trans-silac: sorting out the non-cell-autonomous proteome
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non-cell-autonomous proteins are incorporated into cells that form tight contacts or are invaded by bacteria, but identifying the full repertoire of transferred proteins has been a challenge. here we introduce a quantitative proteomics approach to sort out non-cell-autonomous proteins synthesized by other cells or intracellular pathogens. our approach combines stable-isotope labeling of amino acids in cell culture (silAc), high-purity cell sorting and bioinformatics analysis to identify the repertoire of relevant non-cell-autonomous proteins. this ‘trans-silAc’ method allowed us to discover many proteins transferred from human B to natural killer cells and to measure biosynthesis rates of Salmonella enterica proteins in infected human cells. trans-silAc should be a useful method to examine protein exchange between different cells of multicellular organisms or pathogen and host.
The dual nature of the cell, being both an autonomous entity and a vital component in the construction of multicellular organisms is at the foundation of the modern cell theory1. But the recent recognition of extensive transfer of proteins among interacting cells, particularly immune cells, supports the notion that under certain circumstances, cellular autonomy is compromised2,3.
The poorly understood mechanisms that lymphocytes use to acquire proteins from target cells have been termed ‘trogocytosis’, ‘internalization’, ‘absorption’, ‘acquisition’, ‘snatching’, ‘stripping’, ‘shaving’ and ‘trapping’2–7. The most studied trogocytic event is a fast process that is cell contact– and actin cytoskeleton– dependent2,3,5, and the proteins that transfer by trogocytosis can remain intact and functional8–10. To date, only membrane-associated proteins have been described to transfer; Ras proteins, which inter-act only with the inner leaflet of the plasma membrane, transfer very efficiently when cell-cell contact is established8,11. Moreover, the whole repertoire of proteins that can transfer in vitro among lymphocytes during culture has not been defined yet.
Cells can also be forced to accept non-cell-autonomous proteins, as in the cases of bacteria that infect host cells or deliver proteins via secretion systems, including various virulence factors that are
trans-silAc: sorting out the non-cell-autonomous proteomeOded Rechavi1,5, Matan Kalman2, Yuan Fang3, Helly Vernitsky1,4, Jasmine Jacob-Hirsch4, Leonard J Foster3, Yoel Kloog1,6 & Itamar Goldstein4,6
detrimental to host cells12. Some intracellular bacteria rely on con-tinuous production of proteins in host cells, and their patterns of expression can be regulated by the host’s environment12.
Global identification of non-cell-autonomous proteins has been challenging. Previous studies using flow cytometry–based anal-ysis have only identified a limited number of plasma membrane– associated proteins that transfer among lymphocytes2,3,5. Flow cytometry and other methods, including microscopy and protein biotinylation9,13, are time consuming and typically can be used to detect only a few non-cell-autonomous proteins per experiment.
Here we describe a quantitative proteomics method, trans– stable-isotope labeling of amino acids in cell culture (trans-SILAC), which can be used to differentiate non-cell-autonomous proteins, acquired during cell-cell contact, from the endogenous proteome. We took advantage of the SILAC method in which cell lines are metabolically labeled with heavy-isotope amino acids and com-pared to an unlabeled reference cell sample; differences in pro-tein abundance between the samples can be detected by liquid chromatography–tandem mass spectrometry (LC-MS/MS)14,15. Here we labeled one cell type (protein donor) with ‘heavy’ amino acids and left the other cell type (protein recipient) unlabeled, such that the transferred non-cell-autonomous proteins can be clearly identified in the recipient cell by their mass shift. We used digital fluorescence-activated cell sorting (FACS) to carefully sort out the protein-recipient cell population before LC-MS/MS analysis and a stringent bioinformatics approach to identify non-cell-autonomous proteins. Using trans-SILAC we scanned the non-cell-autonomous proteome exchanged among conjugated lymphocytes. In another application, we followed the translation rate of Salmonella enterica–derived non-cell-autonomous proteins labeled with ‘heavy’ amino acids in host cells during the early phase of the bacterial infection.
resultsexperimental system to probe cell-cell protein transfer We used a well-defined cellular system8,9 to study cell contact– and actin cytoskeleton–dependent protein transfer. We used the
1Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel. 2Department of Biochemistry, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel. 3Centre for High-Throughput Biology, Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, Canada. 4Cancer Research Center, Chaim Sheba Medical Center, Tel Hashomer and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. 5Present address: Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, New York, USA. 6These authors contributed equally to this work. Correspondence should be addressed to Y.K. ([email protected]) or I.G. ([email protected]).Received 11 May; accepted 10 august; published online 10 octobeR 2010; doi:10.1038/nMeth.1513
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B cell line 721.221 (B721), a classical natural killer (NK) cell target, as the heavy isotope–labeled protein donor and freshly isolated ‘light’ human NK cells as the acquiring cell population (Fig. 1). As untagged enhanced GFP (EGFP) does not transfer among cells8, we used B721 cells stably expressing EGFP (B721-EGFP) to identify these cells. Previously we had shown that H-Ras transfers robustly from B721 to NK cells and that this transfer correlates with the transfer of the transmembrane protein CD86 (ref. 8) (Supplementary Fig. 1). To identify only non-cell-autonomous proteins that transfer by an actin cytoskeleton–dependent mecha-nism, we added a control condition in which we disturbed the actin cytoskeleton by latrunculin-B (LatB) treatment. Calibration of LatB inhibition showed that 1 μM LatB efficiently abolished the transfer of H-Ras and CD86. Therefore, we used LatB-treated co-cultures as controls for actin cytoskeleton–independent cell-cell protein transfer in this experimental system (Fig. 2 and Supplementary Figs. 1 and 2).
