An Introduction to Analytical Challenges, Approaches, and Applications in Mass Spectrometry–Based Secretomics
2023; Elsevier BV; Volume: 22; Issue: 9 Linguagem: Inglês
10.1016/j.mcpro.2023.100636
ISSN1535-9484
AutoresSascha Knecht, H. Christian Eberl, Norbert Kreisz, Ukamaka Juliet Ugwu, Tatiana Starikova, Bernhard Küster, Stephanie Wilhelm,
Tópico(s)Advanced biosensing and bioanalysis techniques
Resumo•Overview of challenges, approaches, and opportunities of LC-MS–based secretomics.•Guidance for unbiased analysis of secreted proteins from cultured cells.•Strength and weaknesses of different secretomics approaches. The active release of proteins into the extracellular space and the proteolytic cleavage of cell surface proteins are key processes that coordinate and fine-tune a multitude of physiological functions. The entirety of proteins that fulfill these extracellular tasks are referred to as the secretome and are of special interest for the investigation of biomarkers of disease states and physiological processes related to cell-cell communication. LC-MS–based proteomics approaches are a valuable tool for the comprehensive and unbiased characterization of this important subproteome. This review discusses procedures, opportunities, and limitations of mass spectrometry–based secretomics to better understand and navigate the complex analytical landscape for studying protein secretion in biomedical science. The active release of proteins into the extracellular space and the proteolytic cleavage of cell surface proteins are key processes that coordinate and fine-tune a multitude of physiological functions. The entirety of proteins that fulfill these extracellular tasks are referred to as the secretome and are of special interest for the investigation of biomarkers of disease states and physiological processes related to cell-cell communication. LC-MS–based proteomics approaches are a valuable tool for the comprehensive and unbiased characterization of this important subproteome. This review discusses procedures, opportunities, and limitations of mass spectrometry–based secretomics to better understand and navigate the complex analytical landscape for studying protein secretion in biomedical science. Multicellular organisms depend on the dynamic interplay of different organs, tissues, and cell types to sense and respond adequately to changes in the environment. The organization and orchestration of such responses is dependent on an efficient communication of signals between cells. On the molecular level, proteins play a major role as signaling cues for the transmission and reception of signals and can herby act either close by or in far distance. As such, an important class of proteins are those that are either actively released by a cell into the extracellular environment or reach the extracellular milieu via tissue leakage. The entirety of these proteins in the extracellular space are designated as the secretome (1Uhlen M. Karlsson M.J. Hober A. Svensson A.S. Scheffel J. Kotol D. et al.The human secretome.Sci. Signal. 2019; 12eaaz0274Google Scholar). Functionally, secreted proteins make up a diverse group of proteins covering growth factors, extracellular matrix constituents, cytokines, or hormones. According to the UniProtKB (accessed December 2022, keyword: secreted), 2097 of 20,401 reviewed proteins in total are annotated as secreted, suggesting that approximately 10% of the human proteome are potentially released via the classical secretory pathway or unconventional secretion processes. However, a growing number of experimental data have shown that protein secretion can be uncoupled from the classical endoplasmic reticulum (ER)-Golgi pathway, suggesting that unconventional protein secretion is an important factor which contributes to the active release of proteins into the extracellular space under certain conditions (2Villarreal L. Mendez O. Salvans C. Gregori J. Baselga J. Villanueva J. Unconventional secretion is a major contributor of cancer cell line secretomes.Mol. Cell Proteomics. 