Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA.
Nat Commun. 2024 Nov 1;15(1):9432. doi: 10.1038/s41467-024-53628-0.
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.
鉴定调节元件之间特定于细胞类型的 3D 染色质相互作用有助于破译基因调控并解释与疾病相关的非编码变体。然而，由于输入细胞数量有限，当前的 3D 基因组学技术通常无法实现这种分辨率。因此，我们提出了 ChromaFold，这是一种深度学习模型，仅使用单细胞 ATAC 测序 (scATAC-seq) 数据即可预测包括调节相互作用在内的 3D 接触图。 ChromaFold 使用伪总体染色质可及性、元细胞之间的共可及性以及 CTCF 基序轨迹作为输入，并采用轻量级架构在标准 GPU 上进行训练。在人类样本中经过 scATAC-seq 和 Hi-C 配对数据训练后， ChromaFold 可以准确预测各种人类和小鼠测试细胞类型的 3D 接触图和峰级相互作用。与使用 ATAC-seq 和 CTCF ChIP-seq 的领先接触图预测模型相比， ChromaFold 仅使用 scATAC-seq 即可实现最先进的性能。最后，在复杂组织中对配对的 scATAC-seq 和 Hi-C 进行微调，可实现细胞亚群之间染色质相互作用的反卷积。
