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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics.

Chen Yang1, Xianyang Zhang1, Jun Chen2

  • 1Department of Statistics, Texas A&M University, College Station, Texas, 77843, USA.

Biorxiv : the Preprint Server for Biology
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

ChatSpatial unifies spatial transcriptomics tools into a conversational workflow, simplifying complex analyses. This platform ensures reproducible results by using pre-validated schemas, allowing researchers to focus on biological discovery.

Keywords:
MCPagentic orchestrationartificial intelligencebioinformatics integrationcancer researchcomputational biologynatural language processingsoftware platformspatial transcriptomics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Spatial transcriptomics enables molecular-level tissue architecture analysis.
  • Analyzing spatial transcriptomics data involves numerous computational methods across different ecosystems.
  • Current methods require significant researcher effort for tool integration, hindering biological discovery.

Purpose of the Study:

  • To present ChatSpatial, a novel platform for unifying spatial transcriptomics analysis.
  • To enable researchers to conduct complex analyses through a conversational interface.
  • To improve reproducibility and efficiency in spatial transcriptomics research.

Main Methods:

  • ChatSpatial utilizes a Large Language Model (LLM) with pre-validated tool schemas.
  • Domain expertise is embedded in schema descriptions for context-aware parameter inference.
  • The platform is built on the Model Context Protocol (MCP), integrating over 60 methods across 15 categories.
  • It supports a unified workflow spanning both Python and R ecosystems.

Main Results:

  • ChatSpatial successfully replicated two published studies on ovarian cancer and oral squamous cell carcinoma.
  • Validation across seven LLM platforms demonstrated near-deterministic reproducibility at the workflow level.
  • The platform facilitates exploratory cross-method analyses and practical triangulation across independent analytical frameworks.

Conclusions:

  • ChatSpatial significantly reduces the effort required for spatial transcriptomics data analysis.
  • The schema-enforced orchestration ensures high reproducibility for multi-step spatial analyses.
  • This platform empowers researchers to accelerate biological discovery by streamlining computational workflows.