Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags.
Authors
O'Connell, Jeremy D
Paulo, Joao A
Thakurta, Sanjukta
Rose, Christopher M
Huttlin, Edward L
Gygi, Steven P
Publication Date
2018-01-05Journal Title
J Proteome Res
ISSN
1535-3893
Publisher
American Chemical Society (ACS)
Volume
17
Issue
1
Pages
590-599
Language
eng
Type
Article
This Version
VoR
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
O'Brien, J. J., O'Connell, J. D., Paulo, J. A., Thakurta, S., Rose, C. M., Weekes, M., Huttlin, E. L., & et al. (2018). Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags.. J Proteome Res, 17 (1), 590-599. https://doi.org/10.1021/acs.jproteome.7b00699
Abstract
Mass spectrometry (MS) has become an accessible tool for whole proteome quantitation with the ability to characterize protein expression across thousands of proteins within a single experiment. A subset of MS quantification methods (e.g., SILAC and label-free) monitor the relative intensity of intact peptides, where thousands of measurements can be made from a single mass spectrum. An alternative approach, isobaric labeling, enables precise quantification of multiple samples simultaneously through unique and sample specific mass reporter ions. Consequently, in a single scan, the quantitative signal comes from a limited number of spectral features (≤11). The signal observed for these features is constrained by automatic gain control, forcing codependence of concurrent signals. The study of constrained outcomes primarily belongs to the field of compositional data analysis. We show experimentally that isobaric tag proteomics data are inherently compositional and highlight the implications for data analysis and interpretation. We present a new statistical model and accompanying software that improves estimation accuracy and the ability to detect changes in protein abundance. Finally, we demonstrate a unique compositional effect on proteins with infinite changes. We conclude that many infinite changes will appear small and that the magnitude of these estimates is highly dependent on experimental design.
Keywords
Staining and Labeling, Models, Statistical, Proteomics, Software
Sponsorship
Wellcome Trust (108070/Z/15/Z)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1021/acs.jproteome.7b00699
This record's URL: https://www.repository.cam.ac.uk/handle/1810/273296
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