The paper introduce ovrlpy, a computational tool designed to identify and mitigate vertical signal doublets in imaging-based spatial transcriptomics. While these technologies capture data in three dimensions, standard analysis often collapses this information into a flat two-dimensional projection, which erroneously merges transcripts from overlapping cells. To address this, the software uses a virtual subslicing strategy to calculate a Vertical Signal Integrity (VSI) score, highlighting regions where tissue folds or cellular overlaps compromise data quality. Testing on mouse brain and liver datasets reveals that these vertical artifacts are widespread, particularly affecting glial and vascular cells. By filtering out these low-integrity signals, researchers can achieve a higher signal-to-noise ratio and more accurate cell-type identification. Ultimately, the authors emphasize that accounting for the vertical dimension is essential for ensuring the biological validity of high-resolution spatial maps.

References:

• Tiesmeyer S, Müller-Bötticher N, Malt A, et al. Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy[J]. Nature Biotechnology, 2026: 1-5.