2013; 12: 1046-1060Google Scholar, 3Zhang M. Liu L. Lin X. Wang Y. Li Y. Guo Q. et al.A translocation pathway for vesicle-mediated unconventional protein secretion.Cell. 2020; 181: 637-652.e615Google Scholar, 4Phulphagar K. Kuhn L.I. Ebner S. Frauenstein A. Swietlik J.J. Rieckmann J. et al.Proteomics reveals distinct mechanisms regulating the release of cytokines and alarmins during pyroptosis.Cell Rep. 2021; 34108826Google Scholar, 5Meissner F. Scheltema R.A. Mollenkopf H.J. Mann M. Direct proteomic quantification of the secretome of activated immune cells.Science. 2013; 340: 475-478Google Scholar, 6Dong L.F. Kovarova J. Bajzikova M. Bezawork-Geleta A. Svec D. Endaya B. et al.Horizontal transfer of whole mitochondria restores tumorigenic potential in mitochondrial DNA-deficient cancer cells.Elife. 2017; 6e22187Google Scholar, 7Hurwitz S.N. Rider M.A. Bundy J.L. Liu X. Singh R.K. 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Biol. 2015; 427: 1202-1210Google Scholar). Secreted proteins are in the center of modified signaling pathways of numerous diseases, such as cancer (12Planque C. Kulasingam V. Smith C.R. Reckamp K. Goodglick L. Diamandis E.P. Identification of five candidate lung cancer biomarkers by proteomics analysis of conditioned media of four lung cancer cell lines.Mol. Cell Proteomics. 2009; 8: 2746-2758Google Scholar, 13Makridakis M. Vlahou A. Secretome proteomics for discovery of cancer biomarkers.J. Proteomics. 2010; 73: 2291-2305Google Scholar), cardiovascular (14Ranganath 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: 244-258Google Scholar), neurodegenerative (15Carvalho M.M. Teixeira F.G. Reis R.L. Sousa N. Salgado A.J. Mesenchymal stem cells in the umbilical cord: phenotypic characterization, secretome and applications in central nervous system regenerative medicine.Curr. Stem Cell Res. Ther. 2011; 6: 221-228Google Scholar), and chronic liver diseases (16Kim K. Kim K.H. Targeting of secretory proteins as a therapeutic strategy for treatment of Nonalcoholic Steatohepatitis (NASH).Int. J. Mol. Sci. 2020; 21: 2296Google Scholar) or obesity (17Pardo M. Roca-Rivada A. Seoane L.M. Casanueva F.F. Obesidomics: contribution of adipose tissue secretome analysis to obesity research.Endocrine. 2012; 41: 374-383Google Scholar) and constitute important targets for drugs and diagnostic procedures to track pathogenic processes, disease progression, or pharmacological responses (1Uhlen M. Karlsson M.J. Hober A. Svensson A.S. Scheffel J. Kotol D. et al.The human secretome.Sci. Signal. 2019; 12eaaz0274Google Scholar). The systematic investigation of proteins that are actively and passively released by cells is therefore the subject of secretomics. In this review, we provide a broad survey of established and emerging concepts of LC-MS–based secretomics, illustrate the challenges that are associated with the analysis of secreted proteins, and give examples of biomedical applications. LC-MS–based proteomics has proven to be a key analytical tool for biomedical research and for the investigation of intracellular signaling pathways, protein–protein interactions or for the identification of drug targets, and posttranslational modifications (PTMs). However, a systematic investigation of secreted proteins and intercellular signaling by LC-MS–based proteomics has been less frequently performed because of technical and biological challenges. Nonetheless, LC-MS–based proteomics is a valuable tool for the characterization and in-depth analysis of secretomes, as it gives access to an unbiased and comprehensive view of the entirety of secreted proteins. The possibility to identify thousands of proteins and simultaneously obtain quantitative data differentiates LC-MS–based secretomics approaches from antibody-based readouts that only allow the analysis of a predefined set of proteins and require prior knowledge of the proteins in the sample. A successful investigation of secreted proteins by mass spectrometry, however, requires a deeper understanding of the challenges and limitations which are inherently connected with secretome analysis in general and with the analysis by mass spectrometry in particular. Here, we provide an overview of the main technical and biological challenges