Analyzing Assay Specificity in Metabolomics using Unique Ion Signature Simulations

Abstract

Targeted, untargeted and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have enjoyed popularity in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex co-isolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite identification in complex backgrounds using precursor and fragment ion mass-to-charge separation, comparing three benchmarked MS methods (MS1, MRM, DIA). We simulated MS1, MRM and DIA using the NIST LC-MS library as a complex background (8274 compounds at collision energy=35) and compared these methods with regards to unambiguous detection using unique ion signatures. Our simulations show that data generated with DIA with 25 Da mass windows outperformed MS1-only and MRM-based methods by a factor of 13.6-fold and 8.7-fold, respectively. Additionally, we use saturation analysis to show that for highly complex samples, a large portion of MS1-only detection (44.9%) is ambiguous while MRM (6.6%) and DIA (0.6%) present lower ambiguity. Our analysis demonstrates the power of using both high resolution precursor and high resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both DDA and MRM with regards to unique compound identification potential.