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• W2809106958 abstract "Analysis of secretomes critically underpins the capacity to understand the mechanisms determining interactions between cells and between cells and their environment. In the context of cancer cell micro-environments, the relevant interactions are recognized to be an important determinant of tumor progression. Global proteomic analyses of secretomes are often performed at a single time point and frequently identify both classical secreted proteins (possessing an N-terminal signal sequence), as well as many intracellular proteins, the release of which is of uncertain biological significance. Here, we describe a mass spectrometry-based method for stable isotope dynamic labeling of secretomes (SIDLS) that, by dynamic SILAC, discriminates the secretion kinetics of classical secretory proteins and intracellular proteins released from cancer and stromal cells in culture. SIDLS is a robust classifier of the different cellular origins of proteins within the secretome and should be broadly applicable to nonproliferating cells and cells grown in short term culture. Analysis of secretomes critically underpins the capacity to understand the mechanisms determining interactions between cells and between cells and their environment. In the context of cancer cell micro-environments, the relevant interactions are recognized to be an important determinant of tumor progression. Global proteomic analyses of secretomes are often performed at a single time point and frequently identify both classical secreted proteins (possessing an N-terminal signal sequence), as well as many intracellular proteins, the release of which is of uncertain biological significance. Here, we describe a mass spectrometry-based method for stable isotope dynamic labeling of secretomes (SIDLS) that, by dynamic SILAC, discriminates the secretion kinetics of classical secretory proteins and intracellular proteins released from cancer and stromal cells in culture. SIDLS is a robust classifier of the different cellular origins of proteins within the secretome and should be broadly applicable to nonproliferating cells and cells grown in short term culture. Protein secretion critically supports a diverse range of cellular functions including cell-cell and cell-matrix interactions, as well as specialized functions such as hormone or digestive enzyme release. The constitutive secretion of proteins is a property of all cells, whereas regulated secretion (i.e. dependent on release of preformed stores after increased intracellular Ca2+) occurs in specialized cells including neurons, endocrine and exocrine cells. It is now appreciated that an understanding of secretomes (the totality of secreted proteins) is of crucial importance in health and disease (1Ranganath S.H. Levy O. Inamdar M.S. Karp J.M. Harnessing the mesenchymal stem cell secretome for the treatment of cardiovascular disease.Cell Stem Cell. 2012; 10: 244-258Abstract Full Text Full Text PDF PubMed Scopus (623) Google Scholar, 2Alvarez-Llamas G. Szalowska E. de Vries M.P. Weening D. Landman K. Hoek A. Wolffenbuttel B.H. Roelofsen H. Vonk R.J. Characterization of the human visceral adipose tissue secretome.Mol. Cell. Proteomics. 2007; 6: 589-600Abstract Full Text Full Text PDF PubMed Scopus (184) Google Scholar, 3Makridakis M. Vlahou A. Secretome proteomics for discovery of cancer biomarkers.J. Proteomics. 2010; 73: 2291-2305Crossref PubMed Scopus (202) Google Scholar, 4Wu C.C. Hsu C.W. Chen C.D. Yu C.J. Chang K.P. Tai D.I. Liu H.P. Su W.H. Chang Y.S. Yu J.S. Candidate serological biomarkers for cancer identified from the secretomes of 23 cancer cell lines and the human protein atlas.Mol. Cell. Proteomics. 