A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows

Abstract

Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.

Chemical Elicitors Induce Rare Bioactive Secondary Metabolites in Deep-Sea Bacteria under Laboratory Conditions

“…In the control condition of B003_789, a small cluster represented by hyrtiazepine (11) is found ( Figure 7 b, in white), an indole alkaloid first isolated from a marine sponge with enzyme inhibition properties [ 19 , 47 , 48 ]. The expression of these compounds was inhibited by EDTA elicitation.…”

“…If not treated correctly, dataset dereplication with MS/MS databases of known compounds is further compromised, since spectral identification can be misled by spectral similarities to ionization variants instead of to the real metabolite. In addition to proprietary and publicly available LC-MS/MS data processing and mining tools, many advances in LC-MS/MS data handling have been recently reported, such as TidyMS for data extraction [ 19 ], mzAdan [ 20 ] and feature-based molecular networking [ 21 ] to account for ionization variants, and CANOPUS for systematic compound class annotation based on MS/MS data [ 22 ]. While these works were being reported, we developed our own tool, the NP 3 MS workflow, for processing and mining of LC-MS/MS data obtained from unpurified natural product chemical libraries.…”

New software tools, databases, and resources in metabolomics: updates from 2020

“…TidyMS , is a Python package for preprocessing of untargeted LC–MS/MS derived metabolomics data that reads raw data fro-m a .mzML file format, generates spectra and total ion chromatograms (TICs), allows peak picking, feature detection, reads processed data from xcms , MZmine 2 among others, offers functionalities for data matrix curation, normalization, imputation, scaling, quality metrics, QC-based batch corrections and interactive visualization of results (Riquelme et al 2020 ).…”

Mass Spectrometry-Based Zebrafish Toxicometabolomics: A Review of Analytical and Data Quality Challenges

“…Following data preprocessing, data cleaning (feature reducing) (e.g., remove background ions and features with low precision and detectability), signal drift correction, and imputation of missing values are commonly applied [ 111 , 112 , 113 ]. Then, data transformation and statistical analysis (univariate and/or multivariate techniques) are performed to identify relevant features for a specific condition, followed by further structure elucidation and biological interpretation.…”

“…Pooled QC samples were identified as one of the most commonly applied quality measurements in untargeted LC-MS-based metabolomics studies [ 117 ]. The importance of QC pooled samples for instrumental source conditioning, carry-over assessments, data filtering, signal correction, and determination of precision has been shown by recent software developments and applications [ 111 , 121 , 122 ].…”