The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Multiplexed and data‐independent tandem mass spectrometry for global proteome profiling
Abstract
One of the most important early developments in the field of proteomics was the advent of automated data acquisition routines that allowed high‐throughput unattended data acquisition during HPLC introduction of peptide mixtures to a tandem mass spectrometer. Prior to this, data acquisition was orders of magnitude less efficient being based entirely on lists of predetermined ions generated in a prior HPLC‐MS experiment. This process, known generically as data‐dependent analysis, empowered the development of shotgun proteomics where hundreds to thousands of peptide sequences are matched per experiment. In their most popular implementation, the most abundant ionized species from every precursor ion scan at each moment in chromatographic time are successively selected for isolation, activation and tandem mass analysis. While extremely powerful, this strategy has one primary limitation in that detectable dynamic range is restricted (in a top‐down manner) to the peptides that ionize the best. To circumvent the serial nature of the data‐dependent process and increase detectable dynamic range, the concepts of multiplexed and data‐independent acquisition (DIA) have emerged. Multiplexed‐data acquisition is based on more efficient co‐selection and co‐dissociation of multiple precursor ions in parallel, the data from which is subsequently de‐convoluted to provide polypeptide sequences for each individual precursor ion. DIA has similar goals, but there is no real‐time ion selection based on prior precursor ion scans. Instead, predefined m/z ranges are interrogated either by fragmenting all ions entering the mass spectrometer at every single point in chromatographic time; or by dividing the m/z range into smaller m/z ranges for isolation and fragmentation. These approaches aim to fully utilize the capabilities of mass spectrometers to maximize tandem MS acquisition time and to address the need to expand the detectable dynamic range, lower the limit of detection, and improve the overall confidence of peptide identifications and relative protein quantification measurements. This review covers all aspects of multiplexed‐ and data‐independent tandem mass spectrometry in proteomics, from experimental implementations to advances in software for data interpretation. © 2013 Wiley Periodicals, Inc. Mass Spec Rev 33: 452–470, 2014.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Eng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401
Gillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877
Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., & Hamon, C. (2003) · Analytical Chemistry · doi:10.1021/ac0262560 · Cited by 2,378
Progress with gene‐product mapping of the Mollicutes: Mycoplasma genitalium
Wasinger, V. C., Cordwell, S. J., Cerpa‐Poljak, A., Yan, J. X., Gooley, A. A., Wilkins, M. R., Duncan, M. W., Harris, R., Williams, K. L., & Humphery‐Smith, I. (1995) · ELECTROPHORESIS · doi:10.1002/elps.11501601185 · Cited by 962
Michalski, A., Damoc, E., Hauschild, J.-P., Lange, O., Wieghaus, A., Makarov, A., Nagaraj, N., Cox, J., Mann, M., & Horning, S. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.011015 · Cited by 832
Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra
Venable, J. D., Dong, M.-Q., Wohlschlegel, J., Dillin, A., & Yates, J. R. (2004) · Nature Methods · doi:10.1038/nmeth705 · Cited by 794
Cited by
Mass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334
Current approaches and challenges for the metabolite profiling of complex natural extracts
Wolfender, J.-L., Marti, G., Thomas, A., & Bertrand, S. (2015) · Journal of Chromatography A · doi:10.1016/j.chroma.2014.10.091 · Cited by 635
Meier, F., Geyer, P. E., Virreira Winter, S., Cox, J., & Mann, M. (2018) · Nature Methods · doi:10.1038/s41592-018-0003-5 · Cited by 348
Gillet, L. C., Leitner, A., & Aebersold, R. (2016) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-071015-041535 · Cited by 323
Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280
Metadata
- Authors
- Chapman, J. D., Goodlett, D. R., & Masselon, C. D.
- Venue
- Mass Spectrometry Reviews
- Publisher
- Wiley
- Published
- 2013-11-26
- Volume
- 33
- Issue
- 6
- Pages
- 452-470
- ISSN
- 0277-7037 (ISSN-L)
- 1098-2787
- OpenAlex
- W1803908051
- Cited by
- 259
- References
- 69
- FWCI
- 9.57
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multiplexed and data‐independent tandem mass spectrometry for global proteome profiling. https://app.cochat.ai/papers/10.1002/mas.21400
- The paper
- Chapman, J. D., Goodlett, D. R., & Masselon, C. D. (2014). Multiplexed and data‐independent tandem mass spectrometry for global proteome profiling. Mass Spectrometry Reviews. https://doi.org/10.1002/mas.21400
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.