2010; 9: 1100-1117Abstract Full Text Full Text PDF PubMed Scopus (161) Google Scholar). For example, the secretomes of cancer and stromal cells contribute strongly to the cellular microenvironment that determines tumor progression (5Hanahan D. Weinberg R.A. Hallmarks of cancer: the next generation.Cell. 2011; 144: 646-674Abstract Full Text Full Text PDF PubMed Scopus (42734) Google Scholar). Thus, secretome studies have proven attractive both because they may provide insight into mechanisms of disease and because they facilitate the discovery of biomarkers that can be used for diagnosis, staging and monitoring of therapy. Despite considerable progress in developing methods for secretome profiling (6Holmberg C. Ghesquiere B. Impens F. Gevaert K. Kumar J.D. Cash N. Kandola S. Hegyi P. Wang T.C. Dockray G.J. Varro A. Mapping proteolytic processing in the secretome of gastric cancer-associated myofibroblasts reveals activation of MMP-1, MMP-2, and MMP-3.J. Proteome Res. 2013; 12: 3413-3422Crossref PubMed Scopus (43) Google Scholar, 7Rieckmann J.C. Geiger R. Hornburg D. Wolf T. Kveler K. Jarrossay D. Sallusto F. Shen-Orr S.S. Lanzavecchia A. Mann M. Meissner F. Social network architecture of human immune cells unveiled by quantitative proteomics.Nat. Immunol. 2017; 18: 583-593Crossref PubMed Scopus (194) Google Scholar, 8Gauthier N.P. Soufi B. Walkowicz W.E. Pedicord V.A. Mavrakis K.J. Macek B. Gin D.Y. Sander C. Miller M.L. Cell-selective labeling using amino acid precursors for proteomic studies of multicellular environments.Nat. Methods. 2013; 10: 768-773Crossref PubMed Scopus (41) Google Scholar) there remain problematical issues in interpretation of the data. Such studies frequently identify “classical” secreted proteins defined by an N-terminal signal sequence, but they also identify many intracellular proteins, the apparent secretion of which is often of uncertain significance and not readily discriminated from tissue leakage/cell death (9Brown K.J. Formolo C.A. Seol H. Marathi R.L. Duguez S. An E. Pillai D. Nazarian J. Rood B.R. Hathout Y. Advances in the proteomic investigation of the cell secretome.Expert Rev. Proteomics. 2012; 9: 337-345Crossref PubMed Scopus (88) Google Scholar). Interpretation is further compounded by the fact that many studies are performed at a single time point, such that kinetic differences in the release of different components of the secretome are obscured. The classification of secretome proteins by gene ontology (GO) 1The abbreviations used are:GOgene ontologySIDLSstable isotope dynamic labeling of secretomesCAMscancer-associated myofibroblasts. 1The abbreviations used are:GOgene ontologySIDLSstable isotope dynamic labeling of secretomesCAMscancer-associated myofibroblasts. terms or predictions from computational tools/algorithms such as SignalP (10Petersen T.N. Brunak S. von Heijne G. Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions.Nat. Methods. 2011; 8: 785-786Crossref PubMed Scopus (7096) Google Scholar) or SecretomeP (11Bendtsen J.D. Jensen L.J. Blom N. Von Heijne G. Brunak S. Feature-based prediction of non-classical and leaderless protein secretion.Protein Eng. Des. Sel. 2004; 17: 349-356Crossref PubMed Scopus (938) Google Scholar) can be used to segregate classically secreted proteins from intracellular proteins. However, experimental approaches that support this classification would be of obvious advantage. For example, a triple-labeling, single time point approach was adopted by Kristensen and colleagues (12Kristensen L.P. Chen L. Nielsen M.O. Qanie D.W. Kratchmarova I. Kassem M. Andersen J.S. Temporal profiling and pulsed SILAC labeling identify novel secreted proteins during ex vivo osteoblast differentiation of human stromal stem cells.Mol. Cell. Proteomics. 2012; 11: 989-1007Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar), in which they pointed out that the extent of labeling could be used to discriminate newly synthesized secretome proteins and those that were mobilized from pre-existing stores. Here, we extend this thinking by describing a mass spectrometry (MS)-based strategy using stable isotope dynamic labeling of secretomes (SIDLS) that discriminates between classical secretory proteins and intracellular proteins within the secretome of cultured cells. The method differs from traditional SILAC, in which proteins are labeled for a fixed period to ensure all are fully labeled. Further, it differs from the single time point pulsed SILAC approach (12Kristensen L.P. Chen L. Nielsen M.O. Qanie D.W. Kratchmarova I. Kassem M. Andersen J.S. Temporal profiling and pulsed SILAC labeling identify novel secreted proteins during ex vivo osteoblast differentiation of human stromal stem cells.Mol. Cell. Proteomics. 