Biological functions are typically performed by groups of cells that predominantly express the same genes yet display a continuum of phenotypes. The long-term goal of this project is to understand how such variations influence functional properties at the population level, which is a fundamental problem in cell biology with critical implications for public health. As a model system, we have been using the bacterial chemotaxis system of Escherichia coli because it involves non-trivial functions, such as signal detection, amplification, memory and adaptation, and it is well-characterized molecularly. During the previous funding cycle, we developed microfluidics and computational technology to measure protein abundance, swimming behavior, and performance of the same individual cells in a race up a gradient of attractant. These data revealed that chemotactic performance depends nonlinearly on swimming phenotype, which in turn depends nonlinearly on protein abundances. These nonlinearities have important consequences: because the average of a nonlinear function is different from the nonlinear function of the average, the population could outcompete the performance of its mean phenotype in some conditions. This result illustrates a basic and ubiquitous mechanism by which phenotypic diversity can modulate function in cell biology, even in the absence of any interactions among cells. In this next funding cycle, we plan to examine the consequence of this mechanism for signal transduction by combining our microfluidics and computational framework with single-cell FRET technology developed by long- term collaborator Dr. Thomas Shimizu. This new combined platform enables high-throughput single-cell measurements of signaling dynamics in microfluidics chambers. Using this approach, we will examine how temporal variations in individual cells, due to spontaneous fluctuations in the pathway (Aim 1) and to cell cycle regulation (Aim 2), affect their ability to process signals.
These aims will also quantify the contribution of these processes to the standing variation in an isogenic population. Finally, in Aim 3 we will examine how phenotypic diversity shapes the population?s capability to process signals. Taken together, the proposed aims will go beyond the population-average characterization of this signaling network to reveal how diverse individual cells process signals while growing and fluctuating, and how this diversity shapes the population?s signal transduction capabilities beyond those of its mean phenotype.
Cell-to-cell variability complicates therapies because it diversifies the response of individual cells to identical signals. Common sources of variations among clonal cells are gene expression noise, fluctuations in signaling networks, and regulatory processes. Going beyond the population-average characterization of signaling pathways, this project will quantify how diverse individual cells within a population process signals while growing and fluctuating, and how this diversity shapes the population?s signal transduction capabilities beyond those of the mean phenotype in the same population.
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