FridrichMethod Spatial Epigenomics Agent
The Spatial Epigenomics Agent analyzes spatial epigenomic data combining chromatin accessibility (ATAC-seq), histone modifications (CUT&Tag), and DNA methylation with spatial coordinates. It maps regulatory landscapes across tissue architecture to understand cell-state regulation in spatial context.
When to Use This Skill
- When analyzing spatial ATAC-seq data (Slide-seq + ATAC, DBiT-seq).
- To map chromatin accessibility across tissue microenvironments.
- For spatial profiling of histone modifications (H3K27ac, H3K4me3, H3K27me3).
- When integrating spatial epigenomics with spatial transcriptomics.
- To identify spatially-variable regulatory elements and enhancers.
Core Capabilities
Spatial ATAC Analysis: Process spatial chromatin accessibility data to identify open chromatin regions with spatial coordinates.
Spatial CUT&Tag: Analyze spatially-resolved histone modification profiles (H3K27ac for enhancers, H3K4me3 for promoters).
Spatial Methylation: Map DNA methylation patterns across tissue sections using spatial bisulfite methods.
Multi-Modal Integration: Combine spatial epigenomics with spatial transcriptomics for regulatory network inference.
Regulatory Element Mapping: Identify spatially-variable enhancers, promoters, and silencers.
3D Chromatin Organization: Integrate with MERFISH/seqFISH+ for spatial chromatin organization.
Technologies Supported
| Technology | Epigenetic Mark | Resolution | Method |
|---|---|---|---|
| Spatial-ATAC-seq | Open chromatin | ~10-50μm | Microfluidic barcoding |
| DBiT-seq | ATAC + expression | ~10μm | Deterministic barcoding |
| Spatial-CUT&Tag | Histone marks | ~50μm | Cleavage under targets |
| Spatial-MethylSeq | DNA methylation | Variable | Bisulfite conversion |
| MERFISH + epigenetics | 3D organization | Single-cell | Imaging-based |
Workflow
Input: Spatial epigenomics data (BAM files + spatial coordinates) or processed peak matrices.
Preprocessing: Alignment, deduplication, peak calling with spatial awareness.
Spatial Clustering: Identify spatial domains with similar epigenetic profiles.
Peak Annotation: Map peaks to genomic features (promoters, enhancers, gene bodies).
Motif Analysis: Identify transcription factor binding motifs in spatially-variable peaks.
Integration: Combine with expression data for regulatory inference.
Output: Spatial peak maps, regulatory networks, domain annotations.
Example Usage
User: "Analyze this spatial ATAC-seq dataset to identify spatially-variable regulatory elements in the tumor microenvironment."
Agent Action:
python3 Skills/Genomics/Spatial_Epigenomics_Agent/spatial_epigenomics.py \
--input spatial_atac_fragments.tsv.gz \
--coordinates spot_coordinates.csv \
--peaks macs2_peaks.bed \
--spatial_variable true \
--motif_db jaspar_2024 \
--integrate_with spatial_rna.h5ad \
--output spatial_epi_results/
Analysis Modules
1. Spatial Peak Calling
- Adapted MACS2/Genrich for spatial data
- Spatial autocorrelation of accessibility
- Pseudo-bulk and single-spot approaches
2. Spatial Domain Detection
- Graph-based clustering (Leiden, Louvain)
- Hidden Markov Random Fields
- Deep learning segmentation
3. Transcription Factor Analysis
- ChromVAR for TF activity scores
- SCENIC+ for spatial regulon inference
- Motif enrichment in spatial domains
4. Enhancer-Gene Linking
- Activity-by-contact (ABC) model adaptation
- Spatial correlation of enhancer accessibility with gene expression
- Chromatin loop integration
Integration with Spatial Transcriptomics
Spatial ATAC-seq Spatial RNA-seq
| |
v v
Peak Matrix Expression Matrix
| |
+--------> Integration <-+
|
v
Regulatory Network
(Enhancer -> TF -> Gene)
Key Metrics
| Metric | Description | Typical Range |
|---|---|---|
| TSS Enrichment | Signal at transcription start sites | >4 good quality |
| FRiP | Fraction reads in peaks | >30% |
| Spatial autocorrelation | Moran's I for epigenetic features | 0.2-0.8 |
| Spots per gene | Detection sensitivity | 100-500 |
Prerequisites
- Python 3.10+
- SnapATAC2, ArchR for ATAC analysis
- Squidpy, Scanpy for spatial analysis
- MACS2/Genrich for peak calling
Related Skills
- Spatial_Transcriptomics - For gene expression spatial mapping
- Epigenomics_MethylGPT_Agent - For methylation analysis
- Single_Cell - For non-spatial epigenomics
Applications
- Tumor Microenvironment: Map regulatory programs across tumor-stroma boundary
- Development: Track enhancer activation during tissue morphogenesis
- Neuroanatomy: Brain region-specific regulatory landscapes
- Disease Mechanisms: Spatial dysregulation in pathology
Author
AI Group - Biomedical AI Platform