2012; 11: 989-1007Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar) through dynamic labeling, in which the progressive incorporation of label into proteins is monitored over time. We demonstrate that a time dependence of labeling is of considerable value in the study of cell secretomes. A kinetic approach exploits the different labeling kinetics of classical secretory proteins that exhibit rapid incorporation of label compared with the much slower labeling of the bulk of intracellular proteins, even though some of the latter are present in the secretome. By monitoring the rate of incorporation of labeled amino acids into newly synthesized proteins as they appear in the media, we can differentiate those proteins that have been destined for secretion from those with low rates of labeling or low turnover relative to the growth rate of the cells, a feature of intracellular proteins. gene ontology stable isotope dynamic labeling of secretomes cancer-associated myofibroblasts. gene ontology stable isotope dynamic labeling of secretomes cancer-associated myofibroblasts. Human primary cancer-associated myofibroblasts (CAMs) were derived from resected human esophageal squamous cancer tissue, obtained from patients as described previously (13McCaig C. Duval C. Hemers E. Steele I. Pritchard D.M. Przemeck S. Dimaline R. Ahmed S. Bodger K. Kerrigan D.D. Wang T.C. Dockray G.J. Varro A. The role of matrix metalloproteinase-7 in redefining the gastric microenvironment in response to Helicobacter pylori.Gastroenterology. 2006; 130: 1754-1763Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar). Esophageal squamous cell cancer cells, OE21, were purchased from American Type Culture Collection (Manassas, VA). All cells were maintained at 37 °C, in 5% v/v CO2, and cultured in DMEM, supplemented with 10% v/v FBS as previously described (14Kumar J.D. Holmberg C. Kandola S. Steele I. Hegyi P. Tiszlavicz L. Jenkins R. Beynon R.J. Peeney D. Giger O.T. Alqahtani A. Wang T.C. Charvat T.T. Penfold M. Dockray G.J. Varro A. Increased expression of chemerin in squamous esophageal cancer myofibroblasts and role in recruitment of mesenchymal stromal cells.PLoS ONE. 2014; 9: e104877Crossref PubMed Scopus (40) Google Scholar). Cells (1 × 106) were seeded in complete medium (DMEM) in five T75 flasks giving 80–90% confluency, per flask. The following day, the cell-conditioned medium on each flask was changed to fresh 37 °C heavy-labeled ((13C6)-labeled l-lysine) DMEM (10 ml volume per dish, serum-free). At the following time intervals - 30 min, 1 h, 2 h, 6 h, and 24 h - all 10 ml of now heavy-labeled cell-conditioned DMEM from each flask was collected for subsequent secretome profiling (Fig. 1A), as follows. Each medium/secretome preparation was centrifuged at 800 × g for 7 min to remove debris and the protein component within each was concentrated by mixing, with agitation, with 25 μl StrataClean resin (Agilent Technologies Ltd., Wokingham, UK). The resin beads were washed twice in 25 mm ammonium bicarbonate (ambic). Each secretome-loaded StrataClean suspension was re-suspended in 80 μl of 25 mm ambic and 5 μl of 1% (w/v) RapiGest (Waters, Hertfordshire, UK) in 25 mm ambic, prior to on-bead proteolytic digestion with trypsin (MS grade Trypsin Gold, Promega). The samples were heated at 80 °C for 10 min after which proteins were reduced, by the addition of 5 μl of 60 mm DTT at 60 °C for 10 min, before being cooled prior to addition of 5 μl of 180 mm iodoacetamide and incubation at RT for 30 min in the dark. Trypsin (1 μg) was added and the samples incubated at 37 °C overnight on a rotary mixer. Peptide digests were subsequently acidified by the addition of 1 μl of trifluoroacetic acid (TFA) and incubated at 37 °C for 45 min. Following centrifugation at 17,000 × g for 30 min, 10 μl of each clarified supernatant (peptide mixture) was prepared for nano LC-MS/MS. Peptide digests (2 μl) from each sample were loaded onto a trap column (Acclaim PepMap 100, 2 cm × 75 μm inner diameter, C18, 3 μm, 100 Å) at 5 μl/min with an aqueous solution containing 0.1% (v/v) TFA and 2% (v/v) acetonitrile. After 3 min, the trap column was set in-line with an