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Organellar proteomics to create the cell map
Organellar proteomics to create the cell map Catherine E Au1, Alexander W Bell1, Annalyn Gilchrist1, Johan Hiding2, Tommy Nilsson2 and John JM Bergeron1 The elucidation of a complete, accurate, and permanent representation of the proteome of the mammalian cell may be achievable piecemeal by an organellar based approach. The small volume of organelles assures high protein concentrations. Providing isolated organelles are homogenous, this assures reliable protein characterization within the sensitivity and dynamic range limits of current mass spec based analysis. The stochastic aspect of peptide selection by tandem mass spectrometry for sequence determination by fragmentation is dealt with by multiple biological replicates as well as by prior protein separation on 1-D gels. Applications of this methodology to isolated synaptic vesicles, clathrin coated vesicles, endosomes, phagosomes, endoplasmic reticulum, and Golgi apparatus, as well as Golgi-derived COPI vesicles, have led to mechanistic insight into the identity and function of these organelles. Addresses 1 Department of Anatomy and Cell Biology, McGill University, 3640 University Street, Montreal, Quebec, Canada H3A 2B2 2 Department of Medical and Clinical Genetics, Institute of Biomedicine and the Proteomics Centre at the Sahlgrenska Academy, Go¨teborg University, 413 90 Go¨teborg, Sweden Corresponding author: Bergeron, John JM ([email protected])
Current Opinion in Cell Biology 2007, 19:376–385 This review comes from a themed issue on Membranes and Organelles Edited by Peter McPherson and Thierry Galli Available online 3rd August 2007 0955-0674/$ – see front matter # 2007 Elsevier Ltd. All rights reserved.
Cell map proteomics The experimental strategy of organellar proteomics, outlined in Figure 2, is composed of three main steps: sample preparation, mass spectrometric analysis and data evaluation. On the basis of experience, efforts to secure the highest enrichment for an organelle pay off handsomely in the ease of characterization by tandem mass spectrometry. Longstanding protocols exist for the enrichment of most cellular organelles (e.g. [3,4]). Nevertheless, organelle-specific enzyme activity determinations combined with quantitative Western blotting is now used routinely in combination with quantitative electron microscopy to determine relative enrichment and structural integrity. Once purified, the protein components of the organelle are separated by polyacrylamide gel electrophoresis (PAGE). Traditionally, this is done by two-dimensional (2D) PAGE followed by silver staining. The density of each spot is then a convenient measure for relative abundance of each protein when comparing the same protein-spot between gels. The main drawback with 2D PAGE is that a large proportion of the proteome will never be seen because of the current inability of most proteins to enter the first dimension or to be resolved by their isoelectric point. Moreover, recent work shows that a significant number of spots seen on the gel contain more than one protein species, sometimes up to five proteins. Though developments are being made, 2D PAGE has been overtaken by 1D SDS-PAGE. The entire sample is run in one lane that is cut into 1–2 mm slices after separation for further processing. The entire lane is processed. This allows for a representation of the proteome. Following separation by PAGE, proteins are in-gel digested with trypsin and the peptides extracted from the gel.
DOI 10.1016/j.ceb.2007.05.004
Proteomics is rapidly gaining recognition as an effective experimental strategy to determine the makeup of organelles including their function(s) and identity, with identity defined as those molecular entities (i.e. GTPases) that are the internal spatial signals that distinguish organelles from each other [1]. Here, we highlight some recent technical advances and contributions to the organellar map of the cell (Figure 1) with a focus on those occurring subsequent to the efforts reviewed by Yates et al. [2]. We also discuss whether a vision of the cell map constructed of complete, accurate and permanent (CAP) proteomes of each organelle is already becoming a reality. Current Opinion in Cell Biology 2007, 19:376–385
The mass spectrometry (MS) analysis itself has inbuilt parameters that, to a certain extent, determine the degree of success of the entire exercise. There are two uncertainties in current MS characterization of peptides derived from proteins in biological samples. First, tryptic peptides have highly different efficiencies in their ionization and propensity to ‘fly’. Recently, attempts have been made to determine the biochemical attributes that factor into these properties [5]. By examining the properties of peptides that have been observed in past and ongoing proteomics efforts, the probability of flight can be determined empirically for each proteotypic peptide. High probability peptides can then be used as markers or signature peptides for each protein enabling identification with high confidence. Second, there is a stochastic aspect to further selection of the peptide that ‘flies’ in the www.sciencedirect.com
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Figure 1
Organellar proteomics. Selected highlights updated from Yates et al. [2] and Brunet et al. [22]. New developments highlighted here include the synaptic vesicle [23], endosomes [29], the LOPIT methodology applied to endomembranes from Arabadopsis thaliana [35], and studies of the early secretory pathway of rodent liver parenchyma by Foster et al. [38] and Gilchrist et al. [7]. The remaining proteomics studies for the indicated organelles are as described in Yates et al. [2].
MS instrument for presentation to a collision cell for further fragmentation. The finite sampling time taken to select and fragment the parent tryptic peptides and assess the masses for these fragments represents the duty cycle. Sampling and fragmentation are incomplete and stochastic at present. It is, however, these MS-fragmented peptides that are used to deduce the amino acid sequence of the parent peptide. Here, the quality and frequency of ‘tandem’ mass spectra pointing to a specific peptide sequence varies between instrument platforms impacting identification and quantitation. Efforts to benchmark different MS platforms across the globe are underway through HUPO-based initiatives to circumvent this variable, for example, the HUPO proteomics standards initiative enables data comparison, exchange and verification via standards for data representation [6]. Conceivably, commercially available peptide mixes will be needed to be included in future proteomics work to validate the performance and accuracy of the instrument(s) used.
Data analysis Although the NCBI non-redundant database has several entries for each protein, this database is employed because it is comprehensive, with multiple protein isoforms represented, allowing the most complete assignment of tandem MS to peptide sequences. This is necessary to maximize the assignment of tandem MS www.sciencedirect.com
to peptide sequences but not including natural posttranslational modifications. In our experience, ca. 50% of all tandem mass spectra are assigned with greater than 95% confidence with a false positive rate of less than 1.5% [7]. Matching peptide sequences to proteins involves first a redundant list of peptide identifications, then the creation of a minimum list of proteins that accommodate all the peptides, followed by further minimization of redundant proteins via BLAST (95% identity) as well as literature analysis to remove errors in the database itself. This is especially important for poorly annotated genomes such as the rat database [8]. The proteins are assigned to functional groups (e.g. using gene ontology (GO) which is a controlled vocabulary developed by the Gene Ontology Consortium, used to describe gene products by their biological process, cellular component and molecular function) or literaturebased functional groups [7]. Transmembrane domains and signal peptide predictions are annotated and also verified by literature analysis. The final annotated listing of identified proteins takes into account redundancies in the protein database, the plethora of non-descriptive names/descriptions and errors generated by gene prediction tools [7]. The relative number of MS-fragmented peptides is further used as a basis of a quantitative method to score protein abundance. This running quantitation of their Current Opinion in Cell Biology 2007, 19:376–385
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Figure 2
Cell map proteomics. For organellar proteomics, highly enriched organelle fractions (a) are characterized by a combination of biochemical assays, Western blots and electron microscopy. The proteins are then resolved by 1D SDS-PAGE. (b) Two different amounts (25 and 75 mg protein) of rough microsomes isolated from rat liver homogenates [7], stained with Coomassie Brilliant Blue G, are shown. The stained gel is then robotically processed wherein gel slices of 1.8 mm (83 slices per gel lane) of the complete gel lane are diced into cubes (1 mm) and collected in a 96-well tray. (c) The tray is then transferred to a second robot for in-gel digestion with trypsin after reduction (with dithiothreitol) and alkylation (with iodoacetamide), and the peptides are extracted and transferred to a second 96-well tray. The extracted peptides are then analysed by in-line LC–MS employing reversed phase chromatography with acetonitrile gradients in the presence of 0.1% formic acid to resolve the peptides in the mixture. MS analysis employs data-directed analysis (d) whereby the MS cycles between MS and tandem MS modes, selecting the most intense doubly or triply charged ions (most likely charge state of tryptic peptides at low pH) for fragmentation, with a specified delay time before selecting the same m/z another time. For the Cell Map project, a 1,4,5 duty cycle is employed. This corresponds to a 1 s MS scan followed by up to five tandem MS scans of 1 s each, for up to four selected ions fragmented. Shown (d) are typical MS (upper) and tandem MS (lower) scans. Each of the tandem MS are then converted into digital format by listing the m/z value and the charge state of the ion plus the m/z values and intensities of all fragmentation ions (three peaklists are shown (e)). Typically for a one-hour LC separation 400 fragmentation spectra are recorded. For database search analysis, all individual peaklists for a gel slice are concatenated into a single list. The matching of the tandem MS to tryptic peptides in a protein database involves the theoretical digestion of all proteins and fragmentation of all tryptic peptides: experimental and calculated peptide masses are first matched, within a specified mass tolerance, followed by a scoring of the matching of the experimental and theoretical fragment ions, with the best fit indicated by the highest score. Confidence levels take into account the size of the database, reflecting on the probability of a random match. The matching of experimental fragment ions (red) to the table of theoretical fragment ions (upper) and the matching of theoretical ions (black) in the tandem MS (lower) are shown (f). After matching tandem MS to peptides, the cognate proteins are tabulated. Search results for three gel slices, listing gel band identification, gi number and protein description are shown (g). Subsequently the lists of proteins are grouped into one list for the sample (gel lane), and the minimum number of proteins required to account for all peptides (matched tandem MS) is generated. The final step, generation of the fully annotated protein list (h) is the most labour-intensive step in the process. This step entails further grouping, first by BLAST analysis to gather protein isoforms into a single group followed by literature analysis to assign proper names and to eliminate database errors. Results for repeat experiments are added to the annotated jobset and updated, and data are presented/analysed on a per sample (gel lane) basis.
