Presented by Dr. Nils Eling, University of Zurich The development of highly multiplexed imaging technologies has led to deeper insights into the spatial organization of healthy and diseased tissues. In particular, the structure of the immune compartment within the tumour microenvironment is a predictor for immuno-oncology treatment success. The IMMUcan (Integrated immunoprofiling of large adaptive cancer patient cohorts) project studies immune-tumour interactions within the tumour microenvironment and its impact on therapeutic interventions. As part of this initiative, the team acquires multiplexed immunofluorescence and imaging mass cytometry data of thousands of patient samples from five cancer types. While multiplexed immunofluorescence captures the expression of about seven proteins across all cells of the tumour section, imaging mass cytometry focuses on measuring smaller regions (about 1 mm squared) with higher content (about 40 proteins). To study detailed immune-tumour interactions using imaging mass cytometry, it is therefore crucial to perform informed selection of regions of interest that capture the cell types of interest. We have now developed a set of computational tools that guide and facilitate the selection of regions for imaging mass cytometry based on multiplexed immunofluorescence measurements. First, a custom-made, user-guided workflow robustly identifies the same cell-types across different patients and cancer indications. Via an online tool, we are able to select three to four regions containing about 50% of tumour cells and a diversity of immune cells of interest. The Python package napping was developed to transfer the selected regions’ coordinates onto brightfield images of consecutive sections to be measured by imaging mass cytometry. Upon data acquisition and quality control, we use the cytomapper Bioconductor package to manually label cell types on imaging mass cytometry images and train a random forest classifier for cell type classification. Based on this workflow, we were able to identify broad (e.g. T cells) and rare (e.g. exhausted CD8+ T cells) cell types across all acquired samples with good agreement to matched multiplexed immunofluorescence data.