analytical column (Easy-Spray PepMap® RSLC 50 cm × 75 μm inner diameter, C18, 2 μm, 100 Å) (Dionex, Sunnyvale, CA). Peptides were loaded in 0.1% (v/v) formic acid and eluted with a linear gradient of 3.8 - 40% buffer B (HPLC grade acetonitrile 80% (v/v) with 0.1% (v/v) formic acid) over 95 min at 300 nl/min, followed by a washing step (5 min at 99% solvent B) and an equilibration step (15 min at 3.8% solvent). All peptide separations were carried out using an Ultimate 3000 nano system (Dionex). The column was operated at a constant temperature of 40 °C and the LC system was coupled to a Q-Exactive mass spectrometer (Thermo Fisher), as described previously (15Pratt J.M. Simpson D.M. Doherty M.K. Rivers J. Gaskell S.J. Beynon R.J. Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes.Nat. Protoc. 2006; 1: 1029-1043Crossref PubMed Scopus (304) Google Scholar). The Q-Exactive was operated in data-dependent mode with survey scans acquired at a resolution of 70,000 at m/z 200. Up to the 10 most abundant peptides of charge state between 2+ and 4+ were selected for fragmentation by higher energy collisional dissociation with an isolation window of 2.0 Th and normalized collision energy of 30. The maximum ion injection times for the survey scan and the MS/MS scans were 250 and 100 ms, respectively, and the ion target value was set to 1E6 for survey scans and 1E5 for the MS/MS scans. Repetitive sequencing of peptides was minimized through dynamic exclusion of the sequenced peptides for 20 s. Acquired MS data were searched and analyzed using Andromeda (16Cox J. Neuhauser N. Michalski A. Scheltema R.A. Olsen J.V. Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment.J. Proteome Res. 2011; 10: 1794-1805Crossref PubMed Scopus (3448) Google Scholar) and MaxQuant 1.5.8.3 (17Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9150) Google Scholar) against a reviewed human UniProt protein database (date: 03/09/2016 containing 20,203 entries), using the default settings; briefly: the minimum required peptide length was seven amino acids long, trypsin/P was specified as the proteolytic enzyme and a single missed cleavage was allowed. Cysteine carbamidomethylation was set as a fixed modification and methionine oxidation was allowed as a variable modification. The initial precursor and fragment ion maximum mass deviations were set to 20 ppm and 0.5 Da, respectively. Peptide and protein false discovery rates (FDRs) were set to 1%, the “requant” function activated and “match between runs” enabled with the default parameters. In supplemental material (supplemental Fig. S5), we include copies of relevant figures in the main text (Figs. 3A/3B, 6A/6B, and 8A/8B) where the requant function was disabled, to demonstrate that although the total number of proteins is lower, the results and conclusions of this study are unchanged.Fig. 6Rates of label exchange for individual proteins and complete secretomes, discriminated based on SignalP scores. First-order rate constants at which newly synthesized proteins acquire heavy isotopic label, for every protein in the CAM (A) and OE21 (B) secretomes. Physiologically-secreted proteins (SignalP >0.5; red lines) clearly acquire new (13C6)lysine at a higher rate compared with intracellular proteins that merely “leak” from the cell (SignalP <0.5; blue lines). Some proteins with low SignalP scores appear to be readily secreted or have very high turnover (blue dashed lines); however, manual inspection of the raw MS data for these proteins revealed them to be artifacts (see main text for explanation). Relating “global” secretome kinetics to protein classification on the basis of a computational prediction of the presence or not of a signal peptide (SignalP score), reveals an impressive discrimination in CAMs (C) and, to a lesser extent, in cancer cells (Panel D), thus raising questions about the secretome behavior of cells in vivo in the tumor microenvironment. An equivalent plot with the MaxQuant requant function disabled is provided in supplemental material.