Techniques for systematically monitoring protein translation have lagged far behind methods for measuring messenger RNA (mRNA) levels. Here, we present a ribosome-profiling strategy that is based on the deep sequencing of ribosome-protected mRNA fragments and enables genome-wide investigation of translation with subcodon resolution. We used this technique to monitor translation in budding yeast under both rich and starvation conditions. These studies defined the protein sequences being translated and found extensive translational control in both determining absolute protein abundance and responding to environmental stress. We also observed distinct phases during translation that involve a large decrease in ribosome density going from early to late peptide elongation as well as widespread regulated initiation at non-adenine-uracil-guanine (AUG) codons. Ribosome profiling is readily adaptable to other organisms, making high-precision investigation of protein translation experimentally accessible.The ability to monitor the identity and quantity of proteins that a cell produces would inform nearly all aspects of biology. Microarray-based measurements of mRNA abundance have revolutionized the study of gene expression (1). However, for several reasons there is a critical need for techniques that directly monitor protein synthesis. First, mRNA levels are an imperfect proxy for protein production because mRNA translation is subject to extensive regulation (2-4). Second predicting the exact protein product from the transcript sequence is not possible because of effects such as internal ribosome entry sites, initiation at non-AUG codons, and nonsense read-through (5,6). Finally, programmed ribosomal pausing during protein synthesis is thought to aid the cotranslational folding and secretion of some proteins (7-9).Polysome profiling, in which mRNAs are recovered from translating ribosomes for subsequent microarray analysis, can provide a useful estimate of protein synthesis (10). However, this approach suffers from limited resolution and accuracy. Additionally, upstream open reading frames (uORFs)-short translated sequences found in the 5′ untranslated region (5′UTR) of many genes-result in ribosomes that are bound to an mRNA and yet are not translating the encoded gene (11). Advances in quantitative proteomics circumvent some of these problems (2,3), but there currently are substantial limits on their ability to independently determine protein sequences and measure low-abundance proteins.The position of a translating ribosome can be precisely determined by using the fact that a ribosome protects a discrete footprint [∼30 nucleotides (nt)] on its mRNA template from nuclease digestion (12). We reasoned that advances in deep-sequencing technology, which NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript make it possible to read tens of millions of short (∼35 base pairs) DNA sequences in parallel (13), would allow the full analysis of ribosome footprints from cells. Here, we present a ribosome-...

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.