Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging

Abstract

Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR (https://github.com/CeMOS-Mannheim/moleculaR) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

Deep MALDI-MS Spatial ‘Omics guided by Quantum Cascade Laser Mid-infrared Imaging Microscopy

“…1b(ii)). Consistent with recent observations in (brain) tumor patient samples where transcripts of glycerophospholipid (GPL) remodeling enzymes were overexpressed compared to surrounding non-tumor tissue 2 , GPLs such as phosphatidylinositol PI 34:1 were more prominent in cancer cells, whereas lyso-GPLs, e.g. LPI 18:0, were more abundant in fibroblasts (Fig.…”

“…In addition to these challenges, method development and validation in MSI have generally been hampered by the lack of reliable analytical ground truths for segmentation and molecular identities 22, 23 , as the spatial and molecular composition of investigated tissues is typically unknown. To this end, synthetic datasets 2 , expert crowdsourcing 22 single-cell fluorescence 24 , or histopathology annotations 25 have been proposed as ground truths. To address this key challenge in MSI method development and validation, we propose that genetic mouse models with defined alterations in metabolism be used as qualitative ground truth: In case of QCL-IRI-guided MSI workflows we employed arylsulfatase A-deficient (ARSA-/-) mice, a model of human metachromatic leukodystrophy (MLD).…”

“…Mass spectrometry imaging (MSI) is a fundamental label-free technology in spatial biology. It enables spatially resolved visualization, investigation and probabilistic mapping of lipids, metabolites, peptides, drugs or N-glycans in tissue sections in biomedical science as well as in clinical and industrial research [1][2][3][4] . Integration of whole tissue data sets sequentially acquired from the same or adjacent tissue sections by MSI and orthogonal technologies such as (mid-)infrared imaging (IRI), Raman imaging or spatial transcriptomics, often referred to as correlative spatial multi-omics [5][6][7] , have opened up new avenues for scientific inquiry.…”

Spatial Omics Imaging of Fresh-Frozen Tissue and Routine FFPE Histopathology of a Single Cancer Needle Core Biopsy: A Freezing Device and Multimodal Workflow

“…With the advent of spatially resolved omics technologies, such as the MSI of proteins, lipids, and metabolites ("spatial lipidomics/metabolomics") [11][12][13][14][15], MSI-guided microproteomics [16], microscopy-guided micro-proteomics [17], spatial transcriptomics, and multi-omics [18,19] for studies of complex molecular alterations in pathophysiology in tissue specimens, their potential to be used in clinical diagnosis, prognosis, or targeted therapy is manifold. They allow for the investigation of intra-tumor heterogeneity and pathophysiological processes including tumor genesis, progression or metastasis.…”

Matrix selection for the visualization of small molecules and lipids in brain tumors using untargeted MALDI-TOF mass spectrometry imaging

“…2-HG inhibits the activity of enzymes that are involved in DNA and histone modifications, leading to epigenetic changes in gene expression that promote cancer cell growth and survival. Metabolic activity is not uniformly altered in brain cancer tumors, reflecting complex and multifactorial regulation, that in turn depends on the cellular microenvironment and underlying genetic profile of the malignant cells [6,7]. For example, oxygen availability determines the rate of aerobic glycolysis utilization throughout brain cancer tumors.…”