Single cell time-resolved quorum responses reveal dependence on cell density and configuration

Abstract

Bacterial communication via quorum sensing has been extensively investigated in recent years. Bacteria communicate in a complex manner through the production, release, and reception of diffusible low molecular weight chemical signaling molecules. Much work has focused on understanding the basic mechanisms of quorum sensing. As more and more bacteria grow resistant to conventional antibiotics, the development of drugs that do not kill bacteria but instead interrupt their communication is of increasing interest. This study presents a method for analyzing bacterial communication by investigating single cell responses. Most conventional analysis methods for bacterial communication are based on the averaged response from many bacteria, masking how individual cells respond to their immediate environment. We applied a fiber-optic microarray to record cellular communication from single cells. Single cell quorum sensing systems have previously been employed, but the highly ordered array reported here is an improvement because it allows us to simultaneously investigate cellular communication in many different environments with known cellular densities and configurations. We employed this method to detect how genes under quorum regulation are induced or repressed over time on the single cell level and to determine whether cellular density and configuration are indicative of the single cell temporal patterns of gene expression.

FIGURE 1.

General AHL structure. The R group can be a methyl, hydroxyl, or an oxo-group, and the acyl chain length varies depending on the bacteria (13).

FIGURE 2.

Illustration of the 36 wells surrounding an E. coli bacterium (light gray, center) on the fiber bundle. This area is defined as a microenvironment. The figure illustrates two different configurations of three AHL-producing bacteria (dark gray) with respect to the E. coli, resulting in two different microenvironments on the fiber array. The cell densities in the two microenvironments are the same because they both contain three AHL-producing bacteria.

FIGURE 3.

A, average fluorescence intensity (AU) of all E. coli on the fiber over the duration of the experiment for different concentrations of AHL. Each column is the average of three fiber experiments. Error bars indicate S.D. B, average fluorescence intensity (AU) I_n/I₀ of all E. coli on the fiber at different AHL-producing cell densities. The curves are representative examples. These preliminary experiments were used to determine the appropriate timeframes for subsequent experiments.

FIGURE 4.

Clustering of temporal GFP fluorescence profiles from E. coli when exposed to different scenarios. Each graph shows two clusters generated using k-means analysis of data from a fiber experiment. The curves are the average shapes of all GFP fluorescence profiles in the cluster, and the average number of surrounding AHL-producing cells for all E. coli in that cluster is given. A, AHL-producing bacteria and E. coli were mixed on the fiber. B, the E. coli strain was placed on the fiber, and synthetic AHL was added. C, E. coli and AHL-producing bacteria were mixed on the fiber, and communication was inhibited. It should be noted that these curves are averaged curves from all normalized GFP fluorescence profiles in the clusters, such that the maximum value of the mean curve will not be 1 in most cases.

FIGURE 5.

Distribution of bacteria on the fiber. The numbers of responding E. coli on each fiber-optic bundle with different numbers of AHL-producing cells within a 36-well microenvironment were calculated. The data are from six different fibers, with three uninhibited scenarios and three inhibited scenarios. The data are normalized with respect to the most prevalent configuration because experiments with different numbers of E. coli were compared. Error bars indicate S.D.