Imaging mass cytometry of a lung tumor core from a tissue microarray. 2026 EPFL/AIMM - CC-BY-SA 4.0
Researchers at EPFL have created the world's largest open-access collection of spatial proteomics tissue images, bringing together samples from more than 5,000 patients. Called spora, this new resource aims to accelerate AI research into how tissues function and how diseases develop.
How cells are arranged within tissue can reveal as much about disease as the cells themselves. When doctors examine a cancer tumor, for example, they don't just look at which cells are present, they also want to know how those cells are arranged and how they interact with each other. That's because the way cancer cells interact with neighboring immune cells can offer important clues about how a disease develops or responds to treatment.
Scientists can study these relationships using spatial proteomics, a technology which captures this organization by measuring dozens of proteins while preserving their locations. Because proteins help control what cells do, these images offer a valuable picture of how healthy and diseased tissues work.
But the resulting datasets are difficult to combine. Research laboratories around the world use different equipment, examine different proteins and store their results in different ways. Combining this information is complicated and time-consuming, making it difficult to use large amounts of data in both conventional studies, and now to train artificial intelligence.
To overcome this challenge, a team led by Charlotte Bunne's Artificial Intelligence in Molecular Medicine group at EPFL, in both the School of Life and the School of Computer and Communication Sciences, has developed spora, a spatial proteomics reference atlas - the world's largest, freely available collection of spatial proteomics tissue images and medical data, designed to make research easier.
Two years of work
Released in collaboration with the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), the collection includes 13,546 multiplex images from 5,477 patients, spanning 14 tissue origins and five imaging technologies. Where available, it also includes paired histopathology images, connecting molecular measurements with the tissue images used in routine pathology.
Creating spora took two years of work by scientists and biomedical experts from EPFL, University Hospital Zurich, the University of Zurich, Geneva University Hospitals and the University of Geneva. The researchers had to organize thousands of images, standardize protein annotations, match medical information and identify individual cells so that data from different studies could be compared.
"Gathering and harmonizing data across cohorts takes enormous time, creating a major hurdle for both large-scale AI development and biological discovery," says Benedikt von Querfurth, a doctoral student at EPFL and first author of the study. "We want to allow researchers to start with ready to use data so that they can focus on the models and biological questions."
Toward better predictions for the patient
The collection retains links to the original studies and their annotations. Differences in the proteins measured, imaging conditions, and available clinical information remain visible, helping researchers decide which datasets are appropriate for their questions.
In addition to the 'atlas' itself, spora provides researchers with tools to access and analyze the data, develop AI models and evaluate how well they perform across different studies. "This is important because a model can perform well within one study and still struggle when the patients, laboratory, or measurement technology change," says Lukas Klein, a PostDoc at EPFL and the University Hospital Zurich. "Shared datasets and evaluation protocols let us examine those differences systematically. That is essential if we want AI models to capture biology that extends beyond a single cohort."
Although spora currently focuses mainly on cancer research, the team hopes to expand it to include more tissues, diseases and imaging technologies. "Our goal is to turn what we learn across thousands of tissue samples into insights that can help individual patients," says Bunne. "Working closely with clinicians, we aim to translate these insights into better predictions of how a patient's disease will progress and which treatments are most likely to benefit them."
