Automation in Metabolite Identification Workflows with Software-Assisted Processing of Mass Spectrometry Data
73rd ASMS Conference on Mass Spectrometry. June 2025
Savannah M Mason1; Ismael Zamora1; Luca Morettoni1; Paula Cifuentes1; Ramon Adalia1
1Mass Analytica, S.L., Sant Cugat del Vallés, Spain
Abstract
Introduction
The identification of metabolites using mass spectrometry is a crucial component of drug discovery and development. In recent years, the development of software-assisted approaches for metabolite identification have resulted in the expedited analysis of LCMS data. Despite these advances, challenges remain, particularly in the submission of data for processing, which can be tedious for the user. In this work, we develop automation to facilitate metabolite identification workflows. We demonstrate how automation may be used to parse information from a sample list and process data into a database. Following software-assisted peak finding and structural elucidation, we further automate filters to sort the peaks and generate a report, resulting in an expedited workflow for metabolite identification.
Methods
This work used automation to generate experiments and process mass spectrometry data in a database for application in metabolite identification workflows. A script was executed, which directed the system to monitor a designated folder for an instrument sample list and LCMS data files. The presence of these files triggered both the creation of experiments in a database and the processing of the data for metabolite identification. Filters were applied to simplify analysis of the peaks before user intervention. Data processing and analysis were performed using MassMetaSite 4.7 in the ONIRO 1.6.2 server with LCMS data from Agilent, Bruker, Sciex, Thermo, and Waters.
Preliminary data
Following incubation and LCMS data acquisition, the automation described herein significantly reduced user involvement in the laborious tasks of this metabolite identification workflow. The sample list, which was automatically generated from a mass spectrometer, was parsed to obtain the information necessary to define the protocol and create experiments within the ONIRO database. Using MassMetaSite, the LCMS data was analyzed to find metabolite peaks and elucidate the structures.
Several process tasks were also automated during data analysis to consolidate tasks which are otherwise tedious for the user during data review. Peaks were automatically filtered and discarded based on various parameters, such as mass error, isotope similarity score, and negative control area ratio. In experiments with multiple timepoints, calculations were performed to provide kinetic analysis, such as AUC and the area comparison between a given incubation sample and the 0 minute sample. An AI-based peak selection model was applied, which provided suggestions for peak selection and removal. The peaks which the model suggested to remove with high probability were automatically hidden. Finally, the system generated metabolite identification reports after approval of the experiments.
This workflow reduced the time required of a user by automating the parsing of a sample list, creation of experiments, processing of mass spectrometry data, and filtering of metabolite peaks. Upon review of the data by the user, the system automatically generated a metID report. This automation has the potential to significantly decrease the time between LCMS data acquisition and report generation, providing faster access to information to better understand the metabolism and design compounds with improved properties.
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