that need to be considered for the analysis of secretomes. A major challenge of secretomics experiments is the distinction of truly secreted proteins from the substantial number of background proteins, potentially masking the true biology of the experiment. Data analysis and the interpretation of results therefore benefit from a thoughtful experimental design that should include time-matched untreated control samples. The experimental design should furthermore be based on a well-controlled treatment to call treatment-specific effects and to be able to rule out the impact of cell death. A direct comparison of treated samples with their time-matched controls then allows to evaluate which proteins were differentially released upon a stimulus and which are basally secreted or have leaked into the sample. The experimental design has also a critical influence on the depth and sensitivity of the analysis. For example, hepatocyte model cell lines such as HepaRG cells are highly secretory active upon cytokine treatments (18Knecht S. Eberl H.C. Bantscheff M. Interval-based secretomics unravels acute-phase response in hepatocyte model systems.Mol. Cell Proteomics. 2022; 21100241Google Scholar), allowing the execution of serum-free secretomics experiments in a 12-well plate format and still enable the identification of a multitude of different acute-phase response proteins (ng/ml range). However, the identification and quantification of cytokines, for example, which are usually only present in very low abundances (pg/ml range), may require millions of immune cells to adjust to the detection limit for the LC-MS instrumentation and therefore might require the use of big culture dishes. Protein quantification across different experimental conditions or over different time points to a stimulus or perturbation is another important design element for secretomics experiments. The toolbox of quantitative proteomics approaches includes label-based methods, which are reliant on the labeling with stable isotopes, and label-free strategies, both comprising advantages and disadvantages as reviewed in (19Bantscheff M. Lemeer S. Savitski M.M. Kuster B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present.Anal. Bioanal. Chem. 2012; 404: 939-965Google Scholar, 20Rozanova S. Barkovits K. Nikolov M. Schmidt C. Urlaub H. Marcus K. Quantitative mass spectrometry-based proteomics: an overview.Methods Mol. Biol. 2021; 2228: 85-116Google Scholar). For protein identification, mass spectrometers are mostly operated in a data-dependent acquisition (DDA) mode in which the most abundant peptides are selected during the MS1 scan for the subsequent fragmentation and MS2 analysis. Depending on the cell type and the biological background of the secretomics experiment, digestion of proteins with low molecular weight and low abundance, such as secreted cytokines, typically result in low peptide numbers. Accordingly, in DDA mode, the low abundance of peptides may result in a low signal intensity in the MS1 scan that prevents the selection for a subsequent fragmentation and MS2 analysis. Data-independent acquisition (DIA) approaches (21Bruderer R. Bernhardt O.M. Gandhi T. Miladinovic S.M. Cheng L.Y. Messner S. et al.Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues.Mol. Cell Proteomics. 2015; 14: 1400-1410Google Scholar, 22Gillet L.C. Navarro P. Tate S. Rost H. Selevsek N. Reiter L. et al.Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.Mol. Cell Proteomics. 2012; 11 (, O111.016717)Google Scholar) such as SWATH-MS (23Ludwig C. Gillet L. Rosenberger G. Amon S. Collins B.C. Aebersold R. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial.Mol. Syst. Biol. 2018; 14e8126Google Scholar) are gaining increasing popularity and have also been successfully applied in secretomics experiments (24Tushaus J. Muller S.A. Kataka E.S. Zaucha J. Sebastian Monasor L. Su M. et al.An optimized quantitative proteomics method establishes the cell type-resolved mouse brain secretome.EMBO J. 2020; 39e105693Google Scholar). In DIA, all peptides within a defined mass-to-charge (m/z) window are subjected to fragmentation, thus enhancing proteome coverage and reproducibility. A common observation of secretomics experiments is the presence of hundreds of intracellular proteins, while only a small fraction of proteins are annotated as secreted or extracellular (5Meissner F. Scheltema R.A. Mollenkopf H.J. Mann M. Direct proteomic quantification of the secretome of activated immune cells.Science. 