Abstract
Identification of molecular biomarkers for human diseases is one of the most important disciplines in translational science as it helps to elucidate their origin and early progression. Thus, it is a key factor in better diagnosis, prognosis, and treatment. Proteomics can help to solve the problem of sample complexity when the most common primary sample specimens were analyzed: organic fluids of easy access. The latest developments in high-throughput and label-free quantitative proteomics (SWATH-MS), together with more advanced liquid chromatography, have enabled the analysis of large sample sets with the sensitivity and depth needed to succeed in this task. In this chapter, we show different sample processing methods (major protein depletion, digestion, etc.) and a micro LC-SWATH-MS protocol to identify/quantify several proteins in different types of samples (serum/plasma, saliva, urine, tears).
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References
Anjo SI, Santa C, Manadas B (2017) SWATH-MS as a tool for biomarker discovery: from basic research to clinical applications. Proteomics 17:3–4
Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537:347–355
Darling AL, Uversky VN (2018) Intrinsic disorder and posttranslational modifications: the darker side of the biological dark matter. Front Genet 9:1–18
FDA-NIH Biomarker Working Group (2016) BEST (Biomarkers, EndpointS, and other Tools) Resource. Food and Drug Administration, Silver Spring, MD
Füzéry AK, Levin J, Chan MM, Chan DW (2013) Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin Proteomics 10:1
Pavlou MP, Diamandis EP, Blasutig IM (2013) The long journey of cancer biomarkers from the bench to the clinic. Clin Chem 59:147–157
Anderson NL (2010) The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum. Clin Chem 56:177–185
Vidova V, Spacil Z (2017) A review on mass spectrometry-based quantitative proteomics: targeted and data independent acquisition. Anal Chim Acta 964:7–23
Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422:198–207
Hu A, Noble WS, Wolf-Yadlin A (2016) Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 5:F1000 Faculty Rev-419
Michalski A, Cox J, Mann M (2011) More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J Proteome Res 10:1785–1793
Meyer JG, Schilling B (2017) Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev Proteomics 14:419–429
Doerr A (2014) DIA mass spectrometry. Nat Methods 12:35
Wolf-Yadlin A, Hu A, Noble WS (2016) Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 5:1–12
Kolbowski L, Bernhardt OM, Reiter L, Rappsilber J (2019) Data-independent acquisition improves quantitative cross-linking mass spectrometry. Mol Cell Proteomics 18:786–795
Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, Bonner R, Aebersold R (2012) 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 11:1–17
Chapman JD, Goodlett DR, Masselon CD (2014) Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrom Rev 33:452–470
Kang Y, Burton L, Lau A, Tate S (2017) SWATH-ID: an instrument method which combines identification and quantification in a single analysis. Proteomics 17:e1500522
Lin Q, Tan HT, Chung MCM (2019) Mass spectrometry of proteins. Methods Mol Biol 1977:3–15
Shilov IV, Seymour SL, Patel AA, Loboda A, Tang WH, Keating SP, Hunter CL, Nuwaysir LM, Schaeffer DA (2007) The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol Cell Proteomics 6:1638–1655
Röst HL, Aebersold R, Schubert OT (2017) Automated swath data analysis using targeted extraction of ion chromatograms. Methods Mol Biol 1550:289–307
Lin Q, Tan HT, Chung MCM (2019) Methods in molecular biology. Humana, New York, pp 3–15
