Abstract
Proteomic tools can only be implemented in clinical settings if high-throughput, automated, sensitive, and accurate methods are developed. This has driven researchers to the edge of mass spectrometry (MS)-based proteomics capacity. Here we provide an overview of recent achievements in mass spectrometric technologies and instruments. This includes development of high and ultra definition-MSE (HDMSE and UDMSE) through implementation of ion mobility (IM) MS towards sensitive and accurate label-free proteomics using ultra performance liquid chromatography (UPLC). Label free UPLC-HDMSE is less expensive than labeled-based quantitative proteomics and has no limits regarding the number of samples that can be analyzed and compared, which is an important requirement for supporting clinical applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lee JM, Kohn EC (2010) Proteomics as a guiding tool for more effective personalized therapy. Ann Oncol 21:205–210
Tchabo NE, Liel MS, Kohn EC (2005) Applying proteomics in clinical trials: assessing the potential and practical limitations in ovarian cancer. Am J Pharmacogenomics 5:141–148
Wulfkuhle JD, Edmiston KH, Liotta LA, Petricoin EF 3rd (2006) Technology insight: pharmacoproteomics for cancer—promises of patient-tailored medicine using protein microarrays. Nat Clin Pract Oncol 3:256–268
Patel VJ, Thalassinos K, Slade SE, Connolly JB, Crombie A, Murrell JC et al (2009) A comparison of labeling and label-free mass spectrometry-based proteomics approaches. J Proteome Res 8:3752–3759
Michalski A, Cox J, Mann M (2011) More than 100,000 detectable peptides species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J Proteome Res 10:1785–1793
Geromanos SJ, Hughes C, Golick D, Ciavarini S, Gorenstein MV, Richardson K et al (2011) Simulating and validating proteomics data and search results. Proteomics 11:1189–1211
Geromanos SJ, Vissers JP, Silva JC, Dorschel CA, Li GZ, Gorenstein MV et al (2009) The detection, correlation, and comparison of peptide precursor and product ions from data independent LC-MS with data dependent LC-MS/MS. Proteomics 9:1683–1695
Chakraborty AB, Berger SJ, Gebler JC (2007) Use of an integrated MS-multiplexed MS/MS data acquisition strategy for high-coverage peptide mapping studies. Rapid Commun Mass Spectrom 21:730–744
Tyndall AM, Starr LH, Powell CF (1928) The mobility of ions in air. Part IV. Investigations by two new methods. Proc R Soc Lond A 121:172–184
Tyndall AM, Grindley GC, Sheppard PA (1928) The mobility of ions in air. Part V. The transformation of positive Ions at short ages. Proc R Soc Lond A 121:185–194
Bradbury NE (1932) Photoelectric currents in gases between parallel plates as a function of the potential difference. Phys Rev 40:980
Bradbury NE, Nielsen RA (1936) Absolute values of the electron mobility in hydrogen. Phys Rev 49:388–393
von Helden G, Hsu M-T, Kemper PR, Bowers MT (1991) Structures of carbon cluster ions from 3 to 60 atoms: linears to rings to fullerenes. J Chem Phys 95:3835–3837
Valentine SJ, Kulchania M, Srebalus Barnes CA, Clemmer DE (2001) Multidimensional separations of complex peptide mixtures: a combined high-performance liquid chromatography/ion mobility/time-of-flight mass spectrometry approach. Int J Mass Spectrom 212:97–109
Ruotolo BT, Giles K, Campuzano I, Sandercock AM, Bateman RH, Robinson CV (2005) Evidence for macromolecular protein rings in the absence of bulk water. Science 310:1658–1661
Giles K, Williams JP, Campuzano I (2011) Enhancements in travelling wave ion mobility resolution. Rapid Commun Mass Spectrom 25:1559–1566
Ruotolo BT, Benesch JL, Sandercock AM, Hyung SJ, Robinson CV (2008) Ion mobility-mass spectrometry analysis of large protein complexes. Nat Protoc 3:1139–1152
