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Updated: Feb 12, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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ovrlpyを用いた空間トランスクリプトミクスデータにおける3Dシグナルオーバーラップの同定

Sebastian Tiesmeyer^1,2, Niklas Müller-Bötticher^1,2, Alexander Malt^1,2

¹Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Center of Digital Health, Berlin, Germany.

Nature biotechnology

|February 10, 2026

Abstract:

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.

キーワード:

空間トランスクリプトミクス計算生物学 3D画像解析細胞セグメンテーション転写産物割り当て

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