Experimental and Data Analysis Considerations for Three-Dimensional Mass Spectrometry Imaging in Biomedical Research

Mass spectrometry imaging (MSI) enables the visualization of molecular distributions on complex surfaces. It has been extensively used in the field of biomedical research to investigate healthy and diseased tissues. Most of the MSI studies are conducted in a 2D fashion where only a single slice of the full sample volume is investigated. However, biological processes occur within a tissue volume and would ideally be investigated as a whole to gain a more comprehensive understanding of the spatial and molecular complexity of biological samples such as tissues and cells. Mass spectrometry imaging has therefore been expanded to the 3D realm whereby molecular distributions within a 3D sample can be visualized. The benefit of investigating volumetric data has led to a quick rise in the application of single-sample 3D-MSI investigations. Several experimental and data analysis aspects need to be considered to perform successful 3D-MSI studies. In this review, we discuss these aspects as well as ongoing developments that enable 3D-MSI to be routinely applied to multi-sample studies.

Strategies for managing multi-patient 3D mass spectrometry imaging data

Mass spectrometry imaging (MSI) has emerged as a powerful tool in biomedical research to reveal the localization of a broad scale of compounds ranging from metabolites to proteins in diseased tissues, such as malignant tumors. MSI is most commonly used for the two-dimensional imaging of tissues from multiple patients or for the three-dimensional (3D) imaging of tissue from a single patient. These applications are potentially introducing a sampling bias on a sample or patient level, respectively. The aim of this study is therefore to investigate the consequences of sampling bias on sample representativeness and on the precision of biomarker discovery for histological grading of human bladder cancers by MSI. We therefore submitted formalin-fixed paraffin-embedded tissues from 14 bladder cancer patients with varying histological grades to 3D analysis by matrix-assisted laser desorption/ionization (MALDI) MSI. We found that, after removing 20% of the data based on novel outlier detection routines for 3D-MSI data based on the evaluation of digestion efficacy and z-directed regression, on average 33% of a sample has to be measured in order to obtain sufficient coverage of the existing biological variance within a tissue sample. SIGNIFICANCE: In this study, 3D MALDI-MSI is applied for the first time on a cohort of bladder cancer patients using formalin-fixed paraffin-embedded (FFPE) tissue of bladder cancer resections. This work portrays the reproducibility that can be achieved when employing an optimized sample preparation and subsequent data evaluation approach. Our data shows the influence of sampling bias on the variability of the results, especially for a small patient cohort. Furthermore, the presented data analysis workflow can be used by others as a 3D FFPE data-analysis pipeline working on multi-patient 3D-MSI studies.