One of the challenges in treating and studying cancers is that the solid tumor microenvironment (TME) is composed of a complex mixture of cells that exist in multiple biological states. Compounding the issue is the fact that distinct regions of the same tumor exhibit significant differences which are informed by regional variation in the TME. Addressing these challenges has become an important area of technological innovation, including using single cell RNA sequencing and multiplexed ion beam imaging. Despite such exciting and important advancements, there are still significant gaps in our ability to study spatial heterogeneity in the TME. In particular, the ability to study metabolism at the cellular level in situ would be an important advance for cancer research. This is especially true because a cell’s location can inform its metabolic state, which in turn dictates its function. Although seminal insights into cellular metabolism have come from a combination of methods including in vitro culture and ex vivo analysis of metabolic flux, the logical next step is to understand the metabolism of individual cells in situ.
Justin Perry and Kayvan Keshari now plan to optimize and implement the use of matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) coupled to Fourier-transformed ion cyclotron resonance (FT-ICR) imaging to perform untargeted metabolite analysis of mouse breast cancer models. Optimization of this technology will allow the measurement of relative metabolite levels at single cell resolution and co-register these metabolite profiles onto fluorescent or histology-stained serial sections. They will develop the computational tools and algorithms necessary to perform three-dimensional MALDI MS imaging in cell types across whole tumors. They will also advance the use of MALDI MS imaging by adopting conventional infusion of carbon-13 isotopes to perform proof-of-principle cellular metabolic flux analysis in mouse breast cancer tissue in situ. Understanding these heterogeneous cellular states will lead to better insight into the clinical course of patients and advance the realization of personalized approaches to cancer therapy.