ATAC-seq for Single Cells: Benefits and Limitations

ATAC-seq for single cells at Single Cell Discoveries

ATAC-seq for single cells — Like any biomedical technique, single-cell ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing: scATAC-seq) has benefits and limitations. Some biological questions it is perfectly fit to answer, others fall just outside the scope of its capabilities. Hence, it is crucial for any user to understand what the assay can do very well and when other technologies are more likely to succeed.

Below, we list how we think about the greatest advantages and most important disqualifications for ATAC-seq. Since ATAC-seq and single-cell ATAC-seq specifics overlap, we cover both versions, although Single Cell Discoveries currently only offers single-cell ATAC-seq. Anyone with remaining questions who wants to discuss whether scATAC-seq fits their biological question, please get in touch.

Jump to a section:

  1. What ATAC-seq exactly shows
  2. General benefits of ATAC-seq
  3. General limitations of ATAC-seq
  4. Case study: ATAC-seq versus CHIP-seq
  5. Single-cell versus bulk ATAC-seq
  6. Integrate scATAC-seq with scRNA-seq

What ATAC-seq exactly shows

What does ATAC-seq tell you? ATAC-seq and its single-cell equivalent tell you what nuclear DNA was accessible to the Tn5 transposase at the time of the experiment, which in general confers open chromatin regions. Open chromatin generally relates to active DNA sites, such as those bound by transcription factors, or expressed genes, so ATAC-seq results tell you about the epigenetic and transcriptional state of the cells in a sample. For example, this can provide information about a tissue’s dynamics during development, its disease state, or how it reacts to certain conditions such as therapeutics.

Single-cell ATAC-seq

Where bulk ATAC-seq produces the average profile of all cells in a sample, single-cell ATAC-seq produces these profiles at the level of individual cells. Click here to jump to a comparison between the two forms for more information on what’s won and what’s lost at single-cell resolution.

Learn more about Tn5 transposase and other scATAC-seq elements in our previous blog posts.

Single-Cell ATAC-seq: the Basics  How scATAC-seq works

 

General benefits of ATAC-seq

ATAC-seq provides a comprehensive view of the epigenetic landscape of all cells within a population. This allows you to:

  1. Uncover regulatory elements: ATAC-seq identifies enhancers, promoters, and other regulatory elements, offering insights into the transcriptional regulation governing a tissue. The dominant method for this is detecting specific motifs, i.e. short DNA sequences, in the open chromatin profile that confer to a landing site for specific proteins, such as transcription factors or other regulatory proteins.
  2. Understand mechanisms of diseases with epigenetic players, including cancer, autoimmune diseases, neurodegenerative disorders, and mental illness (see Moosavi & Ardekani, 2016)
  3. Characterize timing of developmental events, gene expression inheritance, and lineage determination. This can, e.g., be important information for unearthing the mechanism of action of epigenetic anticancer drugs.

Compared to other technologies that study the epigenetic landscape of tissues, ATAC-seq has the following technical advantages:

  1. Time efficiency: a simple, two-step protocol, ATAC-seq provides users with high-speed results. This also enables high scalability and means that ATAC-seq is fit for larger assays in drug development and (pre)clinical research.
  2. Sensitivity: ATAC-seq is generally revered as the most accurate method for identifying all regions of open chromatin at bulk and single-cell level. Part of the explanation lies in its high signal-to-noise ratio.
  3. Low input material: instead of the millions of input cells necessary for techniques such as ChIp-seq or DNase-seq, you can perform ATAC-seq on input of a magnitude of several thousands of cells. This makes it more suitable for researchers with precious tissue, such as disease or embryonic tissue.

General limitations of ATAC-seq

It is important to realize that ATAC-seq captures one layer of epigenetic data, that of chromatin accessibility. Which layers of epigenetic data are missed?

Epigenetic data not captured by ATAC-seq

  1. DNA methylation – the most common epigenetic modification. Frequently aberrant in various cancer types and serving as a biomarker for some.
  2. Histone modifications – histone acetylation and methylation confer to active or inactive chromatin states comparable to chromatin accessibility information. Frequently aberrant in cancer and implicated in neurological diseases such as Alzheimer’s.
  3. Chromosome interactions –interactions between two separate chromosomes (contrast: interactions of DNA regions within a chromosome) and other spatial properties of large chromosome parts are too large-scale for ATAC-seq.

It follows logic that we do not advise ATAC-seq for biological questions specifically targeting one or more of the epigenetic layers mentioned above. However, since the chromatin accessibility profile confers the gene expression and regulatory element profile, ATAC-seq is very broadly applicable.

ATAC-seq in relation to other epigenetic assays
ATAC-seq in relation to other epigenetic assays

Source: Mehromohamadi et al. (2021)CC-BY 4.0

Other limitations

When we look at how ATAC-seq functions within the layer of chromatin accessibility, there are still some technical limitations that could introduce bias.

  1. Not all proteins with epigenetic properties have a known corresponding motif. Chromatin remodeling proteins, for example, do not prefer binding to a specific DNA sequence. This makes it difficult to infer the activity of such factors based on open chromatin information alone. Since these proteins have, for example, an impact on cell fate decisions in early embryonic development, [Zaret & Mango, 2016], ATAC-seq data can be insufficient for related research questions.
  2. Tn5 transposase has a bias due to sequence-dependent binding, which skews the accuracy of the open chromatin profile. However, this bias can be corrected for a great part by data analytics tools.
  3. Some bound chromatin regions might open and become tagged during sample processing and create false positives.

