spatial-epigenomics-agent

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---

name: 'spatial-epigenomics-agent'

description: 'AI-powered spatial epigenomics analysis combining chromatin accessibility, histone modifications, and DNA methylation with spatial coordinates for tissue architecture mapping.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

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

1. Spatial ATAC Analysis: Process spatial chromatin accessibility data to identify open chromatin regions with spatial coordinates.

1. Spatial CUT&Tag: Analyze spatially-resolved histone modification profiles (H3K27ac for enhancers, H3K4me3 for promoters).

1. Spatial Methylation: Map DNA methylation patterns across tissue sections using spatial bisulfite methods.

1. Multi-Modal Integration: Combine spatial epigenomics with spatial transcriptomics for regulatory network inference.

1. Regulatory Element Mapping: Identify spatially-variable enhancers, promoters, and silencers.

1. 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

1. Input: Spatial epigenomics data (BAM files + spatial coordinates) or processed peak matrices.

1. Preprocessing: Alignment, deduplication, peak calling with spatial awareness.

1. Spatial Clustering: Identify spatial domains with similar epigenetic profiles.

1. Peak Annotation: Map peaks to genomic features (promoters, enhancers, gene bodies).

1. Motif Analysis: Identify transcription factor binding motifs in spatially-variable peaks.

1. Integration: Combine with expression data for regulatory inference.

1. 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

1. Tumor Microenvironment: Map regulatory programs across tumor-stroma boundary
2. Development: Track enhancer activation during tissue morphogenesis
3. Neuroanatomy: Brain region-specific regulatory landscapes
4. Disease Mechanisms: Spatial dysregulation in pathology

Author

AI Group - Biomedical AI Platform

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