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bioRxiv · 10.64898/2026.09.20.752837

MCseg: AI agent-guided workflow search for no-code cell segmentation and transcript attribution in spatial transcriptomics

Chan, C.-R.·Chang, N.-W.·Wang, C.-Y.·Tan, H.-Y.·Lin, S.-J.
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Abstract

Cell-level analysis of high-resolution spatial transcriptomics depends on accurate segmentation and transcript assignment, yet current workflows often trade transcript capture for boundary purity and can require substantial image-analysis expertise. We developed MCseg, a downloadable no-code platform whose fixed segmentation engine was derived by an AI-agent-guided search in which an AI agent iteratively proposed and evaluated combinations of image-processing and segmentation operations against Xenium-derived cell boundaries. In a lung adenocarcinoma development set, fixed-parameter MCseg increased mean panoptic quality from 0.432 to 0.472 relative to an Optuna-tuned two-diameter Cellpose baseline, while a reference-guided calibration analysis reached 0.554. In an independent expert-annotated colorectal cancer region, MCseg showed higher lineage recall and micro-F1 than the StarDist-based ENACT workflow among cells covered by both methods. Across 15 colorectal cancer regions, MCseg increased neighborhood expression discordance and reduced lineage-exclusive co-expression relative to Space Ranger at similar UMI density. The fixed workflow also transferred to fresh-frozen breast cancer without tissue-specific architecture search, illustrating an agent-guided route to reproducible, locally deployable cell-level spatial transcriptomic analysis.

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Chan, C.-R., Chang, N.-W., Wang, C.-Y., Tan, H.-Y., Lin, S.-J.. 2026-09-25. MCseg: AI agent-guided workflow search for no-code cell segmentation and transcript attribution in spatial transcriptomics. https://doi.org/10.64898/2026.09.20.752837

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