Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues and are impractical for performing time-course studies. The analysis of the minute amounts of sweat sampled from the fingertip enables a solution to this challenge. Sweat sampling from the fingertip is non-invasive and robust and can be accomplished repeatedly by untrained personnel. This matrix represents a rich source for metabolomic phenotyping, which is exemplified by the detection of roughly 50000 features per sample. Moreover, the determined limits of detection demonstrate that the ingestion of 0.2 mg of a xenobiotic may be sufficient for its detection in sweat from the fingertip. The feasibility of short interval sampling of sweat from the fingertips was confirmed in three time-course studies after coffee consumption or ingestion of a caffeine capsule, successfully monitoring all known caffeine metabolites. Fluctuations in the rate of sweat production were accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. Biomonitoring using sweat from the fingertip has far reaching implications for personalized medical diagnostics and biomarker discovery.
Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.
The major benefits of integrating ion mobility (IM) into LC–MS methods for small molecules are the additional separation dimension and especially the use of IM-derived collision cross sections (CCS) as an additional ion-specific identification parameter. Several large CCS databases are now available, but outliers in experimental interplatform IM-MS comparisons are identified as a critical issue for routine use of CCS databases for identity confirmation. We postulate that different routine external calibration strategies applied for traveling wave (TWIM-MS) in comparison to drift tube (DTIM-MS) and trapped ion mobility (TIM-MS) instruments is a critical factor affecting interplatform comparability. In this study, different external calibration approaches for IM-MS were experimentally evaluated for 87 steroids, for which TWCCSN2, DTCCSN2 and TIMCCSN2 are available. New reference CCSN2 values for commercially available and class-specific calibrant sets were established using DTIM-MS and the benefit of using consolidated reference values on comparability of CCSN2 values assessed. Furthermore, use of a new internal correction strategy based on stable isotope labelled (SIL) internal standards was shown to have potential for reducing systematic error in routine methods. After reducing bias for CCSN2 between different platforms using new reference values (95% of TWCCSN2 values fell within 1.29% of DTCCSN2 and 1.12% of TIMCCSN2 values, respectively), remaining outliers could be confidently classified and further studied using DFT calculations and CCSN2 predictions. Despite large uncertainties for in silico CCSN2 predictions, discrepancies in observed CCSN2 values across different IM-MS platforms as well as non-uniform arrival time distributions could be partly rationalized.
Applications of ion mobility (IM) coupled to high-resolution mass spectrometry, i.e., quadrupole time-of-flight (QTOF) instruments, have experienced a significant growth in recent years, especially in the ‘omics fields including metabolomics. Several types of instrumental platforms are now commercially available and exploit different operation principles for the IM separation. In this contribution, we discuss the current state of commercial IM–QTOFMS technology and data acquisition strategies relevant to metabolomics studies. Particular focus is placed on the strengths and weaknesses of the application of this technology both for data generation and emerging strategies and opportunities within data processing workflows that take full advantage of the added IM dimension.
Steroids play key roles in various biological processes and are characterized by many isomeric variants, which makes their unambiguous identification challenging. Ion mobility-mass spectrometry (IM-MS) has been proposed as a suitable platform for this application, particularly using collision cross section (CCS) databases obtained from different commercial IM-MS instruments. CCS is seen as an ideal additional identification parameter for steroids as long-term repeatability and interlaboratory reproducibility of this measurand are excellent and matrix effects are negligible. While excellent results were demonstrated for individual IM-MS technologies, a systematic comparison of CCS derived from all major commercial IM-MS technologies has not been performed. To address this gap, a comprehensive interlaboratory comparison of 142 CCS values derived from drift tube (DTIM-MS), traveling wave (TWIM-MS), and trapped ion mobility (TIM-MS) platforms using a set of 87 steroids was undertaken. Besides delivering three instrument-specific CCS databases, systematic comparisons revealed excellent interlaboratory performance for 95% of the ions with CCS biases within ±1% for TIM-MS and within ±2% for TWIM-MS with respect to DTIM-MS values. However, a small fraction of ions (<1.5%) showed larger biases of up to 7% indicating that differences in the ion conformation sampled on different instrument types need to be further investigated. Systematic differences between CCS derived from different IM-MS analyzers and implications on the applicability for nontargeted analysis are critically discussed. To the best of our knowledge, this is the most comprehensive interlaboratory study comparing CCS from three different IM-MS technologies for analysis of steroids and small molecules in general.
Steroids play key roles in various biological processes and are characterized by many isomeric variants which makes their unambiguous identification challenging. Ion mobility-mass spectrometry (IM-MS) has been proposed as a suitable platform for this application, particularly using collision cross section (CCS) databases obtained from differ-ent commercial IM-MS instruments. CCS is foreseen as an ideal additional identification parameter for steroids as long-term repeatability and interlaboratory reproducibility of this measurand are excellent and matrix effects are negligible. While excellent results were demonstrated for individual IM-MS technologies, a systematic comparison of CCS derived from all major commercial IM-MS technologies has not been performed. To address this gap, a comprehensive interlabor-atory comparison of 142 CCS values derived from drift tube (DTIM-MS), traveling wave (TWIM-MS) and trapped ion mo-bility (TIM-MS) platforms using a set of 87 steroids was undertaken. Besides delivering three instrument-specific CCS databases, systematic comparisons revealed excellent interlaboratory performance for 95% of the ions with CCS biases within ±1% for TIM-MS and within ±2% for TWIM-MS with respect to DTIM-MS values. However, a small fraction of ions (<1.5 %) showed larger biases of up to 7% indicating that differences in the ion conformation sampled on different in-strument types need to be further investigated. Systematic differences between CCS derived from different IM-MS analyz-ers and implications on the applicability for non-targeted analysis are critically discussed. To the best of our knowledge this is the most comprehensive interlaboratory study comparing CCS from three different IM-MS technologies for analysis of steroids and small molecules in general.
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