Michael C. Konopka

The role of physiological heterogeneity in microbial population behavior

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.

Crowding and Confinement Effects on Protein Diffusion In Vivo

The first in vivo measurements of a protein diffusion coefficient versus cytoplasmic biopolymer volume fraction are presented. Fluorescence recovery after photobleaching yields the effective diffusion coefficient on a 1-mum-length scale of green fluorescent protein within the cytoplasm of Escherichia coli grown in rich medium. Resuspension into hyperosmotic buffer lacking K+ and nutrients extracts cytoplasmic water, systematically increasing mean biopolymer volume fraction, , and thus the severity of possible crowding, binding, and confinement effects. For resuspension in isosmotic buffer (osmotic upshift, or Delta, of 0), the mean diffusion coefficient, , in cytoplasm (6.1 +/- 2.4 microm2 s(-1)) is only 0.07 of the in vitro value (87 microm2 s(-1)); the relative dispersion among cells, sigmaD/ (standard deviation, sigma(D), relative to the mean), is 0.39. Both and sigmaD/ remain remarkably constant over the range of Delta values of 0 to 0.28 osmolal. For a Delta value of > or =0.28 osmolal, formation of visible plasmolysis spaces (VPSs) coincides with the onset of a rapid decrease in by a factor of 380 over the range of Delta values of 0.28 to 0.70 osmolal and a substantial increase in sigmaD/. Individual values of D vary by a factor of 9 x 10(4) but correlate well with f(VPS), the fractional change in cytoplasmic volume on VPS formation. The analysis reveals two levels of dispersion in D among cells: moderate dispersion at low Delta values for cells lacking a VPS, perhaps related to variation in phi or biopolymer organization during the cell cycle, and stronger dispersion at high Delta values related to variation in f(VPS). Crowding effects alone cannot explain the data, nor do these data alone distinguish crowding from possible binding or confinement effects within a cytoplasmic meshwork.

Cytoplasmic Protein Mobility in Osmotically Stressed Escherichia coli

Facile diffusion of globular proteins within a cytoplasm that is dense with biopolymers is essential to normal cellular biochemical activity and growth. Remarkably, Escherichia coli grows in minimal medium over a wide range of external osmolalities (0.03 to 1.8 osmol). The mean cytoplasmic biopolymer volume fraction ((phi)) for such adapted cells ranges from 0.16 at 0.10 osmol to 0.36 at 1.45 osmol. For cells grown at 0.28 osmol, a similar phi range is obtained by plasmolysis (sudden osmotic upshift) using NaCl or sucrose as the external osmolyte, after which the only available cellular response is passive loss of cytoplasmic water. Here we measure the effective axial diffusion coefficient of green fluorescent protein (D(GFP)) in the cytoplasm of E. coli cells as a function of (phi) for both plasmolyzed and adapted cells. For plasmolyzed cells, the median D(GFP) (D(GFP)(m)) decreases by a factor of 70 as (phi) increases from 0.16 to 0.33. In sharp contrast, for adapted cells, D(GFP)(m) decreases only by a factor of 2.1 as (phi) increases from 0.16 to 0.36. Clearly, GFP diffusion is not determined by (phi) alone. By comparison with quantitative models, we show that the data cannot be explained by crowding theory. We suggest possible underlying causes of this surprising effect and further experiments that will help choose among competing hypotheses. Recovery of the ability of proteins to diffuse in the cytoplasm after plasmolysis may well be a key determinant of the time scale of the recovery of growth.

Global Molecular Analyses of Methane Metabolism in Methanotrophic Alphaproteobacterium, Methylosinus trichosporium OB3b. Part II. Metabolomics and 13C-Labeling Study

In this work we use metabolomics and 13C-labeling data to refine central metabolic pathways for methane utilization in Methylosinus trichosporium OB3b, a model alphaproteobacterial methanotrophic bacterium. We demonstrate here that similar to non-methane utilizing methylotrophic alphaproteobacteria the core metabolism of the microbe is represented by several tightly connected metabolic cycles, such as the serine pathway, the ethylmalonyl-CoA (EMC) pathway, and the citric acid (TCA) cycle. Both in silico estimations and stable isotope labeling experiments combined with single cell (NanoSIMS) and bulk biomass analyses indicate that a significantly larger portion of the cell carbon (over 60%) is derived from CO2 in this methanotroph. Our13 C-labeling studies revealed an unusual topology of the assimilatory network in which phosph(enol) pyruvate/pyruvate interconversions are key metabolic switches. A set of additional pathways for carbon fixation are identified and discussed.

Respiration response imaging for real time detection of microbial function at the single cell level

ABSTRACT The ability to detect specific functions of uncultured microbial cells in complex natural communities remains one of the most difficult tasks of environmental microbiology. Here we present respiration response imaging (RRI) as a novel fluorescence microscopy-based approach for the identification of microbial function, such as the ability to use C1 substrates, at a single-cell level. We demonstrate that RRI could be used for the investigation of heterogeneity of a single microbial population or for functional profiling of microbial cells from complex environmental communities, such as freshwater lake sediment.

Single Cell Methods for Methane Oxidation Analysis

Respiration is widely used for evaluation of the metabolic capabilities or physiological state of the microbial culture. This chapter describes novel approaches for characterization of respiration at a single cell level: (1) flow cytometry-based redox sensing (FCRS) of actively metabolizing microbes; (2) respiration response imaging (RRI) for real-time detection of substrate stimulated redox responses of individual cells; (3) respiration detection system: microobservation chamber (RDS: MC), a single cell analysis system for carrying out the physiological and genomic profiling of cells capable of respiring C(1) compounds. The techniques are suitable for description of physiological heterogeneity among cells in a single microbial population and could be used to characterize distribution of methylotrophic ability among microbial cells in the natural environmental samples.

Respiration Rate Measurements of Single Bacterial Cells

One of the limitations of a population-based analysis method is that it averages over a large number of cells. While this helps average out fluctuations in the measured signal, it potentially can cover up subpopulations that could be functionally important. Using a single-cell approach, one can individually measure cells and look at the variation within the isogenic cell population.Respiration can be an indicator of physiological state and therefore is an excellent target for analysis of heterogeneity within the sample. Single-cells are isolated in microwells containing Pt-porphyrin embedded microspheres on a glass chip. These microwells are diffusionally sealed with a lid actuator which can be raised at the end of the measurement to reoxygenate the sample. Since the phosphorescence lifetime of the Pt-porphyrin depends on the oxygen concentration, the Pt-porphyrin embedded microspheres can be used as an oxygen sensor. Monitoring the consumption of oxygen in the sealed microwells over time allows a respiration rate to be calculated for the individual cells. This method is compatible with other optical imaging techniques.