Application of Proteomic Marker Ensembles to Subcellular Organelle Identification
2009; Elsevier BV; Volume: 9; Issue: 2 Linguagem: Inglês
10.1074/mcp.m900432-mcp200
ISSN1535-9484
AutoresAlexander Y. Andreyev, Zhouxin Shen, Ziqiang Guan, Andrea C. Ryan, Eoin Fahy, Shankar Subramaniam, Christian R.H. Raetz, Steven P. Briggs, Edward A. Dennis,
Tópico(s)Mitochondrial Function and Pathology
ResumoCompartmentalization of biological processes and the associated cellular components is crucial for cell function. Typically, the location of a component is revealed through a co-localization and/or co-purification with an organelle marker. Therefore, the identification of reliable markers is critical for a thorough understanding of cellular function and dysfunction. We fractionated macrophage-like RAW264.7 cells, both in the resting and endotoxin-activated states, into six fractions representing the major organelles/compartments: nuclei, mitochondria, cytoplasm, endoplasmic reticulum, and plasma membrane as well as an additional dense microsomal fraction. The identity of the first five of these fractions was confirmed via the distribution of conventional enzymatic markers. Through a quantitative liquid chromatography/mass spectrometry-based proteomics analysis of the fractions, we identified 50-member ensembles of marker proteins ("marker ensembles") specific for each of the corresponding organelles/compartments. Our analysis attributed 206 of the 250 marker proteins (∼82%) to organelles that are consistent with the location annotations in the public domain (obtained using DAVID 2008, EntrezGene, Swiss-Prot, and references therein). Moreover, we were able to correct locations for a subset of the remaining proteins, thus proving the superior power of analysis using multiple organelles as compared with an analysis using one specific organelle. The marker ensembles were used to calculate the organelle composition of the six above mentioned subcellular fractions. Knowledge of the precise composition of these fractions can be used to calculate the levels of metabolites in the pure organelles. As a proof of principle, we applied these calculations to known mitochondria-specific lipids (cardiolipins and ubiquinones) and demonstrated their exclusive mitochondrial location. We speculate that the organelle-specific protein ensembles may be used to systematically redefine originally morphologically defined organelles as biochemical entities. Compartmentalization of biological processes and the associated cellular components is crucial for cell function. Typically, the location of a component is revealed through a co-localization and/or co-purification with an organelle marker. Therefore, the identification of reliable markers is critical for a thorough understanding of cellular function and dysfunction. We fractionated macrophage-like RAW264.7 cells, both in the resting and endotoxin-activated states, into six fractions representing the major organelles/compartments: nuclei, mitochondria, cytoplasm, endoplasmic reticulum, and plasma membrane as well as an additional dense microsomal fraction. The identity of the first five of these fractions was confirmed via the distribution of conventional enzymatic markers. Through a quantitative liquid chromatography/mass spectrometry-based proteomics analysis of the fractions, we identified 50-member ensembles of marker proteins ("marker ensembles") specific for each of the corresponding organelles/compartments. Our analysis attributed 206 of the 250 marker proteins (∼82%) to organelles that are consistent with the location annotations in the public domain (obtained using DAVID 2008, EntrezGene, Swiss-Prot, and references therein). Moreover, we were able to correct locations for a subset of the remaining proteins, thus proving the superior power of analysis using multiple organelles as compared with an analysis using one specific organelle. The marker ensembles were used to calculate the organelle composition of the six above mentioned subcellular fractions. Knowledge of the precise composition of these fractions can be used to calculate the levels of metabolites in the pure organelles. As a proof of principle, we applied these calculations to known mitochondria-specific lipids (cardiolipins and ubiquinones) and demonstrated their exclusive mitochondrial