Measurement of the copy number of the master quorum-sensing regulator of a bacterial cell

Quorum sensing is the mechanism by which bacteria communicate and synchronize group behaviors. Quantitative information on parameters such as the copy number of particular quorum-sensing proteins should contribute strongly to understanding how the quorum-sensing network functions. Here we show that the copy number of the master regulator protein LuxR in Vibrio harveyi, can be determined in vivo by exploiting small-number fluctuations of the protein distribution when cells undergo division. When a cell divides, both its volume and LuxR protein copy number N are partitioned with slight asymmetries. We have measured the distribution functions describing the partitioning of the protein fluorescence and the cell volume. The fluorescence distribution is found to narrow systematically as the LuxR population increases while the volume partitioning is unchanged. Analyzing these changes statistically, we have determined that N = 80-135 dimers at low cell density and 575 dimers at high cell density. In addition, we have measured the static distribution of LuxR over a large (3,000) clonal population. Combining the static and time-lapse experiments, we determine the magnitude of the Fano factor of the distribution. This technique has broad applicability as a general, in vivo technique for measuring protein copy number and burst size.

💡 Research Summary

Quorum sensing (QS) enables bacterial populations to coordinate collective behaviors through the production and detection of extracellular signal molecules. In Vibrio harveyi, the master transcriptional regulator LuxR integrates these signals and drives the expression of QS‑controlled genes. Understanding how LuxR functions at the molecular level requires quantitative knowledge of its intracellular copy number, yet direct measurement of low‑abundance proteins in living cells remains challenging. In this study, the authors present an elegant in‑vivo method that exploits the stochastic fluctuations arising during cell division to infer the absolute number of LuxR molecules per cell.

The experimental design hinges on a LuxR–GFP fusion protein whose fluorescence intensity serves as a proxy for the number of LuxR dimers. Using time‑lapse fluorescence microscopy, the authors tracked individual V. harveyi cells through successive divisions, simultaneously measuring the fluorescence intensity of each daughter cell and the corresponding cell volume. While cell volume partitioning remained tightly centered around a 1:1 split with negligible variance, the fluorescence partitioning displayed a systematic broadening that depended on the underlying protein copy number. The authors modeled this partitioning as a binomial (or more precisely a beta‑binomial) process, where each LuxR dimer is allocated randomly to one of the two daughter cells. The variance of the fluorescence distribution, σ_F², can be expressed as a function of the mean fluorescence, Ī, the copy number N, the variance of volume partitioning (σ_V²), and an additive measurement‑noise term. By fitting the experimentally observed σ_F² versus Ī relationship, they solved for N.

At low cell density (LCD), when autoinducer concentrations are low and LuxR expression is repressed, the analysis yielded an estimated 80–135 LuxR dimers per cell (≈160–270 monomers). At high cell density (HCD), where autoinducer accumulation derepresses luxR transcription, the copy number rose dramatically to roughly 575 dimers (≈1150 monomers). These values provide the first direct quantification of LuxR abundance across the QS activation spectrum.

To complement the dynamic measurements, the authors performed a static population analysis on a clonal culture of ~3,000 cells. By measuring the distribution of fluorescence intensities across this large sample, they calculated the mean (μ) and variance (σ²) of LuxR copy number. The resulting Fano factor (F = σ²/μ) exceeded unity, indicating that LuxR expression is not a simple Poisson process but exhibits “bursting” behavior—episodes of rapid transcription and translation followed by periods of quiescence. The magnitude of the burst size can be inferred from the Fano factor, offering insight into the intrinsic noise of the QS network.

Methodologically, the study demonstrates several strengths. First, the LuxR‑GFP fusion was validated to retain functional activity, ensuring that fluorescence accurately reflects native LuxR levels. Second, the dual‑channel imaging setup allowed simultaneous acquisition of fluorescence and phase‑contrast images, facilitating precise volume measurements. Third, the statistical framework accounts for both biological variability (unequal partitioning) and technical noise, enhancing the robustness of the copy‑number estimates.

The implications of these findings are manifold. Quantifying LuxR copy number clarifies how V. harveyi translates extracellular autoinducer concentrations into intracellular transcriptional responses. The observed increase in LuxR abundance from LCD to HCD suggests a mechanism whereby the cell amplifies the QS signal, potentially sharpening the transition between low‑ and high‑density states. Moreover, the measured burstiness indicates that stochastic fluctuations could contribute to phenotypic heterogeneity within a clonal population, influencing the timing of QS‑dependent behaviors such as bioluminescence, virulence factor production, or biofilm formation.

Beyond LuxR, the authors propose that their partition‑fluctuation approach constitutes a general, label‑free strategy for determining the copy numbers of any low‑abundance protein in bacteria, provided a suitable fluorescent tag can be attached without disrupting function. This technique bypasses the need for single‑molecule counting or quantitative Western blots, offering a rapid, high‑throughput alternative that can be applied to diverse microbial systems and regulatory networks.

In summary, the paper introduces a novel, statistically rigorous method to infer protein copy numbers from division‑induced fluctuations, applies it to the master QS regulator LuxR, and reveals density‑dependent changes in LuxR abundance and expression noise. These quantitative insights deepen our mechanistic understanding of quorum sensing and open new avenues for probing stochastic gene regulation in living cells.