2013; 340: 475-478Google Scholar, 25Deshmukh A.S. Cox J. Jensen L.J. Meissner F. Mann M. Secretome analysis of lipid-induced insulin resistance in skeletal muscle cells by a combined experimental and bioinformatics workflow.J. Proteome Res. 2015; 14: 4885-4895Google Scholar). Even under the best culture conditions, the cell culture will never have a 100% viability and a small but not neglectable number of cells will undergo apoptosis or necrosis, which might lead to interferences with the analysis. Sample handling and treatment, for example, excessive washing steps, can further aggravate this problem promoting membrane leakage or cell death. Achieving high cell viability is mandatory for secretomics assays and the contribution of cell death and membrane leakage to the secretome should be monitored. Assessment of the culture quality can be done through quantification of lactate dehydrogenase release into the cell culture supernatant or through trypan-blue staining of cells (25Deshmukh A.S. Cox J. Jensen L.J. Meissner F. Mann M. Secretome analysis of lipid-induced insulin resistance in skeletal muscle cells by a combined experimental and bioinformatics workflow.J. Proteome Res. 2015; 14: 4885-4895Google Scholar). Furthermore, it has been suggested to use the contribution of highly abundant intracellular proteins to the overall mass spectrometry (MS) signal intensity in the secretome sample, such as structural ribosomal proteins or cytoskeleton components (ACTB, TUBB), as an internal quality measure (2Villarreal L. Mendez O. Salvans C. Gregori J. Baselga J. Villanueva J. Unconventional secretion is a major contributor of cancer cell line secretomes.Mol. Cell Proteomics. 2013; 12: 1046-1060Google Scholar, 26Mendez O. Villanueva J. Challenges and opportunities for cell line secretomes in cancer proteomics.Proteomics Clin. Appl. 2015; 9: 348-357Google Scholar, 27Mbeunkui F. Fodstad O. Pannell L.K. Secretory protein enrichment and analysis: an optimized approach applied on cancer cell lines using 2D LC-MS/MS.J. Proteome Res. 2006; 5: 899-906Google Scholar). Increased MS signal intensities of intracellular proteins can be an indication for the contamination of the secretome due to apoptotic processes or compromised cell membranes. The majority of secretome studies are performed in vitro (28Brown K.J. Formolo C.A. Seol H. Marathi R.L. Duguez S. An E. et al.Advances in the proteomic investigation of the cell secretome.Expert Rev. Proteomics. 2012; 9: 337-345Google Scholar, 29Hathout Y. Approaches to the study of the cell secretome.Expert Rev. Proteomics. 2007; 4: 239-248Google Scholar) using mammalian cell culture systems. These cells rely on the addition of (bovine) serum or serum-like supplements to provide a favorable environment for cell growth and normal cellular functions. However, the presence of serum or media supplements poses a major challenge as it leads to a large dynamic range of protein abundances with several orders of magnitude that need to be covered by the mass spectrometer. Highly abundant serum proteins, such as albumin with concentrations of up to 5 g/L in the medium, hamper the detection of cell-derived secreted proteins that typically feature concentrations in a range of low ng/ml (30Chevallet M. Diemer H. Van Dorssealer A. Villiers C. Rabilloud T. Toward a better analysis of secreted proteins: the example of the myeloid cells secretome.Proteomics. 2007; 7: 1757-1770Google Scholar, 31Mukherjee P. Mani S. Methodologies to decipher the cell secretome.Biochim. Biophys. Acta. 2013; 1834: 2226-2232Google Scholar). To circumvent the dynamic range limitation, the standard approach for secretomics analysis is serum-free cell culture (5Meissner F. Scheltema R.A. Mollenkopf H.J. Mann M. Direct proteomic quantification of the secretome of activated immune cells.Science. 