCE AND IMPACT OF THE STUDY

The results confirm the hypothesis that Peptococcaceae are important anaerobic benzene degraders.

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from ¹⁴C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.

Duygan et al. develop a supervised machine learning algorithm, CellCognize, to quantify cell type diversity from multidimensional flow cytometry data. Their model achieves 80% prediction accuracy, detects shifts in microbial communities of unknown composition and quantifies population growth and biomass productivity. Their work will be useful to study microbiota in human health or engineered systems.

Araújo DS, Quiroga MV, Amado AM, Neto ADD, Sarmento H, Metz SD, Unrein F. flowDiv: a new pipeline for analyzing flow cytometric diversity

BACKGROUND

Flow cytometry (FCM) is one of the most commonly used technologies for analysis of numerous biological systems at the cellular level, from cancer cells to microbial communities. Its high potential and wide applicability led to the development of various analytical protocols, which are often not interchangeable between fields of expertise. Environmental science in particular faces difficulty in adapting to non-specific protocols, mainly because of the highly heterogeneous nature of environmental samples. This variety, although it is intrinsic to environmental studies, makes it difficult to adjust analytical protocols to maintain both mathematical formalism and comprehensible biological interpretations, principally for questions that rely on the evaluation of differences between cytograms, an approach also termed cytometric diversity. Despite the availability of promising bioinformatic tools conceived for or adapted to cytometric diversity, most of them still cannot deal with common technical issues such as the integration of differently acquired datasets, the optimal number of bins, and the effective correlation of bins to previously known cytometric populations.

RESULTS

To address these and other questions, we have developed flowDiv, an R language pipeline for analysis of environmental flow cytometry data. Here, we present the rationale for flowDiv and apply the method to a real dataset from 31 freshwater lakes in Patagonia, Argentina, to reveal significant aspects of their cytometric diversities.

CONCLUSIONS

flowDiv provides a rather intuitive way of proceeding with FCM analysis, as it combines formal mathematical solutions and biological rationales in an intuitive framework specifically designed to explore cytometric diversity.

Initial evenness determines diversity and cell density dynamics in synthetic microbial ecosystems

The effect of initial evenness on the temporal trajectory of synthetic communities in comprehensive, low-volume microcosm studies remains unknown. We used flow cytometric fingerprinting and 16S rRNA gene amplicon sequencing to assess the impact of time on community structure in one hundred synthetic ecosystems of fixed richness but varying initial evenness. Both methodologies uncovered a similar reduction in diversity within synthetic communities of medium and high initial evenness classes. However, the results of amplicon sequencing showed that there were no significant differences between and within the communities in all evenness groups at the end of the experiment. Nevertheless, initial evenness significantly impacted the cell density of the community after five medium transfers. Highly even communities retained the highest cell densities at the end of the experiment. The relative abundances of individual species could be associated to particular evenness groups, suggesting that their presence was dependent on the initial evenness of the synthetic community. Our results reveal that using synthetic communities for testing ecological hypotheses requires prior assessment of initial evenness, as it impacts temporal dynamics.

Bacterial characterization of Beijing drinking water by flow cytometry and MiSeq sequencing of the 16S rRNA gene

Flow cytometry (FCM) and 16S rRNA gene sequencing data are commonly used to monitor and characterize microbial differences in drinking water distribution systems. In this study, to assess microbial differences in drinking water distribution systems, 12 water samples from different sources water (groundwater, GW; surface water, SW) were analyzed by FCM, heterotrophic plate count (HPC), and 16S rRNA gene sequencing. FCM intact cell concentrations varied from 2.2 × 10(3) cells/mL to 1.6 × 10(4) cells/mL in the network. Characteristics of each water sample were also observed by FCM fluorescence fingerprint analysis. 16S rRNA gene sequencing showed that Proteobacteria (76.9-42.3%) or Cyanobacteria (42.0-3.1%) was most abundant among samples. Proteobacteria were abundant in samples containing chlorine, indicating resistance to disinfection. Interestingly, Mycobacterium, Corynebacterium, and Pseudomonas, were detected in drinking water distribution systems. There was no evidence that these microorganisms represented a health concern through water consumption by the general population. However, they provided a health risk for special crowd, such as the elderly or infants, patients with burns and immune-compromised people exposed by drinking. The combined use of FCM to detect total bacteria concentrations and sequencing to determine the relative abundance of pathogenic bacteria resulted in the quantitative evaluation of drinking water distribution systems. Knowledge regarding the concentration of opportunistic pathogenic bacteria will be particularly useful for epidemiological studies.

