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RESEARCH HIGHLIGHTS PROTEOMICS © 2006 Nature Publishing Group http://www.nature.com/naturemethods Exploring how the organelles are organized New work from several laboratories describes ongoing efforts to explore a new proteomic frontier—mapping and indexing the protein content of the cellular organelles. The cell is more than just a sack of proteins, even if it can sometimes be convenient to think of it that way. Every cell contains dozens of functionally distinct compartments, and it is well established that the Golgi, mitochondria and other organelles each have unique proteomic profiles. But until recently, efforts to cleanly isolate individual organelle types and analyze their protein content have been met with limited success. To improve the quality of their organelle analyses, Max Planck Institute researcher Matthias Mann and his colleagues developed a technique called protein correlation profiling (PCP), in which mass-spectrometric data obtained from gradient-fractionated cell extracts are compared against proteins known to localize to specific organelles, allowing researchers to confidently map organelles to particular fractions. Using PCP with extracts obtained from mouse liver, Mann and his colleagues obtained data that allowed them to confidently assign nearly 1,500 proteins to 10 cellular organelles (Foster et al., 2006). These data not only took Mann’s multinational team a step closer toward assembling a better cellular proteomic map, but it also proved useful for the preliminary identification of organelle-specific cis-regulatory elements and the tentative assembly of networks of coregulated genes. Sometimes dividing the cellular proteome into compartments can also be a pragmatic decision. “The fractionation that we did to enrich our organelles, to some degree, was to get around technical limitations associated with mass spectrometry,” explains Andrew Emili of the University of Toronto. “[With] a crude extract, we’d probably identify far fewer BIOINFORMATICS MORE THAN JUST ‘DOING THE MATH’ Two new articles show how computational tools continue to move beyond mere sequence-based bioinformatic analysis into more advanced arenas of prediction, deduction and network building. As interest grows in the still-young field of organelle proteomics, inventive in silico strategies are essential if researchers are to construct accurate hypotheses from mountains of raw data. Computational and experimental approaches have a symbiotic relationship, explains Vamsi Mootha of the Broad Institute of MIT and Harvard University: “They complement each other—you can’t tease them apart. In order to support highquality computational approaches, you need to begin with highquality datasets.” Mootha recently illustrated this relationship, describing a ‘smarter’ in silico approach for identifying mitochondrial proteins (Calvo et al., 2006). Earlier strategies have largely emphasized motif-based predictors, but the Mootha group’s ‘Maestro’ program takes a more holistic approach, integrating eight different ‘predictors’, based on both structural and experimental data, to generate scores predicting the likelihood of mitochondrial localization. After training Maestro with a ‘gold standard’ set of known positive and negative controls, Mootha’s team confirmed hundreds of known mitochondrial proteins and confidently identified nearly 500 that were previously unidentified. Notably, Maestro also proved capable of tentatively identifying genes associated with several human mitochondrial diseases, including at least one that had not been previously recognized as mitochondrial. Søren Brunak, of the Technical University of Denmark, and his colleagues recently described an alternative computational tool for organelle proteomics and used in silico methods to 420 | VOL.3 NO.6 | JUNE 2006 | NATURE METHODS predict protein complexes in the nucleolus (Hinsby et al., 2006). They began by constructing an interaction atlas for a collection of known human nucleolar proteins based on publicly available interaction data and then subjected each putative complex to component-by-component computational analysis based on dozens of protein features, to predict the likelihood of nucleolar localization. Using conservative parameters, Brunak’s team confidently predicted 15 nucleolar complexes; several of them were expected, but many were rather surprising from a functional standpoint (for example, proteins involved in DNA repair). This work also revealed 11 new nucleolar proteins, which were confirmed by experimental data from Brunak’s collaborator, Matthias Mann, in a process the two call ‘reverse proteomics’. Both groups benefited from smart use of existing data sets, and Mootha suggests that more data should mean more options for future computational efforts. “More generally,” he says, “if we get different types of really good functional genomics data sets, it might be possible to reconstruct all organelles in silico.” Both approaches, however, also illustrate the value of using conservative cutoffs to eliminate ‘junk’ data and to ensure confidence in one’s analysis. “Mapping something often means to throw a lot of information away, and this is, I think, what we try to do with our work,” says Brunak. “We would rather not waste the precious time of the experimentalists!” Michael Eisenstein RESEARCH PAPERS Calvo, S. et al. Systematic identification of human mitochondrial disease genes through integrative genomics. Nat. Genet. 