Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER

Ian C H Lee; Sergey Tumanov; Jason W H Wong; Roland Stocker; Joshua W K Ho

doi:10.1016/j.isci.2023.106881

Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER

iScience. 2023 May 13;26(6):106881. doi: 10.1016/j.isci.2023.106881. eCollection 2023 Jun 16.

Authors

Ian C H Lee^{1

2}, Sergey Tumanov^{3

4}, Jason W H Wong^{1

5}, Roland Stocker^{3

6}, Joshua W K Ho^{1

2

5}

Affiliations

¹ School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
² Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China.
³ Heart Research Institute, 7 Eliza Street, Newtown, NSW 2042, Australia.
⁴ Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
⁵ Centre for PanorOmic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
⁶ School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.

Abstract

Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER.

Keywords: Biological sciences research methodologies; Biological sciences tools; Lipidomics; Metabolomics.