First, to use trans-SILAC to identify ‘heavy’ B721-derived proteins that transfer into the ‘light’ NK cells, we grew the B721-EGFP cells for at least seven cell divisions with heavy isotopologs of lysine and arginine, for a labeling efficiency of ~98%. We then grew the ‘heavy’ B721-EGFP cells with NK cells for 1.5 h with or without LatB. To assess transfer, we monitored the transfer of CD86 from B721 to NK cells. We used a FACSAria digital cell sorter and a stringent multiparameter duplet-discrimination algorithm to sort EGFP+ NK single-cell events with high purity (>99.5%) from the combined cultures (Fig. 2). Then we lysed the sorted NK cells, digested the extracted proteins, fractionated them and analyzed them by LC-MS/MS. Using MaxQuant16 to analyze the data, we identified 2,426 proteins in total and quantified their ‘trans-SILAC ratios’ (the ratio of labeled to nonlabeled peptides; Supplementary Table 1) and spectral counts (Supplementary Table 2) before subsequent bioinformatics analysis.
defining the nK-cell non-cell-autonomous proteomeOur initial analysis of the data included only annotated proteins that contained heavy peptides, namely with a trans-SILAC ratio >0. Whereas we detected heavy peptides for 1,635 proteins in the untreated samples, we detected much fewer heavy peptides (rep-resenting 1,144 proteins) in the presence of LatB (Supplementary Table 1). There was considerable intersection between the peptide sets identified among the two experimental conditions (1,068 of 1,711 proteins; 62%), but a much greater percentage of proteins exclusively contained B cell–derived ‘heavy’ peptides in the untreated medium (567 of 1,711 proteins; 33%) versus LatB treatment condi-tion (76 of 1,711 proteins; 4.4%) (Supplementary Fig. 3a).
We then compared the distribution of the trans-SILAC ratios for all of the detected proteins in LatB-treated and untreated sam-ples. The overall distribution of trans-SILAC ratio for the proteins in the experimental condition was significantly shifted as com-pared to proteins in LatB-treated cells (P = 1.7 × 10−14, Mann-Whitney-Wilcoxon test; Supplementary Fig. 3b). As ‘heavy’ proteins could have only originated from the B721 cells, these results suggested that significantly more proteins were transferred from B to NK cells when the actin cytoskeleton was undisturbed. In agreement with previous studies8,11, we detected transfer of Ras proteins in the absence of LatB, confirming the sensitivity of trans-SILAC in detecting the transfer of proteins expressed in physiological amounts.
To assess which proteins were transferred via an actin cytoskeleton–dependent mechanism, we used a stringent bioinformatics approach to analyze the trans-SILAC data (Supplementary Fig. 4 and Online Methods). The final and inter-mediate lists as well as the results of all analysis steps performed are listed in Supplementary Table 3.
We generated two initial lists: the ‘transfer set’ included proteins assumed to transfer by an actin cytoskeleton– dependent mechanism (with a high trans-SILAC ratio in the experimental condition and a relatively low trans-SILAC ratio in LatB-treated cells; Supplementary Table 3b), and the ‘high LatB set’ included proteins assumed to transfer by an actin cyto-skeleton–independent mechanism (high trans-SILAC ratio in the LatB condition; Supplementary Table 3a). We next ana-lyzed the lists using the database for annotation, visualization and integrated discovery (DAVID) bioinformatics resources17 to cluster these protein lists into enriched biological functions and examined whether proteins with similar biological func-tions have, as a group, significantly higher trans-SILAC ratios (P < 0.05). Only two biological functions, containing just five proteins, were significantly enriched in LatB-treated cells (P < 0.05; Supplementary Table 3d). In contrast, proteins in the ‘transfer set’ clustered into eight annotation source terms (Supplementary Table 3e), such as ‘integral to plasma mem-brane’ (trans-SILAC ratio = 1.23, P = 0.0051, 21 proteins), ‘trans-membrane receptor activity’ (trans-SILAC = 1.72, P = 0.01, 8 proteins) and ‘MHC class II protein complex’ (trans-SILAC = 3.98, P = 0.0013, 3 proteins). Together, these observations supported our hypothesis that groups of proteins with a distinct biological function transfer among lymphocytes by an actin cytoskeleton–dependent process.
Next, we filtered the ‘transfer set’ using a heuristic approach to include only the proteins with high likelihood for transfer by an actin cytoskeleton–dependent process. Each filtering step was
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Figure 1 | Detecting non-cell-autonomous translated proteins by trans-SILAC. Flow diagram of a typical trans-SILAC experiment designed to detect cell-to-cell protein transfer. LatB-treated cells served as control to allow the exclusion of actin cytoskeleton–independent transfer.