38, 576–582 (2006). Hinsby, A.M. et al. A wiring of the human nucleolus. Mol. Cell 22, 285–295 (2006). © 2006 Nature Publishing Group http://www.nature.com/naturemethods RESEARCH HIGHLIGHTS proteins.” Emili and his colleagues isolated 4 different cellular compartments—cytosol, plasma membrane, nucleus and mitochondria—via gradient separation, and then compared the proteomic differences between these organelles in different mouse tissues (Kislinger et al., 2006). Their study yielded tissue- and organellespecific data for nearly 5,000 different proteins, and their comparisons of proteomic profiles against high-quality microarray data revealed a surprisingly tight relationship between mRNA and protein expression levels. Kathryn Lilley, of the University of Cambridge, encountered similar problems to Emili’s in her initial studies of Arabidopsis sp. proteomics. “We were seeing the same proteins over and over again, because we were just sampling the abundant cytosolic proteins,” she explains. She and her colleagues used localization of organelle proteins using isotope tagging, a technique called LOPIT, to perform an organelle enrichment study of their own, with a particular emphasis on the identification of membrane proteins (Dunkley et al., 2006). They consistently detected approximately 700 proteins in multiple experiments, 60% of which were putative membrane proteins, and more than 75% of which could be confidently assigned to a particular organelle after careful computational analysis of the protein content in various cellular fractions. Technical limitations have posed a serious obstacle to studies like these in the past, and Mann is quick to credit much of his data quality to the equipment at his disposal. “We used very new instrumentation,” he says, “[and] so we were able to get much more accurate data.” Emili agrees: “If I had my way, a mass spectrophotometer would be like a PCR machine, and every lab would have one. We were in a luxury position... being able to dedicate an instrument to this.” All three researchers agree that this is a field rapidly coming of age. “I think it’s an exciting time to be involved in organelle proteomics,” says Lilley. “There are so many different biological questions that require a knowledge of where proteins are and where they traffic to upon given perturbations that have largely been ignored in the past because we haven’t had the tools.” Authoritatively indexing the organelle proteome will require more effort, as well as powerful computational tools to maximize the value of the data. Mann sees this work as a starting point for answering far more interesting questions about cellular dynamics: “For example, if you have insulin signaling, how exactly does it signal into the mitochondria?” He concludes, “I think there will be more looking in these functional directions, and not just trying to build a catalog.” Emili also sees this blossoming of ‘reductionist’ proteomics as an important step toward understanding fundamentals of global protein organization and behavior. “I think we’re going to take it to the next level,” he says. “My view of where the field is going is that in five years, we’ll not only be measuring the levels of protein in various organs and cell types and tissues, but we’ll [also] know who they’re associated with and we’ll have some holistic sense of the posttranslational modifications.” Michael Eisenstein RESEARCH PAPERS Dunkley, T.P. et al. Mapping the Arabidopsis organelle proteome. Proc. Natl. Acad. Sci. USA 103, 6518–6523 (2006). Foster, L.J. et al. A mammalian organelle map by protein correlation profiling. Cell 125, 187–199 (2006). Kislinger, T. et al. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125, 173–186 (2006). NEWS IN BRIEF PROTEOMICS Biochemical suppression of small-molecule inhibitors: a strategy to identify inhibitor targets and signaling pathway components Peterson et al. describe a biochemical alternative to the widely used genetic suppressor screen. Cells treated with a smallmolecule inhibitor are incubated with concentrated cytosolic fractions from untreated cells; closer analysis of the fractions that reverse the inhibitor phenotype can reveal drug targets— including multiprotein complexes—and proteins that act downstream of these targets. Peterson, J.R. et al. Chem. Biol. 13, 443–452 (2006). GENE REGULATION Preventing gene silencing with human replicators Transcriptional silencing presents a serious obstacle to the efficacy and safety of insertion-based gene therapy. Previous research has shown that transcriptionally active chromosomal regions tend to undergo replication early in S phase, and Fu et al. demonstrate that the extent of silencing can be greatly reduced by the incorporation of active replicator sequences into transgenes. Fu, H. et al. Nat. Biotechnol. 24, 572–576 (2006). IMAGING AND VISUALIZATION Assembly of the brainstem cochlear nuclear complex is revealed by intersectional and subtractive genetic fate maps Analyzing the development of complex tissues often requires the ability to distinguish between similar but distinct cell populations. As a tool for such mapping projects, Farago et al. have developed a indicator system that allows them to visually differentiate cells that simultaneously express two genes of interest from cells where only one of the two is being expressed. Farago, A.F. et al. Neuron 50, 205–218 (2006). MICROFLUIDICS Microfabricated bioprocessor for integrated nanoliterscale Sanger DNA sequencing Whereas some scientists foresee the impending demise of Sanger sequencing, Blazej et al. still see advantages in this venerable technique. They describe a microfabricated lab-on-a-chip system capable of accurate Sanger sequencing from one femtomole of template DNA and discuss the possibility of developing similar nanoscale bioprocessors for other genomic applications. Blazej, R.G. et al. Proc. Natl. Acad. Sci. USA 103, 7240–7245 (2006). MICROSCOPY STED microscopy reveals that synaptotagmin remains clustered after synaptic vesicle exocytosis Stimulated emission depletion (STED) considerably improves the resolution of fluorescence microscopy, allowing the visualization of objects tens of nanometers in diameter with a minimum of effort by the investigator. Willig et al. demonstrate the power of STED microscopy, imaging the clustering of synaptic vesicles at the presynaptic membranes of rat neurons. Willig, K.I. et al. Nature 440, 935–939 (2006). NATURE METHODS | VOL.3 NO.6 | JUNE 2006 | 421