Benchmarking SILAC Proteomics Workflows and Data Analysis Platforms

Stable isotope labeling by amino acids in cell culture (SILAC) is a powerful metabolic labeling technique with broad applications and various study designs. SILAC proteomics relies on the accurate identification and quantification of all isotopic versions of proteins and peptides during both data acquisition and analysis. However, a comprehensive comparison and evaluation of SILAC data analysis platforms is currently lacking. To address this critical gap and offer practical guidelines for SILAC proteomics data analysis, we designed a comprehensive benchmarking pipeline to evaluate various in vitro SILAC workflows and commonly used data analysis software. Ten different SILAC data analysis workflows using five software packages (MaxQuant, Proteome Discoverer, FragPipe, DIA-NN, and Spectronaut) were evaluated for static and dynamic SILAC labeling with both DDA and DIA methods. For benchmarking, we used both in-house generated and repository SILAC proteomics datasets from HeLa and neuron culture samples. We assessed twelve performance metrics for SILAC proteomics including identification, quantification, accuracy, precision, reproducibility, filtering criteria, missing values, false discovery rate, protein half-life measurement, data completeness, unique software features, and speed of data analysis. Each method/software has its strengths and weaknesses when evaluated for these performance metrics. Most software reaches a dynamic range limit of 100 folds for accurate quantification of light/heavy ratios. We do not recommend using Proteome Discoverer for SILAC DDA analysis despite its wide use in label-free proteomics. To achieve greater confidence in SILAC quantification, researchers could use more than one software packages to analyze the same dataset for cross-validation. In summary, this study offers the first systematic evaluation of various SILAC data analysis platforms, providing practical guidelines to support decision-making in SILAC proteomics study design and data analysis.