» Articles » PMID: 39770023

Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research

Overview
Journal Molecules
Publisher MDPI
Specialty Biology
Date 2025 Jan 8
PMID 39770023
Authors
Affiliations
Soon will be listed here.
Abstract

Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings. Nevertheless, challenges persist in areas such as sample collection, quantification, quality control, and data interpretation. This review summarizes recent advances in targeted metabolomics and lipidomics, emphasizing their applications in clinical research. Advancements, including microsampling, dynamic multiple reaction monitoring, and integration of ion mobility mass spectrometry, are highlighted. Additionally, the review discusses the critical importance of data standardization and harmonization for successful clinical implementation.

Citing Articles

From Complexity to Clarity: Expanding Metabolome Coverage With Innovative Analytical Strategies.

Aarika K, Rajyalakshmi R, Nalla L, Gajula S J Sep Sci. 2025; 48(2):e70099.

PMID: 39968702 PMC: 11836935. DOI: 10.1002/jssc.70099.

References
1.
Ammerlaan W, Trezzi J, Lescuyer P, Mathay C, Hiller K, Betsou F . Method validation for preparing serum and plasma samples from human blood for downstream proteomic, metabolomic, and circulating nucleic acid-based applications. Biopreserv Biobank. 2014; 12(4):269-80. DOI: 10.1089/bio.2014.0003. View

2.
. Lipidomics needs more standardization. Nat Metab. 2020; 1(8):745-747. DOI: 10.1038/s42255-019-0094-z. View

3.
Gegner H, Naake T, Aljakouch K, Dugourd A, Kliewer G, Muller T . A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens. Clin Proteomics. 2024; 21(1):49. PMC: 11225228. DOI: 10.1186/s12014-024-09501-9. View

4.
Allwright M, Guennewig B, Hoffmann A, Rohleder C, Jieu B, Chung L . ReTimeML: a retention time predictor that supports the LC-MS/MS analysis of sphingolipids. Sci Rep. 2024; 14(1):4375. PMC: 10883992. DOI: 10.1038/s41598-024-53860-0. View

5.
Wasito H, Hermann G, Fitz V, Troyer C, Hann S, Koellensperger G . Yeast-based reference materials for quantitative metabolomics. Anal Bioanal Chem. 2021; 414(15):4359-4368. PMC: 9142427. DOI: 10.1007/s00216-021-03694-w. View