2013; 340: 475-478Google Scholar, 25Deshmukh A.S. Cox J. Jensen L.J. Meissner F. Mann M. Secretome analysis of lipid-induced insulin resistance in skeletal muscle cells by a combined experimental and bioinformatics workflow.J. Proteome Res. 2015; 14: 4885-4895Google Scholar, 27Mbeunkui F. Fodstad O. Pannell L.K. Secretory protein enrichment and analysis: an optimized approach applied on cancer cell lines using 2D LC-MS/MS.J. Proteome Res. 2006; 5: 899-906Google Scholar, 30Chevallet M. Diemer H. Van Dorssealer A. Villiers C. Rabilloud T. Toward a better analysis of secreted proteins: the example of the myeloid cells secretome.Proteomics. 2007; 7: 1757-1770Google Scholar, 32Kleifeld O. Doucet A. auf dem Keller U. Prudova A. Schilling O. Kainthan R.K. et al.Isotopic labeling of terminal amines in complex samples identifies protein N-termini and protease cleavage products.Nat. Biotechnol. 2010; 28: 281-288Google Scholar). However, a shift from serum-supplemented to serum-free cell culture conditions still bears the risk of residual serum proteins that contaminate the secretome. Even after extensive washing of cells, serum proteins can still be present in the secretome potentially masking very-low abundant proteins. Additionally, it should be considered that residual bovine-derived proteins bear the risk of distorting the results when the raw data are not properly filtered and quantitative information of bovine-derived peptides are included in the downstream analysis. To further improve data reliability, stable isotope labeling with amino acids in cell culture (SILAC) can be applied to label the cellular proteome (33Ong S.E. Mann M. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC).Nat. Protoc. 2006; 1: 2650-2660Google Scholar). SILAC is a metabolic labeling strategy that employs stable isotope-labeled amino acids which are added to the cell culture medium and incorporated into proteins by the endogenous protein translational machinery. The application of SILAC labeling (34Kuhn P.H. Koroniak K. Hogl S. Colombo A. Zeitschel U. Willem M. et al.Secretome protein enrichment identifies physiological BACE1 protease substrates in neurons.EMBO J. 2012; 31: 3157-3168Google Scholar) can help to discriminate between residual serum contaminants and cell-derived proteins (2Villarreal L. Mendez O. Salvans C. Gregori J. Baselga J. Villanueva J. Unconventional secretion is a major contributor of cancer cell line secretomes.Mol. Cell Proteomics. 2013; 12: 1046-1060Google Scholar, 35Faca V.M. Ventura A.P. Fitzgibbon M.P. Pereira-Faca S.R. Pitteri S.J. Green A.E. et al.Proteomic analysis of ovarian cancer cells reveals dynamic processes of protein secretion and shedding of extra-cellular domains.PLoS One. 2008; 3: e2425Google Scholar) but does not solve the dynamic range problem. However, SILAC labeling requires the use of dialyzed serum to achieve comprehensive labeling of proteins, which was reported to potentially change the cellular proteome (36Imami K. Sugiyama N. Tomita M. Ishihama Y. Quantitative proteome and phosphoproteome analyses of cultured cells based on SILAC labeling without requirement of serum dialysis.Mol. Biosyst. 2010; 6: 594-602Google Scholar). Nevertheless, metabolic labeling approaches, like azidohomoalanine (AHA) (37Eichelbaum K. Winter M. Berriel Diaz M. Herzig S. Krijgsveld J. Selective enrichment of newly synthesized proteins for quantitative secretome analysis.Nat. Biotechnol. 2012; 30: 984-990Google Scholar) or labeling of secreted proteins with azido sugars, have been developed that allow for the selective enrichment of secreted proteins under serum-containing cell culture conditions, which thus help to overcome the dynamic range issue. The different secretomics approaches will be discussed later. Another strategy to allow deeper secretome analysis and to overcome the dynamic range issue is the removal of highly abundant serum proteins like albumin by immunoaffinity-based depletion (38Pernemalm M. Lewensohn R. Lehtio J. Affinity prefractionation for MS-based plasma proteomics.Proteomics. 