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accompanied by a functional analysis to evaluate the identity of the proteins that were removed from or remained in the list (Supplementary Table 3f–i). This procedure eventually yielded a final list of 172 non-cell-autonomous proteins that transfer
upon cell contact by actin cytoskeleton–dependent mechanisms: the ‘final transfer set’ (Supplementary Table 3j).
To validate the cell-cell transfer of selected proteins from this ‘final transfer set’, we used a FACS-based B cell to NK cell transfer assay8,18. We labeled 17 candidate proteins only in donor cells (HEK293 or B721 cells) either by transfecting HEK293 cells with plasmids encoding EGFP-tagged intracellular proteins, or by tag-ging cell-surface proteins on B721 cells by specific fluorochrome-conjugated monoclonal antibodies. Then we grew these donor cells with NK cells, with or without LatB. The results of the FACS-based experiments were in agreement with the LC-MS/MS data (Fig. 3 and Supplementary Table 4). Moreover, proteins that had high (>0.18) trans-SILAC ratios in both the initial ‘transfer’ and ‘LatB’ sets did not transfer, confirming that purging proteins with high trans-SILAC ratios in the LatB-treatment condition prevented the inclusion of false positive actin cytoskeleton–independent events. Furthermore, we found that proteins with low trans-SILAC ratios in either treatment condition did not transfer. Note that the com-parison among the LC-MS/MS and FACS derived data is inher-ently qualitative because we transfected target cells with plasmids encoding EGFP-tagged proteins for the FACS validation.
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Figure 2 | Sorting of highly purified human CD56+ NK cells. (a–c) The plots depict the various analysis and sorting steps of the stringent duplet-discrimination algorithm used to detect and sort NK single-cell events based on their light-scatter characteristics and EGFP signals. Shown are side scatter area (SSC-A) versus forward scatter area (FSC-A) (a), and FSC-H (height) versus FSC-W (width) of all viable cells in B721 cell (green) and initial NK cell (red) gates (b) and of the cells included in the initial NK single-cell gate (c). (d) The events included in the initial NK single-cell gate were additionally purged of EGFP+ events to obtain the final EGFP− NK single-cell gate. (e,f) Analysis of cells collected from LatB-treated (e) or untreated (f) cultures of B721 and NK cells for cell-surface CD86 before cell sorting. Green dots depict B721-EGFP cell and NK-B721 cell doublets, and red dots denote only events that represent single NK cells with high probability. (g,h) For quality control, the single NK cells sorted from either the LatB-treated (g) or untreated (h) cultures of B721 and NK cells were analyzed for purity. Percentages of EGFP+ events in the sorted cells is indicated. CD86-APC-A, CD86 allophycocyanin signal area; GFP-A, EGFP signal area. Results shown are from a typical experiment of more than five experiments.
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(a) Analysis of the transfer of CD11a and CD43 from B721 to NK cells in LatB-treated (left) and untreated cultures (right; numbers indicate percentage of events in each quadrant). Axis values represent arbitrary logarithmic fluorescent units of the indicated fluorescently tagged mAb. (b,c) Overlapping, vertically displaced histograms (FlowJo software) for the analysis of the transfer of indicated GFP-tagged intracellular proteins from HEK293 to NK cells (b) and of cell-surface proteins labeled by fluorochrome-conjugated monoclonal antibodies from B721 to NK cells (c) relative to NK-cells-alone cultures. Numbers to the right of histograms represent the mean fluorescence intensity for that analysis. Data were collected from ~10,000 single cell events.
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To validate that the transfer was cell contact–dependent we used a transwell assay system9 and found that the transfer of the selected proteins, positively vali-dated by the FACS-based assay, was indeed halted when we separated the B721-cell from the NK-cell cultures by a transwell membrane (Supplementary Table 4).
The DAVID-based functional analysis of the ‘final transfer set’ (Supplementary Table 3k) revealed that 70% (120 of 172 proteins) of the transferring proteins were membrane associated (gene ontology (GO) terms ‘intrinsic to membrane’ or ‘intracellular membrane-bound organelle’). To iden-tify functional protein networks in the ‘final transfer set’, we used the network explorer feature of the Ingenuity Pathways Analysis (IPA) platform (Ingenuity Systems). IPA revealed that the transfer-ring proteins accumulate in specific networks within the adopting NK cells with a high degree of interconnectivity (Supplementary Table 5). One such network identified by IPA with a high degree of interconnectivity (IPA score = 37, n = 23 proteins) was ‘cancer, immuno-logical disease, hematological disease’ (Fig. 4 and Supplementary Table 5). We validated the transfer of eight proteins within this net-work by FACS: HLA-DRA, CD58, ITGA4 (CD49d), K-Ras, H-Ras, Rab10, RALA and RALB (Fig. 3 and Supplementary Table 4).
Note that the final ‘transfer’ list of 172 proteins that transfer by contact and actin cytoskeleton–dependent mechanisms probably does not contain all the proteins that can transfer, and additional proteins may transfer in other contexts.
Finding Salmonella protein translation rate in host cells Salmonella infecting host cells represent an interspecies transfer of intracellular proteins. Once Salmonella adhere to host cells, they secrete proteins into the host cytosol sequentially via two type-3 secretion systems (T3SS; encoded on the Salmonella pathogenic-ity islands19). After Salmonella modulate their own internalization into the host, the bacteria may survive for more than a day, during which time they can replicate inside the Salmonella-containing vacuoles and adjust as needed the composition of the bacterial proteome in several stages19.