Wang J, Pérez-Santiago J, Katz JE, Mallick P, Bandeira N (2010) Peptide identification from mixture tandem mass spectra. Mol Cell Proteomics 9:1476–1485
Parker SJ, Rost H, Rosenberger G, Collins BC, Malmström L, Amodei D, Venkatraman V, Raedschelders K, Van Eyk JE, Aebersold R (2015) Identification of a set of conserved eukaryotic internal retention time standards for data independent acquisition mass spectrometry. Mol Cell Proteomics 14:2800–2813
Holewinski RJ, Parker SJ, Matlock AD, Venkatraman V, Van Eyk JE (2016) Methods for SWATH™: data independent acquisition on triplet of mass spectrometers. Methods Mol Biol 1410:265–279
Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R (2018) Data-independent acquisition-based SWATH - MS for quantitative proteomics: a tutorial. Mol Syst Biol 14:1–23
Ahn SM, Simpson RJ (2007) Body fluid proteomics: prospects for biomarker discovery. Proteomics Clin Appl 1:1004–1015
Manuscript A (2010) Human body fluid proteome analysis. Proteomics 6:6326–6353
Csősz É, Kalló G, Márkus B, Deák E, Csutak A, Tőzsér J (2017) Quantitative body fluid proteomics in medicine—a focus on minimal invasiveness. J Proteomics 153:30–43
Humphrey SP, Williamson RT (2001) A review of saliva normal composition, flow, and function. J Prosthet Dent 85:162–169
De Almeida PDV, Grégio AMT, Machado MÂN, De Lima AAS, Azevedo LR (2008) Saliva composition and functions: a comprehensive review. J Contemp Dent Pract 9:072–080
Schulz BL, Cooper-White J, Punyadeera CK (2013) Saliva proteome research: current status and future outlook. Crit Rev Biotechnol 33:246–259
Shaila M, Pai GP, Shetty P (2013) Salivary protein concentration, flow rate, buffer capacity and pH estimation: a comparative study among young and elderly subjects, both normal and with gingivitis and periodontitis. J Indian Soc Periodontol 17:42–46
Ventura TM d S, Ribeiro NR, Dionizio AS, Sabino IT, Buzalaf MAR (2018) Standardization of a protocol for shotgun proteomic analysis of saliva. J Appl Oral Sci 26:e20170561
Wang K, Wang Y, Wang X, Ren Q, Han S, Ding L, Li Z, Zhou X, Li W, Zhang L (2018) Comparative salivary proteomics analysis of children with and without dental caries using the iTRAQ/MRM approach. J Transl Med 16:1–13
Torabi M, Drahansky M, Paridah M, Moradbak A, Mohamed A, abdulwahab taiwo Owolabi F, Asniza M, Abdul Khalid SH (2016) We are IntechOpen, the world’s leading publisher of Open Access books Built by scientists, for scientists TOP 1 %, Intech, vol. i, no. tourism, p. 13
Jessie K, Hashim OH, Rahim ZHA (2008) Protein precipitation method for salivary proteins and rehydration buffer for two-dimensional electrophoresis. Biotechnology 7(4): 686–693
Wessel D, Flügge UI (1984) A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Anal Biochem 138:141–143
Fullard RJ, Snyder C (1990) Protein levels in nonstimulated and stimulated tears of normal human subjects. IOVS 31(6)
Tiffany JM (2003) Tears in health and disease. Eye 17:923–926
Zhou L, Zhao SZ, Koh SK, Chen L, Vaz C, Tanavde V, Li XR, Beuerman RW (2012) In-depth analysis of the human tear proteome. J Proteomics 75:3877–3885
Li N, Wang N, Zheng J, Liu XM, Lever OW, Erickson PM, Li L (2005) Characterization of human tear proteome using multiple proteomic analysis techniques. J Proteome Res 4:2052–2061
Green-Church KB, Nichols KK, Kleinholz NM, Zhang L, Nichols JJ (2008) Investigation of the human tear film proteome using multiple proteomic approaches. Mol Vis 14:456–470
Ablamowicz AF, Nichols JJ (2017) Concentrations of MUC16 and MUC5AC using three tear collection methods. Mol Vis 23:529–537
Lema I, Brea D, Rodríguez-González R, Díez-Feijoo E, Sobrino T (2010) Proteomic analysis of the tear film in patients with keratoconus. Mol Vis 16:2055–2061
