scATAC-seq

The assay relies on a hyperactive Tn5 transposase pre-loaded with sequencing adapters. This enzyme simultaneously fragments DNA and tags open chromatin regions in a process called tagmentation. Because Tn5 preferentially inserts into nucleosome-free DNA, the resulting fragments are enriched for active regulatory elements like promoters and enhancers. In the single-cell format, each cell's tagmented fragments are barcoded, enabling the reconstruction of cell-specific accessibility profiles.

After sequencing, reads are aligned to a reference genome and aggregated into a count matrix where rows are genomic peaks and columns are cell barcodes. Peak calling identifies regions with statistically significant enrichment of Tn5 insertion events over background. Key considerations include:

• Nucleosome-free regions produce sharp, narrow peaks at transcription start sites
• Flanking nucleosomes generate broader signal patterns
• Tools like MACS2 are adapted for the sparsity and unique fragment distribution of single-cell data

The standard dimensionality reduction technique for scATAC-seq is Latent Semantic Indexing, adapted from natural language processing. The peak-by-cell matrix is term frequency-inverse document frequency (TF-IDF) normalized to up-weight rare peaks that define cell types and down-weight ubiquitous peaks. Singular value decomposition is then performed on this normalized matrix. The first LSI component often captures sequencing depth rather than biology and is typically excluded from downstream clustering.

Because scATAC-seq measures regulatory regions rather than transcripts, gene activity scores are computed as a proxy for gene expression. This metric sums accessibility signals in the gene body and a defined promoter region (e.g., TSS ± 2kb). While correlated with RNA expression, activity scores are an indirect measurement. They enable:

• Linking chromatin state to putative gene regulation
• Cross-modality integration with scRNA-seq data
• Annotation of cell types using expression-based marker gene signatures

Differentially accessible peaks between cell clusters are scanned for over-represented transcription factor binding motifs. This analysis infers which transcription factors drive cell-type-specific regulatory programs. Tools like chromVAR compute per-cell motif deviation scores by comparing observed motif accessibility against a background expectation, producing a continuous matrix of transcription factor activity across all cells. This transforms sparse peak data into a biologically interpretable, dense feature space.

Correlating accessibility across cells identifies co-accessible peaks—regions that tend to be open or closed together. These correlations reveal physical chromatin interactions and link distal enhancers to their target gene promoters. The resulting cis-regulatory networks map the topology of gene regulation. Key applications include:

• Connecting non-coding disease variants to effector genes
• Identifying enhancer hijacking in cancer
• Reconstructing lineage-specific regulatory circuitry

The physical availability of genomic DNA to regulatory proteins and transcriptional machinery. In scATAC-seq, accessible regions are primarily promoters, enhancers, and insulators where nucleosomes are depleted. The assay uses a hyperactive Tn5 transposase that preferentially cuts and tags open chromatin, allowing sequencing reads to map precisely to active regulatory elements. Accessibility is a binary or count-based measure per cell per genomic region.

The computational process of identifying genomic regions with significantly enriched Tn5 insertion events compared to a background model. Key tools include:

• MACS2: Widely used for bulk ATAC-seq, adapted for aggregated single-cell profiles
• SnapATAC2: Performs peak calling on cell-type-specific aggregated pseudobulk data
• ArchR: Uses a tile-based approach before merging adjacent enriched tiles into peaks Peaks represent candidate cis-regulatory elements and form the basis of the peak-by-cell count matrix.

A method to infer which transcription factors (TFs) are active by scanning accessible peaks for overrepresented DNA sequence motifs. Tools like HOMER and MEME Suite compare motif frequencies in peak sets against background genomic sequences. In scATAC-seq, this reveals the regulatory grammar driving cell-type-specific accessibility patterns. ChromVAR extends this by computing per-cell motif deviation scores, measuring whether a TF's binding sites are more or less accessible than expected across the cell population.

A proxy for gene expression derived from chromatin accessibility data. Since scATAC-seq does not measure transcripts directly, gene activity is estimated by aggregating accessibility signals in the gene body and proximal regulatory regions (e.g., ±2kb from the TSS). Tools like Signac and ArchR compute these scores to enable:

• Linking distal enhancers to target genes
• Correlating accessibility with scRNA-seq data
• Annotating cell types using marker gene activity This bridges the gap between epigenomic state and transcriptional output.

The correlation of chromatin accessibility between distal enhancers and gene promoters across single cells. Cicero (Monocle suite) and ArchR identify co-accessible peak pairs, inferring physical enhancer-promoter loops without Hi-C data. High co-accessibility suggests regulatory interaction. These linkages are critical for:

• Assigning non-coding GWAS variants to target genes
• Building gene regulatory networks (GRNs)
• Understanding disease-associated regulatory mechanisms at single-cell resolution

A high-resolution analysis that identifies the precise binding locations of transcription factors within accessible peaks. When a TF binds DNA, it protects the bound nucleotides from Tn5 transposase cleavage, creating a characteristic depletion pattern—a footprint—flanked by accessible cut sites. Tools like TOBIAS and HINT-ATAC perform footprinting on aggregated scATAC-seq profiles to:

• Distinguish bound vs. unbound motifs
• Reconstruct TF binding dynamics across cell states
• Identify pioneer factors that open closed chromatin