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

[1]  D. Watson,et al.  A study of six representative methods of plasma bilirubin analysis , 1961, Journal of clinical pathology.

[2]  G. Berlyne.  DETERMINATION OF GLOMERULAR FILTRATION-RATE , 1966 .

[3]  N. Mantel.  The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[4]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[5]  J. Gower.  Generalized procrustes analysis , 1975 .

[6]  C. Ruckley,et al.  A 5 year follow up of lords dilation for hemorrhoids , 1976 .

[7]  J. Gerich,et al.  Glucose and ketone body kinetics in diabetic ketoacidosis. , 1983, Clinics in endocrinology and metabolism.

[8]  B. Kowalski,et al.  Multivariate instrument standardization , 1991 .

[9]  B. Kowalski,et al.  The parsimony principle applied to multivariate calibration , 1993 .

[10]  V. Hu.  The Cell Cycle , 1994, GWUMC Department of Biochemistry Annual Spring Symposia.

[11]  D. Kell,et al.  Correction of mass spectral drift using artificial neural networks. , 1996, Analytical chemistry.

[12]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[13]  J. T. Magee,et al.  On mass spectrometer instrument standardization and interlaboratory calibration transfer using neural networks , 1997 .

[14]  V. Steinmetz,et al.  A Methodology for Sensor Fusion Design: Application to Fruit Quality Assessment , 1999 .

[15]  김삼묘,et al.  “Bioinformatics” 특집을 내면서 , 2000 .

[16]  A. Höskuldsson.  Variable and subset selection in PLS regression , 2001 .

[17]  Steven D. Brown,et al.  Transfer of multivariate calibration models: a review , 2002 .

[18]  J. Roger,et al.  Fusion of aroma, FT-IR and UV sensor data based on the Bayesian inference. Application to the discrimination of white grape varieties , 2003 .

[19]  Joel S. Parker,et al.  Adjustment of systematic microarray data biases , 2004, Bioinform..

[20]  José Manuel Andrade,et al.  Procrustes rotation in analytical chemistry, a tutorial , 2004 .

[21]  I. Wilson,et al.  High resolution "ultra performance" liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. , 2005, Journal of proteome research.

[22]  A. Smilde,et al.  Fusion of mass spectrometry-based metabolomics data. , 2005, Analytical chemistry.

[23]  A. Smilde,et al.  Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. , 2006, Analytical chemistry.

[24]  R. Abagyan,et al.  XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. , 2006, Analytical chemistry.

[25]  S. Wagner,et al.  Tools in metabonomics: an integrated validation approach for LC-MS metabolic profiling of mercapturic acids in human urine. , 2007, Analytical chemistry.

[26]  Douglas B. Kell,et al.  Proposed minimum reporting standards for data analysis in metabolomics , 2007, Metabolomics.

[27]  Oliver Fiehn,et al.  Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry. , 2008, Trends in analytical chemistry : TRAC.

[28]  D. Kell,et al.  Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system. , 2008, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[29]  P. Elliott,et al.  High-throughput 1H NMR-based metabolic analysis of human serum and urine for large-scale epidemiological studies: validation study. , 2008, International journal of epidemiology.

[30]  Marcelo Nascimento Martins,et al.  A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers. , 2008, Analytica chimica acta.

[31]  J. Rabinowitz,et al.  Analytical strategies for LC-MS-based targeted metabolomics. , 2008, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[32]  Andrew B. Nobel,et al.  Merging two gene-expression studies via cross-platform normalization , 2008, Bioinform..

[33]  I. Wilson,et al.  LC-MS-based methodology for global metabolite profiling in metabonomics/metabolomics , 2008 .

[34]  D. Kell,et al.  Mass Spectrometry Tools and Metabolite-specific Databases for Molecular Identification in Metabolomics , 2009 .

[35]  P. Fortina,et al.  The reverse Warburg effect: Aerobic glycolysis in cancer associated fibroblasts and the tumor stroma , 2009, Cell cycle.

[36]  Frans M van der Kloet,et al.  Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. , 2009, Journal of proteome research.

[37]  Joshua D. Knowles,et al.  Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. , 2009, Analytical chemistry.

[38]  J. Thomas-Oates,et al.  Metabolomic applications of HILIC-LC-MS. , 2010, Mass spectrometry reviews.

[39]  Iven Van Mechelen,et al.  A generic linked-mode decomposition model for data fusion , 2010 .

[40]  I. Wilson,et al.  Does the mass spectrometer define the marker? A comparison of global metabolite profiling data generated simultaneously via UPLC-MS on two different mass spectrometers. , 2010, Analytical chemistry.

[41]  J. Meulman,et al.  Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets. , 2010, Analytical chemistry.

[42]  Timothy M. D. Ebbels,et al.  Intra- and inter-omic fusion of metabolic profiling data in a systems biology framework , 2010 .

[43]  A. Smilde,et al.  On the increase of predictive performance with high-level data fusion. , 2011, Analytica Chimica Acta.

[44]  Faramarz Valafar,et al.  Empirical comparison of cross-platform normalization methods for gene expression data , 2011, BMC Bioinformatics.

[45]  R. Breitling,et al.  Toward global metabolomics analysis with hydrophilic interaction liquid chromatography-mass spectrometry: improved metabolite identification by retention time prediction. , 2011, Analytical chemistry.

[46]  G. Dismukes,et al.  An LC-MS-based chemical and analytical method for targeted metabolite quantification in the model cyanobacterium Synechococcus sp. PCC 7002. , 2011, Analytical chemistry.

[47]  Lutgarde M. C. Buydens,et al.  Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis , 2011, BMC Bioinformatics.

[48]  Douglas B. Kell,et al.  Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets , 2011, Bioinform..

[49]  Joshua D. Knowles,et al.  Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry , 2011, Nature Protocols.

[50]  J. W. Allwood,et al.  Is serum or plasma more appropriate for intersubject comparisons in metabolomic studies? An assessment in patients with small-cell lung cancer. , 2011, Analytical chemistry.

[51]  Ronald J. Moore,et al.  A reversed-phase capillary ultra-performance liquid chromatography–mass spectrometry (UPLC-MS) method for comprehensive top-down/bottom-up lipid profiling , 2012, Analytical and Bioanalytical Chemistry.

[52]  Calibration transfer of partial least squares jet fuel property models using a segmented virtual standards slope-bias correction method , 2012 .