Lipid research is attracting greater attention, as these molecules are key components to understand cell metabolism and the connection between genotype and phenotype. The study of lipids has also been fueled by the development of new and powerful technologies, able to identify an increasing number of species in a single run and at decreasing concentrations. One of such key developments has been the image techniques that enable the visualization of lipid distribution over a tissue with cell resolution. Thanks to the spatial information reported by such techniques, it is possible to associate a lipidome trait to individual cells, in fixed metabolic stages, which greatly facilitates understanding the metabolic changes associated to diverse pathological conditions, such as cancer. Furthermore, the image of lipids is becoming a kind of new molecular histology that has great chances to make an impact in the diagnostic units of the hospitals. Here, we examine the current state of the technology and analyze what the next steps to bring it into the diagnosis units should be. To illustrate the potential and challenges of this technology, we present a case study on clear cell renal cell carcinoma, a good model for analyzing malignant tumors due to their significant cellular and molecular heterogeneity.
Keywords: Digital histopathology; Lipidomics; MALDI-imaging; Molecular histology; Renal cell carcinoma; cancer.
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