Lalli PM, Corilo YE, de Sa GF, Daroda RJ, de Souza V, Souza GHMF et al (2011) Intrinsic mobility of gaseous cationic and anionic aggregates of ionic liquids. ChemPhysChem 12:1444–1447
Lalli PM, Corilo YE, Fasciotti M, Riccio MF, de Sa GF, Daroda RJ et al (2013) Baseline resolution of isomers by traveling wave ion mobility mass spectrometry: investigating the effects of polarizable drift gases and ionic charge distribution. J Mass Spectrom 48:989–997
Distler U, Kuharev J, Navarro P, Levin Y, Schild H, Tenzer S (2014) Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics. Nat Methods 11:167–170
Distler U, Kuharev J, Navarro P, Tenzer S (2016) Label-free quantification in ion mobility-enhanced data-independent acquisition proteomics. Nat Protoc 11:795–812
Giles K, Pringle SD, Worthington KR, Little D, Wildgoose JL, Bateman RH (2004) Applications of a travelling wave-based ratio-frequency-only stacked ring ion guide. Rapid Commun Mass Spectrom 18:2401–2414
Morris HR, Paxton T, Dell A, Langhorne J, Berg M, Bordoli RS et al (1996) High sensitivity collisionally-activated decomposition tandem mass spectrometry on a novel quadrupole/orthogonal-acceleration time-of-flight mass spectrometer. Rapid Commun Mass Spectrom 10:889–896
Yu YQ, Gilar M, Lee PJ, Bouvier ES, Gebler JC (2003) Enzyme-friendly, mass spectrometry-compatible surfactant for ion-solution enzymatic digestion of proteins. Anal Chem 75:6023–6028
Gilar M, Olivova P, Daly AE, Gebler JC (2005) Two-dimensional separation of peptides using RP-RP-HPLC system with different pH in first and second separation dimensions. J Sep Sci 28:1694–1703
Silva JC, Gorenstein MV, Li GZ, Vissers JP, Geromanos SJ (2006) Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol Cell Proteomics 5:144–156
Williams JP, Brown JM, Campuzano I, Sadler PJ (2010) Identifying drug metallation sites on peptides using electron transfer dissociation (ETD), collision induced dissociation (CID) and ion mobility-mass spectrometry (IM-MS). Chem Commun 46:5458–5460
Li GZ, Vissers JP, Silva JC, Golick D, Gorenstein MV, Geromanos SJ (2009) Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics 9:1696–1719
Turtoi A, Mazzucchelli GD, De Pauw E (2010) Isotope coded protein label quantification of serum proteins--comparison with the label-free LC-MS and validation using the MRM approach. Talanta 80:1487–1495
Lucas JE, Thompson JW, Dubois LG, McCarthy J, Tillmann H, Thompson A et al (2012) Metaprotein expression modeling for label-free quantitative proteomics. BMC Bioinformatics 13:74
Benjamin AM, Thompson JW, Soderblom EJ, Geromanos SJ, Henao R, Kraus VB et al (2013) A flexible statistical model for alignment of label-free proteomics data-incorporating ion mobility and product ion information. BMC Bioinformatics 14:364
Thalassinos K, Vissers JP, Tenzer S, Levin Y, Thompson JW, Daniel D et al (2012) Design and application of a data-independent precursor and product ion repository. J Am Soc Mass Spectrom 23:1808–1820
Adeola HA, Soares NC, Paccez JD, Kaestner L, Blackburn JM, Zerbini LF (2015) Discovery of novel candidate urinary protein biomarkers for prostate cancer in a multiethnic cohort of South African patients via label-free mass spectrometry. Proteomics Clin Appl 9:597–609
Beretov J, Wasinger VC, Millar EK, Schwartz P, Graham PH, Li Y (2015) Proteomic analysis of urine to identify breast cancer biomarker candidates using a label-free LC-MS/MS approach. PLoS One 10:e0141876
Fan NJ, Chen HM, Song W, Zhang ZY, Zhang MD, Feng LY, Gao CF (2016) Macrophage mannose receptor 1 and S100A9 were identified as serum diagnostic biomarkers for colorectal cancer through a label-free quantitative proteomic analysis. Cancer Biomark 16:235–243
