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Updated: Jun 30, 2025

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Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq
Published on: November 13, 2017
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txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility
Hao Zhang1,2, Ryan M Mulqueen3, Natalie Iannuzo1
1Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA.
Genome Biology
|March 23, 2024
Summary
We developed a scalable single-cell ATAC-seq method to profile over 400,000 nuclei. This technique revealed technical artifacts in a common mouse model, impacting gene regulatory element interpretation.
Area of Science:
- Genomics
- Molecular Biology
- Epigenetics
Background:
- Single-cell ATAC-seq (scATAC-seq) is crucial for understanding cell-type-specific gene regulation.
- Existing methods face limitations in scalability and throughput.
- Accurate interpretation of scATAC-seq data is essential for biological discovery.
Purpose of the Study:
- To develop a high-throughput, large-scale single-cell ATAC-seq method.
- To profile a diverse range of human and mouse tissues at single-nucleus resolution.
- To identify and characterize technical artifacts in commonly used mouse models.
Main Methods:
- Integration of Tn5 transposase-based pre-indexing with 10× Genomics single-cell barcoding.
- Processing and profiling of up to 200,000 nuclei per reaction across multiple samples.
- Application of the method to diverse tissues including human cortex, mouse brain, and lung, as well as a CC16 knockout model.
Main Results:
- Successfully profiled 449,953 nuclei from various tissues.
- Identified significant, previously underappreciated technical artifacts in the CC16 knockout mouse model.
- Demonstrated that these artifacts, stemming from 129 mouse strain genetic material, cause cell-type-specific alterations in regulatory elements, confounding study interpretations.
Conclusions:
- The developed method enables unprecedented scale for single-cell ATAC-seq.
- Technical artifacts in mouse models can profoundly impact epigenetic interpretations.
- Careful consideration of genetic background is critical for accurate analysis of epigenomic data.

