QuantyFey: An open-source tool for targeted LC-MS quantification with integrated drift correction

This is the authors' abstract. We don't add key points for this paper.

BACKGROUND: Signal intensity drift is a well-recognized issue in quantitative LC-MS(MS) analysis, especially during long analytical sequences or when internal standards (IS) are unavailable. While IS correction is widely supported by commercial platforms, other correction strategies, such as quality control (QC)-based drift correction, or quantification bracketing, are unavailable. This limits the ability of analysts to maintain data quality in more complex or resource-limited experimental setups. To address this, we developed QuantyFey, an open-source, vendor-independent tool for external calibration-based quantification, with support for multiple drift correction strategies. RESULTS: We applied QuantyFey to a targeted LC-MS/MS dataset comprising amino acids, amino acid-related metabolites, and biogenic amines measured in porcine plasma. The dataset was affected by substantial signal drift across the run. A calibration standard was used as a proxy for a QC sample and different drift correction strategies were compared: IS correction, QC-based drift correction, custom bracketing and weighted bracketing. Concentrations of all compounds were calculated using different drift correction strategies and manual tuning of calibration functions, and remaining intensity drift was assessed. Both QC-based and IS-based correction significantly reduced drift effects. Custom- or weighted bracketing methods also improved quantification accuracy but demonstrated variable performance across compounds. This study highlights the importance of evaluating compound-specific behavior when selecting drift correction strategies. SIGNIFICANCE: QuantyFey offers a transparent and accessible framework for quantitative LC-MS(MS) analysis, especially in situations where drift correction is critical and IS are limited. Its flexible design allows for compound-specific evaluation and quantification, making it a practical tool for handling complex datasets. By supporting tailored drift correction strategies, QuantyFey addresses key challenges in maintaining data quality and reproducibility.