Protein expression patterns of the yeast mating response

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Microfluidics, in combination with time-lapse microscopy, is a transformative technology that significantly enhances our ability to monitor and probe biological processes in living cells. However, high-throughput microfluidic devices mostly require sophisticated preparatory and setup work and are thus hard to adopt by non-experts. In this work, we designed an easy-to-use microfluidic chip, which enables tracking of 48 GFP-tagged yeast strains, with each strain under two different stimulus conditions, in a single experiment. We used this technology to investigate the dynamic pattern of protein expression during the yeast mating differentiation response. High doses of pheromone induce cell cycle arrest and the shmoo morphology, whereas low doses of pheromone lead to elongation and chemotrophic growth. By systematically analyzing the protein dynamics of 156 pheromone-regulated genes, we identified groups of genes that are preferentially induced in response to low-dose pheromone (elongation during growth) or high-dose pheromone (shmoo formation and cell cycle arrest). The protein dynamics of these genes may provide insights into the mechanisms underlying the differentiation switch induced by different doses of pheromone.

1. Introduction

Cells respond to extracellular signals through intercellular signaling pathways. To help the cell adapt to the new environments, the reactions of the proteins involved in the signaling pathway often have dynamic features. Systematic studies of dynamic protein expression patterns after external stimulation may confine the possible signaling pathway networks and reveal the underlying mechanisms of the signaling networks. The budding yeast mating pathway is a typical mitogenactivated protein kinase (MAPK) system that plays an important role in many other cellular processes.1 The studies of the yeast mating pathway have revealed important information about eukaryotic signaling networks.2–5 In addition, yeast would exhibit different phenotypes depending on the extracellular pheromone concentration.6,7 This phenomenon makes the yeast mating pathway a prototypical cell fate decision process. The pheromone-induced behaviour in yeast cells involves important biological processes, such as cell mating, cell polarization and cell fusion. The mating pathway in yeast cells has been studied for decades, and the primary process before transcription is almost completely understood. An extracellular pheromone can be recognized by a receptor in the cell membrane, and then the signal is transmitted through the MAPK cascade involving the proteins STE5, STE11, STE7, FUS3 and KSS1. Eventually, the transcription factor STE12 would be activated, and bind to the pheromone response element (PRE), and then regulate the 1. Introduction Cells respond to extracellular signals through intercellular signaling pathways. To help the cell adapt to the new environments, the reactions of the proteins involved in the signaling pathway often have dynamic features. Systematic studies of dynamic protein expression patterns after external stimulation may confine the possible signaling pathway networks and reveal the underlying mechanisms of the signaling networks. The budding yeast mating pathway is a typical mitogenactivated protein kinase (MAPK) system that plays an important role in many other cellular processes.1 The studies of the yeast mating pathway have revealed important information about eukaryotic signaling networks.2–5 In addition, yeast would exhibit different phenotypes depending on the extracellular pheromone concentration.6,7 This phenomenon makes the yeast mating pathway a prototypical cell fate decision process. The pheromone-induced behaviour in yeast cells involves important biological processes, such as cell mating, cell polarization and cell fusion. The mating pathway in yeast cells has been studied for decades, and the primary process before transcription is almost completely understood. An extracellular pheromone can be recognized by a receptor in the cell membrane, and then the signal is transmitted through the MAPK cascade involving the proteins STE5, STE11, STE7, FUS3 and KSS1. Eventually, the transcription factor STE12 would be activated, and bind to the pheromone response element (PRE), and then regulate the through pumps with a constant flow rate of 400 ml h1 . After all the strains were loaded into the observation chambers, yeast cells were cultured in SD medium in the microfluidic chip for approximately 60 minutes before the medium containing the alpha-factor was injected into the chambers (more details of the device design, fabrication and operation can be found in the ESI† and Fig. S1). System setup and automated image acquisition We set up an automated image acquisition system using a Nikon Ti-E microscope, a computer system and syringe pumps (Fig. 2(a)). The system can automatically acquire phase difference and fluorescence images in 96 selected positions through NIS-Elements advanced research software. We obtained a series of images for all 96 positions, and the time interval between two images was 5 minutes. The entire process of image acquisition would take 10 hours. The temperature was maintained at 30 1C throughout the entire experiment using a temperature control system. Image processing and data processing Using our microfluidic chip and image acquisition system, we could obtain sequential phase difference pictures and fluorescence pictures containing more or fewer yeast cells. In the phase difference pictures, the intracellular region appears black, while the cell margin was bright white and the extracellular region was also black. This feature helps to distinguish the cellular region in the phase difference picture. We used the Dynamic Directional Gradient Vector Flow (DDGVF) algorithm,19 which has been widely used in cell segmentation for precise identification of cell margin; then we could obtain mask pictures that cover the yeast cells. Using mask pictures, phase difference pictures and fluorescence pictures, we could also track yeast cells and read out the cellular GFP concentration using a Matlab program provided by the lab Hao Nan Lab at UCSD; thus, the average GFP concentration data could be obtained eventually . Although we could obtain single-cell protein expression data, we did not focus our attention on this information; instead, we studied the average protein expression of approximately 20–50 cells.