2009; 9: 1420-1427Google Scholar) prior to MS analysis. However, depletion of highly abundant proteins can be accompanied by a nonspecific loss of cytokines and other sticky protein species (39Granger J. Siddiqui J. Copeland S. Remick D. Albumin depletion of human plasma also removes low abundance proteins including the cytokines.Proteomics. 2005; 5: 4713-4718Google Scholar), narrowing down the biological relevance of such secretome data. The sample complexity can be further reduced by standard protein fractionation methods using high pH, strong cation exchange, or strong anion exchange fractionation (40Weng Y. Sui Z. Shan Y. Jiang H. Zhou Y. Zhu X. et al.In-depth proteomic quantification of cell secretome in serum-containing conditioned medium.Anal. Chem. 2016; 88: 4971-4978Google Scholar, 41Han D. Jin J. Woo J. Min H. Kim Y. Proteomic analysis of mouse astrocytes and their secretome by a combination of FASP and StageTip-based, high pH, reversed-phase fractionation.Proteomics. 2014; 14: 1604-1609Google Scholar). Besides the presence of serum, secretome analysis is further complicated by the complex composition of the basal cell culture medium. The presence of different medium ingredients such as salts, carbohydrates, vitamins, and amino acids can interfere with subsequent sample processing steps and the MS analysis leading to signal suppression. The high dilution and sample complexity often requires high volumes of cell culture supernatant that can be used for subsequent protein concentration and clean-up steps prior to LC-MS analysis. Different strategies have been used to address these challenges: protein precipitation with TCA (30Chevallet M. Diemer H. Van Dorssealer A. Villiers C. Rabilloud T. Toward a better analysis of secreted proteins: the example of the myeloid cells secretome.Proteomics. 2007; 7: 1757-1770Google Scholar, 42Poschmann G. Prescher N. Stuhler K. Quantitative MS workflow for a high-quality secretome analysis by a quantitative secretome-proteome comparison.Methods Mol. Biol. 2021; 2228: 293-306Google Scholar) or SP3 (18Knecht S. Eberl H.C. Bantscheff M. Interval-based secretomics unravels acute-phase response in hepatocyte model systems.Mol. Cell Proteomics. 2022; 21100241Google Scholar), ultrafiltration with low-molecular weight cut-off filters (2Villarreal L. Mendez O. Salvans C. Gregori J. Baselga J. Villanueva J. Unconventional secretion is a major contributor of cancer cell line secretomes.Mol. Cell Proteomics. 2013; 12: 1046-1060Google Scholar) but also lyophilization or speed vacuum centrifugation to concentrate the sample (43Xie L. Tsaprailis G. Chen Q.M. Proteomic identification of insulin-like growth factor-binding protein-6 induced by sublethal H2O2 stress from human diploid fibroblasts.Mol. Cell Proteomics. 2005; 4: 1273-1283Google Scholar) have been applied. However, all these different strategies have strengths and shortcomings. TCA precipitation for example can lead to protein loss, co-precipitation of contaminants like salts or lipids, and can introduce protein modifications. However, TCA precipitation is relatively fast, requires minimal incubation time, it is easily scalable, cost effective, and compatible with a variety of sample types. The use of low-molecular weight cut-off filter can cause protein loss due to selective retention of larger proteins. If proteins of interest have a molecular weight close to the filter's cutoff, they might partially or completely pass the filter, leading to reduced protein yield. This can be particularly problematic for low abundance proteins. In terms of speed, scalability, throughput, and compatibility with chemical-labeling approaches for relative quantification, an SP3-based sample preparation workflow might be beneficial. However, SP3 can lead to loss of low-abundant proteins. PTMs affect many biological processes and are of importance for the functional diversity of the proteome. Hence it is not surprising that PTMs, such as glycosylations, phosphorylations, sulfations, and citrullinations, also play a crucial role for secreted proteins and changes in PTM decoration are associated with numerous diseases, such as cancer (44Rodrigues J.G. Balmana M. Macedo J.A. Pocas J. Fernandes A. de-Freitas-Junior J.C.M. et al.Glycosylation in cancer: selected roles in tumour progression, immune modulation and metastasis.Cell Immunol. 