Coloring in the Details: Cellular Portraits for Precision Treatment
Each colorectal cancer patient poses a challenge to their physicians. What appears as one disease is actually a collection of biologically distinct cancers, each responding differently to available therapies. Current precision medicine approaches help some patients, yet many still do not benefit, because suitable drug matches remain unavailable. Researchers have developed three-dimensional tumour models that better reflect how cancers behave in patients and so recover accurate matches. While these models provide valuable drug response information, they typically measure only cell growth or viability rather than the complex cellular changes during treatment.
The ColoPaint project has addressed this challenge by extending the use of Cell Painting - a versatile and popular microscopy imaging technique - to study colorectal cancer in more relevant laboratory models. To use Cell Painting is like taking sophisticated cellular snapshots, blending multiple fluorescent dyes to capture detailed images of cell shape and composition, laying bare its current condition. By recording thousands of cellular properties from these images, we can quantify subtle changes in how cancer cells respond to different treatments.
We first used Cell Painting to create a library for colorectal cell lines by systematically profiling their drug responses. The work comprises 52 different common treatments and preclinical drugs across various colon cancer models, establishing a resource that captures the disease's diversity at the cellular level. This resource maps how some of the different subtypes of colorectal cancer respond to treatments, potentially helping researchers identify new therapeutic strategies.
We then extended the Cell Painting technology from traditional flat cell cultures to three-dimensional spheroid models that better mimic how tumours grow in the body. We have developed new methods combining tissue-clearing, advanced microscopy, and smart normalization to see deep inside these clumps of cancer cells in a scalable manner, allowing us to capture drug responses that were previously invisible. This approach confirmed that cancer cells behave differently in 3D environments compared to flat cultures, revealing distinct response patterns when exposed to common chemotherapies.
We then continued developing this technology to study patient-derived tumoroids - a patient's own mini-tumour grown directly from biopsies. Our work has demonstrated the technical feasibility of using Cell Painting for precision medicine applications at scale, where treatment decisions could be guided by how individual patient tumours respond to different drugs in a dish. While the detailed analysis is still ongoing, we can start to measure the unique appearance of those mini tumours, which could be helpful in telling each distinct tumour apart in the future.
The project's outcomes contribute to the growing field of precision medicine by demonstrating how advanced imaging can extract extremely rich and accessible information from patient material. We believe our work provides valuable data for future studies and lays the groundwork for advancing the currently running COSENSE-1 to our future clinical trial COSENSE-2.
The ColoPaint grant supported the development of Cell Painting methodology for 3D colorectal cancer models and the exploration of clinical translation.
Our open dataset and analysis tools (BioImage Archive, GitHub, Python package) provide the research community resources for drug discovery and mechanism-of-action studies in 3D. This manuscript is currently in review. We actively disseminated findings through scientific conferences, including a contribution talks at BNMI2025 in Gothenburg and Cytodata2025 in Berlin, suggesting early recognition. Two additional manuscripts are being prepared, plus one collaborative publication. We have secured funding from The Norwegian Cancer Society for finding new markers indicating drug sensitivity in patient-derived tumouroids, in the project COSENSE-2: The Mona Lisa project, designed to directly inform our next clinical intervention trial COSENSE-2, and funding from EP PerMed for the project ERKetype, where Cell Painting will be included to study mechanisms and combinations of KRAS inhibitors, a new classes of drugs now entering clinical use.
The methodology offers the potential to reduce animal testing in drug development by expanding analytical options for 3D cellular models. As advanced cellular models increasingly replace animal experiments for drug candidate screening, our approach supports the 3Rs principles (replacement, reduction, refinement) in pharmaceutical research, contributing to more ethical and cost-effective drug discovery.
The mobility grant established lasting collaborations between NTNU, Uppsala University, creating a lasting Nordic network in clinical morphological profiling. Industry partnerships with AstraZeneca and SINTEF demonstrates commercial interest and potential for technology transfer from academic research to pharmaceutical applications. Specifically, the project implemented 3D and patient-derived tumoroid Cell Painting methodology at SINTEF, ensuring long-term access to this technology strengthens Norwegian capacity for advanced cellular analysis, supporting future research independence.
We developed workflows enabling analysis of patient-derived tumour samples using Cell Painting, creating new possibilities for precision medicine. While validation studies are needed, the approach offers phenotype-based analysis as an alternative to traditional genomic approaches in cancer diagnostics. The goal remains clear: to create diagnostic tools that analyse patient tumour samples and guide personalized therapy selection, potentially improving treatment outcomes for colorectal cancer patients.
Colon cancer treatment poses a challenge for physicians that lack sufficient decision criteria for personalized patient therapy. Current approaches in clinical research investigate how to adopt patient material to grow 3D spheroids and determine their viability against several candidate therapies based on simple and limited readouts. While these approaches provide rapid functional information to guide physicians towards choosing a personalized therapy for the patient, they lack in-depth information about the heterogeneity and cellular diversity within the patient’s tumor. Cell Painting is a state-of-the-art high-content microscopy technique, applied to characterize planar (2D) cell cultures, that by using an optimized mixture of dyes, allows for the phenotypic profiling of single cells down to the organelle level. In cells treated with a given drug, this technology provides a plethora of information about the single-cell responses, allowing for the identification of potential resistance mechanisms, and far exceeding traditional cell viability readouts. This project aims at applying Cell Painting to patient-derived samples, bridging the current clinical state-of-the-art with morphological profiling, providing a new level of functional personalized medicine by going from single data points and tumor-averaged measurements, to a full breadth of measurements from each individual cell. We envision that by using Cell Painting on patient-derived tumor samples, its composition and heterogeneity will be uncovered, providing highly valuable information about potential resistance, as well as sensitivity to a given treatment, within the cellular diversity of the tumor. After establishing this novel readout, it will be incorporated into the existing functional personalized medicine pipelines, aiding physicians to identify potential cancer therapies. Finally, the proposed project will lay the foundation for a long-term collaboration between the involved labs in Norway and Sweden.
