Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Department of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Nat Methods. 2022 May;19(5):547-553. doi: 10.1038/s41592-022-01461-y. Epub 2022 May 2.
In this work, we describe NEAT-seq (sequencing of nuclear protein epitope abundance, chromatin accessibility and the transcriptome in single cells), enabling interrogation of regulatory mechanisms spanning the central dogma. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive T cell subsets and identify examples of TFs with regulatory activity gated by transcription, translation and regulation of chromatin binding. We also link a noncoding genome-wide association study single-nucleotide polymorphism (SNP) within a GATA motif to a putative target gene, using NEAT-seq data to internally validate SNP impact on GATA3 regulation.
在这项工作中，我们描述了 NEAT-seq（单细胞中核蛋白表位丰度、染色质可及性和转录组的测序），可用于研究跨越中心法则的调控机制。我们使用一组驱动 T 细胞亚群的主转录因子 (TFs) 对 CD4 记忆 T 细胞进行了这项技术的应用，并确定了一些 TF 具有转录、翻译和染色质结合调控门控的调控活性的例子。我们还使用 NEAT-seq 数据将 GATA 基序内的一个非编码全基因组关联研究单核苷酸多态性 (SNP) 与一个假定的靶基因联系起来，内部验证 SNP 对 GATA3 调控的影响。