abundance termed redundant peptide counting is gathering validation in the field [5,7,9,10,11,12–14,15,16– 20,21]. It allows for quantitative comparison of proteins between fractions such that the relative abundance of a given protein can be determined. A protein that is synthesized in the ER and traverses the secretory pathway to be incorporated into the plasma membrane will be seen in multiple fractions. Knowing how much (in a relative Current Opinion in Cell Biology 2007, 19:376–385
sense) is seen in each fraction, it then gives rise to an intracellular distribution of each protein as deduced by proteomics [7]. Importantly, this simple way of determining relative protein abundance among sub-cellular fractions is an effective way of determining the degree of contamination. It also offers a means to identify biomarkers relevant for disease when comparing material from patient and control groups. www.sciencedirect.com
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Some of the recent additions in organellar proteomics (highlights in Figure 1 are those not described previously [2,22]) include a complete characterization of the brainderived synaptic vesicle, a near complete proteome of the rat liver endoplasmic reticulum and Golgi apparatus, a quantitative effort for plant organelles, and a characterization of endosomes, with a discovery emanating from the proteomics characterization.
Synaptic vesicles and clathrin-coated vesicles from rat brain Using classical methods to isolate synaptic vesicles from rat brain homogenates, Takamori et al. [23] washed vesicles in sodium carbonate at pH 11, which selects for membrane proteins [24], followed by either 1D or 2D PAGE to separate the membrane proteins by their molecular weight. After in-gel trypsinization and MS analysis, they concluded that 410 different proteins had been characterized. Although no effort was made to provide a quantitative assessment of the MS data, they pursued quantitative Western blotting using standard curves of their cognate recombinant proteins to evaluate protein abundance. Co-purification of proteins with isolated synaptic vesicles enabled the authors to conclude that 7 of the 40 Rabs and related proteins identified by MS were constituents of the synaptic vesicles and that 12 of the 16 SNAREs characterized by MS were concluded to be genuine residents. For 14 proteins quantified by Western blotting, synaptophysin, synaptobrevin II and the glutamate transporter VGlut2 were the most abundant. Taken together with their estimate of the lipid makeup of their preparations, they concluded that proteins overwhelm the surface of the synaptic vesicle with an excess of trafficking proteins to assure efficient membrane fusion reactions during synaptic transmission. This proteomics-based characterization of synaptic vesicles now enables a comparison with the previously published proteomics-based characterization of clathrincoated vesicles (CCVs), also isolated from rat brain homogenates [10]. These investigators used redundant peptide counting [10,14] to test the hypothesis that clathrincoated vesicles from brain have as their major cargo the synaptic vesicle proteins for recycling. Indeed, of the 32 proteins that Blondeau et al. [10] classified as synaptic vesicles proteins, 31 were observed by Takamori et al. [23]. By contrast, of the 18 coat proteins, 1 endocytic accessory and 7 novel proteins that were observed by Blondeau et al. [10] and validated to be involved in endocytosis and/or clathrin trafficking, only 9 were detected by Takamori et al. [23]. Hence, isolated synaptic vesicles lack largely the key endocytic components of clathrin-coated vesicles from brain. Of further noteworthy significance, of the eight novel proteins identified by Blondeau et al. [10], seven have since been validated as new constituents of the clathrin and membrane trafwww.sciencedirect.com
ficking machinery of clathrin-coated vesicles [25,26] (PS McPherson, personal communication). This alone must be seen as a major accomplishment for proteomics.
Clathrin-coated vesicles from brain, liver and HeLa cells Further to the clathrin-coated vesicles isolated from rat brain [10] and liver [14] homogenates, Borner et al. [27] performed an isolation of clathrin-coated vesicles from HeLa cells and a ‘mock CCV’ preparation from clathrindepleted HeLa cells [27]. They wished to tackle the issue of contamination of organellar preparations in a different manner, and to identify bona fide constituents of the clathrin-coated vesicle by identifying proteins that were enriched in the clathrin-coated vesicle fraction over the ‘mock CCV’ fraction. In this way they intended to sidestep the need for highly purified organellar fractions. To detect enrichment of a protein in the CCV sample, Borner et al. [27] used iTRAQ [28]. This method requires the labelling of digested peptides from the CCV sample with one tag, and the labeling of digested peptides from the ‘mock CCV’ sample with a different tag. These tags are detectable using mass spectrometry, and enrichment of a protein is determined by the ratio of the tags to each other. Even though 1D SDS-PAGE revealed no difference between the clathrin-coated vesicles and the ‘mock CCV preparation’, remarkably, Borner et al. [27] were able to uncover new constituents of the clathrin-coated vesicle. Their use of iTRAQ enabled discernment of lowabundance proteins specifically associated with clathrincoated vesicles. Blondeau et al. [10] identified 209 proteins in their brain-derived clathrin-coated vesicles and declared that 184 were bona fide constituents. In comparison, Borner et al. [27] identified 53 proteins as bona fide constituents. Of the 53 proteins identified as residents by Borner et al. [27], 23 were identified by Blondeau et al., in brain CCVs [10] and 22 were characterized by Girard et al. [14] in CCVs from rat liver. However, within the 53 proteins, Borner et al. [27] did not detect a number of well-validated CCV proteins such as the sigma subunit of the AP-2 complex, AAK1, CVAK104 and NECAP2. Thus, although the approach by Borner et al. [27] may allow for a more stringent assessment of bona fide CCV proteins, it appears to be less sensitive to identifying CCV proteins, probably owing to the fact that the vesicles are substantially less enriched, complicating direct comparisons. Furthermore, the Blondeau et al. [10] sample from brain homogenates contained many more plasma membrane derived (AP-2 marker) CCVs than intracellular ones (AP1:AP-2 ratio was estimated to be 1:5); the CCV sample of Girard et al. [14] from liver homogenates was estimated to be 2:1 AP-1:AP-2, and the CCV preparation from HeLa cell homogenates [27] is >5:1 AP-1:AP-2, so this Current Opinion in Cell Biology 2007, 19:376–385
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may account for some of the differences. Indeed, for highly enriched CCVs isolated from rat liver and rat brain homogenates, respectively, all 34 proteins are common [10,14]. ARF1 and the Rabs 4B, 5C GTPases are candidates that define the identity of CCVs following the criteria of Behnia and Munro [1]. Indeed Rabs 4B, 5C were also characterized as CCV residents in the study of Borner et al. [27].
Endosomes and p14 Another noteworthy discovery based on an organellar proteomics effort is the elucidation of endosomal p14, originally characterized by a proteomics study of enriched endosomal fractions isolated from EpH4-mammary epithelial cell [29]. The protein, p14, was subsequently found to be a scaffold for ERK1 signalling [30,31] extending the concept of endosomes as signalling entities [32]. Most recently, mutations in the 30 -UTR of p14 were identified in four offspring of a Mennonite family. The mutation resulted in reduced expression of p14 protein, a deficiency of endosomal biogenesis, and immunodeficiency likely to be linked to a defect in secretory lysosome formation [33].
