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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data.

Hiren Madhu1, João Felipe Rocha1, Tinglin Huang1

  • 1Yale Univeristy, USA.

Arxiv
|October 3, 2025
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Summary
This summary is machine-generated.

HEIST, a new foundation model, integrates spatial transcriptomics and proteomics data. It captures cellular context and gene expression, revealing novel cell subpopulations and improving predictions without retraining.

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Area of Science:

  • Computational Biology
  • Genomics
  • Proteomics

Background:

  • Single-cell omics data provide insights into cellular heterogeneity.
  • Spatial omics data offer cellular context alongside molecular counts.
  • Existing models struggle to integrate spatial information with complex cellular programs.

Purpose of the Study:

  • To develop a foundation model for spatial transcriptomics and proteomics.
  • To infer how cellular regulation adapts to microenvironmental cues.
  • To create a generalizable model for diverse omics datasets.

Main Methods:

  • Introduced HEIST, a hierarchical graph transformer foundation model.
  • Modeled tissues as hierarchical graphs (spatial cell graph and gene co-expression networks).
  • Utilized intra-level and cross-level message passing for embedding computation.
  • Pretrained on 22.3M cells from 124 tissues across 15 organs.

Main Results:

  • HEIST embeddings revealed spatially informed subpopulations missed by prior models.
  • Demonstrated generalizability to spatial proteomics data without retraining.
  • Achieved state-of-the-art performance in clinical outcome prediction, cell type annotation, and gene imputation.

Conclusions:

  • HEIST effectively integrates spatial and molecular information for deeper biological insights.
  • The hierarchical graph approach enhances model generalizability across different omics technologies.
  • HEIST advances the analysis of complex biological systems by considering cellular context.