Synapse - ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility Journal Article

Authors:	Gao, V. R.; Yang, R.; Das, A.; Luo, R.; Luo, H.; McNally, D. R.; Karagiannidis, I.; Rivas, M. A.; Wang, Z. M.; Barisic, D.; Karbalayghareh, A.; Wong, W.; Zhan, Y. A.; Chin, C. R.; Noble, W. S.; Bilmes, J. A.; Apostolou, E.; Kharas, M. G.; Béguelin, W.; Viny, A. D.; Huangfu, D.; Rudensky, A. Y.; Melnick, A. M.; Leslie, C. S.
Article Title:	ChromaFold predicts the 3D contact map from single-cell chromatin accessibility
Abstract:	Identifying cell-type-specific 3D chromatin interactions between regulatory elements can help decipher gene regulation and interpret disease-associated non-coding variants. However, achieving this resolution with current 3D genomics technologies is often infeasible given limited input cell numbers. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps, including regulatory interactions, from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility across metacells, and a CTCF motif track as inputs and employs a lightweight architecture to train on standard GPUs. Trained on paired scATAC-seq and Hi-C data in human samples, ChromaFold accurately predicts the 3D contact map and peak-level interactions across diverse human and mouse test cell types. Compared to leading contact map prediction models that use ATAC-seq and CTCF ChIP-seq, ChromaFold achieves state-of-the-art performance using only scATAC-seq. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. © The Author(s) 2024.
Keywords:	genetics; mouse; animal; metabolism; animals; mice; chromatin; genomics; rodent; cell; single cell analysis; single-cell analysis; procedures; machine learning; chromate; ccctc-binding factor; transcription factor ctcf; deconvolution; humans; human; deep learning; chromatin immunoprecipitation sequencing; three-dimensional modeling
Journal Title:	Nature Communications
Volume:	15
ISSN:	2041-1723
Publisher:	Nature Publishing Group
Date Published:	2024-11-01
Start Page:	9432
Language:	English
DOI:	10.1038/s41467-024-53628-0
PUBMED:	39487131
PROVIDER:	scopus
PMCID:	PMC11530433
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208290714&doi=10.1038%2fs41467-024-53628-0&partnerID=40&md5=ed2745758cae682d500beb78b771506e
Notes:	Article -- MSK corresponding author is Christina Leslie -- Erratum issued, see DOI: 10.1038/s41467-025-56017-3 -- Source: Scopus