Organelle contamination Isolated organelles are well suited to proteomics since the small volume of such structures ensure that protein concentrations in the sample are high. This overcomes the dynamic range problem of current MS-based proteomics efforts [34]. This benefit, however, is counterbalanced by the problem of contamination. Dunkley et al. [35] have addressed this by an isotope tagging technique that enables the assignment of proteins to sub-cellular compartments. The method is called LOPIT (localization of organelle proteins by isotope tagging). LOPIT results in a score for any given protein that reflects its distribution within an iodixanol density gradient. This distribution is measured using isotope tagging with ICAT [35] and more recently iTRAQ [28,36,37]. The scores are calculated using partial least squares discriminant analysis where a model is built using a training set of 12 known organellar proteins. Uncharacterized proteins are localized to an organelle when their scores match those of known organellar residents. This method attempts to overcome the contamination issue, as organelles will have different but overlapping distribution
Figure 3
Morphology of isolated COPI vesicle fractions. EM and morphometry reveal a homogeneous population of vesicles of ca. 55 nm in diameter. Magnification bars are 250 nm for (a) and (b); 100 nm for (c); 50 nm for (d) and 100 nm for (e), which reveals a rare large vesicle. (f) represents the size distribution of vesicles (from Gilchrist et al. [7]). Current Opinion in Cell Biology 2007, 19:376–385
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patterns that can be discriminated from one another using this statistical approach. LOPIT analysis on fractionated endomembranes from Arabadopsis thaliana callus cultures has enabled the simultaneous localization of 527 proteins to the ER, plasma membrane, Golgi apparatus, vacuolar membrane and mitochondria/plastids. A label-free quantitative method was applied by Foster et al. [38] to map proteins of the endomembrane system of mouse liver. They used protein correlation profiling (PCP) to localize 1400 proteins to 10 sub-cellular compartments resolved by rate zonal centrifugation of mouse liver homogenates. However, the problem of contamination may have been considerable. To use the example of the Golgi apparatus, only 19 of the 67 proteins they characterized as Golgi are accepted in the literature as genuine resident Golgi proteins. The remaining 48 proteins are mostly known contaminants as based on literature analysis [7]. By contrast, Gilchrist et al. [7] used highly enriched organelles for their proteomics study. They used a redundant peptide counting method as an index of protein abundance. Of the 598 proteins they assigned to the Golgi apparatus, 405 were also found in the ER, with 193 uniquely assigned to the Golgi. A further 495 proteins were contaminants (from mitochondria, lysosomes, blood cells, nucleus, keratins, peroxisomes and trypsin). Hence, highly enriched subcellular fractions may be preferable for proteomics studies.
Testing of a hypothesis Gilchrist et al. evaluated a controversy concerning the role of COPI vesicles in membrane traffic through the Golgi. Since the existence of COPI vesicles is still debated, proteomics was then first used to validate that such vesicles could be characterized as a homogenous entity derived from the Golgi apparatus. As illustrated in Figure 3, the electron micrographs confirm that a homogenous population of COPI vesicles of ca. 55 nm in diameter could be isolated. A quantitative comparison of the protein makeup of COPI vesicles and Golgi cisternae could then be made. Here, in Figure 4, the question was whether Golgiassociated COPI vesicles were enriched in Golgi resident proteins or secretory cargo. If the latter, then COPI vesicles are likely to ferry cargo forward in a vesicular transport mechanism [47]. If COPI vesicles were diminished in secretory cargo but enriched in resident proteins, then the maturation hypothesis is more likely (Figure 4). Nine highly abundant secretory cargo proteins (Figure 5a) showed their highest concentration in Golgi cisternae (G1, G2, G3 — three different biological preparations of Golgi cisternae), with far lower concentrations in COPI vesicles (C1, C2, C3). By contrast, Golgi resident proteins (mainly type II integral membrane proteins involved in terminal sugar modifications) revealed as high or higher concentrations in COPI vesicles (C1, C2, C3) than in Figure 4
ER–phagosome controversy Further insight into organellar function emanated from the proteomics-based characterization of phagosomes isolated from macrophages [39]. Here, endoplasmic reticulum proteins, initially considered as contaminants [39], were unexpectedly validated as genuine residents of early phagosomes [40]. These findings provided a fundamental insight into the mechanism of antigen cross-presentation [41,42]. As the notion of ER being a contributor to phagocytosis has recently been challenged thereby questioning the validity of proteomics [43], a note regarding this is warranted. In this challenge, non-specific contamination was used as the main explanation for why Gagnon et al. [40] observed ER specific proteins. Using antibodies to the lumenal domain of calnexin, they showed that this ER-based marker could not be localized to any significant degree by immuno-based methods. This, in no way, excludes an ER contribution to phagocytosis. Indeed, by using antibodies to the cytoplasmic domain of calnexin, it is clear that this part of calnexin persists in phagosomes longer than its luminal domain [40]. In addition, independent and substantial confirmation of the involvement of ER in phagocytosis has recently been presented [44,45,46]. Nevertheless, more direct evidence may be necessary to address the arguments of Touret et al. [43]. www.sciencedirect.com
Cisternal maturation model. The cisternal maturation model of biosynthetic cargo progression through the Golgi apparatus states that the cisternae themselves move the cargo forward (upward arrows), and peri-Golgi COPI vesicles (curved arrows) carry Golgi resident sugar transferase enzymes in a retrograde manner so that they are not secreted to the cell surface. Current Opinion in Cell Biology 2007, 19:376–385
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Figure 5
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Golgi cisternae (G1, G2, G3). This observation is consistent with the model shown in Figure 4 but does not exclude additional roles of COPI vesicles or indeed, other means of transport.
Figures 3 and 5 are reprinted from Gilchrist et al. [7].
Concluding remarks
References and recommended reading
Efforts are being made to improve the evaluation of mass spectra and identification of peptides. As it is today, most of the time spent on a proteomics project goes into the manual work of sorting out and annotating each protein identification. Current databases have a long way to go before being user friendly as there is over representation of individual proteins and/or under representation of genes. More often than not, multiple entries are encountered for a given protein making redundant peptide counting a challenge that necessitates manual intervention. Moreover, factual annotation errors persist. The goal of reaching the CAP proteome for each organelle is still in the future but is achievable. By combining highly refined biochemical cell fractions with 1D-gelbased MS analysis, each proteome is near completion. It is likely that independent strategies are needed to achieve CAP status but proteomics goes a long way, already. What is evident is that cell fractionation and biochemistry are now back in full swing. There is a central place for these types of methodologies that will persist for some time. Even if we reach CAP status for a given proteome of a given organelle, each proteome is likely to be cell-type specific and developmentally regulated. It is here that quantitative proteomics will prove its usefulness as we predict that the relative levels of proteins present in organelles will differ between cell types, not just the makeup. This along with other cell-specific variations (i.e. post-translational modifications and isoforms) will be the important determinants in organellar functions. Organellar proteomics in combination with genetic screens (e.g. RNA silencing) with image-based localization (e.g. expression of open reading frames fused to fluorescent proteins) and organellar protein interaction networks [48] may be an effective ‘systems biology’ strategy to understand the cell.
Acknowledgements The designation CAP principle for the complete, accurate and permanent representation of proteomics data was from the elucidation of the principle as it applied to all large-scale efforts in a Gairdner Award ceremony presented by Dr Sidney Brenner in Montreal, 18 October 2004.
Supported by grants from Genome Quebec and Genome Canada for the Cell Map project, as well as the Canadian Institute for Health Research, the Canada Foundation for Innovation and the Swedish Research Council.
Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1.
Behnia R, Munro S: Organelle identity and the signposts for membrane traffic. Nature 2005, 438:597-604.
2.
Yates JR III, Gilchrist A, Howell KE, Bergeron JJ: Proteomics of organelles and large cellular structures. Nat Rev Mol Cell Biol 2005, 6:702-714.
3.
Kartberg F, Hiding J, Nilsson T: Purification of COPI vesicles. In Cell Biology: A Laboratory Handbook. Edited by Celis J, Carter N, Simons K. Academic Press; 2005:45-50.
4.
Paiement J, Young R, Roy L, Bergeron JJ: Isolation of rough and smooth membrane domains of the endoplasmic reticulum from rat liver. In Cell Biology: A Laboratory Handbook, edn 3. Edited by Celis J. 2004:41-44.
5.
Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T et al.: Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 2007, 25:125-131.
6.
HUPO Proteomics Standards Initiative on World Wide Web URL: http://psidev.sourceforge.net/.
7.
Gilchrist A, Au CE, Hiding J, Bell AW, Fernandez-Rodriguez J, Lesimple S, Nagaya H, Roy L, Gosline SJ, Hallett M et al.: Quantitative proteomics analysis of the secretory pathway. Cell 2006, 127:1265-1281.
8.
Gibbs RA, Weinstock GM, Metzker ML, Muzny DM, Sodergren EJ, Scherer S, Scott G, Steffen D, Worley KC, Burch PE et al.: Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 2004, 428:493-521.
9.
Kislinger T, Cox B, Kannan A, Chung C, Hu P, Ignatchenko A, Scott MS, Gramolini AO, Morris Q, Hallett MT et al.: Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 2006, 125:173-186.
10. Blondeau F, Ritter B, Allaire PD, Wasiak S, Girard M, Hussain NK, Angers A, Legendre-Guillemin V, Roy L, Boismenu D et al.: Tandem MS analysis of brain clathrin-coated vesicles reveals their critical involvement in synaptic vesicle recycling. Proc Natl Acad Sci USA 2004, 101:3833-3838. The first use of redundant peptide counting in a large scale project with stoichiometry of protein complexes and novel proteins uncovered. 11. Cox B, Kislinger T, Emili A: Integrating gene and protein expression data: pattern analysis and profile mining. Methods 2005, 35:303-314. The first application of heat maps to quantify and cluster redundant peptide counting. 12. Gao J, Friedrichs MS, Dongre AR, Opiteck GJ: Guidelines for the routine application of the peptide hits technique. J Am Soc Mass Spectrom 2005, 16:1231-1238.
