Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Abstract

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

Keywords: artificial intelligence; imaging mass spectrometry; spatial metabolomics.

Figure 1

The popularity of different technologies in the life sciences and biomedicine and their evolution over time. The plot shows the numbers of PubMed-indexed publications in a given year containing the keywords shown in the figure key. We highlight three time periods, before 2009, from 2009 until 2015, and after 2015, which we discuss in the main text. The inset shows the popularity of several technologies for metabolomics applications from 1995 until 2018. Abbreviations: FTIR, Fourier-transform infrared spectroscopy; NIR, near-infrared spectroscopy.

Figure 2

An imaging mass spectrometry (MS) dataset represents a collection of spectra acquired from a raster of pixels representing the surface of a tissue section. Peaks are often reduced to centroids, especially for Fourier transform ion cyclotron resonance and Orbitrap high-resolving power analyzers. An ion image represents relative intensities of the ion across all pixels. An imaging MS dataset can represent spatial localization of up to 10³ molecules at a false discovery rate (FDR) of 10%.

Figure 3. Steps of a typical data analysis workflow in imaging mass spectrometry. Abbreviations: LC-MS/MS, liquid chromatography with tandem mass spectrometry; MS/MS, tandem mass spectrometry.

Figure 4. Ground truth is needed for methods development (training machine learning and deep learning models) and methods evaluation at the same time, which requires considerable effort.

Figure 5

METASPACE as an example of an open community platform for artificial intelligence developments in the field of spatial metabolomics. METASPACE provides a free cloud engine for metabolite annotation and encourages users to make their data public, thus creating an open knowledge base of spatial metabolomes. The open public data help engage experts to create ground truth necessary for the development of machine learning methods. Implementing these methods in METASPACE improves the platform and adds new functionality that, in turn, attracts more users, thus creating a sustainable open platform for both imaging mass spectrometry practitioners and computational method developers.