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2D, or not 2D? Investigating Vertical Signal Integrity of Tissue Slices

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

2025-01-15 bioinformatics
10.1101/2025.01.13.632601 bioRxiv
Show abstract

Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in 3D. Cell segmentation assigns transcripts to cells and precedes annotation of cell function. However, cell segmentation is usually performed in 2D, thus unable to deal with spatial doublets arising from overlapping cells, resulting 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 3D.

Published in Nature Biotechnology (predicted rank #3) · training set

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