Quantitative challenges and their bioinformatic solutions in mass spectrometry-based metabolomics

Abstract

Improving the quantitative performance of mass spectrometry (MS)-based metabolomics is the key to its successful application in a broad range of research questions. Like other analytical pipelines, there are quantitative challenges in metabolomics. In particular, due to the large amount of data generated from MS, metabolomics data present unique quantitative challenges that conventional wet-lab approaches cannot address. Complementary bioinformatic methods exhibit unique advantages in tackling these problems. However, analytical chemists often underestimate the importance of bioinformatic solutions in the era of omics. This review summarizes the critical quantitative challenges in MS-based metabolomics. It highlights the existing bioinformatic solutions and discusses ongoing issues as future directions for method development. A specific focus is given to liquid chromatography-mass spectrometry (LC-MS)-based metabolomics because of its wide usage. Through this review, we hope to encourage awareness of the existing quantitative biases and their bioinformatic solutions. We also hope to motivate the development of bioinformatic methods for accurate, precise, and robust quantitative metabolomics.