The field of spatial phenotyping has made great strides in the last decade. With  immunohistochemistry we could identify a single protein in situ, whereas nowadays we can identify up to a hundred different proteins at single cell resolution within one tissue section. Moreover, using spatial transcriptomics we can analyse over thousand different RNA transcripts and with mass spectrometry imaging, the full metabolome of a tissue can be visualised. Such developments have greatly increased our understanding of tissue architecture, and paved the way for many new avenues of research. But, tissues, both healthy and diseased, are complex entities and to fully understand their organisation and way of function, it is paramount to combine forces and investigate them from all angles.
Cellular metabolism is essential for tissue homeostasis and cell function, and is, thus, often derailed in a disease context. Indeed, rewiring of cellular metabolism was described as one of the hallmarks of cancer , due to the reprogramming of cancer cells to thrive in an altered metabolite environment. Cancer cells actively absorb nutrients from the microenvironment, potentially limiting the availability for immune cells. Via the secretion of, among others, cytokines and metabolites, constant crosstalk between stromal cells, immune cells and cancer cells occurs. For instance, some fibroblast subsets actively produce high energy nutrients that aid cancer cell proliferation. Moreover, the presence of specific metabolites in the TME can reprogram macrophages towards an inflammatory phenotype and CD8+ T cell activation is direct dependent on glycolysis after the consumption of glucose. Hence, if T cells lack essential nutrients like glucose, or cancer cells produce immunosuppressive metabolites like lactate, this could directly hamper the effectivity of immunotherapeutic interventions, such as PD1/PDL1 blockade therapy.
We developed a multimodal MSI approach for the combined analysis of metabolites and immune phenotypes in a single tissue section. Integrating the experimental workflows of spatial metabolomics by MALDI-MSI and spatial immunophenotyping by IMC, allows the relative quantification of metabolites at single cell level. We utilized frozen tissue sections which were first subjected to MALDI-MSI and, after removing the matrix, we applied IMC using an antibody panel optimized for formalin fixed tissues. After obtaining the data, we perform co-registration of the datasets and identify cells and cellular phenotypes using the protein markers obtained by IMC. Next we determined the metabolite abundance per cell and investigated the metabolic profile of the different cell types. We have shown the applicability of the methodology on colorectal cancer tissue sections and investigated the metabolite profiles of different immune cell subsets in situ. This highlighted that specific cell types, such as cancer cells, plasma B cells and CD204+ macrophages, exhibited distinct glycerophospholipid profiles. Interestingly, not only between but also within immune cell types, such as macrophages, different metabolic profiles could be distinguished.
Our methodology utilizes a single tissue section for both analyses and is thus not hampered by co-registration challenges that arise from consecutive slides. Furthermore, by utilizing fresh frozen tissue that is formalin-fixed after the MALDI-MSI, optimal detection of metabolites is possible while simultaneously obtaining high quality IMC data. In the publication we describe the methodology and provide a 26-target IMC panel that is directly applicable to different tissue types to characterize the metabolite composition of cell subsets. With this, we hope to provide the scientific community with a powerful tool to characterize tissues from multiple angles, broadening our understanding of the metabolic immune microenvironment.
Spatially resolved single-cell metabolic profiling by multimodal MSI