To apply trans-SILAC to measure the expression rates of Salmonella proteins while the bacteria survive and multiply inside the host, we used Salmonella that had been fully SILAC-labeled to infect host HeLa cells and then maintained this culture for 2 h. During this time, we collected aliquots of the cells every 20 min, lysed them and analyzed the samples by LC-MS/MS. In this application, trans-SILAC was not required to identify which proteins are actually transferred, as Salmonella proteins are easily distinguished from human proteins via their unique sequence, but rather the trans-SILAC allowed us to measure how the Salmonella reprogrammed their proteome while inside the host. We used the increase in ‘light’ Salmonella proteins to esti-mate the change in expression of any given protein over time.
During infection, bacterial protein synthesis was substantially delayed in the first 40 min of infection compared to the control log-phase growth of Salmonella alone. However, after 40 min, pro-tein synthesis resumed its regular turnover rate (Supplementary Fig. 5 and Supplementary Table 6), suggesting that Salmonella shuts down its replication machinery during invasion. We also used trans-SILAC to detect specifically Salmonella pathogenicity island (SPI)-1 and SPI-2 secreted effector proteins in various sub-cellular fractions of infected cells (data not shown).
discussionOur trans-SILAC method allowed us to scan the entire proteome for non-cell-autonomous proteins. The particular heuristic analy-sis we used allowed us to sort out the positive hits with high fidelity. The strategy could be fine tuned when applied to different
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Figure 4 | Non-cell-autonomous proteins that form the ‘cancer, immunological disease, hematological disease’ network. The image was created using the Ingenuity Pathways Analysis (IPA) platform (Ingenuity Systems) by overlaying the transferring proteins detected by trans-SILAC (red) onto a global molecular network from the Ingenuity knowledgebase. Red indicates high trans-SILAC ratios, and white indicates proteins that were not in the ‘transfer set’ but form part of this network. For each transferring protein, the bottom number corresponds to the trans-SILAC ratio in the ‘transfer’ condition and the top number is the difference between the ratios of the two experimental conditions (trans-SILAC ratio in ‘transfer’ condition minus trans-SILAC ratio in ’control’ condition). For cases in which no trans-SILAC ratio was available for the ‘control’ condition, both numbers are equal.
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cellular systems and other types of datasets. Such developments should facilitate the use of less stringent bioinformatics filtering algorithms to follow up the positive hits.
Although most work on cell-cell protein transfer has focused on immune cells, this process also occurs outside the immune system, including between cancer and tumor-associated stromal cells20. A recent study even describes cell-cell transfer, via tunneling nanotubes21, of the disease-specific prion protein, PrPsc (ref. 22). And we have observed that T cells also acquire small regulatory RNAs from target cells that function in a transcellular mode to regulate target protein translation23. Trans-SILAC could be used to explore whether conjugated cells exchange, in addition to regulatory RNAs, multiple non-cell-autonomous proteins that jointly may target specific cellular pathways. Our DAVID-based analysis of the ‘final transfer set’ indeed revealed significant enrichment for the annotation term ‘MHC class II protein complex’ and the proteins in this family had the highest trans-SILAC ratio as a group (trans-SILAC ratio = 3.98, P = 0.0013 for the full DAVID analysis). Similar transfer has been previously described to alter the effector functions of adopting lymphocytes9,24,25.
We also applied trans-SILAC to study previously inaccessible aspects of invading bacteria and host interactions. The approach could be optimized to monitor intracellular bacterial protein biosynthesis at higher resolution by fractionation to compare bacterial protein synthesis rates in specific compartments inside host cells. More detailed time-course experiments can be done to track bacterial proteome reprogramming events at different stages of infection.
As no specialized instruments are required, we predict that trans-SILAC should become a routine method to study the non-cell-autonomous proteome exchanged among cells of multi-cellular organisms or pathogens and infected host cells.
methodsMethods and any associated references are available in the online version of the paper at http://www.nature.com/naturemethods/.
Accession codes. Proteomics identifications database (PRIDE): 13639, 13640, 13641 and 13642.
Note: Supplementary information is available on the Nature Methods website.
AcKnowledgmentsO.R. was supported by a scholarship from the Clore Israel Foundation. M.K. was supported by the Edmond J. Safra Program in Bioinformatics at Tel Aviv University. Operating funds for this work came, in part, from the Prajs-Drimmer Institute for the Development of Anti-degenerative Disease Drugs to Y.K., from the Israel Cancer Association to I.G. and Y.K. and from a Canadian Institutes of Health Research Operating grant (MOP-77688) to L.J.F. Mass spectrometry infrastructure used in this project was supported by the Canadian Foundation for Innovation, the British Columbia Knowledge Development Fund and the British Columbia Proteomics Network. Y.F. is supported by a studentship from the Genome Sciences and Technologies graduate program. Expression vectors encoding for EGFP-tagged RALA and RALB proteins were a gift from A. Cox (The University of North Carolina at Chapel Hill) and vectors for Arf4, Rab10 and Rab11a were a gift from D. Cassel (Technion, Israel Institute of Technology).
