This novel approach to biomarker assessment offers a better understanding of the complicated interactions between tumour cells and their environment. Multiplex imaging techniques and digital image analysis are powerful tools that could overcome some issues concerning tumour-microenvironment studies. The prediction of patient response to this type of therapy is mainly based on conventional immunohistochemistry, which is limited by intraobserver variability, semiquantitative assessment, or single-marker-per-slide evaluation. However, not all patients benefit from immunotherapy equally. Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. Basic Protocol 1: Studying cell morphology and cell migration in time‐lapse datasets using TrackMate (Fiji) and CellProfiler Basic Protocol 2: Creating whole plate montages to easily assess adaptability of segmentation parameters ImageJ and CellProfiler are both committed to interoperability between their platforms, with ongoing development to improve how both are leveraged from the other. While both programs can be and are often used separately, these pipelines demonstrate the benefits of using them together for image analysis workflows. No single platform can provide all the key and most efficient functionality needed for all studies. Here, we share two pipelines demonstrating mechanisms for productively and conveniently integrating ImageJ and CellProfiler for (1) studying cell morphology and migration via tracking, and (2) advanced stitching techniques for handling large, tiled image sets to improve segmentation. Although many image analysis problems can be well solved with one or the other, using these two platforms together in a single workflow can be powerful. ImageJ's traditional strength is in single‐image processing and investigation, while CellProfiler is designed for building large‐scale, modular analysis pipelines. ImageJ and CellProfiler have long been leading open‐source platforms in the field of bioimage analysis.
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