Development and application of time staggered/mass staggered-globally optimized targeted mass spectrometry

A Metabolomic Aging Clock Using Human Cerebrospinal Fluid

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that, in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid (CSF) from healthy human subjects and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer's and Parkinson's disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently overpredict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small data set (n = 85) and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of CSF.

Quantifying Precision Loss in Targeted Metabolomics Based on Mass Spectrometry and Nonmatching Internal Standards

In mass spectrometry, reliable quantification requires correction for variations in ionization efficiency between samples. The preferred method is the addition of a stable isotope-labeled internal standard (SIL-IS). In targeted metabolomics, a dedicated SIL-IS for each metabolite of interest may not always be realized due to high cost or limited availability. We recently completed the analysis of more than 70 biomarkers, each with a matching SIL-IS, across four mass spectrometry-based platforms (one GC-MS/MS and three LC-MS/MS). Using data from calibrator and quality control samples added to 60 96-well trays (analytical runs), we calculated analytical precision (CV) retrospectively. The use of integrated peak areas for all metabolites and internal standards allowed us to calculate precision for all matching analyte (A)/SIL-IS (IS) pairs as well as for all nonmatching A/IS pairs within each platform (total n = 1442). The median between-run precision for matching A/IS across the four platforms was 2.7-5.9%. The median CV for nonmatching A/IS (corresponding to pairing analytes with a non-SIL-IS) was 2.9-10.7 percentage points higher. Across all platforms, CVs for nonmatching A/IS increased with increasing difference in retention time (Spearman's rho of 0.17-0.93). The CV difference for nonmatching vs matching A/IS was often, but not always, smaller when analytes and internal standards were close structural analogs.

Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches

This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.