(Figure 5 Legend ) Cargo of COPI vesicles compared to rough microsomes (RM), smooth microsomes (SM) and Golgi fractions. (a) Highly abundant biosynthetic cargo (nine proteins) were plotted over three (1, 2, 3) biological replicates of rough and smooth microsomes (RM and SM), three (1, 2, 3) Golgi (G), three (1, 2, 3) COPI (c) and three (1, 2, 3) COPI GTPgS (g) fractions, and expressed as normalized peptide counts. The maximal value (% total peptides) was set as 1.0, and all other % total peptides were normalized to the maximal value. The COPI GTPgS fractions represent a control population of COPI vesicles generated with non-hydrolysable GTP. In all cases proteins are greatly diminished in COPI vesicles (C1, C2, C3) as compared with that of the Golgi cisternae (G1, G2, G3) fractions. (b) Nine highly abundant Golgi resident proteins belonging to the protein modification category are shown and plotted as in (a). Abbreviations: PPGANTASE-T2 = UDP-N-acetyl-a D-galactosamine polypeptide N-acetylgalactosaminyltrasferase 2; MGAT1 = a-1,3-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase; SCAN-1 (UDPASE) = soluble calcium activated nucleotidase (from Gilchrist et al. [7]). www.sciencedirect.com
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13. Gao J, Opiteck GJ, Friedrichs MS, Dongre AR, Hefta SA: Changes in the protein expression of yeast as a function of carbon source. J Proteome Res 2003, 2:643-649. 14. Girard M, Allaire PD, McPherson PS, Blondeau F: Nonstoichiometric relationship between clathrin heavy and light chains revealed by quantitative comparative proteomics of clathrin-coated vesicles from brain and liver. Mol Cell Proteomics 2005, 4:1145-1154. 15. Liu H, Sadygov RG, Yates JR III: A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 2004, 76:4193-4201. The validation of the counting of tandem mass spectra (redundant peptide counting) to determine protein abundance. 16. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M: Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 2006, 127:635-648. 17. Pang JX, Ginanni N, Dongre AR, Hefta SA, Opitek GJ: Biomarker discovery in urine by proteomics. J Proteome Res 2002, 1:161169. 18. Qian WJ, Jacobs JM, Camp DG II, Monroe ME, Moore RJ, Gritsenko MA, Calvano SE, Lowry SF, Xiao W, Moldawer LL et al.: Comparative proteome analyses of human plasma following in vivo lipopolysaccharide administration using multidimensional separations coupled with tandem mass spectrometry. Proteomics 2005, 5:572-584. 19. Rappsilber J, Ryder U, Lamond AI, Mann M: Large-scale proteomic analysis of the human spliceosome. Genome Res 2002, 12:1231-1245. 20. Zybailov B, Coleman MK, Florens L, Washburn MP: Correlation of relative abundance ratios derived from peptide ion chromatograms and spectrum counting for quantitative proteomic analysis using stable isotope labeling. Anal Chem 2005, 77:6218-6224. 21. Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, Samatova NF: Detecting differential and correlated protein expression in label-free shotgun proteomics. J Proteome Res 2006, 5:2909-2918. A comparison of redundant peptide counting (spectral counting) with other quantitative methods. 22. Brunet S, Thibault P, Gagnon E, Kearney P, Bergeron JJ, Desjardins M: Organelle proteomics: looking at less to see more. Trends Cell Biol 2003, 13:629-638. 23. Takamori S, Holt M, Stenius K, Lemke EA, Gronborg M, Riedel D, Urlaub H, Schenck S, Brugger B, Ringler P et al.: Molecular anatomy of a trafficking organelle. Cell 2006, 127:831-846. 24. Howell KE, Palade GE: Hepatic Golgi fractions resolved into membrane and content subfractions. J Cell Biol 1982, 92:822-832. 25. Conner SD, Schmid SL: CVAK104 is a novel poly-L-lysinestimulated kinase that targets the beta2-subunit of AP2. J Biol Chem 2005, 280:21539-21544. 26. Ritter B, Blondeau F, Denisov AY, Gehring K, McPherson PS: Molecular mechanisms in clathrin-mediated membrane budding revealed through subcellular proteomics. Biochem Soc Trans 2004, 32:769-773. 27. Borner GH, Harbour M, Hester S, Lilley KS, Robinson MS: Comparative proteomics of clathrin-coated vesicles. J Cell Biol 2006, 175:571-578. 28. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S et al.: Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004, 3:1154-1169. 29. Wunderlich W, Fialka I, Teis D, Alpi A, Pfeifer A, Parton RG, Lottspeich F, Huber LA: A novel 14-kilodalton protein interacts with the mitogen-activated protein kinase scaffold mp1 on a late endosomal/lysosomal compartment. J Cell Biol 2001, 152:765-776. Current Opinion in Cell Biology 2007, 19:376–385
30. Lunin VV, Munger C, Wagner J, Ye Z, Cygler M, Sacher M: The structure of the MAPK scaffold, MP1, bound to its partner, p14. A complex with a critical role in endosomal map kinase signaling. J Biol Chem 2004, 279:23422-23430. 31. Teis D, Wunderlich W, Huber LA: Localization of the MP1-MAPK scaffold complex to endosomes is mediated by p14 and required for signal transduction. Dev Cell 2002, 3:803-814. 32. Baass PC, Di Guglielmo GM, Authier F, Posner BI, Bergeron JJ: Compartmentalized signal transduction by receptor tyrosine kinases. Trends Cell Biol 1995, 5:465-470. 33. Bohn G, Allroth A, Brandes G, Thiel J, Glocker E, Schaffer AA, Rathinam C, Taub N, Teis D, Zeidler C et al.: A novel human primary immunodeficiency syndrome caused by deficiency of the endosomal adaptor protein p14. Nat Med 2007, 13:38-45. 34. Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky JR, Resing KA, Ahn NG: Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteomics 2005, 4:1487-1502. 35. Dunkley TP, Watson R, Griffin JL, Dupree P, Lilley KS: Localization of organelle proteins by isotope tagging (LOPIT). Mol Cell Proteomics 2004, 3:1128-1134. 36. Dunkley TP, Hester S, Shadforth IP, Runions J, Weimar T, Hanton SL, Griffin JL, Bessant C, Brandizzi F, Hawes C et al.: Mapping the Arabidopsis organelle proteome. Proc Natl Acad Sci USA 2006, 103:6518-6523. Innovation of LOPIT to localize organelle proteins using isotope tagging and quantitative proteomics. 37. Sadowski P, Dunkley T, Shadforth I, Griffin J, Dupree P, Bessant C, Lilley K: Quantitative proteomic approach to study subcellular localization of membrane proteins. Nat Protoc 2006, 1:17781789. 38. Foster LJ, de Hoog CL, Zhang Y, Zhang Y, Xie X, Mootha VK, Mann M: A mammalian organelle map by protein correlation profiling. Cell 2006, 125:187-199. 39. Garin J, Diez R, Kieffer S, Dermine JF, Duclos S, Gagnon E, Sadoul R, Rondeau C, Desjardins M: The phagosome proteome: insight into phagosome functions. J Cell Biol 2001, 152:165180. 40. Gagnon E, Duclos S, Rondeau C, Chevet E, Cameron PH, SteeleMortimer O, Paiement J, Bergeron JJ, Desjardins M: Endoplasmic reticulum-mediated phagocytosis is a mechanism of entry into macrophages. Cell 2002, 110:119-131. 41. Guermonprez P, Saveanu L, Kleijmeer M, Davoust J, Van Endert P, Amigorena S: ER–phagosome fusion defines an MHC class I cross-presentation compartment in dendritic cells. Nature 2003, 425:397-402. Elucidation of a new model for antigen presentation by proteomics. 42. Houde M, Bertholet S, Gagnon E, Brunet S, Goyette G, Laplante A, Princiotta MF, Thibault P, Sacks D, Desjardins M: Phagosomes are competent organelles for antigen cross-presentation. Nature 2003, 425:402-406. Proteomics based discovery of the pathway for antigen cross presentation in phagosomes. 43. Touret N, Paroutis P, Terebiznik M, Harrison RE, Trombetta S, Pypaert M, Chow A, Jiang A, Shaw J, Yip C et al.: Quantitative and dynamic assessment of the contribution of the ER to phagosome formation. Cell 2005, 123:157-170. Criticism of organellar proteomics as a method to elucidate antigen cross presentation. 44. Ackerman AL, Giodini A, Cresswell P: A role for the endoplasmic reticulum protein retrotranslocation machinery during crosspresentation by dendritic cells. Immunity 2006, 25:607617. Support of the conclusions made by using organellar proteomics to elucidate antigen cross presentation. 45. Becker T, Volchuk A, Rothman JE: Differential use of endoplasmic reticulum membrane for phagocytosis in www.sciencedirect.com
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J774 macrophages. Proc Natl Acad Sci USA 2005, 102:4022-4026. 46. Hatsuzawa K, Tamura T, Hashimoto H, Hashimoto H, Yokoya S, Miura M, Nagaya H, Wada I: Involvement of syntaxin 18, an endoplasmic reticulum (ER)-localized SNARE protein, in ERmediated phagocytosis. Mol Biol Cell 2006, 17:3964-3977.