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Fig. 8Relationship between k and flux of protein from intracellular to extracellular pools. A surrogate measurement of flux into the extracellular protein pool was determined by calculating, flux = k. (P), where k is first-order rate constant at which newly synthesized protein acquired heavy isotopic label and P is the amount of protein secreted over an 18 h period. Symbols are color-coded on the basis of SignalP classification (>0.5 = red; <0.5 = blue) with alpha transparency shading according to the score from SignalP predictions (from 0, high transparency - to 1, low transparency). A, CAMs; B, OE21 cells. Selected proteins of interest in tumor biology are highlighted. An equivalent plot with the MaxQuant requant function disabled is provided in supplemental material.View Large Image Figure ViewerDownload Hi-res image Download (PPT) To analyze the rate of incorporation of heavy stable isotope-labeled amino acids into nascent proteins within secreted proteins, the MaxQuant peptide-level “evidence.txt” output file was analyzed in detail. Initially, peptides from known contaminant proteins as well as those generated by proteolytic mis-cleavage events (thus potentially carrying >1 labeling site) were omitted. Although only lysine-terminated peptides have the potential to carry a dynamic stable isotope label for kinetic measurements, identification of secretome proteins was based on both arginine- and lysine-terminated tryptic peptide matches. It would, of course, be possible in the future to use both labeled lysine and arginine to increase the number of kinetically informative peptides but the principles of the method we describe here would not change as a consequence. Peptide mass spectral “evidence data” for secretome proteins were then split into two lists, according to cell-line (OE21 or CAM). For each peptide passing a 1% FDR threshold in the Andromeda search, the relative isotope abundance (RIA) was calculated at each time point if present in the MS data. RIA is expressed as abundance of heavy, labeled peptide (H), divided by the abundance of all (heavy + light) peptide (RIAt = H/H+L). We applied a set of stringent criteria to produce high quality data-sets for each cell line analyzed. To model the secretome labeling trajectory, the RIA data for at least three time-points were used. We focused on peptides that had been identified and quantified, allowing RIA calculation, at more than one time point in the labeling trajectory, so that we were effectively tracking their RIA behavior over time. Moreover, because the protein content of the secretome increases with time, we only analyzed peptide mass spectral data that included RIA data at 6 h and 24 h post exchange of culture medium. A small number of peptides were rejected from further analysis if they implied labeling profiles that could not be biologically possible in this experimental system, specifically, where the calculated RIA at 30 min or 60 min after medium exchange was greater than that after 6 h or 24 h - these are likely artifacts. Because proteins at t = 0 are completely unlabeled and for fully labeled proteins, RIA = 1, we fitted a simplified version of the general first order equation: RIAt=(1-exp⁡(-k.t)) which generates the optimal fitted curve for a first order rise to plateau labeling (k) from an initial value of 0 to a final value of 1.0. Fitting was achieved using the nls() function in R. To assess changes in the abundance of proteins identified from the 1% FDR Andromeda search in our secretomes, we summed the mass spectral peptide intensity reported by MaxQuant for labeled (heavy) and unlabeled (light) features to obtain a quantification value. These intensity values represent the summed eXtracted Ion Current (XIC) of all isotopic clusters associated with the identified peptide sequence. If more than one peptide was identified and quantified per protein, we calculated the mean abundance of labeled (heavy) and unlabeled (light) peptide species. This was used to monitor changes in abundance of each individual protein in the secretome with time which, for a physiologically secreted protein, should increase. To calculate a measure of the flux of each protein from intracellular to extracellular pools/compartments, we first measured the abundance (P) secreted over an 18 h period by subtracting the amount secreted after 6 h from