de Souza GA, Godoy LMF, Mann M (2006) Identification of 491 proteins in the tear fluid proteome reveals a large number of proteases and protease inhibitors. Genome Biol 7(8):R72
Nagaraj N, Mann M (2011) Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome. J Proteome Res 10:637–645
Adachi J, Kumar C, Zhang Y, Olsen JV, Mann M (2006) The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol 7(9):R80
McDougal WS, William S (2012) Campbell-Walsh urology tenth edition review. Elsevier/Saunders, Philadelphia
Pusch W, Flocco MT, Leung SM, Thiele H, Kostrzewa M (2003) Mass spectrometry-based clinical proteomics. Pharmacogenomics 4:463–476
Kalantari S, Jafari A, Moradpoor R, Ghasemi E, Khalkhal E (2015) Human urine proteomics: analytical techniques and clinical applications in renal diseases. Int J Proteomics 2015:782798
Wu J, Chen YD, Gu W (2010) Urinary proteomics as a novel tool for biomarker discovery in kidney diseases. J Zhejiang Univ Sci B 11:227–237
Shao C, Zhao M, Chen X, Sun H, Yang Y, Xiao X, Guo Z, Liu X, Lv Y, Chen X, Sun W, Wu D, Gao Y (2019) Comprehensive analysis of individual variation in the urinary proteome revealed significant gender differences. Mol Cell Proteomics 18:1110–1122
Zhao M, Li M, Yang Y, Guo Z, Sun Y, Shao C, Li M, Sun W, Gao Y (2017) A comprehensive analysis and annotation of human normal urinary proteome. Sci Rep 7. https://doi.org/10.1038/s41598-017-03226-6
Kong F-M, Zhao L, Wang L, Chen Y, Hu J, Fu X, Bai C, Wang L, Lawrence TS, Anscher MS, Dicker A, Okunieff P, Wang L, Chen Y, Hu J, Fu X (2017) Ensuring sample quality for blood biomarker studies in clinical trials: a multicenter international study for plasma and serum sample preparation. Transl Lung Cancer Res 6:625–634
Arapidi G, Osetrova M, Ivanova O, Butenko I, Saveleva T, Pavlovich P, Anikanov N, Ivanov V, Govorun V (2018) Peptidomics dataset: blood plasma and serum samples of healthy donors fractionated on a set of chromatography sorbents. Data Brief 18:1204–1211
Ignjatovic V, Geyer PE, Palaniappan K, Chaaban J, Omenn G, Baker M, Deutsch E, Schwenk J (2019) Mass spectrometry-based plasma proteomics: considerations from sample collection to achieving translational data. J Proteome Res 18(12):4085–4097
Geyer PE, Voytik E, Treit PV, Doll S, Kleinhempel A, Niu L, Müller JB, Buchholtz M, Bader JM, Teupser D, Holdt LM, Mann M (2019) Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol Med 11:e10427
Lin L, Zheng J, Yu Q, Chen W, Xing J, Chen C, Tian R (2018) High throughput and accurate serum proteome profiling by integrated sample preparation technology and single-run data independent mass spectrometry analysis. J Proteomics 174:9–16
Pietrowska M, Wlosowicz A, Gawin M, Widlak P (2019) Advances in experimental medicine and biology. Springer, New York, pp 57–76
Fernández C, Santos HM, Ruíz-Romero C, Blanco FJ, Capelo-Martínez JL (2011) A comparison of depletion versus equalization for reducing high-abundance proteins in human serum. Electrophoresis 32:2966–2974
Kay R, Barton C, Ratcliffe L, Matharoo-Ball B, Brown P, Roberts J, Teale P, Creaser C (2008) Enrichment of low molecular weight serum proteins using acetonitrile precipitation for mass spectrometry based proteomic analysis. Rapid Commun Mass Spectrom 22:3255–3260
Warder SE, Tucker LA, Strelitzer TJ, McKeegan EM, Meuth JL, Jung PM, Saraf A, Singh B, Lai-Zhang J, Gagne G, Rogers JC (2009) Reducing agent-mediated precipitation of high-abundance plasma proteins. Anal Biochem 387:184–193
He J, Huang M, Wang D, Zhang Z, Li G (2014) Magnetic separation techniques in sample preparation for biological analysis: a review. J Pharm Biomed Anal 101:84–101
Kishikawa N, Kuroda N (2014) Analytical techniques for the determination of biologically active quinones in biological and environmental samples. J Pharm Biomed Anal 87:261–270
