Benchmarking SILAC Proteomics Workflows and Data Analysis Platforms.

Area of Science:

• Proteomics
• Biochemistry
• Computational Biology

Background:

• Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) is a key metabolic labeling technique in proteomics.
• Accurate identification and quantification of isotopic protein variants are crucial for SILAC-based studies.
• A lack of comprehensive evaluation for SILAC data analysis platforms hinders optimal study design and execution.

Purpose of the Study:

• To systematically evaluate and compare various SILAC data analysis workflows and software.
• To provide practical guidelines for researchers performing SILAC proteomics.
• To address the critical gap in understanding the performance of different SILAC analysis platforms.

Main Methods:

• A benchmarking pipeline was developed to assess ten SILAC data analysis workflows across five software packages (MaxQuant, Proteome Discoverer, FragPipe, DIA-NN, Spectronaut).
• Evaluations included static and dynamic SILAC labeling using both Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) methods.
• Performance was assessed using 12 metrics on both in-house and repository datasets from HeLa and neuron cultures.

Main Results:

• Each software package exhibited distinct strengths and weaknesses across the evaluated performance metrics.
• Most platforms demonstrated a dynamic range limit of approximately 100-fold for accurate light/heavy ratio quantification.
• Proteome Discoverer was not recommended for SILAC DDA analysis based on the findings.

Conclusions:

• This study presents the first systematic evaluation of SILAC data analysis platforms.
• Cross-validation using multiple software packages is recommended for enhanced SILAC quantification confidence.
• The findings offer practical guidance for SILAC proteomics study design and data analysis decisions.