Systematic Comparison of Label-Free, SILAC, and TMT Techniques to Study Early Adaption toward Inhibition of EGFR Signaling in the Colorectal Cancer Cell Line DiFi

Abstract

We evaluated the quantification strategies label-free (LF), stable isotope labeling by amino acids in cell culture (SILAC), and tandem mass tags (TMT) and their performance in quantification of proteins and phosphosites (p-sites) to identify the most powerful approach for monitoring cellular signaling. We analyzed the epidermal growth factor receptor (EGFR) signaling network, which plays an essential role in colorectal cancer, and studied its dynamics within 24 h upon treatment with the EGFR-blocking antibody cetuximab, representing the first cellular adaption toward therapy. LF achieved superior coverage but was outperformed by label-based approaches regarding technical variability, especially for quantification of p-sites. TMT showed the lowest coverage and most missing values. We found that its performance considerably decreases when experimental replicates are distributed over several TMT plexes. SILAC showed the highest precision and outstanding performance for quantification of p-sites, rendering it the method of choice for analyzing cellular signaling in cell culture models. On the protein level, we observed only little regulation upon cetuximab treatment, whereas a great fraction of p-sites was significantly regulated. These dynamics represented an initial downregulation of the MAPK pathway, which was partially rescued as early as 24 h after treatment. We identified upregulation and signaling via ERBB3 as well as calcium and cAMP signaling as possible mechanisms bypassing the blockage of EGFR.

A critical comparison of three MS‐based approaches for quantitative proteomics analysis

“…Nevertheless, the precision advantage of label‐based approaches could be less impactful on larger sample sets. In fact, some authors have reported a decrease in performance when multiple isobaric labelling experiments are combined into a single data analysis pipeline, with increasing missing values and decreased precision 27,30 . The same drawback could be even more relevant for MS 1 ‐based quantification methods (SILAC/DML) because of their limited multiplexing capabilities.…”

“…increasing missing values and decreased precision. 27,30 The same drawback could be even more relevant for MS 1 Marco Gaspari https://orcid.org/0000-0002-5411-8800…”

“…Despite ratio compression, the TMT‐MS 2 method performed equally well in assigning protein localization but with higher proteome coverage than all other methods. In another very recent benchmark, Stepath et al 27 have used EGFR signalling in DiFi cells as a model system to compare SILAC, MaxLFQ and TMT performances. Although the MaxLFQ approach provided the highest proteome coverage, the SILAC‐based method was declared the preferred method for cellular signalling studies, especially thanks to its excellent performance in the quantification of p‐sites.…”

AA_stat: intelligent profiling ofin vivoandin vitromodifications from open search results

“…Publicly available proteomic data were used for the AA_stat evaluation. [25][26][27][28][29][30][31][32] Detailed annotation of these datasets is provided in Supporting Information (S-Table 1). Their selection was aimed at having a diverse number of modified amino acid residues per peptide.…”

“…Table 4 summarizes the sets of fixed and variable modifications recommended by AA_stat in comparison with the modification sets from annotation of the respective studies. [24][25][26][27][28][29][30][31] Comparing the annotated and AA_stat-retrieved modifications, both expected and unexpected modifications can be mentioned. For example, Cys-carbamidomethylation was annotated for all data, however, two subsets with enrichments of phosphorylated peptides (#9, #10 in Figure 2) and one subset with R-methylation enrichment (#12 in AA_stat demonstrates a potential for uncovering rare modifications.…”