location. We speculate that the organelle-specific protein ensembles may be used to systematically redefine originally morphologically defined organelles as biochemical entities. One of the basic concepts of cell biology is compartmentalization of the cellular processes within subcellular structures, termed organelles. Organelles were originally identified in the 19th century as the morphological entities that are still reflected in their names (e.g. "nucleus" from the Latin "little nut," "mitochondria" from the Greek "thread" + "grain," or "reticulum" from the Latin "little net"). Later, the progress of biochemistry made it possible to assign to the various organelles their specific biological functions. Thus, detailed information about the location of biochemical reactions became crucial for the understanding of their roles in cell function or dysfunction. Current technology allows the location of a cell component (a protein or a metabolite) to be linked directly to a morphologically defined organelle (or even a suborganellar compartment) by using electron microscopy. However, more typically, the location of a component is determined on the basis of its co-localization with a known marker for the organelle or subcellular compartment. This co-localization can be either visualized microscopically (imaging approach) to preserve some degree of morphological information or determined through co-purification of the component and the marker in a subcellular fractionation (biochemical approach). For both the imaging and the biochemical approaches, optimal organelle markers are of the utmost importance. Conventional markers include proteins, DNA (for nucleus), and even physical/chemical parameters (electric potential for mitochondria and acidic pH for lysosomes). Protein markers are assayed using either an interaction with specific antibodies or their enzymatic activities. Unfortunately, the former is typically non-quantitative, whereas the latter, although semiquantitative, is subject to interference from multiple parameters of the environment as well as substrate and product sharing with non-marker proteins. For a biochemical approach, tightness of the anchoring of a marker to the corresponding organelle is also an issue. Moreover, an inherent problem is that most proteins are located in several organelles/compartments, which may result in false localization conclusions. Our goal was to identify specific, reliable, and universal protein markers for major subcellular organelles/compartments. The following principles were chosen as the basis for our approach. First, the search had to be conducted without a preconceived notion of the nature of the markers (e.g. we did not expect to necessarily confirm conventional markers as optimal). Second, the search had to be conducted in all major organelles/compartments simultaneously. Third, the aim was to identify relatively large panels (ensembles) of markers as opposed to the best single marker. The last two principles allowed us to address the problem of multiple locations of potential marker proteins. Some of them can be eliminated as markers; for others, the impact of multiple locations on further analysis can be negated by averaging of the data for large numbers of proteins (derivation of marker ensembles). To meet these goals, we performed a complete "quantitative" proteomics analysis of all major subcellular fractions in a single cell type. Numerous reports have focused on the proteomes of specific organelles or interrelated sets of organelles in various cell types (for reviews, see Refs. 1Andersen J.S. Mann M. Organellar proteomics: turning inventories into insights.EMBO Rep. 2006; 7: 874-879Crossref PubMed Scopus (166) Google Scholar and 2Yates 3rd, J.R. Gilchrist A. Howell K.E. Bergeron J.J. Proteomics of organelles and large cellular structures.Nat. Rev. Mol. Cell Biol. 2005; 6: 702-714Crossref PubMed Scopus (348) Google Scholar). However, a need for an integral systematic study in a single cell type has been evident for some time (2Yates 3rd, J.R. Gilchrist A. Howell K.E. Bergeron J.J. Proteomics of organelles and large cellular structures.Nat. Rev. Mol. Cell Biol. 2005; 6: 702-714Crossref PubMed Scopus (348) Google Scholar), and the present study is the first step aimed at addressing this need. The marker ensembles that we identified from the proteome data were used to quantify the composition of the subcellular fractions. It is becoming appreciated that a physical association of various organelles makes it next to impossible to completely separate the organelles and obtain pure fractions acceptable for detailed proteomics analysis (e.g. see Ref. 3Forner F. Foster L.J. Campanaro S. Valle G. Mann M. Quantitative proteomic comparison of rat mitochondria from muscle, heart, and liver.Mol. Cell. Proteomics. 