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47. Pelham HR, Rothman JE: The debate about transport in the Golgi — two sides of the same coin? Cell 2000, 102:713-719. 48. Stuart LM, Boulais J, Charriere GM, Hennessy EJ, Brunet S, Jutras I, Goyette G, Rondeau C, Letarte S, Huang H et al.: A systems biology analysis of the Drosophila phagosome. Nature 2007, 445:95-101.
Current Opinion in Cell Biology 2007, 19:376–385
Current Opinion in Cell Biology 2007, 19:376–385 This review comes from a themed issue on Membranes and Organelles Edited by Peter McPherson and Thierry Galli Available online 3rd August 2007 0955-0674/$ – see front matter # 2007 Elsevier Ltd. All rights reserved.
Cell map proteomics The experimental strategy of organellar proteomics, outlined in Figure 2, is composed of three main steps: sample preparation, mass spectrometric analysis and data evaluation. On the basis of experience, efforts to secure the highest enrichment for an organelle pay off handsomely in the ease of characterization by tandem mass spectrometry. Longstanding protocols exist for the enrichment of most cellular organelles (e.g. [3,4]). Nevertheless, organelle-specific enzyme activity determinations combined with quantitative Western blotting is now used routinely in combination with quantitative electron microscopy to determine relative enrichment and structural integrity. Once purified, the protein components of the organelle are separated by polyacrylamide gel electrophoresis (PAGE). Traditionally, this is done by two-dimensional (2D) PAGE followed by silver staining. The density of each spot is then a convenient measure for relative abundance of each protein when comparing the same protein-spot between gels. The main drawback with 2D PAGE is that a large proportion of the proteome will never be seen because of the current inability of most proteins to enter the first dimension or to be resolved by their isoelectric point. Moreover, recent work shows that a significant number of spots seen on the gel contain more than one protein species, sometimes up to five proteins. Though developments are being made, 2D PAGE has been overtaken by 1D SDS-PAGE. The entire sample is run in one lane that is cut into 1–2 mm slices after separation for further processing. The entire lane is processed. This allows for a representation of the proteome. Following separation by PAGE, proteins are in-gel digested with trypsin and the peptides extracted from the gel.
DOI 10.1016/j.ceb.2007.05.004
Proteomics is rapidly gaining recognition as an effective experimental strategy to determine the makeup of organelles including their function(s) and identity, with identity defined as those molecular entities (i.e. GTPases) that are the internal spatial signals that distinguish organelles from each other [1]. Here, we highlight some recent technical advances and contributions to the organellar map of the cell (Figure 1) with a focus on those occurring subsequent to the efforts reviewed by Yates et al. [2]. We also discuss whether a vision of the cell map constructed of complete, accurate and permanent (CAP) proteomes of each organelle is already becoming a reality. Current Opinion in Cell Biology 2007, 19:376–385
The mass spectrometry (MS) analysis itself has inbuilt parameters that, to a certain extent, determine the degree of success of the entire exercise. There are two uncertainties in current MS characterization of peptides derived from proteins in biological samples. First, tryptic peptides have highly different efficiencies in their ionization and propensity to ‘fly’. Recently, attempts have been made to determine the biochemical attributes that factor into these properties [5]. By examining the properties of peptides that have been observed in past and ongoing proteomics efforts, the probability of flight can be determined empirically for each proteotypic peptide. High probability peptides can then be used as markers or signature peptides for each protein enabling identification with high confidence. Second, there is a stochastic aspect to further selection of the peptide that ‘flies’ in the www.sciencedirect.com
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Figure 1
Organellar proteomics. Selected highlights updated from Yates et al. [2] and Brunet et al. [22]. New developments highlighted here include the synaptic vesicle [23], endosomes [29], the LOPIT methodology applied to endomembranes from Arabadopsis thaliana [35], and studies of the early secretory pathway of rodent liver parenchyma by Foster et al. [38] and Gilchrist et al. [7]. The remaining proteomics studies for the indicated organelles are as described in Yates et al. [2].
MS instrument for presentation to a collision cell for further fragmentation. The finite sampling time taken to select and fragment the parent tryptic peptides and assess the masses for these fragments represents the duty cycle. Sampling and fragmentation are incomplete and stochastic at present. It is, however, these MS-fragmented peptides that are used to deduce the amino acid sequence of the parent peptide. Here, the quality and frequency of ‘tandem’ mass spectra pointing to a specific peptide sequence varies between instrument platforms impacting identification and quantitation. Efforts to benchmark different MS platforms across the globe are underway through HUPO-based initiatives to circumvent this variable, for example, the HUPO proteomics standards initiative enables data comparison, exchange and verification via standards for data representation [6]. Conceivably, commercially available peptide mixes will be needed to be included in future proteomics work to validate the performance and accuracy of the instrument(s) used.
Data analysis Although the NCBI non-redundant database has several entries for each protein, this database is employed because it is comprehensive, with multiple protein isoforms represented, allowing the most complete assignment of tandem MS to peptide sequences. This is necessary to maximize the assignment of tandem MS www.sciencedirect.com
to peptide sequences but not including natural posttranslational modifications. In our experience, ca. 50% of all tandem mass spectra are assigned with greater than 95% confidence with a false positive rate of less than 1.5% [7]. Matching peptide sequences to proteins involves first a redundant list of peptide identifications, then the creation of a minimum list of proteins that accommodate all the peptides, followed by further minimization of redundant proteins via BLAST (95% identity) as well as literature analysis to remove errors in the database itself. This is especially important for poorly annotated genomes such as the rat database [8]. The proteins are assigned to functional groups (e.g. using gene ontology (GO) which is a controlled vocabulary developed by the Gene Ontology Consortium, used to describe gene products by their biological process, cellular component and molecular function) or literaturebased functional groups [7]. Transmembrane domains and signal peptide predictions are annotated and also verified by literature analysis. The final annotated listing of identified proteins takes into account redundancies in the protein database, the plethora of non-descriptive names/descriptions and errors generated by gene prediction tools [7]. The relative number of MS-fragmented peptides is further used as a basis of a quantitative method to score protein abundance. This running quantitation of their Current Opinion in Cell Biology 2007, 19:376–385
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Figure 2
Cell map proteomics. For organellar proteomics, highly enriched organelle fractions (a) are characterized by a combination of biochemical assays, Western blots and electron microscopy. The proteins are then resolved by 1D SDS-PAGE. (b) Two different amounts (25 and 75 mg protein) of rough microsomes isolated from rat liver homogenates [7], stained with Coomassie Brilliant Blue G, are shown. The stained gel is then robotically processed wherein gel slices of 1.8 mm (83 slices per gel lane) of the complete gel lane are diced into cubes (1 mm) and collected in a 96-well tray. (c) The tray is then transferred to a second robot for in-gel digestion with trypsin after reduction (with dithiothreitol) and alkylation (with iodoacetamide), and the peptides are extracted and transferred to a second 96-well tray. The extracted peptides are then analysed by in-line LC–MS employing reversed phase chromatography with acetonitrile gradients in the presence of 0.1% formic acid to resolve the peptides in the mixture. MS analysis employs data-directed analysis (d) whereby the MS cycles between MS and tandem MS modes, selecting the most intense doubly or triply charged ions (most likely charge state of tryptic peptides at low pH) for fragmentation, with a specified delay time before selecting the same m/z another time. For the Cell Map project, a 1,4,5 duty cycle is employed. This corresponds to a 1 s MS scan followed by up to five tandem MS scans of 1 s each, for up to four selected ions fragmented. Shown (d) are typical MS (upper) and tandem MS (lower) scans. Each of the tandem MS are then converted into digital format by listing the m/z value and the charge state of the ion plus the m/z values and intensities of all fragmentation ions (three peaklists are shown (e)). Typically for a one-hour LC separation 400 fragmentation spectra are recorded. For database search analysis, all individual peaklists for a gel slice are concatenated into a single list. The matching of the tandem MS to tryptic peptides in a protein database involves the theoretical digestion of all proteins and fragmentation of all tryptic peptides: experimental and calculated peptide masses are first matched, within a specified mass tolerance, followed by a scoring of the matching of the experimental and theoretical fragment ions, with the best fit indicated by the highest score. Confidence levels take into account the size of the database, reflecting on the probability of a random match. The matching of experimental fragment ions (red) to the table of theoretical fragment ions (upper) and the matching of theoretical ions (black) in the tandem MS (lower) are shown (f). After matching tandem MS to peptides, the cognate proteins are tabulated. Search results for three gel slices, listing gel band identification, gi number and protein description are shown (g). Subsequently the lists of proteins are grouped into one list for the sample (gel lane), and the minimum number of proteins required to account for all peptides (matched tandem MS) is generated. The final step, generation of the fully annotated protein list (h) is the most labour-intensive step in the process. This step entails further grouping, first by BLAST analysis to gather protein isoforms into a single group followed by literature analysis to assign proper names and to eliminate database errors. Results for repeat experiments are added to the annotated jobset and updated, and data are presented/analysed on a per sample (gel lane) basis.
abundance termed redundant peptide counting is gathering validation in the field [5,7,9,10,11,12–14,15,16– 20,21]. It allows for quantitative comparison of proteins between fractions such that the relative abundance of a given protein can be determined. A protein that is synthesized in the ER and traverses the secretory pathway to be incorporated into the plasma membrane will be seen in multiple fractions. Knowing how much (in a relative Current Opinion in Cell Biology 2007, 19:376–385
sense) is seen in each fraction, it then gives rise to an intracellular distribution of each protein as deduced by proteomics [7]. Importantly, this simple way of determining relative protein abundance among sub-cellular fractions is an effective way of determining the degree of contamination. It also offers a means to identify biomarkers relevant for disease when comparing material from patient and control groups. www.sciencedirect.com
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Some of the recent additions in organellar proteomics (highlights in Figure 1 are those not described previously [2,22]) include a complete characterization of the brainderived synaptic vesicle, a near complete proteome of the rat liver endoplasmic reticulum and Golgi apparatus, a quantitative effort for plant organelles, and a characterization of endosomes, with a discovery emanating from the proteomics characterization.