Chen H, Deng C, Zhang X (2010) Synthesis of Fe3O4@SiO2@PMMA core-shell-shell magnetic microspheres for highly efficient enrichment of peptides and proteins for MALDI-ToF MS analysis. Angew Chem Int Ed 49:607–611
Zhao M, Xie Y, Deng C, Zhang X (2014) Recent advances in the application of core-shell structured magnetic materials for the separation and enrichment of proteins and peptides. J Chromatogr A 1357:182–193
Kailasa SK, Wu H-F (2010) Surface modified silver selinide nanoparticles as extracting probes to improve peptide/protein detection via nanoparticles-based liquid phase microextraction coupled with MALDI mass spectrometry. Talanta 83:527–534
Shastri L, Kailasa SK, Wu HF (2010) Nanoparticle-single drop microextraction as multifunctional and sensitive nanoprobes: binary matrix approach for gold nanoparticles modified with (4-mercaptophenyliminomethyl)-2-methoxyphenol for peptide and protein analysis in MALDI-TOF MS. Talanta 81:1176–1182
Farokhzad OC, Langer R (2009) Impact of nanotechnology on drug delivery. ACS Nano 3:16–20
Hafner A, Lovrić J, Lak GP, Pepić I (2014) Nanotherapeutics in the EU: an overview on current state and future directions. Int J Nanomedicine 9:1005–1023
Akhavan O, Ghaderi E, Shahsavar M (2013) Graphene nanogrids for selective and fast osteogenic differentiation of human mesenchymal stem cells. Carbon 59:200–211
Monopoli MP, Åberg C, Salvati A, Dawson KA (2012) Biomolecular coronas provide the biological identity of nanosized materials. Nat Nanotechnol 7:779–786
Mahmoudi M, Lynch I, Ejtehadi MR, Monopoli MP, Bombelli FB, Laurent S (2011) Protein−nanoparticle interactions: opportunities and challenges. Chem Rev 111:5610–5637
Caracciolo G (2015) Liposome-protein corona in a physiological environment: challenges and opportunities for targeted delivery of nanomedicines. Nanomedicine 11:543–557
Sakulkhu U, Maurizi L, Mahmoudi M, Motazacker M, Vries M, Gramoun A, Ollivier Beuzelin MG, Vallée JP, Rezaee F, Hofmann H (2014) Ex situ evaluation of the composition of protein corona of intravenously injected superparamagnetic nanoparticles in rats. Nanoscale 6:11439–11450
Wan S, Kelly PM, Mahon E, Stöckmann H, Rudd PM, Caruso F, Dawson KA, Yan Y, Monopoli MP (2015) The “sweet” side of the protein corona: effects of glycosylation on nanoparticle-cell interactions. ACS Nano 9:2157–2166
Hellstrand E, Lynch I, Andersson A, Drakenberg T, Dahlbäck B, Dawson KA, Linse S, Cedervall T (2009) Complete high-density lipoproteins in nanoparticle corona. FEBS J 276:3372–3381
Cedervall T, Lynch I, Lindman S, Berggård T, Thulin E, Nilsson H, Dawson KA, Linse S (2007) Understanding the nanoparticle-protein corona using methods to quntify exchange rates and affinities of proteins for nanoparticles. Proc Natl Acad Sci U S A 104:2050–2055
Mahmoudi M, Bertrand N, Zope H, Farokhzad OC (2016) Emerging understanding of the protein corona at the nano-bio interfaces. Nano Today 11:817–832
Safavi-Sohi R, Maghari S, Raoufi M, Jalali SA, Hajipour MJ, Ghassempour A, Mahmoudi M (2016) Bypassing protein corona issue on active targeting: zwitterionic coatings dictate specific interactions of targeting moieties and cell receptors. ACS Appl Mater Interfaces 8:22808–22818
Monopoli MP, Walczyk D, Campbell A, Elia G, Lynch I, Baldelli Bombelli F, Dawson KA (2011) Physical−chemical aspects of protein corona: relevance to in vitro and in vivo biological impacts of nanoparticles. J Am Chem Soc 133:2525–2534
Tenzer S, Docter D, Kuharev J, Musyanovych A, Fetz V, Hecht R, Schlenk F, Fischer D, Kiouptsi K, Reinhardt C, Landfester K, Schild H, Maskos M, Knauer SK, Stauber RH (2013) Rapid formation of plasma protein corona critically affects nanoparticle pathophysiology. Nat Nanotechnol 8:772–781
