Integrating cytological images and spatial transcriptomics for cell segmentation with DISSECT.

Abstract

Advances in imaging- and sequencing-based spatial transcriptomics have increased molecular throughput and resolution, enabling the measurement and analysis of spatial transcriptomes at single-cell resolution. However, accurate cell segmentation remains challenging because cell morphology, tissue processing and staining methods vary across samples and platforms, limiting the accuracy and generalizability of existing algorithms. Here we show that DISSECT, a cell segmentation model integrating cytological images with spatial transcriptomic profiles, improves spatial single-cell transcriptome reconstruction. DISSECT uses a pretrained deep generative model to denoise multiscale image features, predicts cell instances with an instance-aware detection module and applies image- and transcriptome-derived gradient fields to refine segmentation masks. Benchmarking across multiple datasets showed that DISSECT achieved higher mean average precision than several existing segmentation tools. We further applied DISSECT to three pairs of gastric adenocarcinoma samples collected before and after anti-PD-1 treatment and profiled by Stereo-seq, illustrating its utility for downstream spatial biological interpretation.