Collins MA, An J, Hood BL, Conrads TP, Bowser RP (2015) Label-free LC-MS/MS proteomic analysis of cerebrospinal fluid identifies protein/pathway alterations and candidate biomarkers for amyotrophic lateral sclerosis. J Proteome Res 14:4486–4501
Csősz É, Emri G, Kalló G, Tsaprailis G, Tőzsér J (2015) Highly abundant defense proteins in human sweat as revealed by targeted proteomics and label-free quantification mass spectrometry. J Eur Acad Dermatol Venereol 29:2024–2031
Song SH, Han M, Choi YS, Dan KS, Yang MG, Song J (2014) Proteomic profiling of serum from patients with tuberculosis. Ann Lab Med 34:345–353
Zhang M, Xu W, Deng Y (2013) A new strategy for early diagnosis of type 2 diabetes by standard-free, label-free LC-MS/MS quantification of glycated peptides. Diabetes 62:3936–3942
Lee J, Joo EJ, Lim HJ, Park JM, Lee KY, Park A et al (2015) Proteomic analysis of serum from patients with major depressive disorder to compare their depressive and remission statuses. Psychiatry Investig 1:249–259
Yang H, Lyutvinskiy Y, Herukka SK, Soininen H, Rutishauser D, Zubarev RA (2014) Prognostic polypeptide blood plasma biomarkers of Alzheimer’s disease progression. J Alzheimers Dis 40:659–666
Steeb H, Ramsey JM, Guest PC, Stocki P, Cooper JD, Rahmoune H et al (2014) Serum proteomic analysis identifies sex-specific differences in lipid metabolism and inflammation profiles in adults diagnosed with Asperger syndrome. Mol Autism 5:4
Zhao Z, Wu F, Ding S, Sun L, Liu Z, Ding K, Lu J (2015) Label-free quantitative proteomic analysis reveals potential biomarkers and pathways in renal cell carcinoma. Tumour Biol 36:939–951
Dai P, Wang Q, Wang W, Jing R, Wang W, Wang F et al (2016) Unraveling molecular differences of gastric cancer by label-free quantitative proteomics analysis. Int J Mol Sci 17(1):E69. doi:10.3390/ijms17010069, pii
Théron L, Gueugneau M, Coudy C, Viala D, Bijlsma A, Butler-Browne G et al (2014) Label-free quantitative protein profiling of vastuslateralis muscle during human aging. Mol Cell Proteomics 13:283–294
Martins-de-Souza D, Guest PC, Guest FL, Bauder C, Rahmoune H, Pietsch S et al (2012) Characterization of the human primary visual cortex and cerebellum proteomes using shotgun mass spectrometry-data-independent analyses. Proteomics 12:500–504
Krishnamurthy D, Harris LW, Levin Y, Koutroukides TA, Rahmoune H, Pietsch S et al (2013) Metabolic, hormonal and stress-related molecular changes in post-mortem pituitary glands from schizophrenia subjects. World J Biol Psychiatry 14:478–489
Stelzhammer V, Alsaif M, Chan MK, Rahmoune H, Steeb H, Guest PC et al (2014) Distinct proteomic profiles in post-mortem pituitary glands from bipolar disorder and major depressive disorder patients. J Psychiatr Res 60:40–48
Broek JA, Guest PC, Rahmoune H, Bahn S (2014) Proteomic analysis of post mortem brain tissue from autism patients: evidence for opposite changes in prefrontal cortex and cerebellum in synaptic connectivity-related proteins. Mol Autism 5:41
Acknowledgments
Prof. Daniel Martins-de-Souza is supported by the São Paulo Research Foundation (FAPESP) grants 13/08711-3 and 14/10068-4 and by the Brazilian National Council for Scientific and Technological Development (CNPq) grant 460289/2014-4.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Souza, G.H.M.F., Guest, P.C., Martins-de-Souza, D. (2017). LC-MSE, Multiplex MS/MS, Ion Mobility, and Label-Free Quantitation in Clinical Proteomics. In: Guest, P.C. (eds) Multiplex Biomarker Techniques. Methods in Molecular Biology, vol 1546. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-6730-8_4
Download citation
DOI: https://doi.org/10.1007/978-1-4939-6730-8_4
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-6729-2
Online ISBN: 978-1-4939-6730-8
eBook Packages: Springer Protocols