Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data

Abstract

In this paper, we describe data processing and metabolite identification approaches which lead to a rapid and semi-automated interpretation of metabolomics experiments. Data from metabolite fingerprinting using LC-ESI-Q-TOF/MS were processed with several open-source software packages, including XCMS and CAMERA to detect features and group features into compound spectra. Next, we describe the automatic scheduling of tandem mass spectrometry (MS) acquisitions to acquire a large number of MS/MS spectra, and the subsequent processing and computer-assisted annotation towards identification using the R packages MetShot, Rdisop, and the MetFusion application. We also implement a simple retention time prediction model using predicted lipophilicity logD, which predicts retention times within 42 s (6 min gradient) for most compounds in our setup. We putatively identified 44 common metabolites including several amino acids and phospholipids at metabolomics standards initiative (MSI) levels two and three and confirmed the majority of them by comparison with authentic standards at MSI level one. To aid both data integration within and data sharing between laboratories, we integrated data from two labs and mapped retention times between the chromatographic systems. Despite the different MS instrumentation and different chromatographic gradient programs, the mapped retention times agree within 26 s (20 min gradient) for 90% of the mapped features.

Compound Characterization and Metabolic Profile Elucidation after In Vitro Gastrointestinal and Hepatic Biotransformation of an Herniaria hirsuta Extract Using Unbiased Dynamic Metabolomic Data Analysis

“…To encounter the dynamic nature of the biotransformation process, a data analysis workflow for in vitro gastrointestinal biotransformation was implemented, previously developed and validated using hederacoside C as a model compound [23,27]. Briefly, data were converted to the open-source mzData format to allow further processing [38]. XCMS was used to convert the raw data into features via peak-picking, using following parameters: ppm = 10, peakwidth = c(5, 25, snthresh = 10, noise = 1000, mzdiff = 0.01, prefilter = c(3, 5000), integrate = 1.…”

PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems

“…It is difficult to compare the accuracy of these models since the same prediction error statistics are not always reported. In the published literature we typically found models with mean predictions errors about 0.5 to 2 minutes equivalent to about 5 to 15 % relative error 8,[10][11][12][13][14][35][36][37][38][39][40] . Better accuracy has been achieved for specific compound groups such as peptides 41,42 and polybrominated diphenyl ethers 43 .…”

“…These quantitative structure-retention relationship (QSRR) models share the characteristic that they require a large number of training compounds and the more complex models risk severe overfitting, making them less generally applicable. While these models can be applied to any molecular structure, they currently have limited accuracy, in part due to the limited accuracy of the underlying physicochemical descriptors 14 .…”

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

“…Those models are known as quantitative structure–retention relationships (QSRR) models [74]. As an example, partition-coefficient (log P) is known to show a relevant correlation with experimental RT in reverse phase chromatography [75]. Different studies proposed (QSRR) models to predict metabolite RT [54,76–84] .…”