Author contriButionsO.R. jointly conceived the study with I.G., designed experiments, performed experiments, analyzed data and wrote the paper; M.K. developed analytical tools and analyzed data; Y.F. designed and performed experiments and analyzed data; H.V. performed experiments and analyzed data; J.J.-H. analyzed data; L.J.F. designed experiments, developed analytical tools, analyzed data and wrote
the paper; Y.K. and I.G. jointly supervised the project, designed experiments, analyzed data and wrote the paper.
comPeting FinAnciAl interestsThe authors declare no competing financial interests.
Published online at http://www.nature.com/naturemethods/. reprints and permissions information is available online at http://npg.nature.com/reprintsandpermissions/.
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22. Gousset, K. et al. Prions hijack tunnelling nanotubes for intercellular spread. Nat. Cell Biol. 11, 328–336 (2009).
23. Rechavi, O. et al. Cell contact-dependent acquisition of cellular and viral nonautonomously encoded small RNAs. Genes Dev. 23, 1971–1979 (2009).
24. Domaica, C.I. et al. Tumour-experienced T cells promote NK cell activity through trogocytosis of NKG2D and NKp46 ligands. EMBO Rep. 10, 908–915 (2009).
25. Tabiasco, J. et al. Acquisition of viral receptor by NK cells through immunological synapse. J. Immunol. 170, 5993–5998 (2003).
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online methodsSubjects. This study was approved by the Institutional Ethics Committee at the Chaim Sheba Medical Center. Signed written informed consent was obtained from all subjects. All periph-eral blood samples were obtained from healthy individuals who donated blood.
Antibodies and reagents. Fluorochrome-conjugated monoclonal antibodies to CD86, CD70, CD49d, CD43 and PDGFR were pur-chased from BD Biosciences, monoclonal antibodies to CD11a, CD56, CD14 and CD58 from Beckman Coulter, and monoclonal antibody to CD10 from Dako. The monoclonal antibody to HLA-E was a gift from E. Gazit (Chaim Sheba Medical Center, Israel). Isotopologs of arginine and lysine were purchased from Sigma. Dialyzed serum and media lacking lysine and arginine were pur-chased from Bet-H’aemek.
Plasmids and transfections. HEK293 cells were transfected as described elsewhere8. The expression vectors containing cDNA encod-ing for EGFP-tagged Ras proteins have been described elsewhere8.
Isolation of NK cells. Peripheral blood lymphocytes (PBLs) were isolated by density-gradient centrifugation on Histopaque 1077 (Sigma), as previously described26. Primary CD56+ NK cells were isolated from the PBLs by the use of anti-CD56 microbeads and the MACS cell separation system (Miltenyi Biotec), as described8.
NK cell cultures. Cells were cultured in RPMI-1640 medium sup-plemented with 10% FBS (FBS), 2 mM L-glutamine, 100 U ml−1 penicillin and 100 μg ml−1 streptomycin (all from Gibco) and maintained at 37 °C in a humidified 5% CO2 incubator. NK cells were typically grown for 24–48 h before experiments in medium supplemented with 100 international units (IU) of rhIL-2.
Cell lines. The human B lymphoblastoid cell line 721.221 (B721) was obtained from the American Type Culture Collection. B721 stable transfectants and HEK293 cells were grown as previously described8.
SILAC labeling. B721 cells were cultured through at least seven cell divisions in SILAC I media lacking normal isotopic abun-dance arginine and lysine but supplemented with 1.15 mM [13C6
15N4]arginine and 0.274 mM [13C615N2]lysine (Sigma). The
medium also contained 10% dialyzed serum.
B721 and primary human CD56+ NK cells cultures. NK cells (1 × 106 cells per tube) were grown for 1.5 h with B721 cells (0.5 × 106 cells per tube) stably expressing EGFP in round-bottom tubes cells in 1 ml of culture medium at 37 °C. The culture tubes were centrifuged for 2 min at 200g to promote cell-conjugate forma-tion before co-culturing. After co-culture the cells were treated to disturb cell conjugates. Then single NK cells were sorted on a FACSAria instrument (BD Biosciences).
FACS analysis and cell sorting. Cell samples were analyzed on a FACSCalibur using Cellquest software or on a FACSAria using FACSDiva software (BD Biosciences). FACS data were analyzed using FlowJo 7.2.1 software (Tree Star Inc.). All cell-sorting experiments were performed on FACSAria. Viable
lymphocytes were identified by their distinct FSC and SSC (including pulse width, height and area), propidium iodide exclusion and expression of a distinct cell marker as indicated (for example, CD56).