2006; 5: 608-619Abstract Full Text Full Text PDF PubMed Scopus (247) Google Scholar). Therefore, correlative approaches such as protein correlation profiling (1Andersen J.S. Mann M. Organellar proteomics: turning inventories into insights.EMBO Rep. 2006; 7: 874-879Crossref PubMed Scopus (166) Google Scholar, 3Forner F. Foster L.J. Campanaro S. Valle G. Mann M. Quantitative proteomic comparison of rat mitochondria from muscle, heart, and liver.Mol. Cell. Proteomics. 2006; 5: 608-619Abstract Full Text Full Text PDF PubMed Scopus (247) Google Scholar, 4Andersen J.S. Wilkinson C.J. Mayor T. Mortensen P. Nigg E.A. Mann M. Proteomic characterization of the human centrosome by protein correlation profiling.Nature. 2003; 426: 570-574Crossref PubMed Scopus (1063) Google Scholar) and localization of organelle proteins by isotope tagging (5Dunkley T.P. Watson R. Griffin J.L. Dupree P. Lilley K.S. Localization of organelle proteins by isotope tagging (LOPIT).Mol. Cell. Proteomics. 2004; 3: 1128-1134Abstract Full Text Full Text PDF PubMed Scopus (283) Google Scholar, 6Sadowski P.G. Dunkley T.P. Shadforth I.P. Dupree P. Bessant C. Griffin J.L. Lilley K.S. Quantitative proteomic approach to study subcellular localization of membrane proteins.Nat. Protoc. 2006; 1: 1778-1789Crossref PubMed Scopus (72) Google Scholar) have been suggested to address this problem. These approaches allowed the assignment of protein locations based on co-localization with known markers in a density gradient (1Andersen J.S. Mann M. Organellar proteomics: turning inventories into insights.EMBO Rep. 2006; 7: 874-879Crossref PubMed Scopus (166) Google Scholar, 4Andersen J.S. Wilkinson C.J. Mayor T. Mortensen P. Nigg E.A. Mann M. Proteomic characterization of the human centrosome by protein correlation profiling.Nature. 2003; 426: 570-574Crossref PubMed Scopus (1063) Google Scholar, 5Dunkley T.P. Watson R. Griffin J.L. Dupree P. Lilley K.S. Localization of organelle proteins by isotope tagging (LOPIT).Mol. Cell. Proteomics. 2004; 3: 1128-1134Abstract Full Text Full Text PDF PubMed Scopus (283) Google Scholar, 6Sadowski P.G. Dunkley T.P. Shadforth I.P. Dupree P. Bessant C. Griffin J.L. Lilley K.S. Quantitative proteomic approach to study subcellular localization of membrane proteins.Nat. Protoc. 2006; 1: 1778-1789Crossref PubMed Scopus (72) Google Scholar) or in multiple fractions (7Gilchrist A. Au C.E. Hiding J. Bell A.W. Fernandez-Rodriguez J. Lesimple S. Nagaya H. Roy L. Gosline S.J. Hallett M. Paiement J. Kearney R.E. Nilsson T. Bergeron J.J. Quantitative proteomics analysis of the secretory pathway.Cell. 2006; 127: 1265-1281Abstract Full Text Full Text PDF PubMed Scopus (386) Google Scholar). We took this approach a step further and derived a quantitative composition of the fractions based on the distribution of the marker ensembles. Furthermore, this enabled us to calculate levels of various components (lipids and proteins) in pure organelles from experimental data obtained with less than pure fractions. The choice of a particular cell type for this study was somewhat arbitrary, and the resulting marker ensembles were optimal for the cell type for which they were generated; of course, they may have to be adjusted to be adapted for other cell types. We chose macrophage cells partly because this study was an integral part of a larger subcellular lipidomics/proteomics study under the auspices of the Lipid Metabolites and Pathways Strategy (LIPID MAPS Consortium). The macrophage plays a central role in inflammation and innate and adaptive immunity. The macrophage detects and attacks pathogens and orchestrates a host response by sending signals to other cells and tissues; in this process, the macrophage itself transits from a resting to an activated state. These two states differ vastly in function, morphology, and