Our latest white paper delves into GMet’s unique flow injection analysis (FIA) approach and how the application of our proprietary signal processing and metabolite indentification software delivers performancem, coverage, reproducibility, and sensitivity that can be superior to conventional LC-MS approaches.
Abstract
Flow injection analysis (FIA) is a form of mass spectrometry-based metabolomics where samples are not separated by chromatography prior to detection by the mass analyzer and where ions are annotated based on their mass-to-charge ratio without any intentional fragmentation. In general contexts, this approach can offer extremely high throughput (thousands of samples per day) and can also enable identification of large numbers of features per sample (~1,000 ions annotated is typical). However, questions of the performance of this approach exist with respect to the sensitivity and reliability of the measurements. In this work we demonstrate that through the application of proprietary signal processing and analysis techniques that General Metabolics (GMet) is able to deliver over 90% uniqueness per annotated ion at the level of the molecular formula. In addition, we have been able to demonstrate that the sensitivity of the method is on par or in some cases superior to conventional LC-MS approaches while delivering ~4 logs of linearity for ions. Ion intensities were shown to be largely independent of one another, emphasizing that sample matrix effects do not fundamentally impede the interpretation of metabolite abundance from flow injection analysis data. Importantly, we were able to systematically confirm that in-source fragmentation leads to only limited challenges in the interpretation of GMet’s FIA data. Based on these results we believe that when properly enabled with well-designed signal processing capabilities (as well as other downstream tools to handle the processing and interpretation of large-scale data), FIA offers an attractive combination of coverage as well as annotation confidence for larger screening projects.
