Machine learning meets mass spectrometry: a focused perspective

TL;DR

This paper discusses how machine learning can revolutionize mass spectrometry data analysis by addressing current challenges, enabling new discoveries, and transforming instrumentation and software for better throughput, automation, and information density.

Contribution

It provides a focused perspective on integrating machine learning with mass spectrometry, highlighting challenges and future directions for the field.

Findings

Machine learning can unlock rich information from large mass spectrometry datasets.

Current challenges include data loss and complexity, especially with electrospray ionization.

Advances in ML require improved instrumentation and automation for effective application.

Abstract

Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software), and once met, the field may experience significant transformation.