underlying protein expression profiles, and therefore, we aimed to identify marker ensembles that would be invariant with regard to the activation process. In the present study, the activation paradigm was treatment with Kdo2 1The abbreviations used are:Kdo2(3-deoxy-d-manno-octulosonic acid)2ERendoplasmic reticulumINTp-iodonitrotetrazolium violetCLcardiolipinSCXstrong cation exchangeRP1the first reverse phase columniTRAQisobaric tag for relative and absolute quantitationMRMmultiple reaction monitoringPQDpulsed Q dissociationIPIInternational Protein IndexFDRfalse discovery ratePDCDprogrammed cell death proteinPCNAproliferating cell nuclear antigenCOPIcoat protein complex IAIFapoptosis-inducing factor. -lipid A. This defined, nearly homogeneous reagent is a form of lipopolysaccharide endotoxin that has all the essential biological properties of lipopolysaccharide (8Raetz C.R. Garrett T.A. Reynolds C.M. Shaw W.A. Moore J.D. Smith Jr., D.C. Ribeiro A.A. Murphy R.C. Ulevitch R.J. Fearns C. Reichart D. Glass C.K. Benner C. Subramaniam S. Harkewicz R. Bowers-Gentry R.C. Buczynski M.W. Cooper J.A. Deems R.A. Dennis E.A. Kdo2-Lipid A of Escherichia coli, a defined endotoxin that activates macrophages via TLR-4.J. Lipid Res. 2006; 47: 1097-1111Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar). (3-deoxy-d-manno-octulosonic acid)2 endoplasmic reticulum p-iodonitrotetrazolium violet cardiolipin strong cation exchange the first reverse phase column isobaric tag for relative and absolute quantitation multiple reaction monitoring pulsed Q dissociation International Protein Index false discovery rate programmed cell death protein proliferating cell nuclear antigen coat protein complex I apoptosis-inducing factor. RAW264.7 cells were from ATCC (catalog number TIB-71). Dulbecco's modified Eagle's medium (catalog number 10-013) and Dulbecco's PBS (catalog number 21-031-CV) were from Mediatech. Fetal calf serum with low endotoxin content was from Hyclone (SH30071.03 ANG19242). Kdo2-lipid A was obtained from Avanti Polar Lipids. Iodixanol (OptiPrepTM from Axis-Shield) was obtained through Sigma-Aldrich. The Quant-iTTM DNA assay kit and Vybrant® cytotoxicity assay kit were from Invitrogen. Tris(2-carboxyethyl)phosphine and iodoacetamide were from Fisher (catalog numbers AC36383 and AC12227, respectively). Trypsin was from Roche Applied Science (catalog number 03 708 969 001). Potassium cyanide, EGTA, and magnesium chloride from Fluka were obtained through Sigma. Solvents were chromatography grade and purchased from OmniSolv. All other reagents/kits were from Sigma-Aldrich. All aqueous solutions were prepared using distilled deionized water (catalog number 25-055-CV) from Mediatech. Isolation media were prepared K+- and Na+-free; pH was adjusted by Tris base (Trizma). Three separate cultures of both resting and activated macrophages were generated for subsequent proteomics and lipidomics analyses. The replicates were started 1 week apart to reflect biological variability in its entirety as followed from our error analysis of eicosanoid production (data not shown). A schematic outline of our procedure is shown in Fig. 1. RAW264.7 mouse macrophage-derived cells were maintained between passages 4 and 24 at 37 °C and 10% CO2. The medium was composed of high glucose- and l-glutamine-containing Dulbecco's modified Eagle's medium supplemented with 10% heat-inactivated fetal calf serum, 100 units/ml penicillin, and 100 µg/ml streptomycin. For an experiment, five T-150 flasks of the cells were plated at a density of 36 × 106 cells/flask in 24 ml of the same medium. At 24 h after plating, they were treated (or left untreated) with 100 ng/ml Kdo2-lipid A for another 24 h followed by subcellular fractionation. The cultured medium was removed and used for eicosanoid analysis of Kdo2-lipid A-treated versus control cells as described elsewhere (9Buczynski M.W. Stephens D.L. Bowers-Gentry R.C. Grkovich A. Deems R.A. Dennis E.A. TLR-4 and sustained calcium agonists synergistically produce eicosanoids independent of protein synthesis in RAW264.7 cells.J. Biol. Chem. 2007; 282: 22834-22847Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar) to confirm their activated state. In these experiments, eicosanoid profiles and levels were consistent with the results of whole cell experiments (9Buczynski