that after 24 h. We then multiplied this with the first-order rate constant (k) at which newly synthesized protein acquired heavy isotopic label, to give flux (flux = k. (P)). In order to obtain protein-level kinetic data, RIA data for peptides belonging to the same protein were grouped together and fitted using the nls() function in R. Our high-quality data-sets for both cell lines were then cross-annotated with the output from SignalP (as described in Functional analysis, below), to explore the relationship(s) between labeling kinetics and predicted sub-cellular localization. All mathematical modeling and data visualizations used R (v3.5.0) and ggplot2 (v2.0.1). Extraction and visualization of mass spectral isotopic patterns and XIC data were carried out using the “RforProteomics” package (1.15.0) (18Gatto L. Breckels L.M. Naake T. Gibb S. Visualization of proteomics data using R and bioconductor.Proteomics. 2015; 15: 1375-1389Crossref PubMed Scopus (37) Google Scholar). Abundance and kinetic plots are provided for every protein in supplemental material. All protein hits from the 1% FDR Andromeda search were subsequently used for subcellular localization and GO enrichment analysis. The FASTA amino acid sequence for each protein identified in OE21 or CAM secretomes was extracted from the UniProt database and submitted to SignalP v4.1 (http://www.cbs.dtu.dk/services/SignalP/), to identify classically secreted proteins wherein a threshold SignalP d-score >0.5 defines classical secretion, as described previously (10Petersen T.N. Brunak S. von Heijne G. Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions.Nat. Methods. 2011; 8: 785-786Crossref PubMed Scopus (7096) Google Scholar). UniProt accession numbers for all proteins in each secretome collected over the labeling trajectory were loaded into the R/Bioconductor package “clusterProfiler” (version 3.8.0 (19Yu G. Wang L.G. Han Y. He Q.Y. clusterProfiler: an R package for comparing biological themes among gene clusters.OMICS. 2012; 16: 284-287Crossref PubMed Scopus (11788) Google Scholar)) to allow GO over-representation analyses. We used the “enrichGO” function together with “compareCluster” to track changes in the functional enrichment profile with time, based on a hypergeometric distribution using a background list of all proteins in the H. sapiens annotation database. To remove redundant GO terms the “simplify” function was applied using the “Wang” measure of semantic similarity (20Yu G. Li F. Qin Y. Bo X. Wu Y. Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products.Bioinformatics. 2010; 26: 976-978Crossref PubMed Scopus (643) Google Scholar) (similarity cut-off of 0.5), reporting only terms with the lowest FDR-adjusted p values. A similar approach was taken to obtain GO functional profiles of our secretomes based on labeling kinetics. Secretomes were probed by Western blotting for selected classically secreted proteins, namely MMP1 (antibody BAF901, R&D Systems, Oxfordshire, UK), MMP3 (antibody AF913, R&D Systems), TGFB/TGFβig-h3 (antibody AF2935, R&D Systems) and SCG2 (antibody ab96589, Abcam, Cambridge, UK). In some experiments, the cells were pre-incubated for 30 min with 10 μg.ml−1 brefeldin A (BFA; eBioscience, Ltd., Hatfield, UK), or 10 μg.ml−1 cycloheximide (Sigma, Dorset, UK) and/or 1 μm ionomycin (Sigma). Proteins were resolved by SDS-PAGE and processed for Western blotting as described previously (13McCaig C. Duval C. Hemers E. Steele I. Pritchard D.M. Przemeck S. Dimaline R. Ahmed S. Bodger K. Kerrigan D.D. Wang T.C. Dockray G.J. Varro A. The role of matrix metalloproteinase-7 in redefining the gastric microenvironment in response to Helicobacter pylori.Gastroenterology. 