Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling

Abstract

Liquid chromatography–mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

Antiviral metabolite 3’-Deoxy-3’,4’-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19

“…To facilitate quality assessment and pre-processing, a pooled QC sample was prepared by combining equal parts of each study sample and analysed periodically among study sample analyses. In addition, for assessment of analyte response, 16 a series of QC sample dilutions was created (10 × 100%, 5 × 80%, 3 × 60%, 3 × 40%, 5 × 20%, 10 × 10%, 10 × 1%) and analysed at the start and end of each set of sample analyses.…”

An Open Platform for Large Scale LC-MS-Based Metabolomics

“…38 Furthermore, by diluting the SR across a range of concentrations to form a set of "dilution series" QC samples, features can also be assessed for how well they are captured with linear dynamic range of the instrument, and for the proportionality of their response to dilution. 5 The SR also forms the basis of pre-processing steps such as batch and run-order correction (see data extraction and pre-processing methods section below).…”

“…4 However, the resulting datasets are large and complex, typically containing tens of thousands of variables ranging in measurement quality and exhibiting a high degree of redundancy. 5,6 Consequently, biological interpretation of global profiling data is fruitless without considerable effort toward transforming analytical data into biochemical knowledge. This represents a substantial barrier to the success of LC-MS-based metabolomics in clinical and translational research.…”

Mass Spectrometry-Based Zebrafish Toxicometabolomics: A Review of Analytical and Data Quality Challenges

“…For instance, in the work of Broadhurst et al, the authors did not advise the use of pooled QC serial dilution to filter data based on correlations, since it does not consider a non-linear response, but they mentioned that further work is required to extend the use of this approach [ 122 ]. A recent application of the QC dilution series and advanced filtering proposed by Sands et al showed that dilution QC series, besides its challenges (e.g., poor representation of low abundant metabolites, correct models for a response, account for matrix effects), can be used to estimate the linear range and analyte response and improve the data quality of global metabolic studies [ 123 ].…”