A novel Spatial Multi-omics mass spectrometry imaging workflow to assist clinical investigations

V. Denti;G. Capitoli;I. Piga;G. Paglia;F. Magni;A. Smith

2022-01-01

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Abstract

1. Introduction The field of spatial omics defines the gathering of different techniques that allow the detection of significant alterations of biomolecules in the context of their native tissue or cellular structures. As such, they extend the landscape of biological changes occurring in complex and heterogeneous pathological tissues, such as cancer. However, additional molecular levels, such as lipids and glycans, must be studied to define a more comprehensive molecular snapshot of disease and fully understand the complexity and dynamics beyond pathological condition [1]. Among the spatial-omics techniques, matrix-assisted laser desorption / ionisation (MALDI) – mass spectrometry imaging (MSI) offers a powerful insight into the chemical biology of pathological tissues in a multiplexed approach where several hundreds of biomolecules can be examined within a single experiment [2]. Thus, MALDI-MSI has been readily employed for spatial multi-omics studies of proteins, peptides and N-Glycans on clinical formalin-fixed paraffin-embedded (FFPE) tissue samples. 2. Experimental In this work, we describe a spatial multi-omics MALDI-MSI workflow which enables the sequential analysis of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section(Figure1). In doing so, we first highlight the feasibility using technical replicates of murine brain tissue. Following, as a proof-of-concept, the approach was applied to four clear cell renal cell carcinoma (ccRCC) specimens to assess the ability of this multiplexed MALDIMSI approach to more comprehensivelycharacterise the tumour tissue when combiningthe multi-level molecular information. 3. Results First, comparing the average spectra obtained from lipid, N-Glycan, and tryptic peptide imaging of the three technical replicates whichwere analysed on three separate days, respectively, a high degree of similarity can beobserved. Accordingly, the mean CV% obtained from each of the sequential MALDI- MSI analysis lay within a range comparable to the CV threshold of 20% recommended by theEuropean Medicine Agency (EMA) for analytical techniques. When the spatial distribution of the detected lipid, N-Glycan, and tryptic peptides was evaluated, it soon became clear that each molecular class was able to better underline different regions of the brain tissue, indicatingtheir complementary nature. This was also consistent among the three technical replicates.Additionally, the potentiality of this spatial multi-omics MALDIMSI workflow in ccRCC pathological tissue was assessed and, asobserved in murine brain tissue, each of the molecular levels showed tendencies to better underline different histopathological regions. To confirm the capability of each molecular class to distinguish among the different histopathological regions of ccRCC tissue, qualitative statistical analyses were performed.In support of what was observed in the MALDI-MS images themselves, each dataset led to a complementary separation of the histopathological regions. When these individual datasets were then combined into one large multi-omics dataset, the histopathological regions were separated with greater power and, in fact, could all easily be distinguished from one another. This was not possible using the lipidomics, N-Glycan, or tryptic peptide dataset in isolation. 4. Conclusions Taken together, the spatial multi-omics MALDIMSI workflow presented hereprovides the ability to map the distribution of lipids, N-Glycans, and tryptic peptides on a single FFPE tissue section. Whilst this study focuses on known histopathological regions as proof of concept, it underlines the increased molecular coverage that is obtained by using a multiplexed MALDI-MSI approach and can lead to a more comprehensive characterisation of diseased tissue [3]. Finally, these findings also pave the way for further development of more powerful bioinformatics tools which canbe used to mine these spatial multiomic datasets and uncover hidden molecular patterns which arise as a result of the relationship between these multiple molecular levels.