Koh WL, Tham PH, Yu H, Leo HL, Yong Kah JC (2016) Aggregation and protein corona formation on gold nanoparticles affect viability and liver functions of primary rat hepatocytes. Nanomedicine (Lond) 11:2275–2287
Walkey CD, Chan WCW (2012) Understanding and controlling the interaction of nanomaterials with proteins in a physiological environment. Chem Soc Rev 41:2780–2799
Lundqvist M, Stigler J, Cedervall T, Berggård T, Flanagan MB, Lynch I, Elia G, Dawson K (2011) The evolution of the protein corona around nanoparticles: a test study. ACS Nano 5:7503–7509
Tenzer S, Docter D, Rosfa S, Wlodarski A, Kuharev J, Rekik A, Knauer SK, Bantz C, Nawroth T, Bier C, Sirirattanapan J, Mann W, Treuel L, Zellner R, Maskos M, Schild H, Stauber RH (2011) Nanoparticle size is a critical physicochemical determinant of the human blood plasma corona: a comprehensive quantitative proteomic analysis. ACS Nano 5:7155–7167
Laurent S, Ng EP, Thirifays C, Lakiss L, Goupil GM, Mintova S, Burtea C, Oveisi E, Hébert C, De Vries M, Motazacker MM, Rezaee F, Mahmoudi M (2013) Corona protein composition and cytotoxicity evaluation of ultra-small zeolites synthesized from template free precursor suspensions. Toxicol Res (Camb) 2:270–279
Mahmoudi M, Monopoli MP, Rezaei M, Lynch I, Bertoli F, Mcmanus JJ, Dawson KA (2013) The protein corona mediates the impact of nanomaterials and slows amyloid beta fibrillation. Chembiochem 14:568–572
Lundqvist M, Stigler J, Elia G, Lynch I, Cedervall T, Dawson KA (2008) Nanoparticle size and surface properties determine the protein corona with possible implications for biological impacts. Proc Natl Acad Sci U S A 105:14265–14270
Ehrenberg MS, Friedman AE, Finkelstein JN, Oberdörster G, McGrath JL (2009) The influence of protein adsorption on nanoparticle association with cultured endothelial cells. Biomaterials 30:603–610
Caracciolo G, Callipo L, De Sanctis SC, Cavaliere C, Pozzi D, Laganà A (2010) Surface adsorption of protein corona controls the cell internalization mechanism of DC-Chol-DOPE/DNA lipoplexes in serum. Biochim Biophys Acta Biomembr 1798:536–543
Docter D, Bantz C, Westmeier D, Galla HJ, Wang Q, Kirkpatrick JC, Nielsen P, Maskos M, Stauber RH (2014) The protein corona protects against size- and dose-dependent toxicity of amorphous silica nanoparticles. Beilstein J Nanotechnol 5:1380–1392
Mahmoudi M, Laurent S, Shokrgozar MA, Hosseinkhani M (2011) Toxicity evaluations of superparamagnetic iron oxide nanoparticles: cell “vision” versus physicochemical properties of nanoparticles. ACS Nano 5:7263–7276
Mahmoudi M, Saeedi-Eslami SN, Shokrgozar MA, Azadmanesh K, Hassanlou M, Kalhor HR, Burtea C, Rothen-Rutishauser B, Laurent S, Sheibani S, Vali H (2012) Cell “vision”: complementary factor of protein corona in nanotoxicology. Nanoscale 4:5461–5468
Zhang H, Burnum KE, Luna ML, Petritis BO, Kim JS, Qian WJ, Moore RJ, Heredia-Langner A, Webb-Robertson BJM, Thrall BD, Camp DG, Smith RD, Pounds JG, Liu T (2011) Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size. Proteomics 11:4569–4577
Caracciolo G, Pozzi D, Capriotti AL, Cavaliere C, Foglia P, Amenitsch H, Laganà A (2011) Evolution of the protein corona of lipid gene vectors as a function of plasma concentration. Langmuir 27:15048–15053
Laurent S, Burtea C, Thirifays C, Rezaee F, Mahmoudi M (2013) Significance of cell “observer” and protein source in nanobiosciences. J Colloid Interface Sci 392:431–445
Caracciolo G, Pozzi D, Capriotti AL, Cavaliere C, Piovesana S, La Barbera G, Amici A, Laganà A (2014) The liposome-protein corona in mice and humans and its implications for in vivo delivery. J Mater Chem B 2:7419–7428
