Published August 10, 2024 | Version v1
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Quantitative analysis of cis-regulatory elements in transcription with KAS-ATAC-seq

Description

Cis-regulatory elements (CREs) are pivotal in orchestrating gene expression throughout diverse biological systems. Accurate identification and in-depth characterization of functional CREs are crucial for decoding gene regulation networks during cellular processes. In this study, we develop Kethoxal-Assisted Single-stranded DNA Assay for Transposase-Accessible Chromatin with Sequencing (KAS-ATAC-seq) to quantitatively analyze the transcriptional activity of CREs. A main advantage of KAS-ATAC-seq lies in its precise measurement of ssDNA levels within both proximal and distal ATAC-seq peaks, enabling the identification of transcriptional regulatory sequences. This feature is particularly adept at defining Single-Stranded Transcribing Enhancers (SSTEs). SSTEs are highly enriched with nascent RNAs and specific transcription factors (TFs) binding sites that define cellular identity. Moreover, KAS-ATAC-seq provides a detailed characterization and functional implications of various SSTE subtypes. Our analysis of CREs during mouse neural differentiation demonstrates that KAS-ATAC-seq can effectively identify immediate-early activated CREs in response to retinoic acid (RA) treatment. Our findings indicate that KAS-ATAC-seq provides more precise annotation of functional CREs in transcription. Future applications of KAS-ATAC-seq would help elucidate the intricate dynamics of gene regulation in diverse biological processes.

Data availability

The raw and processed data of Opti-KAS-seq, KAS-seq, KAS-ATAC-seq, and ATAC-seq experiments performed using HEK293T cells and mouse embryonic stem cells have been deposited in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under the accession number: GSE256232. All published datasets reanalyzed in this study were summarized in Supplementary Data 3. The raw data generated in this study are provided in the Source Data file. Source data are provided with this paper.

All the bioinformatic scripts used in this study are available at https://github.com/Ruitulyu/KAS-Analyzer. Bar plots were created with GraphPad Prism 7. Correlation heatmaps were generated using the corrplot package in R (version 3.6.3). Scatter plots were made using the ggpubr package in R (version 3.6.3), which facilitates the creation of ggplot2-based graphs.

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Additional details

Identifiers

DOI
10.1038/s41467-024-50680-8
Other
oai:uchicago.tind.io:13132

Funding

National Institutes of Health
R01HG012780
National Institutes of Health
RM1HG008935

UChicago Information

Division(s)
Biological Sciences Division, Physical Sciences Division
Department(s)
Biochemistry and Molecular Biology, Chemistry
Center(s) or Institute(s)
Institute for Biophysical Dynamics