TidyMS: a Python-based tool for preprocessing LC-MS data in untargeted metabolomics and lipidomics workflows

GABRIEL RIQUELME; NICOLÁS ZABALEGUI; PABLO MARCHI; CHRISTINA M. JONES; MARÍA EUGENIA MONGE

Conferencia Virtual

Simposio; 9th International Singapore Lipid Symposium (iSLS), On-site-On-line,; 2021

Singapore Lipidomics Incubator

One of the challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), data curation involves removing biologically non-relevant features (retention time, m/z pairs) and retaining only those analytically robust enough to allow subsequent data analysis and interpretation. Our research group has recently developed ?TidyMS?,1 a new Python library for LC-MS data preprocessing, with the aim of performing quality control (QC) procedures in untargeted metabolomics and lipidomics workflows. TidyMS includes tools to work with both raw MS data and data matrices. It provides functionality to build customized pipelines for data curation to obtain cleaned matrices for subsequent statistical analysis, and aims at ensuring accuracy and reliability in LC?MS measurements. The capabilities of TidyMS were illustrated with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to successfully preprocess data from a new suite of candidate human plasma reference materials currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma to be used in metabolomics and lipidomics studies. Overall, TidyMS offers a rapid and reproducible workflow that can be used in an automated or semi-automated way, and it is an open and free tool available to all users.