Detecting immunological synapses through analysis of Imaging Flow Cytometry data Bram van den Broek1, Rolf Harkes1, Anita Karomi2, Christina Metoikidou3, Sofía Ibañez Moleroo2 1 BioImaging Facility, The Netherlands Cancer Institute, Amsterdam 2 Functional Oncogenomics, The Netherlands Cancer Institute, Amsterdam 3 Precision Cancer Immunotherapy, The Netherlands Cancer Institute, Amsterdam b.vd.broek@nki.nl ImageStream Analysis Immunological synapses are transient structures formed at the interface between lymphocytes and antigen-presenting cells. Too assess the efficacy of cancer immunotherapy responses, we present a bioimage analysis workflow for quantifying cell interactions and identifying immune synapses in cancer patient samples using Imaging Flow Cytometry (IFC). IFC acquires brightfield and fluorescence images of cells as they pass through the analyzer/sorter, and provides an opportunity too capture cellular interactions in large samples. Overcoming the limitations of the existing proprietary analysis software, we preprocess and merge exported tiff files intoo multichannel image stacks and employ Fiji scripting for in-depth image analysis. By combining several open -source components (Cellpose, CLIJ2) the integrated workflow allows for automated cell segmentation based on fluorescence markers and brightfield images, classification of various immune and tumor cell types, detection and quantification of cell-cell interfaces, and assessment of marker enrichment at immune synapses. Imaging Flow Cytometry Outlook Powered by References Abstract • Use more clusters too directly identify all cell types • Membrane enrichment analysis for 3 or more cell types [1] https://github.com/BioImaging-NKI/ImageStreamCombiner [2] https://github.com/BioImaging-NKI/ImageStreamAnalysis [3] Schindelin, J., Arganda-Carreras, I., Frise, E. et al. Fiji: an open-source platform for biological-image analysis. Nature Methods 9(7), 676–682 (2012). [4] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106 (2021). [5] Haase, R. et al., CLIJ: GPU-accelerated image processing for everyone. Nature Methods 17, 5-6 (2020). [6] Legland, D., Arganda-Carreras, I., Andrey, P., MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32(22), 3532-3534 (2016). Existing software does not detect whether cells are touching/interacting. Goal: framework for characterizing immune cell-tumor interactions • Identify different immune cell types • count interactions between all cell types • Quantify immune marker enrichment at T cell – tumor interfaces (immune synapse formation) Cell segmentation brightfield variance filtered tumor marker T cell marker variance filter normalize & add merge ‘membrane’ + nuclei Cellpose segmentation segmented cells Variance filter Normalize & add Immune markers brightfield Cellpose segmentation High troughput multispectral microscopy of flowing cells Cytek ImageStream BD FACSDiscover S8 brightfield tumor marker (nucleus or membrane) immune cell marker (synapse) marker other markers Experimental setup: Add incubated tumor cells & immune cells, or directly from patient. Capture specific interactions between different cell types. Preprocessing: ImageStream Combiner Issues with exported images: x Many single-channel tiff files per dataset (10000+) x No metadata, channel number in the filename x Not all channels contain data x Every image has a different size x Sometimes a channel is missing ImageStream Combiner merges files into multipage ImageJ tiff Channel names handled in the GUI, or imported with .toml file Process multiple datasets in one run All images same size, background replaced by median of medians https://github.com/BioImaging-NKI/ImageStreamCombiner Nuclear tumor marker Membrane tumor marker Identify immune cell types & quantify cell-cell interactions 1. Segmentation, montage, label processing, di