Formation of cell conjugates and analysis of cell-to-cell transfer. To validate the cell-to-cell transfer of tagged proteins, the various B721 or HEK293 transfectants were distributed into U-bottom 96-well plates (30,000 cells per well in 100 ml) to which we added NK cells (60,000 cells per well in 100 ml) to obtain an effector to target ratio of 2:1. The culture plates were centrifuged for 2 min at 200g to promote cell-conjugate formation and then incubated for 1.5 h at 37 °C. The collected cells were resuspended vigorously in 5 mM EDTA-PBS and kept on ice for 30 min to allow cell conjugates to dissociate. Immunofluorescence staining with anti-CD56 or CD45-allophycocyanin monoclonal antibodies, as appropriate, was performed for 30 min at 4 °C. After labeling the cells were washed and again resuspended in 5 mM PBS-EDTA. Data collected from 10,000 single-cell events were then analyzed by multiparametric FACS. Primary NK cells were distinguished from target cells by their smaller size (as defined by their FSC/SSC) and fluorescence (specific monoclonal antibody staining and by not expressing EGFP, in the case of culture with B721-EGFP cells). To exclude NK-target cell conjugates from the analysis we used a very stringent state-of-the-art doublet discrimination algorithm using fluorescence height versus area and fluorescence width versus area pulse measurements to distinguish single NK cells from NK-B721 conjugates, as previously described8,23. This doublet discrimination model is considered very precise in cells, such as lymphocytes, as they are rather homogenous and spherical in shape.
LatB treatments. ‘Donor’ and ‘acceptor’ cells were pre-treated for 1 h with 1 μM LatB before and during culture, as previously described8.
Bacterial infection. S. enterica serovar Typhimurium strain SL1344 auxotrophic for arginine and lysine biosynthesis (ΔargH:ΔlysA) was grown to stationary phase in minimal medium labeled with 175 mg l−1 [13C6]arginine and 300 mg l−1 [2H4]lysine. Bacteria were collected at mid–log phase to infect HeLa cells at a multiplicity of infection of 200 for 2 h. After a 30 min infec-tion, cells were washed twice with PBS and were incubated with 50 μg ml−1 gentamicin for 1 h to kill extracellular bacteria. During 2 h infection, we collected aliquots of the cells every 20 min. The first sample was collected at 0 min of infection. Similarly, aliquots of log-phase bacteria culture were collected every 20 min for 2 h. Lysates were tryptically digested in a solution containing 1% sodium deoxycholate and 50 mM NH4HCO3. Five micrograms of peptides were injected into an LTQ-OrbitrapXL and peak lists were searched against a database containing all human protein sequences in the International Proteome Index plus the sequences of Salmonella strain SL1344. Peptides were quantified in MS-Quant27 by comparing the peak intensity between the heavy form and light from of tryptic peptides and proteins were quantified by integrating peptide data. Bacteria intracellular protein synthesis rates were determined by the percentage of newly synthesized proteins as: percentage = L / (L + H) × 100%, in which L is the abundance of light proteins and H represents the abundance of
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heavy proteins). Because, by default, proteins were quantified as a ratio of heavy to light, the percentage of synthesis equals 1/(1 + ‘SILAC ratio’). The s.d. values for protein SILAC ratios were calculated from peptide ratios and s.d. for percentage of synthesis was calculated as 1/(1 + ’SILAC ratio’ ± s.d.).
Mass spectrometry and proteomic data analysis. Sorted cell pel-lets were lysed in 1% deoxycholate, 50 mM NH4HCO3 (pH 8) and digested in solution as described28. For each replicate of each con-dition, peptide digested from 100 μg protein was resolved by pI using an OFFGEL system (Agilent). Each fraction collected from the OFFGEL was then subjected to LC-MS/MS on a linear trapping Fourier transform mass spectrometer (ThermoFisher Scientific) exactly as described29. Proteins were identified by searching the fragment spectra against the human IPI database (v3.47, 144,389 sequences) using MaxQuant16, allowing a 1% false discovery rate at the protein level. Identifications were based on at least 2 peptides unique by sequence as can be seen in Supplementary Table 1. Out of the proteins identified, all proteins whose IPI could be mapped to an Entrez gene ID were included in the analy-sis (2,418 out of 2,426). In the few cases in which different IPIs were mapped to the same Entrez gene identifier, we left in only proteins that were included in either the ‘high LatB’ or ‘transfer’ sets as described below. Absolute protein expression ratios were calculated as described30 using 0.99 for the protein identification probability (Pi value) for each hit and approximating the expected number of unique tryptic peptides for a protein (Oi value) with between 6 and 30 amino acids in each protein.
Extracting a confident set of transferring proteins. The ‘trans-fer set’ was built using the following six-step protocol. (i) First, we considered only the proteins whose trans-SILAC ratio in the experimental condition was very high (top 30%). (ii) From these proteins, we considered only proteins that either had no trans-SILAC ratio in the control condition, or whose trans-SILAC ratio was very low compared to the experimental condition (top 30% difference between the two trans-SILAC ratios). (iii) We filtered out the proteins that were annotated with GO functions that were enriched in the control condition that were annotated with the GO function ‘biosynthetic process’. (iv) We filtered out the proteins that were annotated to localize to the cell compartment ‘endomembrane system’ or ‘organelle lumen’ and had a very high trans-SILAC ratio in the control condition (top 15%). (v) We filtered out the top 10% most abundant proteins according to the spectral count. (vi) We filtered out proteins whose actin- dependent transfer was inconsistent among the different repli-cates performed.
