In Vivo Quantitative Proteome Profiling: Planning and Evaluation of SILAC Experiments

Kirchner, Marieluise; Selbach, Matthias

doi:10.1007/978-1-61779-885-6_13

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…This lack of data is most likely explained by technical challenge to obtain such net degradation rates. However, new technologies and strategies are now available to track protein degradation in a systematic manner (summarized under the term 'degradomics'), in particular using pulsed stable isotope labeling (SILAC) (Kirchner and Selbach, 2012), N-terminal protein enrichment strategies (Lange and Overall, 2013;Plasman et al, 2013), or 'targeted' proteomics techniques in which predefined sets of proteins are tracked across series of samples (Gallien et al, 2012;Picotti and Aebersold, 2012). These techniques differ as to whether they can measure net degradation (i.e.…”

Section: Regulons and Coordinated Control Of Sink-source Transitionsmentioning

confidence: 99%

Correlation of mRNA and protein abundance in the developing maize leaf

et al. 2014

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SUMMARYTo help understand regulation of maize leaf blade development, including sink-source transitions and induction of C4 photosynthesis, we compared large-scale quantitative proteome and transcriptomes collected at specific stages along the developmental maize leaf blade gradient. Proteome data were based on label-free shotgun proteomics (spectral counting) and transcript data were based on RNA-seq using the same source materials, and had been published previously (Nat Genet, 42, 2010, 1060-1067 The Plant Cell, 22, 2010, 3509-3542). Transcript and protein abundance followed near normal distributions, in contrast with several studies with other organisms. Protein observability correlated with transcript abundance following a 'lazy step function' similar to that in bacteria and yeast. mRNA and protein abundance showed significant positive correlations (up to 0.8) for log-transformed length-weighted normalized spectral abundance factor (NSAF) and reads per kilobase of exon model per million mapped reads (RPKM) and non-weighted abundances (NadjSPC and COV) in dependence of function and development. Correlations were much weaker in the leaf 'sinksource' transition zone, i.e. the zone with massive investments in leaf chloroplast biogenesis and build-up of photosynthetic capacity. Clustering analyses of gene-specific protein-mRNA ratios revealed co-ordinated shifts in control points in gene expression along the leaf blade developmental gradient. The highest proteinmRNA ratio for each gene generally corresponded to leaf developmental stages in which the protein function was most important, with the exception of the 80S ribosome. Specific examples are discussed in the context of C4 photosynthesis, leaf development and sink-source transitions. This large-scale mRNA-protein correlation analysis in plants (maize) using label-free spectral counting for protein quantification and RNA-seq for mRNA abundance will provide a template for future mRNA-protein correlation studies.

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Section: Regulons and Coordinated Control Of Sink-source Transitionsmentioning

confidence: 99%

Correlation of mRNA and protein abundance in the developing maize leaf

et al. 2014

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“…We routinely use the MaxQuant software suite (Cox and Mann 2008;Cox et al 2009Cox et al , 2011Kirchner and Selbach 2012) to process phosphoproteomics data. Alternatively, there are several freely or commercially available software packages (e.g., Mascot, Proteome Discoverer, Scaffold, Trans-Proteomic Pipeline).…”

Section: Lyophilizer Maxquant Software Suitementioning

confidence: 99%

Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)-Based Quantitative Proteomics and Phosphoproteomics in Fission Yeast

Carpy

Koch

Bicho

et al. 2017

Cold Spring Harb Protoc

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Modern mass spectrometry (MS)-based approaches are capable of identifying and quantifying thousands of proteins and phosphorylation events in a single biological experiment. Here we present a (phospho)proteomic workflow based on in-solution proteome digestion of samples labeled by stable isotope labeling by amino acids in cell culture (SILAC) and phosphopeptide enrichment using strong cation exchange (SCX) and TiO 2 chromatographies. These procedures are followed by high-accuracy MS measurement on an Orbitrap mass spectrometer and subsequent bioinformatic processing using MaxQuant software. MATERIALSIt is essential that you consult the appropriate Material Safety Data Sheets and your institution's Environmental Health and Safety Office for proper handling of equipment and hazardous material used in this protocol.RECIPES: Please see the end of this protocol for recipes indicated by . Additional recipes can be found online at http://cshprotocols.cshlp.org/site/recipes. ReagentsAcetonitrile (

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“…Most of the methods discussed here are generally applicable to any organism. Even the SILAC approach, which was originally developed for metabolic labeling of tissue culture cells, has since been extended to a number of model organisms ( Kirchner and Selbach, 2012 ). Thus, we expect that in vivo interaction proteomics will become more widespread.…”

Section: In Vivo Interactionsmentioning

confidence: 99%

Quantitative affinity purification mass spectrometry: a versatile technology to study protein–protein interactions

Meyer

Selbach

2015

Front. Genet.

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While the genomic revolution has dramatically accelerated the discovery of disease-associated genes, the functional characterization of the corresponding proteins lags behind. Most proteins fulfill their tasks in complexes with other proteins, and analysis of protein–protein interactions (PPIs) can therefore provide insights into protein function. Several methods can be used to generate large-scale protein interaction networks. However, most of these approaches are not quantitative and therefore cannot reveal how perturbations affect the network. Here, we illustrate how a clever combination of quantitative mass spectrometry with different biochemical methods provides a rich toolkit to study different aspects of PPIs including topology, subunit stoichiometry, and dynamic behavior.

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In Vivo Quantitative Proteome Profiling: Planning and Evaluation of SILAC Experiments

Cited by 15 publications

References 35 publications

Correlation of mRNA and protein abundance in the developing maize leaf

Correlation of mRNA and protein abundance in the developing maize leaf

Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)-Based Quantitative Proteomics and Phosphoproteomics in Fission Yeast

Quantitative affinity purification mass spectrometry: a versatile technology to study protein–protein interactions

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