Researchers published ovrlpy, a software tool that identifies vertical (z‑axis) signal overlaps and tissue folds in spatial transcriptomics data, exposing a class of artifacts that can confound single‑cell and spatial analyses. The method and accompanying Nature Biotechnology report describe how ovrlpy flags overlapping cell signatures and reduces false interpretations arising from sectioning defects. Labs working with spatial omics can integrate ovrlpy into QC pipelines to improve dataset reliability. The tool’s adoption could tighten reproducibility and change how spatial datasets are processed before downstream inference or clinical biomarker extraction.