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组织中染色质特征、转录组和蛋白质的多重空间图谱分析。

Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues.

作者信息

Guo Pengfei, Mao Liran, Chen Yufan, Lee Chin Nien, Cardilla Angelysia, Li Mingyao, Bartosovic Marek, Deng Yanxiang

机构信息

Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

出版信息

Nat Methods. 2025 Mar;22(3):520-529. doi: 10.1038/s41592-024-02576-0. Epub 2025 Jan 27.

Abstract

The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner. We applied this technology to mouse embryos and mouse brains, generating detailed multimodal tissue maps that identified more cell types and states compared to unimodal data. This analysis uncovered spatiotemporal relationships among histone modifications, chromatin accessibility, gene expression and protein levels during neuron differentiation, and revealed a radial glia niche with spatially dynamic epigenetic signals. Collectively, the spatial multi-omics approach heralds a new era for characterizing tissue and cellular heterogeneity that single-modality studies alone could not reveal.

摘要

细胞的表型和功能状态由多个组学层面的复杂交互式分子层级所调控,这些层面包括基因组、表观基因组、转录组、蛋白质组和代谢组。空间组学方法能够在组织环境中研究这些层面,但通常局限于一两种模式,无法全面呈现细胞特性。在此,我们介绍空间多重测序技术(Spatial-Mux-seq),这是一种多模态空间技术,能够在组织尺度和细胞水平上以空间分辨的方式同时对五种不同模式进行分析:两种组蛋白修饰、染色质可及性、全转录组以及一组蛋白质。我们将该技术应用于小鼠胚胎和小鼠大脑,生成了详细的多模态组织图谱,与单模态数据相比,这些图谱识别出了更多的细胞类型和状态。该分析揭示了神经元分化过程中组蛋白修饰、染色质可及性、基因表达和蛋白质水平之间的时空关系,并揭示了一个具有空间动态表观遗传信号的放射状胶质细胞生态位。总体而言,空间多组学方法开创了一个表征组织和细胞异质性的新时代,这是单模态研究单独无法揭示的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b1/11906265/79e2c8ddecd5/nihms-2043960-f0005.jpg

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