Endogenous expression mapping of malignant melanoma by mass spectrometry imaging

Abstract

Background Currently, only a limited number of molecular biomarkers for malignant melanoma exist. This is the case for both diagnosing the disease, staging, and efficiently measuring the response to therapy by tracing the progression of disease development and drug impact. There is a great need to identify novel landmarks of disease progression and alterations. Methods Matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI-MSI) has been developed within our group to study drug localisation within micro-environmental tissue compartments. Here, we expand further on this technology development and introduce for the first time melanoma tumour tissues to map metabolite localisation utilising high resolution mass spectrometry. MALDI-MSI can measure and localise the distribution pattern of a number of small molecule metabolites within tissue compartments of tumours isolated from melanoma patients. Data on direct measurements of metabolite identities attained at the local sites in tissue compartments has not been readily available as a measure of a clinical index for most cancer diseases. The current development on the mapping of endogenous molecular expression melanoma tumours by mass spectrometry imaging focuses on the establishment of a cancer tissue preparation process whereby a matrix crystal formation is homogenously built on the tissue surface, providing uniform molecular mapping. We apply this micro-preparation technology to disease presentation by mapping the molecular signatures from patient tumour sections. Results We have automated the process with a micro-technological dispensing platform. This provides the basis for thin film generation of the cancer patient tissues prior to imaging screening. Compartmentalisation of the tumour regions are displayed within the image analysis interfaced with histopathological grading and characterisation. Conclusions This enables site localisation within the tumour with image mapping to disease target areas such as melanoma cells, macrophages, and lymphocytes.

Clinical protein science in translational medicine targeting malignant melanoma

“…Recently, important progress has been achieved in tissue preparation combined with novel MSI instrumentation to generate high-quality data. In addition, MSI can now aid in interrogating the spatial distribution of native drug compounds and drug metabolites within tissues at the resolution of a single cell (Sugihara et al 2018;Torok et al 2015Torok et al , 2017Végvári et al 2017). Cold compounds are being measured by MSI, i.e.…”

Imaging Mass Spectrometry for the Classification of Melanoma Based on BRAF/NRAS Mutational Status

“…An interesting case report from Alomari et.al described how IMS was very helpful in diagnosing a benign nevus in a newborn baby whose mother had stage IV melanoma [ 28 ]. IMS is not only applicable for the analysis of proteins/peptides in melanoma tissues but also for measuring and mapping small molecule metabolites in specific sites in the tumor compartments [ 29 ]. This finding could help in the characterization of melanoma phenotypes.…”

Lipid fingerprint‐based histology accurately classifies nevus, primary melanoma, and metastatic melanoma samples

“…22,23 We exploit here the malignancy state-associated changes in lipid metabolism of melanocytic cells for diagnostic benefit and explore the use of LIMS to classify patient samples into nevus, primary melanoma, and metastasis from melanoma. Previous works already explored the metabolite expression in sections of primary melanoma using mass spectrometry, 24 demonstrating that the technique enables extracting the metabolic signature from different cellular populations: melanoma cells, connective tissue, macrophages, and lymphocytes, although the spatial resolution at which images were recorded, 50 μm/pixel, limited somehow precise extraction of such signatures. In a recent work, Lazova et al used mass spectrometry to probe different points across sections and TMA cores of nevus and primary melanoma, concluding that it is possible to build classifiers using the protein fingerprint recovered directly from the tissue.…”