M.W. Stephens D.L. Bowers-Gentry R.C. Grkovich A. Deems R.A. Dennis E.A. TLR-4 and sustained calcium agonists synergistically produce eicosanoids independent of protein synthesis in RAW264.7 cells.J. Biol. Chem. 2007; 282: 22834-22847Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar) (data not shown). The cells were harvested by scraping in Dulbecco's PBS (total of 35 ml), pelleted at 200 × g for 7 min, resuspended in 35 ml of the isolation medium (250 mm sucrose, 10 mm HEPES-Tris, pH 7.4, 1 mm EGTA-Tris), and pelleted again to remove salts. For effective homogenization, the cells were subjected to mild osmotic shock by resuspending in 35 ml of slightly hypotonic medium (same as the isolation medium above but with only 100 mm sucrose) and pelleted. The supernatant was set aside; the cell pellet was carefully transferred into a 7-ml glass Dounce homogenizer, homogenized in 10 ml of the supernatant by 40 strokes of the tight fitting pestle, and recombined with the supernatant. The osmotic shock and the details of homogenization are essential for effective cell lysis, organelle separation, and the final yield. The homogenate was brought to an isotonic state by the addition of 3.2 ml of the hypertonic medium (same as the isolation medium above but with 1.78 m sucrose) and supplemented with 2 mm MgCl2, essential for preservation of the nuclei throughout the preparation. Differential centrifugation parameters were as follows: 200 × g for 10 min to pellet the nuclei/unbroken cells (the initial "nuclear" pellet), 5,000 × g for 10 min to pellet the mitochondria, and 100,000 × g for 1 h to pellet the microsomes. Postnuclear and postmitochondrial supernatants were additionally spun at 300 × g and 5,000 × g for 10 min, respectively, to additionally remove residual nuclei and mitochondria, respectively. The initial nuclear and mitochondrial pellets were additionally washed by resuspending/pelleting in Mg2+-containing and Mg2+-free media, respectively. The supernatant from the 100,000 × g spin was retained as the cytosolic fraction. The nuclear, mitochondrial, and microsomal pellets were additionally separated in the stepwise gradients of iodixanol in an SW-41 bucket rotor. All gradient media were prepared according to the manufacturer's instructions based on the isolation medium above; the media for the nuclear preparation were supplemented with 5 mm MgCl2. Nuclei were purified according to the manufacturer-suggested protocol; briefly, the nuclear pellet was brought to 25% iodixanol (12 ml), the iodixanol gradient was built from the bottom up in three 12-ml tubes (4 ml of 10%, 4 ml of nuclei in 25%, 2.5 ml of 30%, and 1.5 ml of 35%) and spun at 10,000 × g for 20 min. Nuclei banded at the 30/35% interface. The mitochondrial and microsomal pellets were resuspended in the isolation medium, brought to 35% iodixanol (6 ml), and fractionated by flotation for 2 h at 50,000 × g in three 12-ml tubes each. The following iodixanol gradient was used: 2 ml of 10%, 4 ml of 17.5%, 4 ml of 25%, and 2 ml of the corresponding pellet resuspended in the 35% iodixanol. Mitochondria banded at the 17.5/25% interface; plasma membrane and the ER banded at 10/17.5 and 17.5/25% interfaces, respectively. The third fraction originating from the microsomal pellet banded at the most dense 25/35% interface and was termed "dense microsomes." All samples were frozen and stored at −80 °C. Proteomics analysis of each of the three biological replicates was performed in duplicate using the quadruplex or octuplex iTRAQTM (Applied Biosystems, Foster City, CA) approach as follows. For duplicates, the quartets of samples for each iTRAQ run were permuted to enable either direct or indirect calculations of all possible sample-to-sample ratios. TCA was added to samples to a final concentration of 15% (w/v) to precipitate proteins. Samples were incubated at 4 °C for 2 h and then spun down in a refrigerated centrifuge at 4,000 × g for 15 min. The supernatant was discarded. Protein pellets were solubilized in 1 ml of 0.1% RapiGest (Waters) and 75 mm HEPES buffer, pH 7.0. Cysteines were reduced and alkylated using 1 mm tris(2-carboxyethyl)phosphine at 95 °C for 5 min followed by 2.5 mm iodoacetamide at 37 °C in the dark for 15 min. Proteins were digested with trypsin at an enzyme-to-substrate ratio (w/w) of 1:50 overnight. For iTRAQ derivatization, an aliquot of each digested sample (100 µg of total protein) was treated with one tube of one of the iTRAQ reagents in 70% isopropanol at pH 7.2 for 2 h at room temperature. Labeled samples were dried down in a vacuum concentrator. 