SP-Mind让AI自动分析空间蛋白组数据，从图像到发现一气呵成。
SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis
- 用自然语言指令驱动全流程分析，无需针对任务微调
- 在102项任务上表现超越现有开源生物医学智能体
- 适合需要高效、可复现空间蛋白组分析的研究者
空间蛋白组学可在单细胞分辨率下刻画组织结构中的蛋白表达，对理解肿瘤微环境和指导精准医疗至关重要。然而，当前分析流程分散，依赖专家手动编排异构工具，限制了研究的可扩展性和可重复性。我们提出SP-Mind，首个专为空间蛋白组学分析设计的自主式AI代理，能从多重免疫荧光组织图像到下游表型发现实现端到端分析。其具备专家级生物分析技能和专用计算工具，可将自然语言查询转化为完整分析流程，无需任务特异性微调。为严格评估能力，我们构建SP-Bench，涵盖多种组织类型，包含18类共102项任务。在SP-Bench及多个下游任务上的广泛评估显示，SP-Mind性能优于现有开源生物医学智能体基准。代码已公开于https://github.com/tomtommyyuan/spmind。
原文摘要 · Abstract (English)
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, 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 is publicly available at https://github.com/tomtommyyuan/spmind.
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