Here we studied the quantitative behaviour and cell-to-cell variability of a prototypical eukaryotic cell-fate decision system, the mating pheromone response pathway in yeast. We dissected and measured sources of variation in system output, analysing thousands of individual, genetically identical cells. Only a small proportion of total cell-to-cell variation is caused by random fluctuations in gene transcription and translation during the response ('expression noise'). Instead, variation is dominated by differences in the capacity of individual cells to transmit signals through the pathway ('pathway capacity') and to express proteins from genes ('expression capacity'). Cells with high expression capacity express proteins at a higher rate and increase in volume more rapidly. Our results identify two mechanisms that regulate cell-to-cell variation in pathway capacity. First, the MAP kinase Fus3 suppresses variation at high pheromone levels, while the MAP kinase Kss1 enhances variation at low pheromone levels. Second, pathway capacity and expression capacity are negatively correlated, suggesting a compensatory mechanism that allows cells to respond more precisely to pheromone in the presence of a large variation in expression capacity.

Haploid Saccharomyces cerevisiae yeast cells use a prototypic cell signaling system to transmit information about the extracellular concentration of mating pheromone secreted by potential mating partners. The ability for cells to respond distinguishably to different pheromone concentrations depends on how much information about pheromone concentration the system can transmit. Here we show that the MAPK Fus3 mediates fast-acting negative feedback that adjusts the dose-response of downstream system response to match that of receptor-ligand binding. This “dose-response alignment”, defined by a linear relationship between receptor occupancy and downstream response, can improve the fidelity of information transmission by making downstream responses corresponding to different receptor occupancies more distinguishable and reducing amplification of stochastic noise during signal transmission. We also show that one target of the feedback is a novel signal-promoting function of the RGS protein Sst2. Our work suggests that negative feedback is a general mechanism used in signaling systems to align dose-responses and thereby increase the fidelity of information transmission.

Microscope-based cytometry provides a powerful means to study cells in high throughput. Here we present a set of refined methods for making sensitive measurements of large numbers of individual Saccharomyces cerevisiae cells over time. The set consists of relatively simple 'wet' methods, microscope procedures, open-source software tools and statistical routines. This combination is very sensitive, allowing detection and measurement of fewer than 350 fluorescent protein molecules per living yeast cell. These methods enabled new protocols, including 'snapshot' protocols to calculate rates of maturation and degradation of molecular species, including a GFP derivative and a native mRNA, in unperturbed, exponentially growing yeast cells. Owing to their sensitivity, accuracy and ability to track changes in individual cells over time, these microscope methods may complement flow-cytometric measurements for studies of the quantitative physiology of cellular systems.

Although the proteins comprising many signaling systems are known, less is known about their numbers per cell. Existing measurements often vary by more than 10-fold. Here, we devised improved quantification methods to measure protein abundances in the Saccharomyces cerevisiae pheromone response pathway, an archetypical signaling system. These methods limited variation between independent measurements of protein abundance to a factor of two. We used these measurements together with quantitative models to identify and investigate behaviors of the pheromone response system sensitive to precise abundances. The difference between the maximum and basal signaling output (dynamic range) of the pheromone response MAPK cascade was strongly sensitive to the abundance of Ste5, the MAPK scaffold protein, and absolute system output depended on the amount of Fus3, the MAPK. Additional analysis and experiment suggest that scaffold abundance sets a tradeoff between maximum system output and system dynamic range, a prediction supported by recent experiments.quantitative immunoblotting | single-cell fluorescence quantification | genetic algorithmic model optimization T he Saccharomyces cerevisiae pheromone response system has been useful for understanding eukaryotic cell signaling (1-3). In haploid cells, the pheromone response system detects mating pheromone in the extracellular environment secreted by yeast of the opposite mating type. The system triggers a number of responses, including induction of gene expression, arrest in cell cycle progression, changes in morphology, and eventually, mating. The molecules and interactions by which the system operates are relatively well-understood (Fig. 1).Changes in the number of molecules per cell (hereafter called abundances) of signaling proteins can alter quantitative signaling behaviors. For example, in the pheromone response system, overexpression of Fus3 increases pheromone-induced sensitivity to cell cycle arrest (4), and overexpression of Gpa1 decreases pheromone-induced transcription (5). Similarly, changes in the abundances of MAPK cascade scaffold proteins (Ste5 in the yeast pheromone response system) ( Fig. 1) can alter system behavior. In models, when scaffold is limiting, increasing scaffold abundance first increases system output and then, eventually sequesters the associated protein kinases onto separate scaffolds, diminishing the number of fully assembled complexes and system output (6). This peak and decline behavior has been experimentally shown for Ste5 in this yeast system (7) and the Kinase Suppressor of Ras (KSR) scaffold protein in Ras signaling in Xenopus oocytes (8).Previous investigators reported focused and genome-wide inventories of pheromone system protein abundances (9-13). These measurements differed by up to 12-fold (Fig. 2) (14, 15). We thought some discrepancies might be because of differences and systematic biases in quantification methods (Discussion). We developed improved measurement methods and used these methods to quantify system proteins. We used...

We describe general methods to detect and quantify small numbers of specific molecules. We redirected self-splicing protein inteins to create 'tadpoles', chimeric molecules comprised of a protein head covalently coupled to an oligonucleotide tail. We made different classes of tadpoles that bind specific targets, including Bacillus anthracis protective antigen and the enzyme cofactor biotin. We measured the amount of bound target by quantifying DNA tails by T7 RNA polymerase runoff transcription and real-time polymerase chain reaction (PCR) evaluated by rigorous statistical methods. These assays had a dynamic range of detection of more than 11 orders of magnitude and distinguished numbers of molecules that differed by as little as 10%. At their low limit, these assays were used to detect as few as 6,400 protective antigen molecules, 600 biotin molecules and 150 biotinylated protein molecules. In crudely fractionated human serum, the assays were used to detect as few as 32,000 protective antigen molecules. Tadpoles thus enable sensitive detection and precise quantification of molecules other than DNA and RNA.