Pozzi D, Caracciolo G, Digiacomo L, Colapicchioni V, Palchetti S, Capriotti AL, Cavaliere C, Zenezini Chiozzi R, Puglisi A, Laganà A (2015) The biomolecular corona of nanoparticles in circulating biological media. Nanoscale 7:13958–13966
Schöttler S, Klein K, Landfester K, Mailänder V (2016) Protein source and choice of anticoagulant decisively affect nanoparticle protein corona and cellular uptake. Nanoscale 8:5526–5536
Mahmoudi M, Abdelmonem AM, Behzadi S, Clement JH, Dutz S, Ejtehadi MR, Hartmann R, Kantner K, Linne U, Maffre P, Metzler S, Moghadam MK, Pfeiffer C, Rezaei M, Ruiz-Lozano P, Serpooshan V, Shokrgozar MA, Nienhaus GU, Parak WJ (2013) Temperature: the “ignored” factor at the NanoBio interface. ACS Nano 7:6555–6562
Dell’Orco D, Lundqvist M, Linse S, Cedervall T (2014) Mathematical modeling of the protein corona: implications for nanoparticulate delivery systems. Nanomedicine 9:851–858
Smith RA, Andrews KS, Brooks D, Fedewa SA, Manassaram-Baptiste D, Saslow D, Brawley OW, Wender RC (2018) Cancer screening in the United States, 2018: a review of current American Cancer Society guidelines and current issues in cancer screening. CA Cancer J Clin 68:297–316
Hajipour MJ, Laurent S, Aghaie A, Rezaee F, Mahmoudi M (2014) Personalized protein coronas: a “key” factor at the nanobiointerface. Biomater Sci 2:1210–1221
Zheng T, Pierre-Pierre N, Yan X, Huo Q, Almodovar AJO, Valerio F, Rivera-Ramirez I, Griffith E, Decker DD, Chen S, Zhu N (2015) Gold nanoparticle-enabled blood test for early stage cancer detection and risk assessment. ACS Appl Mater Interfaces 7:6819–6827
Corbo C, Molinaro R, Tabatabaei M, Farokhzad OC, Mahmoudi M (2017) Personalized protein corona on nanoparticles and its clinical implications. Biomater Sci 5:378–387
del Pilar Chantada-Vázquez M, López AC, Bravo SB, Vázquez-Estévez S, Acea-Nebril B, Núñez C (2019) Proteomic analysis of the bio-corona formed on the surface of (Au, Ag, Pt)-nanoparticles in human serum. Colloids Surf B Biointerfaces 177:141–148
del Pilar Chantada-Vázquez M, López AC, Vence MG, Vázquez-Estévez S, Acea-Nebril B, Calatayud DG, Jardiel T, Bravo SB, Núñez C (2020) Proteomic investigation on bio-corona of Au, Ag and Fe nanoparticles for the discovery of triple negative breast cancer serum protein biomarkers. J Proteomics 212:103581
Zheng J, Zhou C, Yu M, Liu J (2012) Different sized luminescent gold nanoparticles. Nanoscale 4:4073–4083
López-Cortés R, Oliveira E, Núñez C, Lodeiro C, Páez de la Cadena M, Fdez-Riverola F, López-Fernández H, Reboiro-Jato M, Glez-Peña D, Luis Capelo J, Santos HM (2012) Fast human serum profiling through chemical depletion coupled to gold-nanoparticle-assisted protein separation. Talanta 100:239–245
Bastús NG, Merkoçi F, Piella J, Puntes V (2014) Synthesis of highly monodisperse citrate-stabilized silver nanoparticles of up to 200 nm: kinetic control and catalytic properties. Chem Mater 26:2836–2846
Wu GW, He SB, Peng HP, Deng HH, Liu AL, Lin XH, Xia XH, Chen W (2014) Citrate-capped platinum nanoparticle as a smart probe for ultrasensitive mercury sensing. Anal Chem 86:10955–10960
Lu AH, Salabas EL, Schüth F (2007) Magnetic nanoparticles: synthesis, protection, functionalization, and application. Angew Chem Int Ed 46:1222–1244
Hermida-Nogueira L, Barrachina MN, Izquierdo I, García-Vence M, Lacerenza S, Bravo S, Castrillo A, García Á (2020) Proteomic analysis of extracellular vesicles derived from platelet concentrates treated with Mirasol® identifies biomarkers of platelet storage lesion. J Proteomics 210:103529
Bonzon-Kulichenko E, Pérez-Hernández D, Núñez E, Martínez-Acedo P, Navarro P, Trevisan-Herraz M, Ramos MDC, Sierra S, Martínez-Martínez S, Ruiz-Meana M, Miró-Casas E, García-Dorado D, Redondo JM, Burgos JS, Vázquez J (2011) A robust method for quantitative high-throughput analysis of proteomes by 18O labeling. Mol Cell Proteomics 10:M110.003335
