Abstract 6903: A transcript-only framework for pseudocell boundary inference in high-resolution spatial transcriptomics.

Abstract Background: High-resolution spatial transcriptomics (ST) enables subcellular expression profiling, yet cell-level analysis remains critical for understanding tissue organization. Current cell segmentation methods in ST like bin2cell rely on H144 bins) and total counts (10). The tissue was divided into 60 segments: 48 (80%) for parameter optimization and 12 (20%) for testing. Performance was assessed via Leiden clustering quality on segmented cells using Average Silhouette Width (ASW), Calinski-Harabasz Index (CHI), and Davies-Bouldin Index (DBI). Results: Parameter optimization showed that Gaussian smoothing provided no benefit, whereas an 8-bin radius to capture seed regions from local maxima offered the optimal balance between cell clustering performance and cell detection count. In the independent test set (n=12), HIPSTER demonstrated statistically significant and superior clustering performance compared to bin2cell across all three metrics (ASW, CHI, and DBI). Notably, HIPSTER achieved a higher CHI score, reflecting superior cluster separability, in every single paired comparison without exception. Although HIPSTER detected fewer total cells than bin2cell, this reduction reflects a selective focus on transcriptionally meaningful hotspots, resulting in cleaner boundaries and more biologically coherent clustering. Conclusion: HIPSTER is a robust and effective transcript-only segmentation tool for high-resolution ST data. By defining cells via functional transcriptomic activity rather than H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6903.