Synaptic vesicles and clathrin-coated vesicles from rat brain Using classical methods to isolate synaptic vesicles from rat brain homogenates, Takamori et al. [23] washed vesicles in sodium carbonate at pH 11, which selects for membrane proteins [24], followed by either 1D or 2D PAGE to separate the membrane proteins by their molecular weight. After in-gel trypsinization and MS analysis, they concluded that 410 different proteins had been characterized. Although no effort was made to provide a quantitative assessment of the MS data, they pursued quantitative Western blotting using standard curves of their cognate recombinant proteins to evaluate protein abundance. Co-purification of proteins with isolated synaptic vesicles enabled the authors to conclude that 7 of the 40 Rabs and related proteins identified by MS were constituents of the synaptic vesicles and that 12 of the 16 SNAREs characterized by MS were concluded to be genuine residents. For 14 proteins quantified by Western blotting, synaptophysin, synaptobrevin II and the glutamate transporter VGlut2 were the most abundant. Taken together with their estimate of the lipid makeup of their preparations, they concluded that proteins overwhelm the surface of the synaptic vesicle with an excess of trafficking proteins to assure efficient membrane fusion reactions during synaptic transmission. This proteomics-based characterization of synaptic vesicles now enables a comparison with the previously published proteomics-based characterization of clathrincoated vesicles (CCVs), also isolated from rat brain homogenates [10]. These investigators used redundant peptide counting [10,14] to test the hypothesis that clathrincoated vesicles from brain have as their major cargo the synaptic vesicle proteins for recycling. Indeed, of the 32 proteins that Blondeau et al. [10] classified as synaptic vesicles proteins, 31 were observed by Takamori et al. [23]. By contrast, of the 18 coat proteins, 1 endocytic accessory and 7 novel proteins that were observed by Blondeau et al. [10] and validated to be involved in endocytosis and/or clathrin trafficking, only 9 were detected by Takamori et al. [23]. Hence, isolated synaptic vesicles lack largely the key endocytic components of clathrin-coated vesicles from brain. Of further noteworthy significance, of the eight novel proteins identified by Blondeau et al. [10], seven have since been validated as new constituents of the clathrin and membrane trafwww.sciencedirect.com
ficking machinery of clathrin-coated vesicles [25,26] (PS McPherson, personal communication). This alone must be seen as a major accomplishment for proteomics.
Clathrin-coated vesicles from brain, liver and HeLa cells Further to the clathrin-coated vesicles isolated from rat brain [10] and liver [14] homogenates, Borner et al. [27] performed an isolation of clathrin-coated vesicles from HeLa cells and a ‘mock CCV’ preparation from clathrindepleted HeLa cells [27]. They wished to tackle the issue of contamination of organellar preparations in a different manner, and to identify bona fide constituents of the clathrin-coated vesicle by identifying proteins that were enriched in the clathrin-coated vesicle fraction over the ‘mock CCV’ fraction. In this way they intended to sidestep the need for highly purified organellar fractions. To detect enrichment of a protein in the CCV sample, Borner et al. [27] used iTRAQ [28]. This method requires the labelling of digested peptides from the CCV sample with one tag, and the labeling of digested peptides from the ‘mock CCV’ sample with a different tag. These tags are detectable using mass spectrometry, and enrichment of a protein is determined by the ratio of the tags to each other. Even though 1D SDS-PAGE revealed no difference between the clathrin-coated vesicles and the ‘mock CCV preparation’, remarkably, Borner et al. [27] were able to uncover new constituents of the clathrin-coated vesicle. Their use of iTRAQ enabled discernment of lowabundance proteins specifically associated with clathrincoated vesicles. Blondeau et al. [10] identified 209 proteins in their brain-derived clathrin-coated vesicles and declared that 184 were bona fide constituents. In comparison, Borner et al. [27] identified 53 proteins as bona fide constituents. Of the 53 proteins identified as residents by Borner et al. [27], 23 were identified by Blondeau et al., in brain CCVs [10] and 22 were characterized by Girard et al. [14] in CCVs from rat liver. However, within the 53 proteins, Borner et al. [27] did not detect a number of well-validated CCV proteins such as the sigma subunit of the AP-2 complex, AAK1, CVAK104 and NECAP2. Thus, although the approach by Borner et al. [27] may allow for a more stringent assessment of bona fide CCV proteins, it appears to be less sensitive to identifying CCV proteins, probably owing to the fact that the vesicles are substantially less enriched, complicating direct comparisons. Furthermore, the Blondeau et al. [10] sample from brain homogenates contained many more plasma membrane derived (AP-2 marker) CCVs than intracellular ones (AP1:AP-2 ratio was estimated to be 1:5); the CCV sample of Girard et al. [14] from liver homogenates was estimated to be 2:1 AP-1:AP-2, and the CCV preparation from HeLa cell homogenates [27] is >5:1 AP-1:AP-2, so this Current Opinion in Cell Biology 2007, 19:376–385
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may account for some of the differences. Indeed, for highly enriched CCVs isolated from rat liver and rat brain homogenates, respectively, all 34 proteins are common [10,14]. ARF1 and the Rabs 4B, 5C GTPases are candidates that define the identity of CCVs following the criteria of Behnia and Munro [1]. Indeed Rabs 4B, 5C were also characterized as CCV residents in the study of Borner et al. [27].
Endosomes and p14 Another noteworthy discovery based on an organellar proteomics effort is the elucidation of endosomal p14, originally characterized by a proteomics study of enriched endosomal fractions isolated from EpH4-mammary epithelial cell [29]. The protein, p14, was subsequently found to be a scaffold for ERK1 signalling [30,31] extending the concept of endosomes as signalling entities [32]. Most recently, mutations in the 30 -UTR of p14 were identified in four offspring of a Mennonite family. The mutation resulted in reduced expression of p14 protein, a deficiency of endosomal biogenesis, and immunodeficiency likely to be linked to a defect in secretory lysosome formation [33].
Organelle contamination Isolated organelles are well suited to proteomics since the small volume of such structures ensure that protein concentrations in the sample are high. This overcomes the dynamic range problem of current MS-based proteomics efforts [34]. This benefit, however, is counterbalanced by the problem of contamination. Dunkley et al. [35] have addressed this by an isotope tagging technique that enables the assignment of proteins to sub-cellular compartments. The method is called LOPIT (localization of organelle proteins by isotope tagging). LOPIT results in a score for any given protein that reflects its distribution within an iodixanol density gradient. This distribution is measured using isotope tagging with ICAT [35] and more recently iTRAQ [28,36,37]. The scores are calculated using partial least squares discriminant analysis where a model is built using a training set of 12 known organellar proteins. Uncharacterized proteins are localized to an organelle when their scores match those of known organellar residents. This method attempts to overcome the contamination issue, as organelles will have different but overlapping distribution
Figure 3
Morphology of isolated COPI vesicle fractions. EM and morphometry reveal a homogeneous population of vesicles of ca. 55 nm in diameter. Magnification bars are 250 nm for (a) and (b); 100 nm for (c); 50 nm for (d) and 100 nm for (e), which reveals a rare large vesicle. (f) represents the size distribution of vesicles (from Gilchrist et al. [7]). Current Opinion in Cell Biology 2007, 19:376–385
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patterns that can be discriminated from one another using this statistical approach. LOPIT analysis on fractionated endomembranes from Arabadopsis thaliana callus cultures has enabled the simultaneous localization of 527 proteins to the ER, plasma membrane, Golgi apparatus, vacuolar membrane and mitochondria/plastids. A label-free quantitative method was applied by Foster et al. [38] to map proteins of the endomembrane system of mouse liver. They used protein correlation profiling (PCP) to localize 1400 proteins to 10 sub-cellular compartments resolved by rate zonal centrifugation of mouse liver homogenates. However, the problem of contamination may have been considerable. To use the example of the Golgi apparatus, only 19 of the 67 proteins they characterized as Golgi are accepted in the literature as genuine resident Golgi proteins. The remaining 48 proteins are mostly known contaminants as based on literature analysis [7]. By contrast, Gilchrist et al. [7] used highly enriched organelles for their proteomics study. They used a redundant peptide counting method as an index of protein abundance. Of the 598 proteins they assigned to the Golgi apparatus, 405 were also found in the ER, with 193 uniquely assigned to the Golgi. A further 495 proteins were contaminants (from mitochondria, lysosomes, blood cells, nucleus, keratins, peroxisomes and trypsin). Hence, highly enriched subcellular fractions may be preferable for proteomics studies.
