Combination of injection volume calibration by creatinine and MS signals' normalization to overcome urine variability in LC-MS-based metabolomics studies.

It is essential to choose one preprocessing method for liquid chromatography-mass spectrometry (LC-MS)-based metabolomics studies of urine samples in order to overcome their variability. However, the commonly used normalization methods do not substantially reduce the high variabilities arising from differences in urine concentration, especially for signal saturation (abundant metabolites exceed the dynamic range of the instrumentation) or missing values. Herein, a simple preacquisition strategy based on differential injection volumes calibrated by creatinine (to reduce the concentration differences between the samples), combined with normalization to "total useful MS signals" or "all MS signals", is proposed to overcome urine variabilities. This strategy was first systematically compared with other popular normalization methods by application to serially diluted urine samples. Then, the method has been verified using rat urine samples of pre- and postinoculation of Walker 256 carcinoma cells. The results showed that the calibration of injection volumes based on creatinine values could effectively eliminate intragroup differences caused by variations in the concentrations of urinary metabolites, thus giving better parallelism and clustering effects. In addition, peak area normalization could further eliminate intraclass differences. Therefore, the strategy of combining peak area normalization with calibration of injection volumes of urine samples based on their creatinine values is effective for solving problems associated with urinary metabolomics.

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

[2]  Mark R. Viant,et al.  Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline , 2011, Metabolomics.

[3]  C. Guillou,et al.  Critical aspects of urine profiling for the selection of potential biomarkers using UPLC-TOF-MS. , 2012, Biomedical chromatography : BMC.

[4]  Jesper Kristiansen,et al.  Comparison of uncertainties related to standardization of urine samples with volume and creatinine concentration. , 2004, The Annals of occupational hygiene.

[5]  Stephanie S O'Malley,et al.  Correction of urine cotinine concentrations for creatinine excretion: is it useful? , 2003, Clinical chemistry.

[6]  L K Lowry,et al.  Interpretation of urine results used to assess chemical exposure with emphasis on creatinine adjustments: a review. , 1993, American Industrial Hygiene Association journal.

[7]  Yongmei Song,et al.  Time-course changes in potential biomarkers detected using a metabonomic approach in Walker 256 tumor-bearing rats. , 2011, Journal of proteome research.

[8]  T. Ebbels,et al.  Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery. , 2011, Analytical chemistry.

[9]  Q. Zhan,et al.  Integrated ionization approach for RRLC-MS/MS-based metabonomics: finding potential biomarkers for lung cancer. , 2010, Journal of proteome research.

[10]  Xiaomei Yan,et al.  LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery. , 2011, Journal of proteome research.

[11]  Rebecca C. Miller,et al.  Comparison of specific gravity and creatinine for normalizing urinary reproductive hormone concentrations. , 2004, Clinical chemistry.

[12]  J. Lindon,et al.  Scaling and normalization effects in NMR spectroscopic metabonomic data sets. , 2006, Analytical chemistry.

[13]  G. Siuzdak,et al.  From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics. , 2007, Journal of proteome research.

[14]  R. Spang,et al.  State-of-the art data normalization methods improve NMR-based metabolomic analysis , 2011, Metabolomics.

[15]  Kazuo T. Suzuki,et al.  Comprehensive evaluation of canine renal papillary necrosis induced by nefiracetam, a neurotransmission enhancer. , 2003, European journal of pharmacology.

[16]  H. Senn,et al.  Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. , 2006, Analytical chemistry.

[17]  John T. Wei,et al.  Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression , 2009, Nature.

[18]  R. A. van den Berg,et al.  Centering, scaling, and transformations: improving the biological information content of metabolomics data , 2006, BMC Genomics.

[19]  B. Warrack,et al.  Normalization strategies for metabonomic analysis of urine samples. , 2009, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[20]  C. Guillou,et al.  Comment on "Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery". , 2011, Analytical chemistry.

[21]  Hui Sun,et al.  Urine Metabolomics Analysis for Biomarker Discovery and Detection of Jaundice Syndrome in Patients With Liver Disease* , 2012, Molecular & Cellular Proteomics.

[22]  R. J. O. Torgrip,et al.  A note on normalization of biofluid 1D 1H-NMR data , 2008, Metabolomics.

[23]  H. Ressom,et al.  LC-MS-based metabolomics. , 2012, Molecular bioSystems.