In an international collaboration, the Berlin Institute of Health at Charité (BIH) has developed the software tool ovrlpy, which significantly improves the accuracy of spatial transcriptomics analyses. The results were published in the journal Nature Biotechnology (DOI: 10.1038/s41587-026-03004-8).

Spatial transcriptomics makes it possible to spatially resolve the gene activity of cells within a tissue and to assign transcripts to individual cells. Previous analyses usually only look at tissue sections in two dimensions. However, even very thin sections of five to ten micrometers thick have a complex three-dimensional structure. When interpreted as a pure surface, errors occur due to cell overlaps or unintentional tissue folding, which can lead to incorrect assignments of transcripts and falsify subsequent bioinformatic evaluations.

Ovrlpy analyzes the spatial distribution of transcripts in three dimensions and detects signal inconsistencies that indicate overlaps or convolutions in the vertical axis. Comprehensive studies of various tissues and organs showed that such artifacts occur more frequently than previously thought. By specifically identifying these sources of error, ovrlpy significantly improves the precision of subsequent analyses.

The tool helps spatial technologies such as Spatially Resolved Transcriptomics (Nature Method of the Year 2020) or Spatial Proteomics (Nature Method of the Year 2024) to provide more reliable data – a crucial factor for routine biomedical research, cancer research, neurology, and the development of personalized therapies.

The work was led by Dr. Naveed Ishaque (Group Leader for Computational Oncology in Roland Eils’ Digital Health Department at BIH) and shows that ovrlpy creates a solid foundation for more precise insights in various disciplines.

Original Paper:

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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