Case study: ATAC-seq vs ChIP-seq

ChIP-seq is one of the most popular alternatives to ATAC-seq. It works by enriching for DNA bound to specific proteins by cross-linking DNA and protein and capturing these DNA fragments with antibodies (immunoprecipitation, the IP in ChIP-seq).

Compared to ATAC-seq, its multiple steps are more time-consuming and it requires more input material due to information loss during immunoprecipitation. However, other antibody-based assays such as ChIPmentation and CUT&TAG require less input. Importantly, antibody-based assays can measure histone modifications, which ATAC-seq misses.

Regarding sensitivity, the following can be said about the two techniques.

ChiP-seq can measure direct interactions between proteins and DNA, whereas ATAC-seq measures global chromatin accessibility. For this, ChIP-seq sensitivity depends on the quality of the antibodies. Moreover, ChIP-seq requires experimental controls to identify experimental noise, which is usually not essential for ATAC-seq.

In general, ChIP-seq can offer a higher resolution in identifying specific protein binding sites compared to ATAC-seq. By contrast, ATAC-seq might provide broader but less specific information about accessible chromatin regions.

  • ChiP-seq can measure direct interactions between proteins and DNA, whereas ATAC-seq measures global chromatin accessibility.

ChIP-seq may be unsuccessful in finding meaningful regulatory elements when comparing two or more samples. In this regard, ATAC-seq has the advantage of high resolution and high signal-to-noise ratio, which enables researchers to identify differentially regulated sequences, such as enhancers, more accurately. Depending on your biological question, you may prefer one technology over the other.

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Single-cell versus bulk ATAC-seq

At Single Cell Discoveries, we of course frequently witness the general benefits of single-cell sequencing. Meanwhile, we have experience with the limitations inherent to single-cell ATAC-seq. We list them below.

Opportunities

In traditional bulk ATAC-seq, which measures chromatin accessibility at the ensemble level, it is challenging to dissect the contributions of individual cells within a heterogeneous population. Single-cell ATAC-seq addresses this limitation by allowing the simultaneous examination of chromatin accessibility profiles across numerous individual cells. This allows for the following opportunities:

  1. Resolve cellular heterogeneity: identify rare cell types or subpopulations and understand their unique regulatory profiles, which is challenging with bulk ATAC-seq that provides an average signal across all cells in a sample.
  2. Identify cell states: crucial for understanding developmental trajectories, cell differentiation, and disease-related changes.
  3. Find cell-specific regulatory elements: cell-specific or lineage-specific regulatory elements, enhancers, and promoters help to unravel the regulatory networks driving cellular functions and responses.
  4. Detect rare events: in rare cell populations or during transitional states (e.g., during early development), bulk ATAC-seq may not capture rare events, but scATAC-seq can identify these infrequent chromatin accessibility changes.
  5. Detect non-coding RNAs: while scRNA-seq primarily focuses on protein-coding genes, scATAC-seq can detect regulatory elements associated with non-coding RNAs, which are essential for gene regulation and function.

Limitations

Compared to bulk ATAC-seq, single-cell ATAC-seq has several limitations:

  1. Compared to bulk ATAC-seq data, scATAC-seq signals are binary and sparse. This means that measuring transitions over time from closed to open chromatin is more difficult. Moreover, this sparsity can limit the power to detect rare or transient cell states or regulatory elements. It can be compensated with high-quality handling of the protocol and increased starting material. We circumvent variable nuclei preparation qualities by performing nuclei extraction in-house.
  2. In addition, partly due to the sparsity of the data, interpretation is more complex. Hence, we put focus on assisting with scATAC-seq data analysis at Single Cell Discoveries, and we offer our data consultancy service for quick and complete help with the data.
  3. Single Cell ATAC requires single nucleus suspensions and the technology does not work on whole cells. However, this is often not an issue. The main disadvantage of using nuclei is the loss of data from the cytoplasm, but chromatin is, of course, located in the nucleus so no chromatin accessibility data is lost.
  4. The efficiency of the transposase enzyme used in scATAC-seq can vary between cells, leading to library preparation biases. This can result in the overrepresentation of certain genomic regions or cell types in the data. In addition, scATAC-seq may not cover the entire genome uniformly, potentially missing regulatory elements or regions with exceptionally closed chromatin.

Partly, the limitations of single-cell ATAC-seq can be surmounted by synergizing single-cell ATAC-seq with single-cell RNA sequencing.

Integrate scATAC-seq with scRNA-seq

It is possible to perform scATAC-seq and scRNA-seq on the same tissue with two approaches. One, you can divide a tissue into two and perform the two techniques on the two separate but comparable tissues. Two, you can perform multiome ATAC-seq and generate a single-cell chromatin accessibility and gene expression profile from the same tissue.

What is the added benefit of this synergy?

  1. Validate findings: researchers can discover whether differentially expressed genes (scRNA-seq data) also have significantly differential chromatin accessibility. This cross-validates conclusions based the gene expression and chromatin accessibility profiles of single cells, and conclusions on the layer of cell type identification and tissue dynamics.
  2. Connect gene expression with regulation: researchers can infer differentially expressed genes to be regulated by transcription factors associated with specific motifs in the open chromatin.
  3. Get a more complete picture of total RNA in single cells by detection of non-coding RNAs. While scRNA-seq primarily focuses on protein-coding genes, scATAC-seq can detect regulatory elements associated with non-coding RNAs, which are essential for gene regulation and function. Importantly, VASA-seq is a single-cell RNA sequencing method that does capture total RNA.

Find out when to go for multiome scATAC-seq and when to stick to standalone scATAC-seq or scRNA-seq in our upcoming blog.

More information 

Find more information on single-cell ATAC-seq, 10x Genomics, and our approach in our information guide.

 

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