Focus on Heterogeneity

The role of physiological heterogeneity in microbial population behavior

Journal name:
Nature Chemical Biology
Volume:
6,
Pages:
705–712
Year published:
DOI:
doi:10.1038/nchembio.436
Published online

Abstract

As the ability to analyze individual cells in microbial populations expands, it is becoming apparent that isogenic microbial populations contain substantial cell-to-cell differences in physiological parameters such as growth rate, resistance to stress and regulatory circuit output. Subpopulations exist that are manyfold different in these parameters from the population average, and these differences arise by stochastic processes. Such differences can dramatically affect the response of cells to perturbations, especially stress, which in turn dictates overall population response. Defining the role of cell-to-cell heterogeneity in population behavior is important for understanding population-based research problems, including those involving infecting populations, normal flora and bacterial populations in water and soils. Emerging technological breakthroughs are poised to transform single-cell analysis and are critical for the next phase of insights into physiological heterogeneity in the near future. These include technologies for multiparameter analysis of live cells, with downstream processing and analysis.

At a glance

Figures

  1. 'On'/'off' switch mechanisms (bimodal states) using pigment as an example.
    Figure 1: 'On'/'off' switch mechanisms (bimodal states) using pigment as an example.

    In a population average, it is not possible to distinguish between a state in which all cells have an intermediate phenotype (pink cells) and one in which half are on (red cells), half are off (white cells). An intermediate phenotype implies a cooperative binding mechanism, whereas an on/off switch implies a threshold mechanism with a stochastic component. Determination of the actual mechanism requires analysis at the individual cell level. x macron represents the arithmetic mean.

  2. Subpopulation responding to a perturbation.
    Figure 2: Subpopulation responding to a perturbation.

    In this example, only a subset of the cells show a growth response to a perturbation, whereas the remainder die. The responding cells (white) are those with a low amount of the measured parameter (denoted by red color). As these cells grow, they generate the same initial physiological heterogeneity as in the final population. However, the population average assumes that all cells show a growth response, and the mechanism appears to be a growth lag in the entire population, rather than an immediate growth response of a subpopulation.

  3. Single molecule detection by single-molecule fluorescence microscopy.
    Figure 3: Single molecule detection by single-molecule fluorescence microscopy.

    Examples of using single-molecule fluorescence microscopy to count proteins (using fluorescent protein tagged to protein of interest) or mRNA (using fluorescence in situ hybridization) in single cells.

  4. Single molecule detection by single-molecule force microscopy.
    Figure 4: Single molecule detection by single-molecule force microscopy.

    Single-molecule force microscopy uses a target molecule at the end of an AFM cantilever to probe surface molecules for those with a strong attractive or adhesive force. These force measurements can subsequently be mapped out as an image.

  5. Glass microwell-based single-cell analysis chip.
    Figure 5: Glass microwell–based single-cell analysis chip.

    Allows imaging, detection of fluorescence and phosphorescence lifetimes and measurement of respiration rates via a platinum-porphyrin sensor.

  6. The future of single-cell analysis.
    Figure 6: The future of single-cell analysis.

    Single-cell systems biology that couples live-cell phenotype response measurements with multiplexed omics analyses.

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Affiliations

  1. Department of Chemical Engineering, University of Washington, Seattle, Washington, USA.

    • Mary E Lidstrom &
    • Michael C Konopka
  2. Department of Microbiology, University of Washington, Seattle, Washington, USA.

    • Mary E Lidstrom

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The authors declare no competing financial interests.

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