Stegle Lab

@steglelab.bsky.social

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Our group develops and applies computational approaches to study molecular variations and their phenotypic consequence. We are part of DKFZ and EMBL. Website: https://steglelab.org/

An illustration of a pinball machine representing a cell as the ball, with bumpers as causal signatures and flippers as methods guiding understanding through complex molecular terrain.

Segmentation of Xenium lung cancer tissue was assessed using ground-truth staining. Cellpose served as a reference. 10x over-expanded boundaries (~180 µm²) with ~10% contamination, while Baysor over-segmented, doubling cell counts. Segger balanced precision and recall (~140 µm²), minimizing contamination (~5%) while preserving epithelial structure.

segger optionally regroups unassigned transcripts based on their gene identity and spatial proximity into fragments

#Segger embeds cells & transcripts into a shared space and assigns the cell with the highest score for each transcript within a receptive field.

segger's network architecture.

Accurate cell segmentation in ST data faces challenges like overlapping cells, transcript diffusion, background noise, and missing nuclei. Conservative assignments may over-segment cells, while under-segmentation leads to false positives that contaminate profiles. Effective segmentation must balance splitting cells and merging multiple cells.