Advertisement

High-Resolution Mass Spectrometry Quantification: Impact of Differences in Data Processing of Centroid and Continuum Data

  • L. Vereyken
  • L. Dillen
  • R. J. Vreeken
  • F. CuyckensEmail author
Critical Insight
First Online:

Abstract

High-resolution mass spectrometry (HRMS) in full scan mode acquires all ions present in the sample of interest offering a lot of qualitative information. This, in combination with the improved performance of the new generation HRMS systems, triggers more (bio) analysts to switch from triple quad MS systems to HRMS for quantitative analysis. Quantitative processing of HRMS data is performed based on narrow mass extraction windows rather than on nominal mass product ion chromatograms (SRM or MRM). Optimal processing of HRMS data requires different considerations and software tools and can have an impact on data processing and final results. The selection of centroid versus continuum/profile data for processing, selection of the optimal narrow mass extraction window, using theoretical versus measured accurate mass for the extraction of the ion chromatograms as well as differences in calculations and data handling residing in the different vendor software packages are tackled in the presented manuscript. These differences are illustrated on HRMS data acquired for the same plasma samples on three different platforms, i.e., a Sciex QToF, a Waters QToF, and a Thermo Orbitrap system, and processed in four different software packages, i.e., Sciex Analyst® TF, Waters Masslynx, Waters Unifi, and Thermo Xcalibur. The impact of these differences on quantitative HRMS performance was evaluated on calibration curves of eight small molecule compounds in plasma using four different ways of processing. Simple guidelines are provided for the selection of an optimal mass extraction window for continuum and centroided data.

Graphical Abstract

Keywords

High-Resolution Mass Spectrometry Mass Extraction Window Profile data Continuum data Centroid data Quantification Q-Tof Orbitrap 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

The authors thank Lyle Burton (ABSciex) for developing and providing the research tool to transform continuum raw data files to centroid data files post-acquisition. We thank Anne Van Vlaslaer, Ils Pijpers, and Emmanuel Njumbe Ediage (Janssen R&D) for the analysis of the calibration curves in plasma and Ronald de Vries for fruitful discussions.

Supplementary material

13361_2018_2101_MOESM1_ESM.docx (15 kb)
Supplementary Table 1 Maximum MEW calculations based on measured resolution and mass. *the differences between the system resolution and measured resolution can be mainly explained by the mass dependency of mass resolution on QTof and Orbitrap instruments, experiments on the TripleTof® were performed with an aged detector that had a clear detrimental impact on MS resolution. (DOCX 14 kb)
13361_2018_2101_MOESM2_ESM.docx (33 kb)
Supplementary Table 2 Results of calibration curves for acetaminophen, tolbutamide, norethindrone, midazolam, abiraterone, prednisone, loperamide, and simeprevir n = 5) acquired on a Waters Synapt G2-S in continuum mode and processed after post-acquisition centroiding of the same data A mass accuracy (MA) of 2 mDa is applied according to the specifications of the instrument. Data are processed in four different ways as discussed in the manuscript: (i) smoothed centroid data MEW = 2 × MA or smallest MEW with no loss of data points using unsmoothed data in TargetLynx, (ii) continuum data MEW = FWHM + 2 × MA in TargetLynx, (iii) continuum data 2D processing in Unifi, and (iv) continuum data 3D processing in Unifi, where the selection of the MEW has no impact on the XIC generation. Results obtained with other MEWs of 10 mDa, 20 mDa and the max MEW are given for comparison. Exact nominal mass has been calculated with MassLyx. (DOCX 32 kb)
13361_2018_2101_MOESM3_ESM.docx (53 kb)
Supplemental Figure 1 Comparison of different MEWs (0.5–10–20–33-max MEW (34 mDa)) for simeprevir data (at a concentration of 5 ng/mL) acquired on the Synapt G2-S. Data were centroided post-acquisition. This illustrates the difference in peak evaluation using unsmoothed (a) and smoothed data (b): the loss of data points is only visible with unsmoothed data, while the entire chromatographic peak is lost when the MEW is chosen to small (DOCX 53 kb)

