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Tiesmeyer, S.* ; Müller-Bötticher, N.* ; Malt, A.* ; Ma, L.* ; Marco-Salas, S.* ; Kiessling, P.* ; Horn, P.* ; Guillot, A.* ; Kuemmerle, L. ; Tacke, F.* ; Theis, F.J. ; Kuppe, C.* ; Nilsson, M.* ; Eils, R.* ; Long, B.* ; Ishaque, N.*

Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy.

Nat. Biotechnol., DOI: 10.1038/s41587-026-03004-8 (2026)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in three dimensions. However, cell segmentation, which assigns transcripts to cells, is usually performed in two dimensions and spatial doublets in the vertical dimension result in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds and inaccurate cell segmentation by analyzing transcript localization in three dimensions.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Pattern Recognition (psychology) ; Signal (programming Language) ; Transcriptome ; Signal Processing ; Noisy Data
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Journal Nature Biotechnology
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute for Intelligent Biotechnologies (IBIO)
Grants Deutsche Forschungsgemeinschaft (German Research Foundation)
Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)