Connecting high-resolution 3D chromatin organization with epigenomics

• Published in: Nature communications Vol. 13; no. 1; p. 2054
• Main Authors: Feng, Fan, Yao, Yuan, Wang, Xue Qing David, Zhang, Xiaotian, Liu, Jie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.04.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2397; Cell lines; Chromatin; Chromatin - genetics; Chromosomes; Conformation; Datasets; Deep learning; Epigenomics; Gene Expression Regulation; Gene mapping; Gene regulation; Genomes; High resolution; Human tissues; Humanities and Social Sciences; Humans; multidisciplinary; Regulatory sequences; Regulatory Sequences, Nucleic Acid; Science; Science (multidisciplinary); Transcription
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-29695-6

The resolution of chromatin conformation capture technologies keeps increasing, and the recent nucleosome resolution chromatin contact maps allow us to explore how fine-scale 3D chromatin organization is related to epigenomic states in human cells. Using publicly available Micro-C datasets, we develop a deep learning model, CAESAR, to learn a mapping function from epigenomic features to 3D chromatin organization. The model accurately predicts fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C fails to detect. With existing epigenomic datasets from ENCODE and Roadmap Epigenomics Project, we successfully impute high-resolution 3D chromatin contact maps for 91 human tissues and cell lines. In the imputed high-resolution contact maps, we identify the spatial interactions between genes and their experimentally validated regulatory elements, demonstrating CAESAR’s potential in coupling transcriptional regulation with 3D chromatin organization at high resolution. While large-scale 3D genome architecture is well studied, the limits of resolution have hindered our understanding on the fine scale. Here the authors mapped 1D epigenomic profiles to fine-scale 3D chromatin structures with their deep learning model CAESAR. The model predicted fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C datasets fail to detect.