A transposase system for integrative ChIP-exo and ATAC-seq analysis at single-cell resolution

项目总结 仅从序列预测顺式调控信息尚不可能，但收集顺式调控信息 系统地跨越所有人类细胞类型和跨物种的信息将是可行的，因为 技术进步。在这里，我们建议开发这样的突破性技术，将允许提取 来自任何细胞类型的顺式调控信息，包括不同细胞类型的混合物。这项技术是 基于结合了芯片和ATAC-SEQ技术的转座酶系统，从而允许 同时测量同一细胞中染色质的可及性和转录因子的占有率。它是 在DNA片段进入基因组的效率上的数量级改进方面的创新 文库，该文库允许以单细胞分辨率进行分析。它的分辨率是最先进的， 这使得能够识别体内结合的转录因子的精确足迹。我们将优化规模 和工作流，并开发了初步的分析框架，使该技术具有广泛的适用性 一系列系统。作为原则证明，我们将把这项技术应用于小鼠早期胚胎，并比较 结果与从小鼠胚胎干细胞获得的结果相同。拥有这样的技术将打开通往 史无前例地探索任何细胞类型的顺式调控信息。它将加深我们对 在发育和进化过程中的转录调控网络，并将提供对 突变和人类疾病的潜在机制。

PROJECT SUMMARY Predicting cis-regulatory information from sequence alone is not yet possible, but collecting cis-regulatory information systematically across all human cell types and across species would be feasible with significant progress in technology. Here we propose to develop such breakthrough technology that will allow to extract cis-regulatory information from any cell type, including mixtures of heterogeneous cell types. The technology is based on a transposase system that combines ChIP and ATAC-seq technology, and thus allows the simultaneous measurement of chromatin accessibility and transcription factor occupancy in the same cells. It is innovative in its orders of magnitude improvement in efficiency by which DNA fragments enter the genomic library, which allows the assay to be performed at single-cell resolution. It is cutting-edge in its resolution, which allows the identification of precise footprints of transcription factor bound in vivo. We will optimize scale and workflow, as well as develop an initial analysis framework, to make the technology applicable to a wide range of systems. As proof-of-principle, we will apply the technology to early mouse embryos and compare the results to those obtained from mouse embryonic stem cells. Having such technology will open the door to unprecedented explorations of cis-regulatory information across any cell type. It will deepen our understanding of transcriptional regulatory networks during development and evolution, and will provide insights into mutations and mechanisms underlying human disease.