Cytometric patterns reveal growth states of Shewanella putrefaciens

Bacterial growth is often difficult to estimate beyond classical cultivation approaches. Low cell numbers, particles or coloured and dense media may disturb reliable growth assessment. Further difficulties appear when cells are attached to surfaces and detachment is incomplete.

Therefore, flow cytometry was tested and used for analysis of bacterial growth on the single-cell level. Shewanella putrefaciens was cultivated as a model organism in planktonic or biofilm culture. Materials of smooth and rough surfaces were used for biofilm cultivation. Both aerobic and anaerobic as well as feast and famine conditions were applied. Visualization of growth was also done using Environmental Scanning and Phase Contrast Microscopy. Bioinformatic tools were applied for data interpretation.

Cytometric proliferation patterns based on distributions of DNA contents per cell corresponded distinctly to the various lifestyles, electron acceptors and substrates tested. Therefore, cell cycling profiles of S. putrefaciens were found to mirror growth conditions.

The cytometric patterns were consistently detectable with exception of some biofilm types whose resolution remained challenging. Corresponding heat maps proved to be useful for clear visualization of growth behaviour under all tested conditions. Therefore, flow cytometry in combination with bioinformatic tools proved to be powerful means to determine various growth states of S. putrefaciens, even in constrained environments. The approach is universal and will also be applicable for other bacterial species.

Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics

Optical characteristics of individual bacterial cells of natural communities can be measured with flow cytometry (FCM) in high throughput. The resulting data are visualized in cytometric histograms. These histograms represent individual cytometric fingerprints of microbial communities, e.g., at certain time points or microenvironmental conditions. Up to now four tools for analyzing the variation in these cytometric fingerprints are available but have not yet been systematically compared regarding application: Dalmatian Plot, Cytometric Histogram Image Comparison (CHIC), Cytometric Barcoding (CyBar), and FlowFP. In this article these tools were evaluated concerning (i) the required experience of the operator in handling cytometric data sets, (ii) the detection level of changes, (iii) time demand for analysis, and (iv) software requirements. As an illustrative example, FCM was used to characterize the microbial community structure of electroactive microbial biofilms. Their cytometric fingerprints were determined, analyzed with all four tools, and correlated to experimental and functional parameters. The source of inoculum (four different types of wastewater samples) showed the strongest influence on the microbial community structure and biofilm performance while the choice of substrate (acetate or lactate) had no significant effect in the present study. All four evaluation tools were found suitable to monitor structural changes of natural microbial communities. The Dalmatian Plot was shown to be most sensitive to operator impact but nevertheless provided an overview on community shifts. CHIC, CyBar, and FlowFP showed less operator dependence and gave highly resolved information on community structure variation on different detection levels. In conclusion, experimental and productivity parameters correlated with the biofilm structures and practical process integration details were available from cytometric fingerprint analysis.

Key players and team play: anaerobic microbial communities in hydrocarbon-contaminated aquifers

Biodegradation of anthropogenic pollutants in shallow aquifers is an important microbial ecosystem service which is mainly brought about by indigenous anaerobic microorganisms. For the management of contaminated sites, risk assessment and control of natural attenuation, the assessment of in situ biodegradation and the underlying microbial processes is essential. The development of novel molecular methods, "omics" approaches, and high-throughput techniques has revealed new insight into complex microbial communities and their functions in anoxic environmental systems. This review summarizes recent advances in the application of molecular methods to study anaerobic microbial communities in contaminated terrestrial subsurface ecosystems. We focus on current approaches to analyze composition, dynamics, and functional diversity of subsurface communities, to link identity to activity and metabolic function, and to identify the ecophysiological role of not yet cultured microbes and syntrophic consortia. We discuss recent molecular surveys of contaminated sites from an ecological viewpoint regarding degrader ecotypes, abiotic factors shaping anaerobic communities, and biotic interactions underpinning the importance of microbial cooperation for microbial ecosystem services such as contaminant degradation.