dcf: Peptide design by compatible functions.

<p>This is the final release for revision. It includes programs and scripts to reproduce the manuscript results. Prebuilt versions are currently only available for 64-bit Linux.</p> <p>For more details please see the manuscript and the linked Github repository.</p

Yeast Mating and Image-Based Quantification of Spatial Pattern Formation

<div><p>Communication between cells is a ubiquitous feature of cell populations and is frequently realized by secretion and detection of signaling molecules. Direct visualization of the resulting complex gradients between secreting and receiving cells is often impossible due to the small size of diffusing molecules and because such visualization requires experimental perturbations such as attachment of fluorescent markers, which can change diffusion properties. We designed a method to estimate such extracellular concentration profiles <i>in vivo</i> by using spatiotemporal mathematical models derived from microscopic analysis. This method is applied to populations of thousands of haploid yeast cells during mating in order to quantify the extracellular distributions of the pheromone <b>α</b>-factor and the activity of the aspartyl protease Bar1. We demonstrate that Bar1 limits the range of the extracellular pheromone signal and is critical in establishing <b>α</b>-factor concentration gradients, which is crucial for effective mating. Moreover, haploid populations of wild type yeast cells, but not <i>BAR1</i> deletion strains, create a pheromone pattern in which cells differentially grow and mate, with low pheromone regions where cells continue to bud and regions with higher pheromone levels and gradients where cells conjugate to form diploids. However, this effect seems to be exclusive to high-density cultures. Our results show a new role of Bar1 protease regulating the pheromone distribution within larger populations and not only locally inside an ascus or among few cells. As a consequence, wild type populations have not only higher mating efficiency, but also higher growth rates than mixed <i>MAT</i><b>a</b><i>bar1Δ/MAT</i><b>α</b> cultures. We provide an explanation of how a rapidly diffusing molecule can be exploited by cells to provide spatial information that divides the population into different transcriptional programs and phenotypes.</p></div

Virtual cell populations were randomly generated in order to track the influence of population density on the α-factor distribution.

<p>(A) Maximum information content of the <b>α</b>-factor distribution as calculated by entropy (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003690#pcbi.1003690.s002" target="_blank">Text S1</a>) depending on population density. (B) Average <b>α</b>-factor gradients (relative front/back difference, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003690#pcbi.1003690.s002" target="_blank">Text S1</a>) for individual cells in the populations. (C and D) Calculated <b>α</b>-factor distributions for subpopulations of different densities in wild type and <i>bar1Δ</i>.</p

Microscopic images (left) and derived computational domains and α-factor distributions (right) for <i>BAR1</i> wild type (top) and <i>bar1Δ</i> (bottom).

<p>The microscopic images are an overlay of the bright field, mCherry (for <i>MAT</i><b><i>α</i></b>) and GFP channels (for Fus1 expression in <i>MAT</i><b>a</b>). Individual images are given in Figure S1 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003690#pcbi.1003690.s002" target="_blank">Text S1</a>. Computed images show <i>MAT</i><b><i>α</i></b> in red and <i>MAT</i><b>a</b> in white. The extracellular <b>α</b>-factor distribution is as indicated on the color scales. Note different scales for <b>α</b>-factor for wild type (top) and <i>bar1Δ</i> (bottom).</p

Flow cytometry has been used to quantify the fractions of diploids and <i>MAT</i>a or <i>MATα</i> haploids in the yeast cultures.

<p><i>MAT</i><b>a</b> and <i>MAT</i><b><i>α</i></b> carried constitutively expressed GFP and mCherry constructs, respectively (Rpl9A-GFP and mCherry induced from the <i>TDH3</i> promoter). We observed a switch from many cells carrying either of the two constructs at the initial time point (A, C) to a large subpopulation carrying both constructs (B,D) after 5 hours. Density is indicated by colors from blue to red, two biological replicates of 10.000 cells each.</p

PCR primer for Rpl9a –TagBFP2 tagging.

<p>PCR primer for Rpl9a –TagBFP2 tagging.</p

PCR primer for pBar1-qVenus reporter strain cloning.

<p>PCR primer for pBar1-qVenus reporter strain cloning.</p

The combined experimental and computational methodology used to derive the extracellular distribution of α-factor.

<p>(A) Cells were synchronized by elutriation, fixed by forced sedimentation, and microscopically analyzed. (B) Fus1-GFP was measured in <i>MAT</i><b>a</b> wild type and <i>bar1Δ</i>cells after stimulation with various levels of <b>α</b>-factor (displayed here: <b>α</b>-factor concentration of 500 nM) and used for calibration curves in (D) and (E). (C) Overlays of the bright field, mCherry and GFP channels allowed recording the positions of <i>MAT</i><b>α</b> (in red) and <i>MAT</i><b>a</b> cells for further mathematical analysis. (D) Calibration of the response of <i>bar1Δ</i> cells to given amounts of <b>α</b>-factor allowed to estimate, how much <b>α</b>-factor would reach the cell without degradation by Bar1. (E) Calibration of the response of wild type <i>MAT</i><b>a</b> cells enabled calculation of Bar1 secretion in response to <b>α</b>-factor. (F) The information obtained in panels (B) to (E) was used to calculate the unknown distributions of Bar1 and <b>α</b>-factor in the space between the cells (<b>α</b>-factor distribution is displayed).</p

PCR primer for BAR1 deletion.

<p>PCR primer for BAR1 deletion.</p

Do wild type and mutant cells influence each others' mating success?

<p>(A) Mating in mixed cultures initially containing <i>MAT</i><b>α</b> cells (marked with mCherry) and varying fractions of wild type <i>MAT</i><b>a</b> cells (marked with Rpl9A-GFP) and <i>MAT</i><b>a </b><i>bar1Δ</i> cells (marked with RPl9a-TagBFP2) after 4 h. (B) Confocal merged image of a mixed culture initially containing 50% <i>MAT</i><b>α</b> and 50% <i>MAT</i><b>a </b><i>bar1Δ</i> cells. (C) Confocal merged image of a mixed culture containing 50% <i>MAT</i><b>α</b> cells, 45% <i>MAT</i><b>a </b><i>bar1Δ</i> cells and 5% wild type <i>MAT</i><b>a</b> cells. Some mating events are marked with white arrows. Each percentage represents the mean of two technical replicates with 10.000 counted cells each. Note that colors in the microscopic images and in the table (A) concur.</p