STAN, a computational framework for inferring spatially informed transcription factor activity
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View abstract on PubMed
Summary
This summary is machine-generated.We developed STAN, a computational method to map transcription factor (TF) activity within tissues using spatial transcriptomics. This reveals how TF networks influence cell identity and spatial organization in various diseases and biological contexts.
Area Of Science
- Computational Biology
- Genomics
- Systems Biology
Background
- Transcription factors (TFs) regulate cellular responses and are influenced by microenvironments.
- Spatial transcriptomics (ST) offers insights into tissue microenvironments but has not been fully leveraged to infer TF activity.
- Understanding TF roles in cell identity and spatial organization is crucial.
Purpose Of The Study
- To develop a computational approach for inferring spatially resolved TF activity from ST data.
- To investigate the relationship between TF activity, cell identity, and tissue architecture.
- To enhance the analytical capabilities of ST data for biological discovery.
Main Methods
- Introduced STAN (Spatially informed Transcription factor Activity Network), a linear mixed-effects model.
- Integrated TF-target gene priors, mRNA expression, spatial coordinates, and histological features.
- Applied STAN to diverse ST datasets (lymph node, brain, breast cancer, glioblastoma).
Main Results
- STAN successfully predicted spot-specific TF activities.
- Identified TFs associated with distinct cell types, spatial regions, and pathological zones.
- Revealed TF involvement in ligand-receptor interactions within tissue microenvironments.
Conclusions
- STAN effectively infers TF activity from ST data, providing spatial context.
- The approach enhances the utility of ST for understanding TF roles in cellular function and tissue organization.
- Highlights the intricate interplay between TF networks and spatial biology across various biological systems.
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