Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

• Published in: Analytical chemistry (Washington) Vol. 84; no. 22; pp. 9848 - 9857
• Main Authors: Vaughan, Andrew A, Dunn, Warwick B, Allwood, J. William, Wedge, David C, Blackhall, Fiona H, Whetton, Anthony D, Dive, Caroline, Goodacre, Royston
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 20.11.2012
• Subjects: Analytical chemistry; blood serum; Calibration; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography; Chromatography, Liquid - methods; Correlation analysis; data collection; Exact sciences and technology; General, instrumentation; Humans; least squares; Liquid chromatography; Lung cancer; lung neoplasms; Lung Neoplasms - metabolism; Mass spectrometry; Mass Spectrometry - methods; Metabolites; metabolomics; Metabolomics - methods; Other chromatographic methods; Plasma; Small Cell Lung Carcinoma - metabolism; Spectrometric and optical methods; Statistics as Topic - methods; temporal variation; Metabolomics; Lung disease; Metabolite; Coupled method; Sample; Lung; Instrumentation; Data processing; Liquid chromatography; Calibration; Laboratory; Malignant tumor; Retention time; Survival; Blood plasma; Biological compound; PLS regression; Transfer; Serum; Large scale; Mass spectrometry; Chemometrics; Cancer
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/ac302227c

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography–mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson’s R = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (R = 0.94).