Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry

Abstract

Natural microbial communities affect human life in countless ways, ranging from global biogeochemical cycles to the treatment of wastewater and health via the human microbiome. In order to probe, monitor, and eventually control these communities, fast detection and evaluation methods are required. In order to facilitate rapid community analysis and monitor a community's dynamic behavior with high resolution, we here apply community flow cytometry, which provides single-cell-based high-dimensional data characterizing communities with high acuity over time. To interpret time series data, we draw inspiration from macroecology, in which a rich set of concepts has been developed for describing population dynamics. We focus on the stability paradigm as a promising candidate to interpret such data in an intuitive and actionable way and present a rapid workflow to monitor stability properties of complex microbial ecosystems. Based on single-cell data, we compute the stability properties resistance, resilience, displacement speed, and elasticity. For resilience, we also introduce a method which can be implemented for continuous online community monitoring. The proposed workflow was tested in a long-term continuous reactor experiment employing both an artificial and a complex microbial community, which were exposed to identical short-term disturbances. The computed stability properties uncovered the superior stability of the complex community and demonstrated the global applicability of the protocol to any microbiome. The workflow is able to support high temporal sample densities below bacterial generation times. This may provide new opportunities to unravel unknown ecological paradigms of natural microbial communities, with applications to environmental, biotechnological, and health-related microbiomes. IMPORTANCE Microbial communities drive many processes which affect human well-being directly, as in the human microbiome, or indirectly, as in natural environments or in biotechnological applications. Due to their complexity, their dynamics over time is difficult to monitor, and current sequence-based approaches are limited with respect to the temporal resolution. However, in order to eventually control microbial community dynamics, monitoring schemes of high temporal resolution are required. Flow cytometry provides single-cell-based data in the required temporal resolution, and we here use such data to compute stability properties as easy to interpret univariate indicators of microbial community dynamics. Such monitoring tools will allow for a fast, continuous, and cost-effective screening of stability states of microbiomes. Applicable to various environments, including bioreactors, surface water, and the human body, it will contribute to the development of control schemes to manipulate microbial community structures and performances.

Keywords: constancy; microbial communities; microbial ecology; microbial flow cytometry; resilience; resistance; single-cell analytics; stability properties.

FIG 1

Response of microbial communities to disturbance events and stability properties of resistance, displacement speed, resilience, and elasticity (Table 1). A low-complexity member community (AMC) and, after addition (gray box), a complex community (CMC) were cultivated under the same conditions. Both the AMC and CMC structures were displaced in response to short-term temperature and pH disturbances (Dis: T and Dis: pH, respectively). (A) Deviation from the reference state (s_ref) (red circle), calculated as the Canberra distance, and resistance over time. Dashed horizontal green lines indicate the border of the reference space, blue triangles mark s_max, and filled black circles mark s_end. The determination of resistance (RS) and displacement speed (DS) is shown as a dashed black line. (B) Comparison of the stability properties resistance and displacement speed across all disturbance experiments. (C) Comparison of the stability properties resilience and elasticity for disturbance experiments showing resilience (AMC Dis: pH and CMC Dis: pH).

FIG 2

Community dynamics over the full experiment. Nonmetric multidimensional scaling (NMDS) plot (Bray-Curtis dissimilarity) indicating community dynamics of the artificial microbial community (AMC) (0 to 215 h, filled circles) and the complex microbial community (CMC) (216 to 435 h, open circles). Symbol size increases with increasing sampling time.

FIG 3

Potential community responses to a disturbance. The reference state characterizing the phase prior to the disturbance (s_ref) is marked by the open red circle. Natural variability in the reference state (σ_ref) is indicated by a dashed green line. Symbols connected by black lines indicate how community structure changes in response to the disturbance. The state maximally deviating from the reference state (s_max) is marked by a blue triangle, with a corresponding blue dashed line at its radius (d_max) showing the maximum deviation from s_ref. The solid blue line marks the theoretical s_max value. The final structure, s_end, is marked by a filled black circle. (A) For a microbial community of high resistance (RS values close to 1), the disturbance does not lead to a change in the community structure, and the system remains within the limits of its natural variability. (B and C) For systems of lower resistance, the disturbance leads to a change in community structure beyond its natural variability. For resilient systems (RL > 0), after passing through s_max, the community structure either returns to the reference state, with the filled red circle indicating this return into the reference space (RL close to 1) (B), or at least approaches it so that d_end is <d_max (0 < RL < 1) (C). (D) If the system is not resilient at all (RL = 0), its state continues to deviate more from the reference state, with d_end equal to d_max.