Retention Time Standardization and Registration (RTStaR): An algorithm that matches corresponding and identifies unique species in nanoliquid chromatography-nanoelectrospray ionization-mass spectrometry lipidomic datasets

Abstract

AbstractBioinformatic tools capable of registering, rapidly and reproducibly, large numbers of nanoliquid chromatography-nanoelectrospray ionization-tandem mass spectrometry (nLC-nESI-MS/MS) lipidomic datasets are lacking. We provide here a freely available Retention Time Standardization and Registration (RTStaR) algorithm that aligns nLC-nESI-MS/MS spectra within a single dataset and compares these aligned retention times across multiple datasets. This two-step calibration matches corresponding and identifies unique lipid species in different lipidomes from different matrices and organisms. RTStaR was developed using a population-based study of 1001 human serum samples composed of 71 distinct glycerophosphocholine metabolites comprising a total of 68,572 analytes. Platform and matrix independence were validated using different MS instruments, nLC methodologies, and mammalian lipidomes. The complete algorithm is packaged in two modular ExcelTM workbook templates for easy implementation. RTStaR is freely available from the India Taylor Lipidomics Research Platform http://www.neurolipidomics.ca/rtstar/rtstar.html. Technical support is provided through ldomic@uottawa.ca