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<p>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.</p>

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Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy

  • 期刊论文

  • 发表日期：

    2026年2月10日

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  • Sebastian Tiesmeyer,
  • Niklas Müller-Bötticher,
  • Alexander Malt,
  • Leyao Ma,
  • Sergio Marco-Salas,
  • Paul Kiessling,
  • Paul Horn,
  • Adrien Guillot,
  • Louis B. Kuemmerle,
  • Frank Tacke,
  • Fabian J. Theis,
  • Christoph Kuppe,
  • Mats Nilsson,
  • Roland Eils,
  • Brian Long,
  • Naveed Ishaque

摘要

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.