Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

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 <i>R</i> = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (<i>R</i> = 0.94)

Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

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 <i>R</i> = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (<i>R</i> = 0.94)

Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

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 <i>R</i> = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (<i>R</i> = 0.94)

The sample selection process.

<p>The sample selection process.</p

Metabolites differing between UQ and GDM groups at 2-y follow-up (<i>p</i>&lt;0.05).

<p>Metabolites have been classified according to their molecular structures or known metabolic functions/pathway participation. Within each class, data have been separated in to those with higher and lower ratios and are then presented in order from lowest to highest <i>p</i> value. The molecular weights, calculated as the monoisotopic mass, are included. Ratios with 95% confidence intervals in parentheses are shown. CE Cholesteryl ester; CEHC, 2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman; DG, diglyceride; HEPE, hydroxy-eicosapentaenoic acid; PC, phosphatidylcholine; PG, phosphatidylglycine; The values in parentheses (for example PC(34∶0)) relate to the total fatty acid carbon chain length and number of carbon double bonds (unsaturation) in each metabolite. *Identification by matching of retention time and accurate mass to authentic chemical standard.</p><p>Metabolites differing between UQ and GDM groups at 2-y follow-up (<i>p</i>&lt;0.05).</p

msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics

Tandem mass spectrometry (MS/MS or MS²) is a widely used approach for structural annotation and identification of metabolites in complex biological samples. The importance of assessing the contribution of the precursor ion within an isolation window for MS² experiments has been previously detailed in proteomics, where precursor ion purity influences the quality and accuracy of matching to mass spectral libraries, but to date, there has been little attention to this data-processing technique in metabolomics. Here, we present msPurity, a vendor-independent R package for liquid chromatography (LC) and direct infusion (DI) MS² that calculates a simple metric to describe the contribution of the selected precursor. The precursor purity metric is calculated as “intensity of a selected precursor divided by the summed intensity of the isolation window”. The metric is interpolated at the recorded point of MS² acquisition using bordering full-scan spectra. Isotopic peaks of the selected precursor can be removed, and low abundance peaks that are believed to have limited contribution to the resulting MS² spectra are removed. Additionally, the isolation efficiency of the mass spectrometer can be taken into account. The package was applied to Data Dependent Acquisition (DDA)-based MS² metabolomics data sets derived from three metabolomics data repositories. For the 10 LC-MS² DDA data sets with > ±1 Da isolation windows, the median precursor purity score ranged from 0.67 to 0.96 (scale = 0 to +1). The R package was also used to assess precursor purity of theoretical isolation windows from LC-MS data sets of differing sample types. The theoretical isolation windows being the same width used for an anticipated DDA experiment (±0.5 Da). The most complex sample had a median precursor purity score of 0.46 for the 64,498 XCMS determined features, in comparison to the less spectrally complex sample that had a purity score of 0.66 for 5071 XCMS features. It has been previously reported in proteomics that a purity score of <0.5 can produce unreliable spectra matching results. With this assumption, we show that for complex samples there will be a large number of metabolites where traditional DDA approaches will struggle to provide reliable annotations or accurate matches to mass spectral libraries

Metabolites differing between control and GDM groups at 2-y follow-up (<i>p</i>&lt;0.05).

<p>Metabolites have been classified according to their molecular structures or known metabolic functions/pathway participation. Within each class the data have been separated in to those with higher and lower ratios and are then presented in order from lowest to highest <i>p</i> value. The molecular weights, calculated as the monoisotopic mass, are included. Ratios with 95% confidence intervals in parentheses are shown. CE cholesteryl ester; DG, diglyceride; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycine; PGF, prostaglandin; PI, phosphatidylinositol; PS, phosphatidylserine; The values in parentheses (for example PC(34∶0)) relate to the total fatty acid carbon chain length and number of carbon double bonds (unsaturation) in each metabolite. *Identification by matching of retention time and accurate mass to authentic chemical standard.</p><p>Metabolites differing between control and GDM groups at 2-y follow-up (<i>p</i>&lt;0.05).</p

Phospholipids that differed significantly between control and UQ groups at 2-y follow-up (<i>p</i>&lt;0.05).

<p>*Identification by matching of retention time and accurate mass to authentic chemical standard.</p

Long chain fatty acids that differed significantly between control and UQ groups at 2-y follow-up (<i>p</i>&lt;0.05).

<p>PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, glycerophosphoglycerol; PS, phosphatidylserine.</p

Clinical data for participants during pregnancy and at follow-up in the three study groups.

<p><i>p</i> values calculated applying ANOVA or **Chi-squared tests. #Data are geometric mean and 95% confidence intervals</p><p>Clinical data for participants during pregnancy and at follow-up in the three study groups.</p