References

  1. 1.
    Korfmacher, W.: High-resolution mass spectrometry will dramatically change our drug-discovery bioanalysis procedures. Bioanalysis. 3, 1169–1171 (2011)CrossRefGoogle Scholar
  2. 2.
    Ramanathan, R., Korfmacher, W.: The emergence of high-resolution MS as the premier analytical tool in the pharmaceutical bioanalysis arena. Bioanalysis. 4, 467–469 (2012)CrossRefGoogle Scholar
  3. 3.
    Russell, D.H., Edmondson, R.D.: High-resolution mass spectrometry and accurate mass measurements with emphasis on the characterization of peptides and proteins by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J. Mass Spectrom. 32, 263–276 (1997)CrossRefGoogle Scholar
  4. 4.
    Dillen, L., Cools, W., Vereyken, L., Lorreyne, W., Huybrechts, T., de Vries, R., Ghobarah, H., Cuyckens, F.: Comparison of triple quadrupole and high-resolution TOF-MS for quantification of peptides. Bioanalysis. 4, 565–579 (2012)CrossRefGoogle Scholar
  5. 5.
    Ramanathan, R., Jemal, M., Ramagiri, S., Xia, Y.Q., Humpreys, W.G., Olah, T., Korfmacher, W.A.: It is time for a paradigm shift in drug discovery bioanalysis: from SRM to HRMS. J. Mass Spectrom. 46, 595–601 (2011)CrossRefGoogle Scholar
  6. 6.
    Barbara, J.E., Buckley, D.B., Horrigan, M.J.: Exploring the utility of high-resolution MS with post-acquisition data mining for simultaneous exogenous and endogenous metabolite profiling. Bioanalysis. 5, 1211–1228 (2013)CrossRefGoogle Scholar
  7. 7.
    O’Connor, D., Mortishire-Smith, R., Morrison, D., Davies, A., Dominguez, M.: Ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry for robust, high-throughput quantitative analysis of an automated metabolic stability assay, with simultaneous determination of metabolic data. Rapid Commun. Mass Spectrom. 20, 851–857 (2006)CrossRefGoogle Scholar
  8. 8.
    Bateman, K.P., Kellmann, M., Muenster, H., Papp, R., Taylor, L.: Quantitative-qualitative data acquisition using a benchtop Orbitrap mass spectrometer. J. Am. Soc. Mass Spectrom. 20, 1441–1450 (2009)CrossRefGoogle Scholar
  9. 9.
    Emary, W.B., Zhang, N.R.: Emerging applications for high-resolution MS in discovery bioanalysis. Bioanalysis. 3, 2485–2489 (2011)CrossRefGoogle Scholar
  10. 10.
    Cuyckens, F., Dillen, L., Cools, W., Bockx, M., Vereyken, L., de Vries, R., Mortishire-Smith, R.J.: Identifying metabolite ions of peptide drugs in the presence of an in vivo matrix background. Bioanalysis. 4, 595–604 (2012)CrossRefGoogle Scholar
  11. 11.
    Fung, E.N., Xia, Y.Q., Aubry, A.F., Zeng, J., Olah, T., Jemal, M.: Full-scan high resolution accurate mass spectrometry (HRMS) in regulated bioanalysis: LC-HRMS for the quantitation of prednisone and prednisolone in human plasma. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 879, 2919–2927 (2011)CrossRefGoogle Scholar
  12. 12.
    Bowen, C.L., Kehler, J., Boram, S., Abberley, L.: Modify on the fly: triple quad to high resolution in support of a dermal clinical study requiring an ultra low LLOQ. Bioanalysis. 8, 205–214 (2016)CrossRefGoogle Scholar
  13. 13.
    Fung, E.N., Jemal, M., Aubry, A.F.: High-resolution MS in regulated bioanalysis: where are we now and where do we go from here? Bioanalysis. 5, 1277–1284 (2013)CrossRefGoogle Scholar
  14. 14.
    Huang, M.Q., Lin, Z., Weng, N.: Applications of high-resolution MS in bioanalysis. Bioanalysis. 5, 1269–1276 (2013)CrossRefGoogle Scholar
  15. 15.
    Kaufmann, A., Butcher, P., Maden, K., Walker, S., Widmer, M.: Comprehensive comparison of liquid chromatography selectivity as provided by two types of liquid chromatography detectors (high resolution mass spectrometry and tandem mass spectrometry): “where is the crossover point?”. Anal. Chim. Acta. 673, 60–72 (2010)CrossRefGoogle Scholar
  16. 16.
    Food and Drug Administration homepage: Guidance for industry Bioanalytical method validation. (2013) http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm368107.pdf
  17. 17.
    Sturm, R.M., Jones, B.R., Mulvana, D.E., Lowes, S.: HRMS using a Q-Exactive series mass spectrometer for regulated quantitative bioanalysis: how, when, and why to implement. Bioanalysis. 8, 1709–1721 (2016)CrossRefGoogle Scholar
  18. 18.
    Rochat, B., Kottelat, E., McMullen, J.: The future key role of LC-high-resolution-MS analyses in clinical laboratories: a focus on quantification. Bioanalysis. 4, 2939–2958 (2012)CrossRefGoogle Scholar
  19. 19.
    Glauser, G., Grund, B., Gassner, A.-L., Menin, L., Henry, H., Bromirski, M., Schütz, F., McMullen, J., Rochat, B.: Validation of the mass-extraction-window for quantitative methods using liquid chromatography high resolution mass spectrometry. Anal. Chem. 88, 3264–3271 (2016)CrossRefGoogle Scholar
  20. 20.
    Xia, Y.Q., Lau, J., Olah, T., Jemal, M.: Targeted quantitative bioanalysis in plasma using liquid chromatography/high-resolution accurate mass spectrometry: an evaluation of global selectivity as a function of mass resolving power and extraction window, with comparison of centroid and profile mo. Rapid Commun. Mass Spectrom. 25, 2863–2878 (2011)CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2018

Authors and Affiliations

  1. 1.Janssen Research and DevelopmentBeerseBelgium

Personalised recommendations

Cite article

Buy options