Advances in mass spectrometry imaging for spatial cancer metabolomics

Abstract

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Keywords: DESI; MALDI; SIMS; cancer; glycans; lipids; mass spectrometry imaging; spatial metabolomics.

FIGURE 1

Number of publications reported by Google Scholar since 2010 with the keywords “mass spectrometry imaging” and “cancer.”

FIGURE 2

Schematic illustrations of the ionization steps in (A) MALDI, (B) DESI, and (C) SIMS MSI experiments. Adjusted and reprinted with permissions from Elsevier (A: D. Sturtevant et al., 2016 and C: D. Aoki et al., 2016) and the American Association for the Advancement of Science (B: Z. Takáts et al., 2004).

FIGURE 3

Schematic drawing of sample preparation process for MSI experiments, created with license obtained from BioRender.com (agreement number ZW23CMI9GA). MSI, mass spectrometry imaging.

FIGURE 4

Schematic illustrations of (A) LAESI (C, capillary; CCD, CCD camera with short-distance microscope; CE, counter electrode; CV, cuvette; FL, focusing lenses; HV, high-voltage power supply; L-N₂, nitrogen laser; L-Er:YAG, Er:YAG laser; M, mirrors; OSC, digital oscilloscope; PC-1 to PC-3, personal computers; SH, sample holder; SP, syringe pump), (B) MALDESI and (C) MALDI-2 setups for MSI experiments. Adjusted and reprinted with permissions from American Chemical Society (A: P. Nemes & Vertes, 2007, B: J. S. Sampson et al., 2006, and C: A. Potthoff et al., 2020).

FIGURE 5

Schematic illustration of IMS and MSI techniques that have been integrated for imaging of metabolites, lipids, and proteins. Adjusted and reprinted with permission from Elsevier (M. Sans et al., 2018). IMS, ion mass spectrometry; MSI, mass spectrometry imaging.

FIGURE 6

A typical FTICR MSI lipidomics workflow for cancer studies. Part of the figure was created with license obtained from BioRender.com (agreement numbers XX23PNM7XW, RA23PNM7ZG, TI23PNM80Q, and IF23PNN56N). FTICR, Fourier-transform ion cyclotron resonance; MSI, mass spectrometry imaging.

FIGURE 7

(A) 3D MS images constructed by alignment of 49 metastasizing medulloblastoma tissue sections; different colors indicate distributions of three lipids in the tissue. (B) Reconstructed 3D H&E image (left) and MS image (right) of 162 human oral squamous cell carcinoma tissue sections. Adjusted and reprinted with permissions from Springer Nature (A: Paine et al., 2019) and Elsevier (B: Lotz et al., 2017). 3D, three-dimensional; H&E, hematoxylin and eosin; MS, mass spectrometry.

FIGURE 8

(A) Coregistration of a MS image with a H&E-stained optical image, (B) MS (top) and immunofluorescence (bottom) images of drug metabolites and DNA damage markers in pancreatic cancer tumors, (C) cluster image of the different anatomical regions in a rat hippocampus tissue (top left), average IR absorption spectra per cluster (top right) and average MS spectra per cluster (bottom), and (d) coregistration of a DESI-MS image with Raman image of a mouse brain tissue. Adjusted and reprinted with permissions from Springer Nature (A: Frédéric Dewez et al., 2019) and American Chemical Society (B: Strittmatter et al., 2022, C: Neumann et al., 2018, and D: Bergholt et al., 2018). H&E, hematoxylin and eosin; IR, infrared; MS, mass spectrometry.