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GAGE-seq 可同时在单细胞中对多尺度 3D 基因组结构和基因表达进行分析。

GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells.

机构信息

Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

出版信息

Nat Genet. 2024 Aug;56(8):1701-1711. doi: 10.1038/s41588-024-01745-3. Epub 2024 May 14.

Abstract

The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34 cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

摘要

哺乳动物基因组的组织具有复杂的、多尺度的三维(3D)结构,其功能意义仍然难以捉摸,因为有限的单细胞技术可以同时描绘基因组组织和转录活性。在这里,我们介绍了通过测序进行基因组结构和基因表达分析(GAGE-seq),这是一种可扩展的、稳健的单细胞共检测方法,可以在同一细胞内同时测量 3D 基因组结构和转录组。将 GAGE-seq 应用于小鼠大脑皮层和人类骨髓 CD34 细胞,我们描述了 3D 基因组与基因表达之间错综复杂的关系,表明多尺度 3D 基因组特征可以告知细胞类型特异性基因表达,并将调控元件与靶基因联系起来。与空间转录组数据的整合揭示了小鼠皮层中局部的 3D 基因组变化。在人类造血中观察到 3D 基因组组织和基因表达之间的不一致变化,突出了单细胞水平上复杂的、时变的相互作用。GAGE-seq 为在不同的生物学背景下探索单细胞水平的基因组结构和基因表达关系提供了一种强大、经济有效的方法。

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