Orbi‐SIMS Mediated Metabolomics Analysis of Pathogenic Tissue up to Cellular Resolution

Abstract

Tumors have a complex metabolism that differs from most metabolic processes in healthy tissues. It is highly dynamic and driven by the tumor cells themselves, as well as by the non‐transformed stromal infiltrates and immune components. Each of these cell populations has a distinct metabolism that depends on both their cellular state and the availability of nutrients. Consequently, to fully understand the individual metabolic states of all tumor‐forming cells, correlative mass spectrometric imaging (MSI) up to cellular resolution with minimal metabolite shift needs to be achieved. By using a secondary ion mass spectrometer (SIMS) equipped with an Orbitrap mass analyzer, we present a workflow to image primary murine tumor tissues up to cellular resolution and correlate these ion images with post acquisition immunofluorescence or histological staining. In a murine breast cancer model, we could identify metabolic profiles that clearly distinguish tumor tissue from stromal cells and immune infiltrates. We demonstrate the robustness of the classification by applying the same profiles to an independent murine model of lung cancer, which is accurately segmented by histological traits. Our pipeline allows metabolic segmentation with simultaneous cell identification, which in the future will enable the design of subpopulation‐targeted metabolic interventions for therapeutic purposes.