Single-Cell ATAC-seq, or scATAC-seq, is a method for analysing chromatin accessibility at the single-cell level. It identifies open chromatin regions, including potentially active promoters, enhancers and transcription-factor binding sites.
Unlike conventional ATAC-seq, which provides an averaged profile for the entire sample, scATAC-seq separates signals originating from individual cell populations. This enables analysis of heterogeneous tissues, identification of rare subpopulations and comparison of gene-regulation mechanisms between different cell types and states.
Single-Cell ATAC-seq workflow
- Sample preparation and removal of dead cellsThe number of cells, their viability and the quality of the supplied suspension are assessed. If the proportion of dead cells is high, they can be removed using a magnetic bead-based method. This enriches the sample in live cells and improves the quality of the material used for nuclei isolation.
- Nuclei isolation and sample partitioningA homogeneous suspension of cell nuclei is prepared from the cells. Accessible chromatin regions are then tagged with Tn5 transposase, which fragments the DNA and adds sequencing adapters. The nuclei are partitioned in a microfluidic system together with beads carrying barcoding oligonucleotides.
- Barcoding and library preparationDNA fragments from each nucleus receive a unique barcode. This makes it possible to assign reads to individual cells after sequencing. The tagged DNA fragments are then amplified and used to prepare sequencing libraries.
- SequencingThe libraries are pooled and sequenced on a high-throughput Illumina platform. Paired-end sequencing makes it possible to determine the position and length of accessible-chromatin fragments.
- Bioinformatics analysisReads are assigned to individual nuclei based on their barcodes and mapped to the reference genome. The analysis may include data quality control, identification of open chromatin regions, cell clustering, analysis of differential chromatin accessibility and identification of potential transcription-factor binding motifs. Results are presented as reports, tables and visualisations that enable comparison of cell populations.
Applications of Single-Cell ATAC-seq
ATAC-seq as an alternative to ChIP-seq
ChIP-seq identifies sites associated with a specific transcription factor or histone modification. However, the method requires an appropriate validated antibody, and its use may be limited by the small amount of biological material available. Antibody quality and specificity directly affect the reliability of the results. ATAC-seq does not require antibodies and can analyse the entire open-chromatin landscape at the same time. Based on the accessibility of regulatory regions, the sequence motifs they contain and footprinting analysis, potential transcription factors involved in the process under study can be identified. Single-Cell ATAC-seq additionally makes it possible to assign these differences to specific cell populations. ATAC-seq does not, however, replace ChIP-seq for directly confirming the binding of a specific protein to DNA. It is particularly useful for discovering potential regulators, especially when the list of transcription factors is not yet known or suitable antibodies are unavailable.
Sample Requirements
The input material may be fresh or appropriately preserved tissue, a single-cell suspension or a prepared suspension of cell nuclei. Obtaining a uniform nuclei suspension with preserved integrity, few aggregates and a limited amount of cellular debris is critical. The required amount of material and its preservation method depend on the organism and tissue type and the planned number of nuclei to be analysed.
Single-Cell ATAC-seq bioinformatics analysis
- quality control and data filtering;
- mapping reads to the reference genome;
- identification of open chromatin regions;
- creation of chromatin accessibility profiles for individual nuclei;
- dimensionality reduction and cell clustering;
- identification of differentially accessible regions;
- analysis of promoters and potential enhancers;
- analysis of transcription-factor motifs;
- prediction of gene activity;
- integration with single-cell data from RNA-seq;
- comparison of cell populations between experimental groups.
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