Testing of a hypothesis Gilchrist et al. evaluated a controversy concerning the role of COPI vesicles in membrane traffic through the Golgi. Since the existence of COPI vesicles is still debated, proteomics was then first used to validate that such vesicles could be characterized as a homogenous entity derived from the Golgi apparatus. As illustrated in Figure 3, the electron micrographs confirm that a homogenous population of COPI vesicles of ca. 55 nm in diameter could be isolated. A quantitative comparison of the protein makeup of COPI vesicles and Golgi cisternae could then be made. Here, in Figure 4, the question was whether Golgiassociated COPI vesicles were enriched in Golgi resident proteins or secretory cargo. If the latter, then COPI vesicles are likely to ferry cargo forward in a vesicular transport mechanism [47]. If COPI vesicles were diminished in secretory cargo but enriched in resident proteins, then the maturation hypothesis is more likely (Figure 4). Nine highly abundant secretory cargo proteins (Figure 5a) showed their highest concentration in Golgi cisternae (G1, G2, G3 — three different biological preparations of Golgi cisternae), with far lower concentrations in COPI vesicles (C1, C2, C3). By contrast, Golgi resident proteins (mainly type II integral membrane proteins involved in terminal sugar modifications) revealed as high or higher concentrations in COPI vesicles (C1, C2, C3) than in Figure 4
ER–phagosome controversy Further insight into organellar function emanated from the proteomics-based characterization of phagosomes isolated from macrophages [39]. Here, endoplasmic reticulum proteins, initially considered as contaminants [39], were unexpectedly validated as genuine residents of early phagosomes [40]. These findings provided a fundamental insight into the mechanism of antigen cross-presentation [41,42]. As the notion of ER being a contributor to phagocytosis has recently been challenged thereby questioning the validity of proteomics [43], a note regarding this is warranted. In this challenge, non-specific contamination was used as the main explanation for why Gagnon et al. [40] observed ER specific proteins. Using antibodies to the lumenal domain of calnexin, they showed that this ER-based marker could not be localized to any significant degree by immuno-based methods. This, in no way, excludes an ER contribution to phagocytosis. Indeed, by using antibodies to the cytoplasmic domain of calnexin, it is clear that this part of calnexin persists in phagosomes longer than its luminal domain [40]. In addition, independent and substantial confirmation of the involvement of ER in phagocytosis has recently been presented [44,45,46]. Nevertheless, more direct evidence may be necessary to address the arguments of Touret et al. [43]. www.sciencedirect.com
Cisternal maturation model. The cisternal maturation model of biosynthetic cargo progression through the Golgi apparatus states that the cisternae themselves move the cargo forward (upward arrows), and peri-Golgi COPI vesicles (curved arrows) carry Golgi resident sugar transferase enzymes in a retrograde manner so that they are not secreted to the cell surface. Current Opinion in Cell Biology 2007, 19:376–385
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Figure 5
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Golgi cisternae (G1, G2, G3). This observation is consistent with the model shown in Figure 4 but does not exclude additional roles of COPI vesicles or indeed, other means of transport.
Figures 3 and 5 are reprinted from Gilchrist et al. [7].
Concluding remarks
References and recommended reading
Efforts are being made to improve the evaluation of mass spectra and identification of peptides. As it is today, most of the time spent on a proteomics project goes into the manual work of sorting out and annotating each protein identification. Current databases have a long way to go before being user friendly as there is over representation of individual proteins and/or under representation of genes. More often than not, multiple entries are encountered for a given protein making redundant peptide counting a challenge that necessitates manual intervention. Moreover, factual annotation errors persist. The goal of reaching the CAP proteome for each organelle is still in the future but is achievable. By combining highly refined biochemical cell fractions with 1D-gelbased MS analysis, each proteome is near completion. It is likely that independent strategies are needed to achieve CAP status but proteomics goes a long way, already. What is evident is that cell fractionation and biochemistry are now back in full swing. There is a central place for these types of methodologies that will persist for some time. Even if we reach CAP status for a given proteome of a given organelle, each proteome is likely to be cell-type specific and developmentally regulated. It is here that quantitative proteomics will prove its usefulness as we predict that the relative levels of proteins present in organelles will differ between cell types, not just the makeup. This along with other cell-specific variations (i.e. post-translational modifications and isoforms) will be the important determinants in organellar functions. Organellar proteomics in combination with genetic screens (e.g. RNA silencing) with image-based localization (e.g. expression of open reading frames fused to fluorescent proteins) and organellar protein interaction networks [48] may be an effective ‘systems biology’ strategy to understand the cell.
Acknowledgements The designation CAP principle for the complete, accurate and permanent representation of proteomics data was from the elucidation of the principle as it applied to all large-scale efforts in a Gairdner Award ceremony presented by Dr Sidney Brenner in Montreal, 18 October 2004.
Supported by grants from Genome Quebec and Genome Canada for the Cell Map project, as well as the Canadian Institute for Health Research, the Canada Foundation for Innovation and the Swedish Research Council.
Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1.
Behnia R, Munro S: Organelle identity and the signposts for membrane traffic. Nature 2005, 438:597-604.
2.
Yates JR III, Gilchrist A, Howell KE, Bergeron JJ: Proteomics of organelles and large cellular structures. Nat Rev Mol Cell Biol 2005, 6:702-714.
3.
Kartberg F, Hiding J, Nilsson T: Purification of COPI vesicles. In Cell Biology: A Laboratory Handbook. Edited by Celis J, Carter N, Simons K. Academic Press; 2005:45-50.
4.
Paiement J, Young R, Roy L, Bergeron JJ: Isolation of rough and smooth membrane domains of the endoplasmic reticulum from rat liver. In Cell Biology: A Laboratory Handbook, edn 3. Edited by Celis J. 2004:41-44.
5.
Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T et al.: Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 2007, 25:125-131.
6.
HUPO Proteomics Standards Initiative on World Wide Web URL: http://psidev.sourceforge.net/.
7.
Gilchrist A, Au CE, Hiding J, Bell AW, Fernandez-Rodriguez J, Lesimple S, Nagaya H, Roy L, Gosline SJ, Hallett M et al.: Quantitative proteomics analysis of the secretory pathway. Cell 2006, 127:1265-1281.
8.
Gibbs RA, Weinstock GM, Metzker ML, Muzny DM, Sodergren EJ, Scherer S, Scott G, Steffen D, Worley KC, Burch PE et al.: Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 2004, 428:493-521.
9.
Kislinger T, Cox B, Kannan A, Chung C, Hu P, Ignatchenko A, Scott MS, Gramolini AO, Morris Q, Hallett MT et al.: Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 2006, 125:173-186.
10. Blondeau F, Ritter B, Allaire PD, Wasiak S, Girard M, Hussain NK, Angers A, Legendre-Guillemin V, Roy L, Boismenu D et al.: Tandem MS analysis of brain clathrin-coated vesicles reveals their critical involvement in synaptic vesicle recycling. Proc Natl Acad Sci USA 2004, 101:3833-3838. The first use of redundant peptide counting in a large scale project with stoichiometry of protein complexes and novel proteins uncovered. 11. Cox B, Kislinger T, Emili A: Integrating gene and protein expression data: pattern analysis and profile mining. Methods 2005, 35:303-314. The first application of heat maps to quantify and cluster redundant peptide counting. 12. Gao J, Friedrichs MS, Dongre AR, Opiteck GJ: Guidelines for the routine application of the peptide hits technique. J Am Soc Mass Spectrom 2005, 16:1231-1238.
