Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Transformation, Normalization, and Batch Effect in the Analysis of Mass Spectrometry Data for Omics Studies Read chapter Read first chapter

2017 | OriginalPaper | Chapter

The Analysis of Peptide-Centric Mass-Spectrometry Data Utilizing Information About the Expected Isotope Distribution

Authors : Tomasz Burzykowski, Jürgen Claesen, Dirk Valkenborg

Published in: Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In shotgun proteomics, much attention and instrument time is dedicated to the generation of tandem mass spectra. These spectra contain information about the fragments of, ideally, one peptide and are used to infer the amino acid sequence of the scrutinized peptide. This type of spectrum acquisition is called a product ion scan, tandem MS, or MS2 spectrum. Another type of spectrum is the, often overlooked, precursor ion scan or MS1 spectrum that catalogs all ionized analytes present in a mass spectrometer. While MS2 spectra are important to identify the peptides and proteins in the sample, MS1 spectra provide valuable information about the quantity of the analyte. In this chapter, we describe some properties of MS1 spectra, such as the isotope distribution, and how these properties can be employed for low-level signal processing to reduce data complexity and as a tool for quality assurance. Furthermore, we describe some cases in which advanced modeling of the isotope distribution can be used in quantitative proteomics analysis.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Automated Alignment of Mass Spectrometry Data Using Functional Geometry
next chapter Probabilistic and Likelihood-Based Methods for Protein Identification from MS/MS Data
Literature
1.
Atlas, M., & Datta, S. (2009). A statistical technique for monoisotopic peak detection in a mass spectrum. Journal of Proteomics and Bioinformatics, 2, 202–216.CrossRef
2.
Claesen, J., Dittwald, P., Burzykowski, T., & Valkenborg, D. (2012). An efficient method to calculate the aggregated isotopic distribution and exact center-masses. Journal fo the American Society for Mass Spectrometry, 23, 753–763.CrossRef
3.
Eckel-Passow, J. E., Oberg, A. L., Therneau, T. M., Mason, C. J., Mahoney, D. W., Johnson, K. L., et al. (2006). Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass-spectrometry. Bioinformatics, 2, 305–318.
4.
Geller, O., & Lifshitz, C. (2004). Applying a new algorithm to H/D exchange of multiply protonated cytochrome c. International Journal of Mass Spectrometry, 223, 125–129.CrossRef
5.
Gevaert, K., Impens, F., Ghesquière, B., Van Damme, P., Lambrechts, A., & Vandekerckhove, J. (2008). Stable isotopic labeling in proteomics. Proteomics, 8, 4873–4885.CrossRef
6.
Ghavidel, F. Z., Mertens, I., Baggerman, G., Laukens, K., Burzykowski, T., & Valkenborg, D. (2014). The use of the isotopic distribution as a complementary quality metric to assess tandem mass spectra results. Journal of Proteomics, 98, 150–158.CrossRef
7.
Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., & Aebersold, R. (1999). Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotechnology, 17, 994–999.CrossRef
8.
Impens, F., Colaert, N., Helsens, K., Ghesquière, B., Timmerman, E., De Bock, P. J., et al. (2010). A quantitative proteomics design for systematic identification of protease cleavage events. Molecular and Cellular Proteomics, 9, 2327–2333.CrossRef
9.
Lermyte, F., Konijnenberg, A., Williams, J. P., Brown, J. M., Valkenborg, D., & Sobott, F. (2014). ETD allows for native surface mapping of a 150 kDa noncovalent complex on a commercial Q-TWIMS-TOF instrument. Journal of the American Society for Mass Spectrometry, 25, 343–350.CrossRef
10.
Lermyte, F., Verschueren, T., Brown, J. M., Williams, J. P., Valkenborg, D., & Sobott, F. (2015). Characterization of top-down ETD in a travelling-wave ion guide. Methods, 89, 22–29.
11.
Lermyte, F., Łąckic, M. K., Valkenborg, D., Baggerman, G., Gambin, A., & Sobott, F. (2015). Understanding reaction pathways in top-down ETD by dissecting isotope distributions: A mammoth task. International Journal of Mass Spectrometry, 390, 146–154.
12.
Lopez-Ferrer, D., Ramos-Fernandez, A., Martinez-Bartolome, S., Garca-Ruiz, P., & Vazquez, J. (2006). Quantitative proteomics using 16O/18O labeling and linear ion trap mass spectrometry. Proteomics, 6, S4–S11.CrossRef
13.
Mirgorodskaya, O. A., Kozmin, Y. P., Titov, M. I., Korner, R., Sonksen, C. P., & Roepstorff, P. (2000). Quantitation of peptides and proteins by matrix-assisted laser desorption/ionization mass spectrometry using 18O-labeled internal standards. Rapid Communications in Mass Spectrometry, 14, 1226–1232.CrossRef
