Pipelines and Systems for Threshold-Avoiding Quantification of LC–MS/MS Data
Alejandro Sánchez Brotonsa, Jonatan O. Erikssonb, Marcel Kwiatkowskia c, Justina Clarinda Woltersd, Ido P. Kemae, Andrei Barcarua, Folkert Kuipersd e, Stephan J. L. Bakkerf, Rainer Bischoffa, Frank Suitsg, Peter L. Horvatovicha✉
- aDepartment of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, 9713 AV Groningen, The Netherlands
- bDepartment of Biomedical Engineering, Lund University, 221 84 Lund, Sweden
- cFunctional Proteo-Metabolomics, Department of Biochemistry, University of Innsbruck, A-6020 Innsbruck, Austria
- dDepartment of Pediatrics, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
- eDepartment of Laboratory Medicine, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
- fDepartment of Internal Medicine, Division of Nephrology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
- gIBM Research—Australia, Southbank, 3006 Victoria, Australia
- ✉Corresponding author
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Abstract
The accurate processing of complex liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data from biological samples is a major challenge for metabolomics, proteomics, and related approaches. Here, we present the pipelines and systems for threshold-avoiding quantification (PASTAQ) LC-MS/MS preprocessing toolset, which allows highly accurate quantification of data-dependent acquisition LC-MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parameterization and automatic generation of quality control plots for data and preprocessing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios and allows the detection of peptides over a larger dynamic concentration range compared to widely used proteomics preprocessing tools. The performance of the pipeline is also demonstrated in a biological human serum dataset for the identification of gender-related proteins.