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Mol Cell Proteomics,
2010]
In the biological sciences, model organisms have been used for many decades and have enabled the gathering of a large proportion of our present day knowledge of basic biological processes and their derailments in disease. Although in many of these studies using model organisms, the focus has primarily been on genetics and genomics approaches, it is important that methods become available to extend this to the relevant protein level. Mass spectrometry-based proteomics is increasingly becoming the standard to comprehensively analyze proteomes. An important transition has been made recently by moving from charting static proteomes to monitoring their dynamics by simultaneously quantifying multiple proteins obtained from differently treated samples. Especially the labeling with stable isotopes has proved an effective means to accurately determine differential expression levels of proteins. Among these, metabolic incorporation of stable isotopes in vivo in whole organisms is one of the favored strategies. In this perspective, we will focus on methodologies to stable isotope label a variety of model organisms in vivo, ranging from relatively simple organisms such as bacteria and yeast to Caenorhabditis elegans, Drosophila, and Arabidopsis up to mammals such as rats and mice. We also summarize how this has opened up ways to investigate biological processes at the protein level in health and disease, revealing conservation and variation across the evolutionary tree of life.
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Anal Chem,
2008]
Comparative proteomics has emerged as a powerful approach to determine differences in protein abundance between biological samples. The introduction of stable-isotopes as internal standards especially paved the road for quantitative proteomics for comprehensive approaches to accurately determine protein dynamics. Metabolic labeling with (15)N isotopes is applied to an increasing number of organisms, including Drosophila, C. elegans, and rats. However, (15)N-enrichment is often suboptimal (<98%), which may hamper identification and quantitation of proteins. Here, we systematically investigated two independent (15)N-labeled data sets to explore the influence of heavy nitrogen enrichment on the number of identifications as well as on the error in protein quantitation. We show that specifically larger (15)N-labeled peptides are under-represented when compared to their (14)N counterparts and propose a correction method, which significantly increases the number of identifications. In addition, we developed a method that corrects for inaccurate peptide ratios introduced by incomplete (15)N enrichment. This results in improved accuracy and precision of protein quantitation. Altogether, this study provides insight into the process of protein identification and quantitation, and the methods described here can be used to improve both qualitative and quantitative data obtained by labeling with heavy nitrogen with enrichment less than 100%.