spatial-epigenomics-agent

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.

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.

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

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

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

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

6. 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.

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

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

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

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

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

7. 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