Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS...

Area of Science:

• Metabolomics
• Analytical Chemistry
• Biomarker Discovery

Background:

• Quality control (QC) samples are essential in metabolomics for column conditioning, analytical drift correction, and precision evaluation.
• Pooled QC samples, prepared before or after extraction, account for analytical variance or both analytical and sample preparation variances.

Purpose of the Study:

• To compare three pooled QC sample preparation methods for untargeted metabolomics of tea leaves.
• To evaluate their efficiency in improving data quality across inter-batch correction, measurement precision, and variable importance projection (VIP) candidate selection.
• To investigate the impact of QC preparation on data loss and dataset structure using Orbitrap and QToF mass spectrometry.

Main Methods:

• Untargeted metabolomics analysis of tea (Camellia sinensis) leaves.
• Comparison of three pooled QC sample preparation strategies (two usual, one unusual).
• Data acquisition using Orbitrap and time of flight (QToF) mass spectrometry.
• Evaluation of data processing modalities based on different QC preparations.

Main Results:

• Usual QC sample preparations yielded comparable data quality (precision, dispersion) on both mass spectrometry instruments.
• QC preparation critically influenced variable importance projection (VIP) selection, with up to 54% of biomarker candidates being specific to the QC preparation type.
• Different QC preparations affected data loss and the global structure of the datasets.

Conclusions:

• The choice of QC sample preparation method is crucial for reliable biomarker discovery in untargeted metabolomics.
• Standard QC preparation methods offer consistent data quality across different mass spectrometry platforms.
• Optimizing QC preparation is vital for accurate identification and selection of potential biomarkers.