Advertisement

Stable Isotope Dynamic Labeling of Secretomes (SIDLS) Identifies Authentic Secretory Proteins Released by Cancer and Stromal Cells*

Open AccessPublished:June 18, 2018DOI:https://doi.org/10.1074/mcp.TIR117.000516
      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 (
      • Ranganath S.H.
      • Levy O.
      • Inamdar M.S.
      • Karp J.M.
      Harnessing the mesenchymal stem cell secretome for the treatment of cardiovascular disease.
      ,
      • Alvarez-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.
      ,
      • Makridakis M.
      • Vlahou A.
      Secretome proteomics for discovery of cancer biomarkers.
      ,
      • Wu 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.
      ). For example, the secretomes of cancer and stromal cells contribute strongly to the cellular microenvironment that determines tumor progression (
      • Hanahan D.
      • Weinberg R.A.
      Hallmarks of cancer: the next generation.
      ). 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 (
      • Holmberg 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.
      ,
      • Rieckmann 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.
      ,
      • Gauthier 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.
      ) 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 (
      • Brown 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.
      ). 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)
      The abbreviations used are:
      GO
      gene ontology
      SIDLS
      stable isotope dynamic labeling of secretomes
      CAMs
      cancer-associated myofibroblasts.
      1The abbreviations used are:GO
      gene ontology
      SIDLS
      stable isotope dynamic labeling of secretomes
      CAMs
      cancer-associated myofibroblasts.
      terms or predictions from computational tools/algorithms such as SignalP (
      • Petersen T.N.
      • Brunak S.
      • von Heijne G.
      • Nielsen H.
      SignalP 4.0: discriminating signal peptides from transmembrane regions.
      ) or SecretomeP (
      • Bendtsen J.D.
      • Jensen L.J.
      • Blom N.
      • Von Heijne G.
      • Brunak S.
      Feature-based prediction of non-classical and leaderless protein secretion.
      ) 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 (
      • Kristensen 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.
      ), 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 (
      • Kristensen 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.
      ) 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.