100 µl of water was added to each tube to dissolve the peptides. Samples tagged with four different iTRAQ reagents were pooled together. 1% TFA, pH 1.4 was added to precipitate RapiGest. Samples were incubated at 4 °C overnight and then centrifuged at 16,100 × g for 15 min. Supernatant was collected and centrifuged through a 0.22-µm filter and was used for LC-MS/MS analysis. iTRAQ labeling efficiency was calculated by searching the MS/MS data, specifying four possible iTRAQ modifications: 1) fully labeled, 2) N terminus-labeled only, 3) lysine-labeled only, and 4) non-labeled. Using the above protocol, we obtained higher than 95% iTRAQ labeling efficiency for all data sets. An Agilent 1100 HPLC system (Agilent Technologies, Santa Clara, CA) delivered a flow rate of 300 nl/min to a three-phase capillary chromatography column through a splitter. Using a custom pressure cell, 5-µm Zorbax SB-C18 (Agilent) was packed into fused silica capillary tubing (200-µm inner diameter, 360-µm outer diameter, 20 cm long) to form the first reverse phase column (RP1). A 5-cm-long strong cation exchange (SCX) column packed with 5-µm polysulfoethyl (PolyLC, Inc.) was connected to RP1 using a zero dead volume 1-µm filter (Upchurch, M548) attached to the exit of the RP1 column. A fused silica capillary (100-µm inner diameter, 360-µm outer diameter, 20 cm long) packed with 5-µm Zorbax SB-C18 (Agilent) was connected to the SCX column as the analytical column (the second reverse phase column; Fig. 2). The electrospray tip of the fused silica tubing was pulled to a sharp tip with the inner diameter smaller than 1 µm using a laser puller (Sutter P-2000). The peptide mixtures were loaded onto the RP1 using the custom pressure cell. Columns were not reused. Peptides were first eluted from the RP1 to the SCX column using a 0–80% acetonitrile gradient for 150 min. The peptides were fractionated by the SCX column using a series of salt gradients (from 10 mm to 1 m ammonium acetate for 20 min) followed by high resolution reverse phase separation using an acetonitrile gradient of 0–80% for 120 min (Fig. 2). Typically, it takes 4 days (38 salt fractions) for each full proteome analysis. Spectra were acquired using an LTQ linear ion trap tandem mass spectrometer (Thermo Electron Corp., San Jose, CA) using automated, data-dependent acquisition. The mass spectrometer was operated in positive ion mode with a source temperature of 150 °C. The full MS scan range of 400–2,000 m/z was divided into three smaller scan ranges (400–800, 800–1,050, and 1,050–2,000 m/z) to improve the dynamic range. Both CID and pulsed Q dissociation (PQD) scans of the same parent ion were collected for protein identification and quantitation. Each MS scan was followed by four pairs of CID-PQD MS/MS scans of the most intense ions from the parent MS scan. A dynamic exclusion of 1 min was used to improve the duty cycle of MS/MS scans. About 20,000 MS/MS spectra were collected for each salt step fractionation. The raw data were extracted and searched using Spectrum Mill v3.03 (Agilent). The CID and PQD scans from the same parent ion were merged together. MS/MS spectra with a sequence tag length of 1 or less were considered to be poor spectra and were discarded. The rest of the MS/MS spectra were searched against the International Protein Index (IPI) mouse database (v3.31, 56,555 protein sequences). The enzyme parameter was limited to fully tryptic peptides with a maximum miscleavage of 1. All other search parameters were set to the default settings of Spectrum Mill (carbamidomethylation of cysteines, iTRAQ modification, ±2.5 Da for precursor ions, ±0.7 Da for fragment ions, and a minimum matched peak intensity (scored peak intensity) of 50%). A concatenated forward-reverse database was constructed to calculate the in situ false discovery rate (FDR). The total number of protein sequences in the combined database was 113,110. Cutoff scores were dynamically assigned to each data set to maintain the false discovery rate at less than 1% at the protein level. The resulting spectrum