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

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

The phenotypic and functional states of a cell 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 capture of information from different molecular layers directly in the tissue context. However, current technologies are limited to map one to two modalities at the same time, providing an incomplete representation of cellular identity. Such data is inadequate to fully understand complex biological systems and their underlying regulatory mechanisms. Here we present spatial-Mux-seq, a multi-modal spatial technology that allows simultaneous profiling of five different modalities, including genome-wide profiles of two histone modifications and open chromatin, whole transcriptome, and a panel of proteins at tissue scale and cellular level in a spatially resolved manner. We applied this technology to generate multi-modal tissue maps in mouse embryos and mouse brains, which discriminated more cell types and states than unimodal data. We investigated the spatiotemporal relationship between histone modifications, chromatin accessibility, gene and protein expression in neuron differentiation revealing the relationship between tissue organization, function, and gene regulatory networks. We were able to identify a radial glia spatial niche and revealed spatially changing gradient of epigenetic signals in this region. Moreover, we revealed previously unappreciated involvement of repressive histone marks in the mouse hippocampus. Collectively, the spatial multi-omics approach heralds a new era for characterizing tissue and cellular heterogeneity that single modality studies alone could not reveal.
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组织中染色质特征、转录组和蛋白质的多重空间绘图
细胞的表型和功能状态由多个全息层的复杂交互分子层次结构调节,其中涉及基因组、表观基因组、转录组、蛋白质组和代谢组。空间全息方法能够直接从组织背景中捕捉不同分子层的信息。然而,目前的技术仅限于同时绘制一到两种模式的图谱,无法完整呈现细胞特征。这些数据不足以全面了解复杂的生物系统及其潜在的调控机制。在这里,我们介绍了一种多模态空间技术--spatial-Mux-seq,它可以同时绘制五种不同模态的图谱,包括两种组蛋白修饰和开放染色质的全基因组图谱、全转录组,以及以空间分辨的方式在组织尺度和细胞水平绘制的一组蛋白质图谱。我们应用这项技术生成了小鼠胚胎和小鼠大脑的多模态组织图,与单模态数据相比,它能区分更多的细胞类型和状态。我们研究了神经元分化过程中组蛋白修饰、染色质可及性、基因和蛋白质表达之间的时空关系,揭示了组织组织、功能和基因调控网络之间的关系。我们确定了神经胶质细胞的径向空间位点,并揭示了该区域表观遗传信号的空间变化梯度。此外,我们还揭示了小鼠海马中以前未被认识到的抑制性组蛋白标记的参与。总而言之,空间多组学方法预示着一个新时代的到来,它可以描述组织和细胞的异质性,而这是单个模式研究无法揭示的。
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