Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

Protein and mRNA copy numbers vary from cell to cell in isogenic bacterial populations. However, these molecules often exist in low copy numbers, and are difficult to detect in single cells. Here we carried out quantitative system-wide analyses of protein and mRNA expression in individual cells with single-molecule sensitivity using a newly constructed yellow fluorescent protein fusion library for Escherichia coli. We found that almost all protein number distributions can be described by the gamma distribution with two fitting parameters which, at low expression levels, have clear physical interpretations as the transcription rate and protein burst size. At high expression levels, the distributions are dominated by extrinsic noise. Strikingly, we found that a single cell's protein and mRNA copy numbers for any given gene are uncorrelated.Gene expression is often stochastic because gene regulation takes place at a single DNA locus within a cell. Such stochasticity is manifested in fluctuations of mRNA and protein copy numbers within a cell lineage over time, and in variations of mRNA and protein copy numbers among a population of genetically identical cells at a particular time (1,2,3,4). Because both manifestations of stochasticity are connected, measurement of the latter allows the deduction of the gene expression dynamics in a cell (5). We aim to characterize such mRNA and protein distributions in single bacteria cells at a system-wide level.While single cell mRNA profiling has been carried out with cDNA microarray (6) and mRNA-seq (7), these studies did not have single molecule sensitivity and are not suitable for bacteria, which express mRNA at low copy numbers (8). A fluorescent protein reporter library of Saccharomyces cerevisiae (9) has proven to be extremely useful in protein profiling (10,11). However, the lack of sensitivity in existing flow cytometry or fluorescence microscopy techniques prevented the quantification of one third of the labeled proteins because of their low copy numbers. In recent years, single-molecule fluorescence ** Publisher's Disclaimer: This manuscript has been accepted for publication in Science. This version has not undergone final editing. Please refer to the complete version of record at http://www.sciencemag.org/. The manuscript may not be reproduced or used in any manner that does not fall within the fair use provisions of the Copyright Act without the prior, written permission of AAAS." † To whom correspondence should be addressed. xie@chemistry.harvard.edu. * These authors contributed equally to this work. NIH Public Access Author ManuscriptScience. Author manuscript; available in PMC 2010 August 17. Single-molecule imaging of a YFP reporter libraryWe created a chromosomal YFP fusion library (Fig. 1A), in which each strain has a particular gene tagged with the YFP coding sequence. YFP can be detected with single molecule sensitivity in live bacterial cells (8,18). We converted the C-terminus tags of an existing chromosomally affinity-tagged E. coli library (19,20...

Delivery and toxicity are critical issues facing nanomedicine research. Currently, there is limited understanding and connection between the physicochemical properties of a nanomaterial and its interactions with a physiological system. As a result, it remains unclear how to optimally synthesize and chemically modify nanomaterials for in vivo applications. It has been suggested that the physicochemical properties of a nanomaterial after synthesis, known as its "synthetic identity", are not what a cell encounters in vivo. Adsorption of blood components and interactions with phagocytes can modify the size, aggregation state, and interfacial composition of a nanomaterial, giving it a distinct "biological identity". Here, we investigate the role of size and surface chemistry in mediating serum protein adsorption to gold nanoparticles and their subsequent uptake by macrophages. Using label-free liquid chromatography tandem mass spectrometry, we find that over 70 different serum proteins are heterogeneously adsorbed to the surface of gold nanoparticles. The relative density of each of these adsorbed proteins depends on nanoparticle size and poly(ethylene glycol) grafting density. Variations in serum protein adsorption correlate with differences in the mechanism and efficiency of nanoparticle uptake by a macrophage cell line. Macrophages contribute to the poor efficiency of nanomaterial delivery into diseased tissues, redistribution of nanomaterials within the body, and potential toxicity. This study establishes principles for the rational design of clinically useful nanomaterials.

BackgroundGene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal.Principal FindingsTo overcome gene-set redundancy and help in the interpretation of large gene lists, we developed “Enrichment Map”, a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results.ConclusionsEnrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).

Proteins often function as components of multi-subunit complexes. Despite its long history as a model organism, no large-scale analysis of protein complexes in Escherichia coli has yet been reported. To this end, we have targeted DNA cassettes into the E. coli chromosome to create carboxy-terminal, affinity-tagged alleles of 1,000 open reading frames (approximately 23% of the genome). A total of 857 proteins, including 198 of the most highly conserved, soluble non-ribosomal proteins essential in at least one bacterial species, were tagged successfully, whereas 648 could be purified to homogeneity and their interacting protein partners identified by mass spectrometry. An interaction network of protein complexes involved in diverse biological processes was uncovered and validated by sequential rounds of tagging and purification. This network includes many new interactions as well as interactions predicted based solely on genomic inference or limited phenotypic data. This study provides insight into the function of previously uncharacterized bacterial proteins and the overall topology of a microbial interaction network, the core components of which are broadly conserved across Prokaryota.