Decoding the Metabolic Signatures of Neurodegeneration Diseases: Advances in Mass Spectrometry-Based Metabolomics

TL;DR

This review discusses how mass spectrometry-based metabolomics is helping to uncover metabolic changes in neurodegenerative diseases.

Contribution

The paper highlights recent methodological and bioinformatics advancements in metabolomics for studying neurodegeneration.

Findings

Mass spectrometry improvements allow broader detection and accurate identification of metabolites.

Signature metabolites and key pathways linked to major neurodegenerative diseases are summarized.

Current challenges and future translational directions in metabolomics are explored.

Abstract

The dysregulation of multiple metabolic pathways is a potential contributor to the development of neurodegenerative diseases. Understanding early-stage metabolic alterations is crucial for identifying targets associated with disease development and progression. Recent advances in mass spectrometry-based metabolomics now allow investigators to conduct a comprehensive analysis of small-molecule metabolites in complex biological systems, providing valuable insights regarding the biochemical mechanisms underlying neurodegeneration. This review presents the latest advances in mass spectrometry-based metabolomic approaches and their applications in studying neurodegenerative diseases. We discuss methodology improvements in metabolomics, including sample preparation, chromatography separations, ionization, and fragmentation. These improvements enable broader detection and more accurate identification of metabolites. We also review developments in bioinformatics tools for large-scale data processing, structural annotation, and pathway analysis. Furthermore, the signature metabolites associated with major neurodegenerative diseases and the key metabolic pathways involved are summarized. Finally, we address current analytical and biological challenges in mass spectrometry-based metabolomics while exploring its future directions in translational research.

Figures