CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space
Journal:
bioRxiv
Published Date:
Jun 3, 2026
Abstract
Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, morphological feature extraction, and spatial organization analysis; however, these tools often require manual intervention and lack seamless integration with code-driven automation, limiting efficiency and scalability for complex spatial tissue studies, while also offering limited flexibility for custom analyses by supporting only a fixed set of predefined spatial cellular features. To address these limitations, we propose CodeCytos, a coding-based reasoning agent framework that enables dynamic, programmable interaction with spatial molecular imaging data and streamlines the exploration of custom spatial cellular features across diverse research needs. We demonstrate its utility through case studies on four expert-curated datasets spanning distinct tissue types, including frontal cortex, non-small-cell lung cancer, pancreas, and tonsil, and evaluate it under a realistic minimal prompt setting in which bioscientists pose simple questions without task-specific instructions or prior contextual knowledge, benchmarking multiple large language model backbones with strong coding capabilities. We further show that incorporating domain-agnostic few-shot in-context coding-reasoning examples, randomly sampled from outside the spatial analysis domain, substantially improves performance without requiring costly expert-crafted in-domain demonstrations; overall, CodeCytos outperforms baseline approaches, highlighting the potential of code-driven reasoning agents to support custom feature exploration in spatial molecular imaging and accelerate biomarker discovery.
