An Adaptive Pipeline To Maximize Isobaric Tagging Data in Large-Scale MS-Based Proteomics

Area of Science:

• Proteomics
• Bioinformatics
• Mass Spectrometry

Background:

• Isobaric tagging, such as tandem mass tag (TMT) labeling, is crucial for comparative proteomics.
• Large-scale studies often suffer from missing values and unexploited quantitative data due to data-dependent acquisition and standard processing workflows.
• Current methods leave many tandem mass spectra with quantitative information unutilized.

Purpose of the Study:

• To develop and validate a novel bioinformatic pipeline, Quantify then Identify (QtI), for optimizing TMT-based proteomics data processing.
• To enhance quantification and identification rates while minimizing missing values in large-scale proteomic studies.
• To leverage unexploited quantitative information from unmatched spectra.

Main Methods:

• Developed the Quantify then Identify (QtI) pipeline, a quantification-driven approach for TMT data.
• Implemented innovative features including self-adaptive peak filtering, Peptide Match Rescue, and Optimized Post-Translational Modification.
• Compared QtI against a classical benchmark workflow using human cerebrospinal fluid and plasma datasets.

Main Results:

• QtI significantly improved both quantification and identification rates compared to the benchmark workflow.
• The pipeline substantially reduced missing data, decreasing unexploited tandem mass spectra by 77% and 62% in the tested datasets.
• Preserved and utilized quantitative information from previously unmatched features.

Conclusions:

• The Quantify then Identify (QtI) pipeline offers a superior method for processing large-scale TMT proteomic data.
• QtI effectively addresses the challenges of missing values and data incompleteness in comparative proteomics.
• This approach enhances data utility and analytical power in mass-spectrometry-based proteomic studies.