Fig. 1: Wet and dry lab work up for high-resolution computational mapping of Innate Lymphoid Cells (ILCs) in human primary and secondary lymphoid organs.

After tissue selection (n = 8 independent biological samples per lymphoid tissue type), 4 to 6 serial FFPE slides were cut at 4 µm. The first slide was stained with H&E for morphological quality control. mIF was performed for the 3 panels (NK/ILC1ie + ILC1, ILC2, and ILC3) in parallel for each tissue sample (7 batches, 1 staining cycle of 3 mIF per lymphoid tissue type) on the BOND RX automated staining platform (Leica Biosystems). 6-channels multispectral fluorescence imaging was performed using a Zeiss AxioScan Z1 whole-slide scanner. Object-oriented whole-slide image analysis was carried out using the Halo framework (Indica Labs). Each image was manually annotated by a certified pathologist with three regions of interest (ROI: “interface”, “extra-interface”, and “interface + extra-interface”). In tonsil, appendix, ileum, lymph node, and follicular lymphoma images, the “interface” and “extra-interface” ROIs matched respectively the follicular and extra-follicular zones, the white and red pulp in the spleen, and the medulla and cortex in thymus. Morphometric features and OPAL dye-intensities were quantified, and each object was tagged and classified for a specific phenotype based on its marker content. Spatial analysis and mapping was based on their X-Y location coordinates. Raw data were exported from the Halo database as.csv text files to be analyzed and visualized using Panda, Matplotlib, and Seaborn (Python packages). Partially created in BioRender. Bezombes, C. (2025) https://BioRender.com/shbs5qf.