The trans-SILAC ratio must be considered a measure of how much of a given protein was transferred relative to the amount of that protein in the receiving cell but since the endogenous levels of any given protein are likely to be different between donor and acceptor cells, the ratio does not represent an exact quantification of the amount of transferred protein. This means that a ratio of 0.10 for a highly abundant protein such as actin would repre-sent an enormous movement of protein from donor to acceptor, whereas a ratio of 100 for a transcription factor may only repre-sent a few molecules transferred per cell since that protein might be present at very endogenous low levels in the acceptor. For this reason, it is virtually impossible to apply the conventional rules
used in other proteomics experiments, such as a ratio cut-off or significance versus a ‘control’ ratio. Despite this, trans-SILAC still allowed us to use the measured ratios to extract a set of proteins that have a high likelihood to transfer by following the heuristic approach described in this section.
We also created a second set of proteins (‘high LatB set’), to characterize the proteins that are very likely to transfer by an actin-independent mechanism. This set served to highlight pro-teins identified in the experimental condition that might com-prise the background (false positive detection) and moreover to understand patterns common to this ‘biological noise’. It initially contained all of the proteins that have a very high trans-SILAC ratio in the control condition (top 15%), and subsequently under-went the same filtering steps (steps iii–v) as the ‘transfer’ set, to enable spotting the differences between the two sets that are not a direct outcome of the filtering method.
Below we describe and explain each filtering step and the analy-sis that accompanied it. The final sets, intermediate sets and results of all the analyses performed are available in Supplementary Table 3 and Supplementary Software.
Building the initial ‘transfer’ and ‘high LatB’ sets. For the ‘transfer’ set, we included all proteins that had a high trans-SILAC ratio in the experimental condition (top 30%, trans-SILAC ratio >0.18). For the ‘high LatB’ set, we included the proteins that had a high trans-SILAC ratio in the LatB condition (top 15%, trans-SILAC ratio >0.2, Supplementary Table 3a).
Subtracting the proteins that transfer independently of actin. We compared the trans-SILAC ratios of the two conditions and identified within the ‘experimental’ condition 200 proteins that had no detectable trans-SILAC ratio in the ‘LatB treatment’ con-dition (Supplementary Table 3b). These proteins were included in our following sets for further analysis as they were consid-ered as having a high likelihood to transfer by an actin-depend-ent mechanism. Moreover, 303 proteins that had trans-SILAC ratios in both conditions were further analyzed to identify indi-vidual proteins that had, nevertheless, a high likelihood of actin-dependent transfer. We assessed these ‘overlapping’ proteins by examining a scatter plot of the ‘LatB’ versus ‘transfer’ trans-SILAC ratios for all these 303 proteins (Supplementary Fig. 6). The Pearson correlation coefficient of the plot was 0.8156, indicating overall a positive correlation between the two con-ditions (see also best-fitting regression line), namely between the actin-dependent and independent modes of transfer. Three possible models are consistent with this observation, as fol-lows: (i) existence of a weaker transfer process that transfers the same proteins in LatB-dependent and -independent man-ner; (ii) there is a tendency for abundant proteins to transfer among lymphocytes in both LatB-dependent and -independent processes; and (iii) abundant proteins create more false posi-tive background ‘noise’ in our experimental system. At present we could not distinguish between the above noted possibilities. Therefore, to avoid with high likelihood inclusion of proteins that transfer by actin-independent mechanisms, we included annotated proteins with the top 30% highest difference in the trans-SILAC ratios between the two conditions (difference > 0.117; Supplementary Fig. 6). This method revealed 91 different proteins that were added to the list of 200 proteins detected only
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in the ‘experiment’ condition as described above thus yielding a set of 291 proteins (Supplementary Table 3b).
We alternatively tried to neutralize the effect of protein abun-dances on transfer rate by selecting only proteins that were below the linear regression line in the scatter plot with the top 30% dis-tance from the line (distance > 0.03755; Supplementary Fig. 4). This strategy yielded a very similar list (62 of 91 overlapping pro-teins) with similar enriched functions according to the DAVID functional analysis.
Two rounds of analysis and filtering were preformed based on protein set enrichment analysis using the DAVID Bioinformatics Resources available online that use multiple heterogeneous func-tional gene annotation database sources17. First we examined the ‘experimental’ set and discovered that many biological func-tions were significantly enriched (expression analysis systematic explorer (EASE) score, P < 0.01; a modified Fisher exact test used by DAVID) compared to the background that consisted of all the proteins that appear in the raw mass spectrometry dataset (Supplementary Table 3c). As mostly membrane-associated pro-teins have been previously described to transfer from cell-to-cell by trogocytosis when an immunological synapse (IS) is formed5, some of these enriched functions seemed highly relevant such as the term ‘intrinsic to membrane’ (GO: 0031224, 47 proteins), ‘integral to membrane’ (GO: 0016021, 17 proteins), ‘plasma mem-brane part’ (GO: 0044459, 26 proteins) and ‘membrane-bound organelle’ (GO: 0043227, 136 proteins). The full DAVID analysis for this set is available in Supplementary Table 3c.
Next, based on these DAVID data, we analyzed each of these enriched functions to determine whether sets of proteins possess-ing a similar function tend to have significantly high (P < 0.05) average trans-SILAC values. Empirical P values were calculated for each such set by generating 10,000 random lists of the same size. In the control (LatB) this analysis revealed two enriched functions containing only five proteins that have higher than background trans-SILAC ratios: ‘pyridoxal phosphate binding’ (trans-SILAC ratio = 10.28, P = 0.0005) and ‘vitamin binding’ (trans-SILAC ratio = 8.35, P = 0.0015; Supplementary Table 3d).
