Flow cytometry underpins immunology and clinical diagnostics, yet its analysis is still bottlenecked by manual gating inside fragmented, subscription-locked GUIs like FlowJo and Cytobank. Unlike single-cell sequencing, cytometry data suffers from severe batch-to-batch variability and inconsistent marker panels, so conventionally trained models generalize poorly and expert manual gating stays unavoidable. We built an AI-native framework that lets Claude take flow cytometry data end-to-end — reading an experiment's markers, formulating a gating and phenotyping strategy, executing it, and running downstream analysis to reach conclusions, all in one pipeline. At its core is a vision-language foundation model that performs marker-agnostic gating directly from text prompts, connected to cytometry tools through a Model Context Protocol (MCP) layer and extended via a plugin system that absorbs new methods as the field evolves. This removes the single largest barrier in cytometry analysis: reliance on manual, non-reproducible gating locked inside closed software. By making the workflow automated, reproducible, and agent-driven, it lowers cost, accelerates immunology and diagnostics research, and turns a fragmented toolchain into a single, builder-friendly framework researchers can extend for their own experiments.
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