Abstract
SummaryMeasuring 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).
Highlights
Understanding how protein abundance relates to protein characteristics such as location, function or post-translational modification is an important aspect of understanding biological systems, but reliably estimating protein abundance is non-trivial
A global protein abundance score (PAS) for Arabidopsis proteins observed by mass spectrometry
Over 100 publications describing the proteomes of enriched subcellular regions, organelles and protein complexes are contained in SUBA, PPDB and AtChloro (Sun et al, 2009; Ferro et al, 2010; Hooper et al, 2017)
Summary
Understanding how protein abundance relates to protein characteristics such as location, function or post-translational modification is an important aspect of understanding biological systems, but reliably estimating protein abundance is non-trivial. Assessing expression of proteincoding genes is facilitated by microarray data, or directly measured by quantitative polymerase chain reaction and RNA sequencing. This informs little about actual protein abundance, as global protein expression studies show inconsistent correlation with gene expression (Greenbaum et al, 2003; Gry et al, 2009). The development of mass spectrometry-based protein profiling, or proteomics, has provided an analytical platform that enables the estimation of protein abundance from a biological sample. Relative quantitation of in vivo protein abundance is possible using quantitative mass spectrometry of labelled proteins (Thompson et al, 2003; Ross et al, 2004; Christoforou et al, 2016).
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