Perez-Hernandez D, Gutiérrez-Vázquez C, Jorge I, López-Martín S, Ursa A, Sánchez-Madrid F, Vázquez J, Yáñez-Mó M (2013) The intracellular interactome of tetraspanin-enriched microdomains reveals their function as sorting machineries toward exosomes. J Biol Chem 288:11649–11661
Shevchenko A, Wilm M, Vorm O, Jensen ON, Podtelejnikov AV, Neubauer G, Mortensen P, Mann M (1996) A strategy for identifying gel-separated proteins in sequence databases by MS alone. Biochem Soc Trans 24:893–896
Schilling B, Gibson BW, Hunter CL (2018) Management. Manage Entrep:15–38
Bereman MS (2015) Tools for monitoring system suitability in LC MS/MS centric proteomic experiments. Proteomics 15:891–902
Bruderer R, Bernhardt OM, Gandhi T, Reiter L (2016) High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation. Proteomics 16:2246–2256
Röst HL, Aebersold R, Schubert OT (2017) Management. Manage Entrep 1550:15–38
Lam H, Aebersold R (2011) Building and searching tandem mass (MS/MS) spectral libraries for peptide identification in proteomics. Methods 54:424–431
Zi J, Zhang S, Zhou R, Zhou B, Xu S, Hou G, Tan F, Wen B, Wang Q, Lin L, Liu S (2014) Expansion of the ion library for mining SWATH-MS data through fractionation proteomics. Anal Chem 86:7242–7246
Lam H (2012) Spectral library searching for peptide identification in proteomics. Stat Interface 5:39–46
Schubert OT, Gillet LC, Collins BC, Navarro P, Rosenberger G, Wolski WE, Lam H, Amodei D, Mallick P, Maclean B, Aebersold R (2015) Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc 10:426–441
Deutsch EW, Perez-riverol Y, Chalkley RJ, Wilhelm M, Sachsenberg T, Walzer M, Käll L, Delanghe B, Schymanski EL, Wilmes P, Dorfer V, Kuster B (2019) Expanding the use of spectral libraries in proteomics. J Proteome Res 17:4051–4060
Escher C, Reiter L, Maclean B, Ossola R, Herzog F, Maccoss MJ, Rinner O (2014) Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12:1111–1121
Manadas B, Mendes VM, English J, Dunn MJ (2010) Peptide fractionation in proteomics approaches. Expert Rev Proteomics 7:655–663
Bekker-Jensen DB, Kelstrup CD, Batth TS, Larsen SC, Haldrup C, Bramsen JB, Sørensen KD, Høyer S, Ørntoft TF, Andersen CL, Nielsen ML, Olsen JV (2017) An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst 4:587–599.e4
Barkovits K, Pacharra S, Pfeiffer K, Steinbach S, Eisenacher M, Marcus K, Uszkoreit J (2019) Reproducibility, specificity and accuracy of relative quantification using spectral library-based data-independent acquisition. Mol Cell Proteomics 2019:mcp.RA119.001714
Navarro P, Kuharev J, Gillet LC, Bernhardt OM, MacLean B, Röst HL, Tate SA, Tsou CC, Reiter L, Distler U, Rosenberger G, Perez-Riverol Y, Nesvizhskii AI, Aebersold R, Tenzer S (2016) A multicenter study benchmarks software tools for label-free proteome quantification. Nat Biotechnol 34:1130–1136
MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966–968
Räst HL, Rosenberger G, Navarro P, Gillet L, Miladinoviä SM, Schubert OT, OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 32:219–223
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Chantada-Vázquez, M.d.P., García Vence, M., Serna, A., Núñez, C., Bravo, S.B. (2021). SWATH-MS Protocols in Human Diseases. In: Carrera, M., Mateos, J. (eds) Shotgun Proteomics. Methods in Molecular Biology, vol 2259. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1178-4_7
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DOI: https://doi.org/10.1007/978-1-0716-1178-4_7
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Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1177-7
Online ISBN: 978-1-0716-1178-4
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