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The new software tool ovrlpy improves quality control in spatial transcriptomics, a key technology in biomedical research. Developed by the Berlin Institute of Health at Charité (BIH) in international collaboration, ovrlpy is the first tool to identify cell overlaps and folds in tissue sections, thereby reducing previously unrecognised sources of misinterpretations. The researchers have published their results in the journal Nature Biotechnology.
Spatial transcriptomics is a pioneering field of research in biomedicine, that visualises cellular activity within a tissue by mapping RNA transcripts and assigning this molecular activity to individual cells. So far, such analyses of tissue samples have mostly been interpreted in two dimensions. However, even very thin tissue sections of five to ten micrometres thick, about one-tenth the width of a human hair, have a complex three-dimensional structure. If this 3D arrangement is interpreted only as a flat surface, analytical errors can occur, for example, due to cell overlaps or tissue folds. This impedes the precise assignment of transcripts to individual cells and can distort downstream analysis and interpretation.
Revealing hidden overlaps in tissue
Ovrlpy analyses the spatial distribution of transcripts in three dimensions and detects signal inconsistencies in areas with cell overlaps or accidental tissue folds, thus detecting potential sources of error in the vertical dimension that have so far largely gone unnoticed. Comprehensive analyses of various tissues and organs revealed that such overlaps occur more frequently than previously thought. By specifically identifying these artefacts, ovrlpy makes a significant contribution to improving the precision of subsequent bioinformatic analyses.
"Ovrlpy helps us to identify these sources of error before they lead to false conclusions," says Dr Naveed Ishaque, group leader for Computational Oncology in Roland Eils’ Digital Health department at the BIH and last author of the study. He adds: "This creates the foundation for more robust insights in a wide range of disciplines, whether in cancer research, neurology or the development of personalised therapies."
With the increasing use of spatial technologies such as spatially resolved transcriptomics (Nature’s Method of the Year 2020) or spatial proteomics (Nature’s Method of the Year 2024) in routine biomedical research, ensuring high-quality data is becoming ever more important. Ovrlpy makes a significant contribution to this and enables reliable analyses of the complex architecture and function of tissues.
Tiesmeyer, S., Müller-Bötticher, N., Malt, A. et al. Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy. Nat Biotechnol (2026). DOI: 10.1038/s41587-026-03004-8
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