Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

Area of Science:

• Metabolomics
• Analytical Chemistry
• Bioinformatics

Background:

• Direct-infusion mass spectrometry (DIMS) metabolomics is crucial for understanding biological responses.
• Large-scale metabolomics studies require robust bioanalytical and computational workflows for data quality.
• Reproducibility is a key challenge in multi-batch DIMS metabolomics.

Purpose of the Study:

• To systematically evaluate the reproducibility of a multi-batch DIMS metabolomics study.
• To provide a dataset for assessing batch-correction algorithms in metabolomics.
• To establish a benchmark for DIMS metabolomics using best-practice workflows.

Main Methods:

• Analysis of cardiac tissue extracts from cow and sheep (20 samples).
• Multi-batch analysis across 7 days with 8 batches and concurrent quality control (QC) samples.
• Systematic evaluation of data quality at each workflow step.

Main Results:

• The dataset enables correction of intra- and inter-batch variation using QC spectra.
• Quality of batch correction can be independently assessed using repeatedly measured biological samples.
• The data facilitates evaluation of novel data processing algorithms.

Conclusions:

• This dataset serves as a valuable resource for the metabolomics community.
• It enables rigorous quality assessment and benchmarking of DIMS metabolomics workflows.
• It supports the development and validation of improved computational methods for metabolomics data analysis.