      DISCUSSION

      We describe a dynamic stable isotopic labeling- and mass spectrometry-based approach to characterize the physiological secretome of any cell that can be maintained in culture. Unlike traditional SILAC approaches, our stable isotope dynamic labeling of secretomes (SIDLS) method exploits the kinetics of exchange from light-to-heavy stable isotopic labeling that occurs with protein synthesis de novo over relatively short labeling trajectories, thus dispensing with the need for exogenously-added serum factors required in more long-term cell cultures. This is beneficial as serum often contains an abundance of factors that influence the behavior and physiology of the culture system. SIDLS can confidently discriminate the secretion kinetics of physiologically relevant classical secretory proteins from intracellular proteins that are released from cells either through damage during cell culture, apoptosis or “leakage.” It is therefore a powerful classifier of the different cellular origins of proteins within the secretome and should be broadly applicable, allowing secretome characterization of nonproliferating cells and cells only viable in short term culture. Embedding new knowledge of the rate of synthesis of the secretome constituents improves upon previously described approaches that rely on either the time-consuming labeling of cells to completion with heavy isotopic labels (traditional SILAC approaches), complex click chemistry approaches, or a combination of the two (
      • Kristensen 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.
      ,
      • Eichelbaum K.
      • Krijgsveld J.
      Combining pulsed SILAC labeling and click-chemistry for quantitative secretome analysis.
      ,
      • Henningsen J.
      • Blagoev B.
      • Kratchmarova I.
      Analysis of secreted proteins using SILAC.
      ,
      • Rocha B.
      • Calamia V.
      • Blanco F.J.
      • Ruiz-Romero C.
      Identification of Factors Produced and Secreted by Mesenchymal Stromal Cells with the SILAC Method.
      ,
      • Roelofsen H.
      • Dijkstra M.
      • Weening D.
      • de Vries M.P.
      • Hoek A.
      • Vonk R.J.
      Comparison of isotope-labeled amino acid incorporation rates (CILAIR) provides a quantitative method to study tissue secretomes.
      ).
      Across two different cell lines we obtained global secretome identification of over 2000 proteins and, in parallel, definitively determine the dynamic secretome behavior of a large proportion of these proteins, helping define their true intracellular origin and physiological role (see supplemental Tables S2 and S3 for complete data sets). As expected, classical secreted proteins show a shift in RIA from 0 toward 1 over time (as exemplified by MMP1 in Fig. 2A, red line). This is especially true in the nontransformed cells (CAMs), taking the SignalP score as a classifier of secretion or not (Fig. 5A). Our dynamic labeling strategy, combined with assessment of total abundance in the secretome, allowed us to distinguish, with high confidence, secreted protein assignments from erroneous measurements borne out of chromatographic and MS errors (e.g. co-eluting peptide MS isotopic envelopes skewing RIA calculations), as logic dictates that the total abundance of any secreted protein in the secretome must increase with time. Several proteins that appear to be secreted readily, but which have no known extracellular function, fall into this bracket (e.g. RRP12 in OE21 cells; GRHL1 and AL9A1 in CAMs; CHMP3 in both OE21 cells and CAMs).
      In our data set, some classically secreted proteins show near identical behavior across both cell-lines (see supplemental Fig. S4). Although small, this list of proteins identified in both CAM and OE21 secretomes shows that in general, a commonality exists in the secretome behavior of proteins between stromal and cancer cells that exist within the same microenvironment. But several proteins were removed from our data-sets during stringent filtering of the RIAt data. Indeed, some other classical secreted proteins, for e.g. additional members of the MMP family, did not meet our stringent filtering criteria - for example, where tandem MS evidence existed for both light and heavy peptide features, but only at one time point in the labeling trajectory (all protein data is included in the raw data in supplemental Tables S2 and S3). More in-depth proteomic analyses, for e.g. adopting fractionation approaches of each secretome sample, would increase the number of proteins identified allowing improved cross-comparison(s) to be made. However, it must be noted that, in general, the relationship between the rate of labeling in OE21 cancer cells and rate of labeling in CAMs, for common proteins, is not a strong correlation. Much clearer is a generally lower rate of incorporation of label into newly synthesized protein in the cancer cells (OE21), indicative of defective protein synthesis and/or trafficking though the secretory system in cancer.
      One of the main advantages of SIDLS is that it provides an orthogonal perspective to secretome dynamics. By tracking the appearance of label in secreted proteins, it is possible to build a profile of the speed and duration of response of individual proteins and resolve true secreted proteins from low level intracellular leakage. The marked consonance between proteins that would be labeled as secreted through a high predictive score of a signal peptide and rapid labeling gives a convincing confirmatory perspective on the secretome. For this analysis, we have been very stringent in the retention of proteins, and those for which abundances were too low for recovery by data-dependent acquisition approaches would be recovered by more targeted methods, such as selected reaction monitoring (
      • Holman S.W.
      • Hammond D.E.
      • Simpson D.M.
      • Waters J.
      • Hurst J.L.
      • Beynon R.J.
      Protein turnover measurement using selected reaction monitoring-mass spectrometry (SRM-MS).
      ). It is not too bold to imagine that the use of different labeled precursors in a pulse-labeling strategy would provide new insights into the interaction of co-cultures of cells mediated by their secretomes. Thus, inclusion of the simple expedient of dynamic labeling of secretomes will greatly increase the confidence with which such secretomes are studied.

      DATA AVAILABILITY

      The mass spectrometry proteomics data for the SIDLS dynamic labeling aspect of this study have been deposited to the ProteomeXchange Consortium via the PRIDE (
      • Vizcaino J.A.
      • Csordas A.
      • Del-Toro N.
      • Dianes J.A.
      • Griss J.
      • Lavidas I.
      • Mayer G.
      • Perez-Riverol Y.
      • Reisinger F.
      • Ternent T.
      • Xu Q.W.
      • Wang R.
      • Hermjakob H.
      2016 update of the PRIDE database and its related tools.
      ) partner repository with the data set identifier PXD007231. Equivalent data for the linearity of protein capture by StrataClean are deposited to ProteomeXchange too, with the identifier PXD009838. The output of the MaxQuant searches of the SIDLS dynamic labeling proteomics data are available using MS-Viewer (http://msviewer.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msviewer), using the search key identifier: 88dyh3qzvc. All annotated spectra can be accessed here. That for the StrataClean linearity experiment is also available using MS-Viewer using the search key idenitifier: hjhr9jxzpk.

      Acknowledgments

      We are grateful for support from the University of Liverpool Technology Directorate and the exceptional instrument support provided by Dr Philip Brownridge.

