Intelligent Differential Ion Mobility Spectrometry (iDMS): A Machine Learning Algorithm that Simplifies Optimization of Lipidomic Differential Ion Mobility Spectrometry Parameters

Abstract

Glycosphingolipids such as α- and β-glucosylceramides (GlcCers) and α- and β- galactosylceramides (GalCers) are stereoisomers differentially synthesized by gut bacteria and their mammalian hosts in response to environmental insult. Thus, lipidomic assessment of α- and β-GlcCers and α- and β-GalCers is crucial for inferring biological functions and biomarker discovery. However, simultaneous quantification of these stereoisomeric lipids is difficult due to their virtually identical structures. Differential mobility mass spectrometry (DMS), as an orthogonal separation to high performance liquid chromatography used in electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS), can be used to separate stereoisomeric lipids. Generating LC-ESI-DMS-MS/MS methods for lipidomic analyses is exceedingly difficult demanding intensive manual optimization of DMS parameters that depend on the availability of synthetic lipid standards. Where synthetic standards do not exist, method development is not possible. To address this challenge, I developed a supervised in silico machine learning approach to accelerate method development for ion mobility-based quantification of lipid stereoisomers. I hypothesized that supervised neural network models could be used to learn the relationships between lipid structural characteristics and optimal DMS machine parameter values thereby reducing the total number of empirical experiments required to develop a DMS method and enabling users to “predict” DMS parameters for analytes that lack synthetic standards. Specifically, this thesis describes a supervised learning approach that learns the relationship between two DMS machine parameter values (separation voltage and compensation voltage) and two lipid structural features (N-Acyl chain length and degree of unsaturation). I describe here, iDMS, an algorithm that was trained on 17 lipid species, and can further simulate results of DMS manual method development and suggest optimal parameter values for 47 lipid species. This approach promises to greatly accelerate the development of assays for the detection of lipid stereoisomers in biological samples.