(Figure 5 Legend ) Cargo of COPI vesicles compared to rough microsomes (RM), smooth microsomes (SM) and Golgi fractions. (a) Highly abundant biosynthetic cargo (nine proteins) were plotted over three (1, 2, 3) biological replicates of rough and smooth microsomes (RM and SM), three (1, 2, 3) Golgi (G), three (1, 2, 3) COPI (c) and three (1, 2, 3) COPI GTPgS (g) fractions, and expressed as normalized peptide counts. The maximal value (% total peptides) was set as 1.0, and all other % total peptides were normalized to the maximal value. The COPI GTPgS fractions represent a control population of COPI vesicles generated with non-hydrolysable GTP. In all cases proteins are greatly diminished in COPI vesicles (C1, C2, C3) as compared with that of the Golgi cisternae (G1, G2, G3) fractions. (b) Nine highly abundant Golgi resident proteins belonging to the protein modification category are shown and plotted as in (a). Abbreviations: PPGANTASE-T2 = UDP-N-acetyl-a D-galactosamine polypeptide N-acetylgalactosaminyltrasferase 2; MGAT1 = a-1,3-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase; SCAN-1 (UDPASE) = soluble calcium activated nucleotidase (from Gilchrist et al. [7]). www.sciencedirect.com
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13. Gao J, Opiteck GJ, Friedrichs MS, Dongre AR, Hefta SA: Changes in the protein expression of yeast as a function of carbon source. J Proteome Res 2003, 2:643-649. 14. Girard M, Allaire PD, McPherson PS, Blondeau F: Nonstoichiometric relationship between clathrin heavy and light chains revealed by quantitative comparative proteomics of clathrin-coated vesicles from brain and liver. Mol Cell Proteomics 2005, 4:1145-1154. 15. Liu H, Sadygov RG, Yates JR III: A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 2004, 76:4193-4201. The validation of the counting of tandem mass spectra (redundant peptide counting) to determine protein abundance. 16. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M: Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 2006, 127:635-648. 17. Pang JX, Ginanni N, Dongre AR, Hefta SA, Opitek GJ: Biomarker discovery in urine by proteomics. J Proteome Res 2002, 1:161169. 18. Qian WJ, Jacobs JM, Camp DG II, Monroe ME, Moore RJ, Gritsenko MA, Calvano SE, Lowry SF, Xiao W, Moldawer LL et al.: Comparative proteome analyses of human plasma following in vivo lipopolysaccharide administration using multidimensional separations coupled with tandem mass spectrometry. Proteomics 2005, 5:572-584. 19. Rappsilber J, Ryder U, Lamond AI, Mann M: Large-scale proteomic analysis of the human spliceosome. Genome Res 2002, 12:1231-1245. 20. Zybailov B, Coleman MK, Florens L, Washburn MP: Correlation of relative abundance ratios derived from peptide ion chromatograms and spectrum counting for quantitative proteomic analysis using stable isotope labeling. Anal Chem 2005, 77:6218-6224. 21. Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, Samatova NF: Detecting differential and correlated protein expression in label-free shotgun proteomics. J Proteome Res 2006, 5:2909-2918. A comparison of redundant peptide counting (spectral counting) with other quantitative methods. 22. Brunet S, Thibault P, Gagnon E, Kearney P, Bergeron JJ, Desjardins M: Organelle proteomics: looking at less to see more. Trends Cell Biol 2003, 13:629-638. 23. Takamori S, Holt M, Stenius K, Lemke EA, Gronborg M, Riedel D, Urlaub H, Schenck S, Brugger B, Ringler P et al.: Molecular anatomy of a trafficking organelle. Cell 2006, 127:831-846. 24. Howell KE, Palade GE: Hepatic Golgi fractions resolved into membrane and content subfractions. J Cell Biol 1982, 92:822-832. 25. Conner SD, Schmid SL: CVAK104 is a novel poly-L-lysinestimulated kinase that targets the beta2-subunit of AP2. J Biol Chem 2005, 280:21539-21544. 26. Ritter B, Blondeau F, Denisov AY, Gehring K, McPherson PS: Molecular mechanisms in clathrin-mediated membrane budding revealed through subcellular proteomics. Biochem Soc Trans 2004, 32:769-773. 27. Borner GH, Harbour M, Hester S, Lilley KS, Robinson MS: Comparative proteomics of clathrin-coated vesicles. J Cell Biol 2006, 175:571-578. 28. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S et al.: Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004, 3:1154-1169. 29. Wunderlich W, Fialka I, Teis D, Alpi A, Pfeifer A, Parton RG, Lottspeich F, Huber LA: A novel 14-kilodalton protein interacts with the mitogen-activated protein kinase scaffold mp1 on a late endosomal/lysosomal compartment. J Cell Biol 2001, 152:765-776. Current Opinion in Cell Biology 2007, 19:376–385
30. Lunin VV, Munger C, Wagner J, Ye Z, Cygler M, Sacher M: The structure of the MAPK scaffold, MP1, bound to its partner, p14. A complex with a critical role in endosomal map kinase signaling. J Biol Chem 2004, 279:23422-23430. 31. Teis D, Wunderlich W, Huber LA: Localization of the MP1-MAPK scaffold complex to endosomes is mediated by p14 and required for signal transduction. Dev Cell 2002, 3:803-814. 32. Baass PC, Di Guglielmo GM, Authier F, Posner BI, Bergeron JJ: Compartmentalized signal transduction by receptor tyrosine kinases. Trends Cell Biol 1995, 5:465-470. 33. Bohn G, Allroth A, Brandes G, Thiel J, Glocker E, Schaffer AA, Rathinam C, Taub N, Teis D, Zeidler C et al.: A novel human primary immunodeficiency syndrome caused by deficiency of the endosomal adaptor protein p14. Nat Med 2007, 13:38-45. 34. Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky JR, Resing KA, Ahn NG: Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteomics 2005, 4:1487-1502. 35. Dunkley TP, Watson R, Griffin JL, Dupree P, Lilley KS: Localization of organelle proteins by isotope tagging (LOPIT). Mol Cell Proteomics 2004, 3:1128-1134. 36. Dunkley TP, Hester S, Shadforth IP, Runions J, Weimar T, Hanton SL, Griffin JL, Bessant C, Brandizzi F, Hawes C et al.: Mapping the Arabidopsis organelle proteome. Proc Natl Acad Sci USA 2006, 103:6518-6523. Innovation of LOPIT to localize organelle proteins using isotope tagging and quantitative proteomics. 37. Sadowski P, Dunkley T, Shadforth I, Griffin J, Dupree P, Bessant C, Lilley K: Quantitative proteomic approach to study subcellular localization of membrane proteins. Nat Protoc 2006, 1:17781789. 38. Foster LJ, de Hoog CL, Zhang Y, Zhang Y, Xie X, Mootha VK, Mann M: A mammalian organelle map by protein correlation profiling. Cell 2006, 125:187-199. 39. Garin J, Diez R, Kieffer S, Dermine JF, Duclos S, Gagnon E, Sadoul R, Rondeau C, Desjardins M: The phagosome proteome: insight into phagosome functions. J Cell Biol 2001, 152:165180. 40. Gagnon E, Duclos S, Rondeau C, Chevet E, Cameron PH, SteeleMortimer O, Paiement J, Bergeron JJ, Desjardins M: Endoplasmic reticulum-mediated phagocytosis is a mechanism of entry into macrophages. Cell 2002, 110:119-131. 41. Guermonprez P, Saveanu L, Kleijmeer M, Davoust J, Van Endert P, Amigorena S: ER–phagosome fusion defines an MHC class I cross-presentation compartment in dendritic cells. Nature 2003, 425:397-402. Elucidation of a new model for antigen presentation by proteomics. 42. Houde M, Bertholet S, Gagnon E, Brunet S, Goyette G, Laplante A, Princiotta MF, Thibault P, Sacks D, Desjardins M: Phagosomes are competent organelles for antigen cross-presentation. Nature 2003, 425:402-406. Proteomics based discovery of the pathway for antigen cross presentation in phagosomes. 43. Touret N, Paroutis P, Terebiznik M, Harrison RE, Trombetta S, Pypaert M, Chow A, Jiang A, Shaw J, Yip C et al.: Quantitative and dynamic assessment of the contribution of the ER to phagosome formation. Cell 2005, 123:157-170. Criticism of organellar proteomics as a method to elucidate antigen cross presentation. 44. Ackerman AL, Giodini A, Cresswell P: A role for the endoplasmic reticulum protein retrotranslocation machinery during crosspresentation by dendritic cells. Immunity 2006, 25:607617. Support of the conclusions made by using organellar proteomics to elucidate antigen cross presentation. 45. Becker T, Volchuk A, Rothman JE: Differential use of endoplasmic reticulum membrane for phagocytosis in www.sciencedirect.com
Organellar proteomics to create the cell map Au et al. 385
J774 macrophages. Proc Natl Acad Sci USA 2005, 102:4022-4026. 46. Hatsuzawa K, Tamura T, Hashimoto H, Hashimoto H, Yokoya S, Miura M, Nagaya H, Wada I: Involvement of syntaxin 18, an endoplasmic reticulum (ER)-localized SNARE protein, in ERmediated phagocytosis. Mol Biol Cell 2006, 17:3964-3977.
www.sciencedirect.com
47. Pelham HR, Rothman JE: The debate about transport in the Golgi — two sides of the same coin? Cell 2000, 102:713-719. 48. Stuart LM, Boulais J, Charriere GM, Hennessy EJ, Brunet S, Jutras I, Goyette G, Rondeau C, Letarte S, Huang H et al.: A systems biology analysis of the Drosophila phagosome. Nature 2007, 445:95-101.
Current Opinion in Cell Biology 2007, 19:376–385