2018; 333: 46-57Google Scholar), inflammation (45Loke I. Kolarich D. Packer N.H. Thaysen-Andersen M. Emerging roles of protein mannosylation in inflammation and infection.Mol. Aspects Med. 2016; 51: 31-55Google Scholar, 46Tilvawala R. Nguyen S.H. Maurais A.J. Nemmara V.V. Nagar M. Salinger A.J. et al.The rheumatoid arthritis-associated citrullinome.Cell Chem Biol. 2018; 25: 691-704.e696Google Scholar, 47Romero V. Fert-Bober J. Nigrovic P.A. Darrah E. Haque U.J. Lee D.M. et al.Immune-mediated pore-forming pathways induce cellular hypercitrullination and generate citrullinated autoantigens in rheumatoid arthritis.Sci. Transl. Med. 2013; 5209ra150Google Scholar), or congenital (48Paprocka J. Jezela-Stanek A. Tylki-Szymanska A. Grunewald S. Congenital disorders of glycosylation from a neurological perspective.Brain Sci. 2021; 11: 88Google Scholar) and neurological disorders (49Reily C. Stewart T.J. Renfrow M.B. Novak J. Glycosylation in health and disease.Nat. Rev. Nephrol. 2019; 15: 346-366Google Scholar). As most PTMs can only be found in the sample in substoichiometric levels, PTM analysis requires an enrichment of the modified proteins or peptides. Whereas glycosylations are well-known PTMs on secreted proteins, the function and biological significance of other PTMs for the extracellular proteome, such as phosphorylations, is understudied and still enigmatic. By far, protein glycosylation is the most abundant PTM of proteins that is found in all eukaryotic cells and which is involved in a multitude of biological processes such as protein folding, protein solubility, or cell-cell communication (50Schjoldager K.T. Narimatsu Y. Joshi H.J. Clausen H. Global view of human protein glycosylation pathways and functions.Nat. Rev. Mol. Cell Biol. 2020; 21: 729-749Google Scholar). As protein glycosylation is a whole field on its own which rapidly evolves, we will only cover this briefly. The attachment of glycan structures with multiple hydroxy groups increases the hydrophilicity of proteins and provides crucial structural and functional properties that are of high significance for diverse biological processes such as blood clotting, cross-linking of cells, or the immune response (51Kristic J. Lauc G. Ubiquitous importance of protein glycosylation.Methods Mol. Biol. 2017; 1503: 1-12Google Scholar). Alterations in glycosylation profiles are associated with many diseases with diverse clinical representations, such as the congenital disorders of glycosylation or cancer, where glycoproteins are used as biomarkers (52Drake P.M. Cho W. Li B. Prakobphol A. Johansen E. Anderson N.L. et al.Sweetening the pot: adding glycosylation to the biomarker discovery equation.Clin. Chem. 2010; 56: 223-236Google Scholar, 53Ohtsubo K. Marth J.D. Glycosylation in cellular mechanisms of health and disease.Cell. 2006; 126: 855-867Google Scholar, 54Boersema P.J. Geiger T. Wisniewski J.R. Mann M. Quantification of the N-glycosylated secretome by super-SILAC during breast cancer progression and in human blood samples.Mol. Cell Proteomics. 2013; 12: 158-171Google Scholar). Hence, the investigation and characterization of the glycan structures in health and disease have become more and more relevant. Even though glycosylations are not a unique feature of secreted proteins and can also be found on intracellular proteins, approximately 66% of all secreted proteins and 87% of type I and type II transmembrane proteins (34Kuhn P.H. Koroniak K. Hogl S. Colombo A. Zeitschel U. Willem M. et al.Secretome protein enrichment identifies physiological BACE1 protease substrates in neurons.EMBO J. 2012; 31: 3157-3168Google Scholar) are glycosylated. In contrast, many unconventional secreted proteins (not containing the respective signal peptide) are not glycosylated, which needs to be considered when choosing a suitable secretomics assay, since not all of the presented workflows also allow the exploration of unconven
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