Extending the Range of SLIM-Labeling Applications: From Human Cell Lines in Culture to Caenorhabditis elegans Whole-Organism Labeling.

The simple light isotope metabolic-labeling technique relies on the in vivo biosynthesis of amino acids from U-[12C]-labeled molecules provided as the sole carbon source. The incorporation of the resulting U-[12C]-amino acids into proteins presents several key advantages for mass-spectrometry-based proteomics analysis, as it results in more intense monoisotopic ions, with a better signal-to-noise ratio in bottom-up analysis. In our initial studies, we developed the simple light isotope metabolic (SLIM)-labeling strategy using prototrophic eukaryotic microorganisms, the yeasts Candida albicans and Saccharomyces cerevisiae, as well as strains with genetic markers that lead to amino-acid auxotrophy. To extend the range of SLIM-labeling applications, we evaluated (i) the incorporation of U-[12C]-glucose into proteins of human cells grown in a complex RPMI-based medium containing the labeled molecule, considering that human cell lines require a large number of essential amino-acids to support their growth, and (ii) an indirect labeling strategy in which the nematode Caenorhabditis elegans grown on plates was fed U-[12C]-labeled bacteria (Escherichia coli) and the worm proteome analyzed for 12C incorporation into proteins. In both cases, we were able to demonstrate efficient incorporation of 12C into the newly synthesized proteins, opening the way for original approaches in quantitative proteomics.

Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy.

Simple light isotope metabolic labeling (SLIM labeling) is an innovative method to quantify variations in the proteome based on an original in vivo labeling strategy. Heterotrophic cells grown in U-[12C] as the sole source of carbon synthesize U-[12C]-amino acids, which are incorporated into proteins, giving rise to U-[12C]-proteins. This results in a large increase in the intensity of the monoisotope ion of peptides and proteins, thus allowing higher identification scores and protein sequence coverage in mass spectrometry experiments. This method, initially developed for signal processing and quantification of the incorporation rate of 12C into peptides, was based on a multistep process that was difficult to implement for many laboratories. To overcome these limitations, we developed a new theoretical background to analyze bottom-up proteomics data using SLIM-labeling (bSLIM) and established simple procedures based on open-source software, using dedicated OpenMS modules, and embedded R scripts to process the bSLIM experimental data. These new tools allow computation of both the 12C abundance in peptides to follow the kinetics of protein labeling and the molar fraction of unlabeled and 12C-labeled peptides in multiplexing experiments to determine the relative abundance of proteins extracted under different biological conditions. They also make it possible to consider incomplete 12C labeling, such as that observed in cells with nutritional requirements for nonlabeled amino acids. These tools were validated on an experimental dataset produced using various yeast strains of Saccharomyces cerevisiae and growth conditions. The workflows are built on the implementation of appropriate calculation modules in a KNIME working environment. These new integrated tools provide a convenient framework for the wider use of the SLIM-labeling strategy.

Capillary liquid chromatography coupled with mass spectrometry for analysis of nanogram protein quantities on a wide-pore superficially porous particle column in top-down proteomics.

In top-down proteomics experiments, intact protein ions are subjected to gas-phase fragmentation for MS analysis without prior digestion. This approach is used to characterize post-translational modifications and clipped forms of proteins, avoids several "inference" problems associated with bottom-up proteomics, and is well suited to the study of proteoforms. In the past decade, top-down proteomics has progressed rapidly, taking advantage of MS instrumentation improvements and the efforts of pioneering groups working to improve sample handling and data processing. The potential of this technology has been established through its successful use in a number of important biological studies. However, many challenges remain to be addressed like improving protein separation capabilities such that it might become possible to expand the dynamic range of whole proteome analysis, address co-elution and convoluted mass spectral data, and aid final data processing from peak identification to quantification. In this study, we investigated the use of a wide-pore silica-based superficially porous media with a high coverage phenyl bonding, commercially packed into customized capillary columns for the purpose of top-down proteomics. Protein samples of increasing complexity were tested, namely subunit digests of a monoclonal antibody, components of purified histones and proteins extracted from eukaryotic ribosomes. High quality mass spectra were obtained from only 100 ng of protein sample while using difluoroacetic acid as an ion pairing agent to improve peak shape and chromatographic resolution. A peak width at half height of about 15 s for a 45 min gradient time was observed on a complex mixture giving an estimated peak capacity close to 100. Most importantly, efficient separations were obtained for highly diverse proteins and there was no need to make method specific adjustments, suggesting this is a highly versatile and easy-to-use setup for top-down proteomics.

Quantitative Proteomics in Yeast : From bSLIM and Proteome Discoverer Outputs to Graphical Assessment of the Significance of Protein Quantification Scores.

Simple light isotope metabolic labeling (bSLIM) is an innovative method to accurately quantify differences in protein abundance at the proteome level in standard bottom-up experiments. The quantification process requires computation of the ratio of intensity of several isotopologs in the isotopic cluster of every identified peptide. Thus, appropriate bioinformatic workflows are required to extract the signals from the instrument files and calculate the required ratio to infer peptide/protein abundance. In a previous study (Sénécaut et al., J Proteome Res 20:1476-1487, 2021), we developed original open-source workflows based on OpenMS nodes implemented in a KNIME working environment. Here, we extend the use of the bSLIM labeling strategy in quantitative proteomics by presenting an alternative procedure to extract isotopolog intensities and process them by taking advantage of new functionalities integrated into the Minora node of Proteome Discoverer 2.4 software. We also present a graphical strategy to evaluate the statistical robustness of protein quantification scores and calculate the associated false discovery rates (FDR). We validated these approaches in a case study in which we compared the differences between the proteomes of two closely related yeast strains.