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

Abstract

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.

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

“…Contaminants brought by the growth medium in the HEK123 cell cultures were searched using a dedicated protein sequence database. 9 We measured 12 C incorporation into the identified peptides using our recently described workflow, 3 in which the intensity of every isotopologue in the isotopic clusters is extracted from the .pdresults SQL database containing the data from the Minora node of Proteome Discoverer 2.4 software. 4 For each peptide identified, the sequence was decomposed into the essential amino acids (not labeled) and the nonessential amino acids (labeled), and we extracted their elemental composition.…”

“…The number of carbon atoms was split into those originating from essential amino acids, with a probability of presence of 12 C set to 0.9893, and those originating from the nonessential amino acids with a probability of presence of 12 C set to 1. Taking into account the experimental intensity of the monoisotopic ion (M 0 ) and the +1 isotopologue (M 1 ), the composition of each peptide and the associated probabilities of occurrence their stable isotopes, it is possible to compute the corresponding 12 C enrichment, 3 expressed as the probability of presence of the 12 C element. The workflows implemented in the KNIME environment 10…”

“…In these experiments, all the amino acids required for protein synthesis can be produced by the catabolism of U-[ 12 C]-glucose. It was further extended to Saccharomyces cerevisiae cells with a limited number of mutations used as genetic markers, making the cells auxotrophic for certain amino acids, therefore introducing the notion of essential amino acids. These amino acids must be supplied in the growth medium of the cells in a natural, nonlabeled form.…”

Determining and interpreting protein lifetimes in mammalian tissues

“…Briefly, although atom-based tracers can provide reliable measurements of relative labeling, analysis of the MS spectra is challenging due to the presence of heterogeneous populations of peptides of the same chemical composition that differ only by their isotopic composition (i.e., isotopologs). It is important to mention that this aspect has recently been addressed to simplify the analysis of the isotopolog distribution either by forcing a light isotope labeling shift or by using water labeling and considering only the enrichment of two mass isotopomers [18,61]. The use of heavy essential amino acids simplifies the analysis but only allows protein turnover measurements for peptides containing the heavy…”

TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies

“…21 Notably, the KNIME working environment, a recently described approach utilizing a fully light carbon source, enables the calculation of protein turnover rates in organisms using simple light isotope metabolic labeling (SLIM) and a simplified calculation utilizing two mass isotopologue peaks. 31 A similar "two-isotopologue" approach has been described for heavy water labeling. 32 Recently, a dataindependent acquisition workflow that uses open-source tools (EncyclopeDIA and Skyline) for analysis of SILAC-DIA data was introduced.…”