SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis

摘要

Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understanding tumor microenvironments and guiding precision medicine. However, current analysis workflows remain fragmented, requiring expert manual orchestration of heterogeneous tools and limiting research scalability and reproducibility. We present **SP-Mind**, the first autonomous AI agent designed to unify the spatial proteomics analysis pipeline, from raw multiplexed tissue imaging to downstream phenotype discovery. Equipped with expert-curated biological analysis skills and specialized computational tools, SP-Mind converts natural-language queries into end-to-end analytical workflows without task-specific fine-tuning. To rigorously evaluate its capabilities, we introduce **SP-Bench**, a comprehensive benchmark spanning diverse tissue types and imaging technologies (fluorescence-based and mass spectrometry imaging), comprising 102 tasks across 18 distinct categories. Through extensive evaluation on SP-Bench and established downstream tasks, SP-Mind achieves **state-of-the-art** performance compared to existing open-source biomedical agent baselines. Code and benchmark will be publicly available upon acceptance.