Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network (“AI-TAC”) does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.

尽管我们知道许多序列特异性转录因子 (TF)，但如何在基因组规模上解码和编排顺式调控元件的 DNA 序列以确定免疫细胞的分化却超出了我们的掌握。利用 81 种免疫细胞类型的染色质可及性颗粒图谱，我们询问卷积神经网络 (CNN) 是否可以学会仅根据调控 DNA 序列推断细胞类型特异性染色质可及性。通过定制的架构和 CNN 参数解释的集成方法，我们证明了我们训练有素的网络（“AI-TAC”）通过从头开始重新发现已知调节剂和一些未知调节剂的结合基序来实现这一点。其重要性实际上与功能上一样重要的基序与通过染色质免疫沉淀对几个 TF 确定的位置惊人地重叠。 AI-TAC 建立了 TF 及其相互作用的层次结构，可驱动谱系规范，并识别特定阶段的相互作用，例如 Pax5/Ebf1 与 Pax5/Prdm1，或不同细胞类型中不同 NF-κB 二聚体的作用。 AI-TAC 将 Spi1/Cebp 和 Pax5/Ebf1 分别指定为髓系和 B 谱系命运所需的驱动因素，但似乎没有任何因素是 T 细胞分化所需的主要因素，这可能代表了一种后退途径。经过小鼠训练的 AI-TAC 可以解析人类 DNA，揭示出具有影响力的 TF 的惊人相似的排名，并为 AI-TAC 是一种通用的调控序列解码器提供了额外的支持。因此，深度学习可以揭示预测免疫系统完全分化复杂性的调控语法。