Problem
Research questions and friction points this paper is trying to address.
spatial proteomics
analysis workflow
scalability
reproducibility
autonomous reasoning
SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis
🤖 AI Summary
Current spatial proteomics analysis workflows are fragmented and rely heavily on expert-driven manual integration of heterogeneous tools, limiting scalability and reproducibility. This work proposes the first autonomous reasoning agent tailored for this domain, leveraging a large language model–driven architecture that combines expert-curated biological skills with specialized computational tools to automatically translate natural language queries into end-to-end analytical pipelines—spanning from multiplexed imaging to phenotypic discovery. The approach achieves full pipeline automation without task-specific fine-tuning and introduces SP-Bench, a comprehensive benchmark comprising 102 tasks across 18 categories. Experimental results demonstrate that the proposed method significantly outperforms existing open-source biomedical agents on both SP-Bench and downstream tasks, establishing state-of-the-art performance.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
autonomous AI agent
spatial proteomics
natural-language-driven workflow
SP-Bench
end-to-end analysis pipeline
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