Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry

Abstract

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM−MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM−MS to focus and provide insights into areas requiring further improvement.

Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section

“…Such comparisons allow the researcher to gain better insights into the conformational landscape adopted. − These methods have been well-reviewed ,,− and broadly fall into three categories. In the first category are those that fully evaluate the trajectory of the ion as it interacts with the buffer gas so call trajectories methods (TM) including (MOBCAL-TM and IMoS). ,,,,− The second category includes those that consider the projected area of the candidate structure and use empirical data to determine a CCS (PA, PSA, IMPACT). ,,,, The last category considers the recently emergent machine learning approaches. ,− The first two approaches rely on a reasonable starting structure, and commonly with proteins, molecular dynamics methods, both atomistic and coarse-grained that can be used to provide candidate gas-phase geometries. Such molecular dynamics (MD) evaluation can be computationally very expensive, although refinements to this have been made that integrate CCS values into the conformational searching for suitable candidate geometries.…”

Investigating the Potential of Drift Tube Ion Mobility for the Analysis of Oxidized Lipids

“…11 In addition to increasing the overall peak capacity of the analytical platform by coupling IMS to LC-HRMS, collision cross-section (CCS) values can be derived from the measured mobility and used to support untargeted annotation workflows. 12 Ion mobility experiments can be performed using time-dispersive, spatial-dispersive, or confinement-selective release instrumentation. While the timedispersive instruments with static (e.g., drift tube (DTIMS)) or oscillating electric field (e.g., traveling wave (TWIMS)) have been widely used in lipidomics, new instruments have been introduced mainly to improve the resolution of IMS separation (e.g., cyclic IMS).…”

“…Ion mobility spectrometry (IMS), a gas-phase technique separating ionized analytes based on the size, shape, and charge, has shown promising results in the separation of isobaric and isomeric lipids (e.g., sn -positions, cis/trans orientations, and R/S positions) . In addition to increasing the overall peak capacity of the analytical platform by coupling IMS to LC-HRMS, collision cross-section (CCS) values can be derived from the measured mobility and used to support untargeted annotation workflows . Ion mobility experiments can be performed using time-dispersive, spatial-dispersive, or confinement-selective release instrumentation.…”

Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry

“…Similarly, CSI:FingerID also utilizes MS/MS spectra to assist in searching a molecular structure database. Another application that takes advantage of the intersection of mass spectrometry and ML is in the understanding of metabolite chemistry. , There are also many papers utilizing ML with mass spectrometry to perform rapid screening methodologies for specific analytes of interest. , These ML methods have had powerful results and have been revolutionary in our implementation of mass spectral methods.…”