BioTechniques News
Beatrice Bowlby
Original story from the European Molecular Biology Laboratory (Heidelberg, Germany).
An open-source analysis framework makes it easier to study how cells are organized within tumors, paving the way for new insights into cancer biology.
How cancer develops and responds to treatment depends not only on the tumor cells themselves but also on the complex ecosystem of surrounding immune cells, blood vessels and connective tissue. In B-cell non-Hodgkin lymphoma, these cellular neighborhoods can determine whether a tumor grows, evades the immune system or responds to therapy. Understanding this spatial organization has become one of the major challenges in cancer research.
Now, researchers from the European Molecular Biology Laboratory (EMBL; Heidelberg, Germany), Heinrich Heine University Düsseldorf (HHU; Germany) and University Hospital Düsseldorf (UKD; Germany) have developed spatialproteomics, an open-source software package that makes it much easier to analyze highly complex tissue images. The software enables researchers to move seamlessly from microscopy images to biological insights, helping reveal how different cell types are organized within healthy and diseased tissues.
“Modern imaging technologies allow us to observe tissues in extraordinary detail,” shared Matthias Meyer-Bender (EMBL), first author of the study. “But turning these images into biological knowledge has remained technically challenging. We developed spatialproteomics to make these analyses more accessible, reproducible and scalable.”
Recent advances in multiplexed imaging allow researchers to map thousands to millions of individual cells within a single tissue sample. Unlike conventional pathology methods, which typically examine only a handful of cellular features, these technologies simultaneously capture dozens of molecular markers while preserving the spatial arrangement of cells. This provides an unprecedented view of the tumor microenvironment and the interactions that shape disease.
However, analyzing these datasets requires identifying, classifying and quantifying vast numbers of cells before researchers can investigate their spatial arrangement. Until now, this process often involved combining multiple software packages and custom-built analysis pipelines.
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We also explore the cellular origins of cancer and neurodevelopmental disease, the impact of spatial proteomics and AI on cancer research and a streamlined protocol for conducting single-cell spatial transcriptomics on human kidney tissue.
Spatialproteomics brings these steps together into a single workflow. The software guides researchers from raw microscopy images through cell segmentation, annotation and spatial analysis, making sophisticated image analysis more efficient and easier to reproduce.
To demonstrate the framework’s capabilities, the team analyzed tissue samples from patients with B-cell non-Hodgkin lymphoma. Spatialproteomics enabled the researchers to characterize the spatial distribution of proteins and identify distinct patterns in the organization of tumor and immune cells. These spatial signatures supported the distinction between indolent (slow-growing) and aggressive lymphomas, illustrating how computational analysis can help uncover biologically and clinically relevant tissue features.
“Accessible computational tools are essential if advanced imaging technologies are to realize their full potential,” Meyer-Bender commented. “By lowering technical barriers and standardizing analysis workflows, spatialproteomics allows more researchers to extract meaningful information from complex imaging data.”
Although demonstrated using lymphoma samples in this study, the researchers expect the software to benefit many areas of biomedical research.
“Spatialproteomics is not limited to lymphoma research,” noted co-senior author Peter-Martin Bruch (HHU and UKD). “The framework can be applied to many types of cancer as well as inflammatory diseases and other complex tissues where spatial organization plays a critical role.”
The project brought together complementary expertise across institutions. Clinical and experimental researchers in Düsseldorf collaborated closely with EMBL scientists specializing in bioinformatics, data science and software engineering to develop a tool that addresses both methodological challenges and practical biomedical questions.
“This project demonstrates the value of interdisciplinary collaboration,” Bruch concluded. “By combining clinical expertise with EMBL’s strengths in computational biology and software development, we created a framework that is both technically robust and directly relevant to biomedical research.”
Spatialproteomics is freely available as open-source software, allowing researchers worldwide to integrate it into their own imaging workflows and further expand its capabilities.
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This content is part of our Spotlight: Spatial investigations of complex tissues, in association with Takara Bio.
The post Simplifying highly complex tissue images with a new open-source software appeared first on BioTechniques.
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