Multiple marker abundance profiling: combining selected reaction monitoring and data-dependent acquisition for rapid estimation of organelle abundance in subcellular samples
Web of Science
AuthorHooper, CM; Stevens, TJ; Saukkonen, A; Castleden, IR; Singh, P; Mann, GW; Fabre, B; Ito, J; Deery, MJ; Lilley, KS; ...
Source TitleThe Plant Journal
University of Melbourne Author/sHeazlewood, Joshua
AffiliationSchool of BioSciences
Document TypeJournal Article
CitationsHooper, C. M., Stevens, T. J., Saukkonen, A., Castleden, I. R., Singh, P., Mann, G. W., Fabre, B., Ito, J., Deery, M. J., Lilley, K. S., Petzold, C. J., Millar, A. H., Heazlewood, J. L. & Parsons, H. T. (2017). Multiple marker abundance profiling: combining selected reaction monitoring and data-dependent acquisition for rapid estimation of organelle abundance in subcellular samples. PLANT JOURNAL, 92 (6), pp.1202-1217. https://doi.org/10.1111/tpj.13743.
Access StatusOpen Access
Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently-used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox (http://suba.live).
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