2006; 130: 1754-1763Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar). Secretomes, particulary in the early periods of incubation, are low abundance and we concentrated proteins by adsorption onto StrataClean, a silica-based bead preparation with a high affinity for protein. This captures all secretome proteins, and tryptic digests can be conducted directly on the beads—SDS-PAGE of bead eluate after digestion confirms completeness of digestion (results not shown). Peptides recovered from the on-bead digests were then used directly for LC-MS/MS. To establish the linearity of the StrataClean bead capture, we completed control experiments in which fresh “virgin” (v) culture media was mixed in different proportions (specifically, 0:100, 20:80, 50:50, 80:20 and 100:0) with media that had been cell-conditioned (cc) for 24 h with CAM cells (supplemental Fig. S1, supplemental Table S1). StrataClean was used exactly as described under Experimental Procedures. The label free abundance of recovered proteins exhibited excellent linearity with protein load, confirming the quantitative performance of the protein capture method (supplemental Fig. S1, supplemental Table S1). Further, the assessment of the degree of labeling of protein captured by StrataClean is internally controlled and thus independent of the quantity of protein. Each protein in a secretome has two kinetic parameters of relevance. The first is the change in abundance as a function of time. A protein that is actively secreted should accumulate in the medium, unless there is an opposing removal process that takes the protein back into the cell or which elicits extracellular degradation (a possibility, given the number of endopeptidases that are secreted from cells). Thus, abundance is not enough to define secretome kinetics. The second necessary parameter is flux, or the rate at which the protein flows from the intracellular pool to the extracellular space. Measurement of flux can also discriminate between classically secreted proteins and those that are released from the cell through necrotic or apoptotic changes, provided a labeling method is used to discriminate these pools. If a cell is supplied with labeled precursors, such as amino acids, the newly synthesized and labeled intracellular proteins will enter and equilibrate with the unlabeled pre-existing pool. Thus, although newly synthesized proteins are fully labeled, they are diluted by a large, pre-existing pool of unlabeled protein, and thus the RIA is low. Subsequent leakage from the cell would reflect loss of this minimally-labeled mixture. If a small proportion of this pool is then released from the cell, the fraction of protein molecules that are labeled will be low. By contrast, proteins that are classically secreted by the constitutive pathway do not have a large intracellular reservoir to dilute the labeling of newly synthesized molecules, so all labeled proteins exiting the endoplasmic reticulum/Golgi are immediately secreted from the cell and will exhibit rapid acquisition of complete labeling in the medium. A third class of proteins, those of the regulated secretory pathway which is a feature of neural, exocrine, and endocrine cells, can have a large, stored intracellular pool in secretory vesicles, and thus, newly synthesized protein should enter this pool and may exhibit relatively slow labeling. We reasoned that these different kinetic behaviors could be used to discriminate between classically secreted proteins and those derived from intracellular protein leakage. Tensioned against protein turnover is the change in protein abundance. Secretome protein pools that expand would be expected to exhibit a rapid increase in label enrichment, consistent with a small intracellular pool and physiological secretion. By contrast, continued leakage of an intracellular protein, e.g. through cell damage, will reflect the extent of labeling of the intracellular pool, and unless this is an intrinsically high turnover protein, the degree of labeling will be low. To develop this logic further, a secreted protein pool that is static (not increasing) but which has a high degree of labeling must be subject to rapid removal from the extracellular pool (Fig. 1B). It follows that two measurements, changes in secretome protein abundance and the kinetic profile of labeling, could resolve proteins that are physiologically secreted from those that leak from the cell. In this study, a canc" @default.
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• W2809106958 title "Stable Isotope Dynamic Labeling of Secretomes (SIDLS) Identifies Authentic Secretory Proteins Released by Cancer and Stromal Cells" @default.
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