scores/spectrum scored peak intensities were >14/>50%, >12/>50%, and >14/>50% for 1+ peptides, 2+ peptides, and 3+ peptides, respectively. Proteins that share common peptides were grouped to address the database redundancy issue. The proteins within the same group shared the same set or subset of unique peptides. Protein iTRAQ intensities were calculated by summing the peptide iTRAQ intensities from each protein group. Peptides shared among different protein groups were removed before quantitation. A minimal total iTRAQ intensity of 100 was used to filter out low intensity spectra. Isotope impurities of iTRAQ reagents were corrected using correction factors provided by the manufacturer (Applied Biosystems). Protein identification information (unique scores, numbers of unique peptides, and percent coverage) is summarized in supplemental Table S1. Semiquantitatively, raw protein abundances were calculated by normalization of the data by the total iTRAQ reporter intensities for each sample (supplemental Table S1). Because the same amount of total protein was used in the analysis of each sample, the latter approach is equivalent to normalization to total protein. In all subsequent analyses, abundances of proteins undetected in particular fractions were regarded as missing data rather than zero amounts. Therefore, the duplicate protein abundances were averaged if protein was detected in both iTRAQ runs; otherwise, the single replicate was used. To derive protein distributions among six fractions, these raw protein abundances were normalized either to the sum total of all six fractions (supplemental Table S1) or to protein abundance in the main fraction (supplemental Table S2; selected marker proteins only). To assess the biological variability of the protein distributions, means and S.E. of biological triplicates were calculated for each of the 2,642 detected proteins in each of six fractions from the resting and activated cells (supplemental Table S1) for which duplicate/triplicate data had been obtained. The purity of the fractions was characterized with regard to the intensities of the conventional markers for each organelle/cell compartment. DNA was measured as the marker for nuclei using a Quant-iT DNA assay kit according to the manufacturer's protocol. Measurements were performed using a FluoroMax-2 spectrofluorometer (Horiba Jobin-Yvon). To ensure reproducibility, the sample aliquots were supplemented with 5% ethanol and frozen-thawed prior to the assay. Succinate dehydrogenase served as the marker enzyme for mitochondria. The enzyme quantity was assayed using a partial enzymatic reaction, reduction of p-iodonitrotetrazolium violet (INT), according to the method described by Munujos et al. (10Munujos P. Coll-Cantí J. González-Sastre F. Gella F.J. Assay of succinate dehydrogenase activity by a colorimetric-continuous method using iodonitrotetrazolium chloride as electron acceptor.Anal. Biochem. 1993; 212: 506-509Crossref PubMed Scopus (103) Google Scholar) with minor modifications. The assay medium contained 50 mm Tris-HCl, pH 8.1, 1 mm EGTA, 12 mg/ml detergent Cremaphor EL, and 20 mm succinate. All reactions were performed in triplicate in the 96-well plates in the ELx808iu plate reader (BioTek Instruments). Control reactions in the absence of succinate were set up for each sample to account for the background reduction of INT. The reactions were started with the addition of 2 mm INT and followed for 10 min at 490 nm; the succinate-dependent rates were calculated by subtraction. Cytochrome P450 reductase served as the marker enzyme for the ER. Its quantity was assayed using the NADPH-dependent cytochrome c reductase activity of the enzyme using a cytochrome c reductase (NADPH) assay kit according to the manufacturer's protocol. The measurements were performed in an Uvikon-XL spectrophotometer (BioTek Instruments). K+-dependent phosphatase reaction served as the marker activity for plasma membrane and was measured as K+-stimulated p-nitrophenylphosphatase according to the method of Kashiwamata et al. (11Kashiwamata S. Goto S. Semba R.K. Suzuki F.N. Inhibition by bilirubin of (Na+ + K+)-activated adenosine triphosphatase and K+-activated p-nitrophenylphosphatase activities of NaI-treated microsomes from young ra
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