This DAVID-based analysis of the ‘transfer’ set revealed signif-icant enrichment for eight annotation source terms, all of which refer to antigen presentation, membrane localization or recep-tor activities (Supplementary Table 3e). For example, ‘MHC class II protein complex’ (trans-SILAC ratio = 3.98, P = 0.0013), ‘Integral to plasma membrane’ (trans-SILAC ratio = 1.23, P = 0.0051), and ‘Transmembrane receptor activity’ (trans-SILAC ratio = 1.72, P = 0.01).
Filtering out proteins annotated with the function ‘biosynthetic process’. Different functional networks that are known to be highly abundant and over-represented in mass spectrometry data in general were also very common in our datasets. To formally test this possibility we checked for each GO function the median of its spectral count in the experimental and control conditions (Supplementary Fig. 7). Only GO functions that include more than 150 proteins in each condition (to examine relatively general functions) were included. The analysis revealed that the most abundant terms are translation (GO: 0006412), macromolecule biosynthetic process (GO: 0009059), cellular biosynthetic proc-ess (GO: 0044249) and biosynthetic process (GO: 0009058). All these overrepresented functions are subterms of the ‘biosynthetic
process’ GO term and are also enriched in the ‘high LatB’ set (EASE score = 4 × 10−4). Therefore, proteins that belong to this annotation source term were suspected as ‘background’ and filtered out. This rather conservative filtering strategy was adopted to reduce the risk of including ‘experimental noise’ (Supplementary Table 3f). This and all subsequent filtering steps were applied also to the ‘High LatB’ set.
Filtering proteins by their localization to specific cell com-partments. In another filtering step we used the GO annotation database to examine the cellular localization of the proteins in each of our sets. We wanted to filter out proteins that are located in distinctive cellular compartments which host proteins that were transferred by an actin-independent mechanism, namely, compartments that are enriched in the ‘high LatB’ set. We thus determined what percentage of proteins localized within each compartment in each set (‘experimental’ and ‘high LatB’). The results of the analysis suggested that two cell components con-tained many proteins with high trans-SILAC ratio only in the ‘high LatB’ set: ‘endomembrane system’ and ‘organelle lumen’. Therefore we filtered out from the ‘transfer’ set 12 proteins that localize into these cell components and had a high (>0.2) trans-SILAC ratio in the LatB condition (Supplementary Fig. 8 and Supplementary Table 3g).
Filtering out the most abundant proteins. We filtered out the top 10% most abundant proteins according to the spectral count (top 10% out of all detected proteins, spectral count above 1.294; Supplementary Table 3h). This was done to reduce the amount of false positives by minimizing the chance of includ-ing overrepresented proteins, picked up simply because of their abundance.
Filtering out proteins that were inconsistent among the differ-ent replicates. Finally, a protein was filtered out from the final transfer set if its trans-SILAC ratios in both duplicates of the experimental condition were substantially lower than the overall trans-SILAC ratio in that condition, or if the trans-SILAC ratio of either triplicate of the LatB condition was substantially higher than the overall trans-SILAC ratio in that condition (Supplementary Table 3i). For this case, ‘substantially lower’ was defined to be at least 50% lower than the original ratio. ‘Substantially higher’ was defined to be at least 50% higher, the only exception being when the overall control ratio was zero, in which case substan-tially higher was defined to be of ratio at least 0.2, the cut-off used for the ‘high LatB’ set.
Analysis of the final ‘transfer’ set. To evaluate the nature of the proteins in the final ‘transfer’ set that remained after all the fil-tering (Supplementary Table 3j) we performed another round of DAVID-based analysis (Supplementary Table 3k), which revealed that 70% (120/172) of the transferring proteins interact with membranes or intracellular membrane-bound organelles (GO terms ‘intrinsic to membrane’ or ‘intracellular membrane-bound organelle’).
The dataset containing protein identifiers and correspond-ing values was uploaded into IPA. Each identifier was mapped to its corresponding gene object in the Ingenuity knowledge-base. The proteins were overlaid onto a global molecular
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network developed from information contained in the Ingenuity knowledgebase. Networks of these focus genes were then algo-rithmically generated based on their connectivity (Fig. 4 and Supplementary Table 5).
We also roughly assessed the abundance of B cell–derived proteins (B values) in the NKs according to the spectral counts and the trans-SILAC ratios. This was calculated by the equation (r/(r + 1))s, where r is the trans-SILAC ratios and s is the spectral count (Supplementary Table 3l). The equation is based on the fact that the r is the ratio between the abundance of proteins in the B and the NK cell, and s is roughly the sum of the abundances.
Statistical analysis. P values were calculated by either the non-parametric Wilcoxon rank-sum test or empirically by generating a large random population as appropriate. A P-value of 0.05 or less was considered significant.
Software. The in-house Perl and R scripts used to process the datasets and a readme file with detailed instructions for sorting out the positive hits are available as Supplementary Software.
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27. Mortensen, P. et al. MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J. Proteome Res. 9, 393–403 (2009).
28. Rogers, L.D. & Foster, L.J. The dynamic phagosomal proteome and the contribution of the endoplasmic reticulum. Proc. Natl. Acad. Sci. USA 104, 18520–18525 (2007).
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