14.
Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., et al. (2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular and Cellular Proteomics, 1, 376–386.CrossRef
15.
Palmblad, M., Buijs, J., & Hakansson, P. (2001). Automatic analysis of Hydrogen/Deuterium exchange mass spectra of peptides and proteins using calculations of isotopic distributions. Journal of the American Society for Mass Spectrometry, 12, 1153–1162.CrossRef
16.
Park, S. K., Jin, L. L., Kim, Y., & Yates, J. R., III. (2009). A computational approach to correct arginine-to-proline conversion in quantitative proteomics. Nature Methods, 6, 184–185.CrossRef
17.
Rao, K. C., Carruth, R. T., & Miyagi, M. (2005). Proteolytic 18O-labeling by peptidyl-Lys metalloendopeptidase for comparative proteomics. Journal of Proteome Research, 4, 507–514.CrossRef
18.
Reuben, B., Ritov, Y., Geller, O., McFarland, M., Marshall, A., & Lifshitz, C. (1993). Applying a new algorithm for obtaining site specific rate constants for H/D exchange of the gas phase proton-bound arginine dimer. Chemical Physics Letters, 380, 88–94.CrossRef
19.
Rockwood, A. L., & Van Orden, S. L. (1996). Ultrahigh-speed calculation of isotope distributions. Analytical Chemistry, 68, 2027–2030.CrossRef
20.
Rosman, K. J. R., & Taylor, P. D. P. (1998). Isotopic compositions of the elements 1997. Pure and Applied Chemistry, 70, 217–235.CrossRef
21.
Schmidt, A., Kellermann, J., & Lottspeich, F. (2005). A novel strategy for quantitative proteomics using isotope-coded protein labels. Proteomics, 5, 4–15.CrossRef
22.
Senko, M. W., Beu, S. C., & McLafferty, F. W. (1995). Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distribution. Journal of the American Society for Mass Spectrometry, 6, 229–233.CrossRef
23.
Shadforth, I. P., Dunkley, T. P. J., Lilley, K. S., & Bessant, C. (2005). i-Tracker: For quantitative proteomics using iTRAQ™. BMC Genomics, 6, 145.
24.
Smith, D., Deng, Y., & Zhang, Z. (1997). Probing the non-covalent structure of proteins by amide hydrogen exchange and mass spectrometry. Journal of Mass Spectrometry, 32, 135–146.CrossRef
25.
Stemmann, O., Zou, H., Gerber, S. A., Gygi, S. P., & Kirschner, M. W. (2001). Dual inhibition of sister chromatid separation at metaphase. Cell, 107, 715–726.CrossRef
26.
Valkenborg, D., Jansen, I., & Burzykowski, T. (2008). A model-based method for the prediction of the isotopic distribution of peptides. Journal of the American Society for Mass Spectrometry, 19, 703–712.CrossRef
27.
Valkenborg, D., & Burzykowski, T. (2011). A Markov-chain model for the analysis of high-resolution enzymatically 18O-labeled mass spectra. Statistical Applications in Genetics and Molecular Biology, 10, article 1.
28.
Valkenborg, D., Mertens, I., Lemière, F., Witters, E., & Burzykowski, T. (2012). The isotopic distribution conundrum. Mass Spectrometry Reviews, 31, 96–109.CrossRef
29.
Zhang, Z., & Smith, D. L. (1993). Determination of amide Hydrogen exchange by mass spectrometry: A new tool for protein structure elucidation. Protein Science, 2, 522–531.CrossRef
30.
Zhang, Z., Guan, S., & Marshall, A. (1997). Enhancement of the effective resolution of mass spectra of high-mass biomolecules by maximum-entropy based deconvolution to eliminate the isotopic natural abundance distribution. Journal of the American Society for Mass Spectrometry, 8, 659–670.CrossRef
31.
Zhu, Q., & Burzykowski, T. (2011). A Bayesian Markov-chain-based heteroscedastic regression model for the analysis of 18O-labelled mass spectra. Journal of the American Society for Mass Spectrometry, 22, 499–507.CrossRef
32.
Zhu, Q., Valkenborg, D., & Burzykowski, T. (2010). A Markov-chain-based heteroscedastic regression model for the analysis of high-resolution enzymatically 18O-labeled mass spectra. Journal of Proteome Research, 9, 2669–2677.CrossRef
33.
Ye, X., Luke, B., Andresson, T., & Blonder, J. (2009). 18O stable isotope labeling in MS-based proteomics. Briefings in Functional Genomics and Proteomics, 8, 136–144.CrossRef
34.
Zhu, Q., Kasim, A., Valkenborg, D., & Burzykowski, T. (2011). A Bayesian model-averaging approach to the quantification of overlapping peptides in a MALDI-TOF mass spectrum. International Journal of Proteomics, article ID 928391.
Metadata
Title
The Analysis of Peptide-Centric Mass-Spectrometry Data Utilizing Information About the Expected Isotope Distribution
Authors
Tomasz Burzykowski
Jürgen Claesen
Dirk Valkenborg
Copyright Year
2017
Book
Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Print ISBN: 978-3-319-45807-6

Electronic ISBN: 978-3-319-45809-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-45809-0_3

Premium Partner

    Image Credits