Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise

• Published in: Nature Vol. 441; no. 7095; pp. 840 - 846
• Main Authors: Newman, J.R.S, Ghaemmaghami, S, Ihmels, J, Breslow, D.K, Noble, M, DeRisi, J.L, Weissman, J.S
• Format: Journal Article
• Language: English
• Published: London Nature Publishing 15.06.2006; Nature Publishing Group
• Subjects: Biological and medical sciences; cells; Cellular biology; Culture Media - pharmacology; Deoxyribonucleic acid; DNA; Flow Cytometry; Fundamental and applied biological sciences. Psychology; fungal proteins; Measurement techniques; Microbiology; Mycological methods and techniques used in mycology; Mycology; Noise; protein composition; Proteins; proteome; Proteome - genetics; Proteome - metabolism; Proteomics; Reproducibility of Results; Ribonucleic acid; RNA; RNA, Fungal - genetics; RNA, Fungal - metabolism; RNA, Messenger - genetics; RNA, Messenger - metabolism; Saccharomyces cerevisiae; Saccharomyces cerevisiae - cytology; Saccharomyces cerevisiae - drug effects; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; single-cell proteome; Stochastic Processes; Time Factors; yeasts; Fungi; Yeast; Investigation method; Proteome; Ascomycetes; Saccharomyces cerevisiae; Protein; Thallophyta
• ISSN: 0028-0836; 1476-4687
• DOI: 10.1038/nature04785

A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.