      REFERENCES

        • Ranganath 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: 244-258
        • Alvarez-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: 589-600
        • Makridakis M.
        • Vlahou A.
        Secretome proteomics for discovery of cancer biomarkers.
        J. Proteomics. 2010; 73: 2291-2305
        • Wu 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: 1100-1117
        • Hanahan D.
        • Weinberg R.A.
        Hallmarks of cancer: the next generation.
        Cell. 2011; 144: 646-674
        • Holmberg 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: 3413-3422
        • Rieckmann 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: 583-593
        • Gauthier 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: 768-773
        • Brown 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: 337-345
        • Petersen T.N.
        • Brunak S.
        • von Heijne G.
        • Nielsen H.
        SignalP 4.0: discriminating signal peptides from transmembrane regions.
        Nat. Methods. 2011; 8: 785-786
        • Bendtsen 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: 349-356
        • Kristensen 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: 989-1007
        • McCaig 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: 1754-1763
        • Kumar 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: e104877
        • Pratt 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: 1029-1043
        • Cox 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: 1794-1805
        • Cox 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: 1367-1372
        • Gatto L.
        • Breckels L.M.
        • Naake T.
        • Gibb S.
        Visualization of proteomics data using R and bioconductor.
        Proteomics. 2015; 15: 1375-1389
        • Yu G.
        • Wang L.G.
        • Han Y.
        • He Q.Y.
        clusterProfiler: an R package for comparing biological themes among gene clusters.
        OMICS. 2012; 16: 284-287
        • Yu 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: 976-978
        • Budczies J.
        • Klauschen F.
        • Sinn B.V.
        • Gyorffy B.
        • Schmitt W.D.
        • Darb-Esfahani S.
        • Denkert C.
        Cutoff Finder: a comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization.
        PLoS ONE. 2012; 7: e51862
        • Balabanova S.
        • Holmberg C.
        • Steele I.
        • Ebrahimi B.
        • Rainbow L.
        • Burdyga T.
        • McCaig C.
        • Tiszlavicz L.
        • Lertkowit N.
        • Giger O.T.
        • Oliver S.
        • Prior I.
        • Dimaline R.
        • Simpson D.
        • Beynon R.
        • Hegyi P.
        • Wang T.C.
        • Dockray G.J.
        • Varro A.
        The neuroendocrine phenotype of gastric myofibroblasts and its loss with cancer progression.
        Carcinogenesis. 2014; 35: 1798-1806
        • Varro A.
        • Dockray G.J.
        • Bate G.W.
        • Vaillant C.
        • Higham A.
        • Armitage E.
        • Thompson D.G.
        Gastrin biosynthesis in the antrum of patients with pernicious anemia.
        Gastroenterology. 1997; 112: 733-741
        • Rhodes C.J.
        • Halban P.A.
        Newly synthesized proinsulin/insulin and stored insulin are released from pancreatic B cells predominantly via a regulated, rather than a constitutive, pathway.
        J. Cell Biol. 1987; 105: 145-153
        • Holmberg C.
        • Quante M.
        • Steele I.
        • Kumar J.D.
        • Balabanova S.
        • Duval C.
        • Czepan M.
        • Rakonczay Jr, Z.
        • Tiszlavicz L.
        • Nemeth I.
        • Lazar G.
        • Simonka Z.
        • Jenkins R.
        • Hegyi P.
        • Wang T.C.
        • Dockray G.J.
        • Varro A.
        Release of TGFbetaig-h3 by gastric myofibroblasts slows tumor growth and is decreased with cancer progression.
        Carcinogenesis. 2012; 33: 1553-1562
        • Eichelbaum K.
        • Krijgsveld J.
        Combining pulsed SILAC labeling and click-chemistry for quantitative secretome analysis.
        Methods Mol. Biol. 2014; 1174: 101-114
        • Henningsen J.
        • Blagoev B.
        • Kratchmarova I.
        Analysis of secreted proteins using SILAC.
        Methods Mol. Biol. 2014; 1188: 313-326
        • Rocha B.
        • Calamia V.
        • Blanco F.J.
        • Ruiz-Romero C.
        Identification of Factors Produced and Secreted by Mesenchymal Stromal Cells with the SILAC Method.
        Methods Mol. Biol. 2016; 1416: 551-565
        • Roelofsen H.
        • Dijkstra M.
        • Weening D.
        • de Vries M.P.
        • Hoek A.
        • Vonk R.J.
        Comparison of isotope-labeled amino acid incorporation rates (CILAIR) provides a quantitative method to study tissue secretomes.
        Mol. Cell. Proteomics. 2009; 8: 316-324
        • Holman S.W.
        • Hammond D.E.
        • Simpson D.M.
        • Waters J.
        • Hurst J.L.
        • Beynon R.J.
        Protein turnover measurement using selected reaction monitoring-mass spectrometry (SRM-MS).
        Philos. Trans. A Math. Phys. Eng. Sci. 2016; 374 (pii: 20150362)
        • Vizcaino J.A.
        • Csordas A.
        • Del-Toro N.
        • Dianes J.A.
        • Griss J.
        • Lavidas I.
        • Mayer G.
        • Perez-Riverol Y.
        • Reisinger F.
        • Ternent T.
        • Xu Q.W.
        • Wang R.
        • Hermjakob H.
        2016 update of the PRIDE database and its related tools.
        Nucleic Acids Res. 2016; 44: 11033