Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY, USA.
Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA.
Nat Biotechnol. 2023 Aug;41(8):1140-1150. doi: 10.1038/s41587-022-01612-8. Epub 2023 Jan 9.
Investigating how chromatin organization determines cell-type-specific gene expression remains challenging. Experimental methods for measuring three-dimensional chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a multimodal deep neural network that performs de novo prediction of cell-type-specific chromatin organization using DNA sequence and two cell-type-specific genomic features-CTCF binding and chromatin accessibility. C.Origami enables in silico experiments to examine the impact of genetic changes on chromatin interactions. We further developed an in silico genetic screening approach to assess how individual DNA elements may contribute to chromatin organization and to identify putative cell-type-specific trans-acting regulators that collectively determine chromatin architecture. Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.
研究染色质组织如何决定细胞类型特异性基因表达仍然具有挑战性。用于测量三维染色质组织的实验方法，如 Hi-C，成本高昂且存在技术限制，限制了其广泛应用，特别是在高通量遗传扰动方面。我们提出了 C.Origami，这是一种多模态深度神经网络，使用 DNA 序列和两种细胞类型特异性基因组特征（CTCF 结合和染色质可及性）来进行细胞类型特异性染色质组织的从头预测。C.Origami 能够进行计算机实验，以检查遗传变化对染色质相互作用的影响。我们进一步开发了一种计算机遗传筛选方法，以评估单个 DNA 元件如何可能有助于染色质组织，并确定可能的细胞类型特异性反式作用调节剂，这些调节剂共同决定染色质结构。将这种方法应用于白血病细胞和正常 T 细胞，我们证明了由 C.Origami 实现的细胞类型特异性计算机遗传筛选可用于系统地发现正常和疾病相关生物系统中的新型染色质调控回路。
