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研究揭示三维厚组织块中可扩展的空间单细胞转录组学和翻译组学
作者:小柯机器人 发布时间:2025/11/25 15:05:38

麻省理工学院和哈佛大学的布罗德研究所科研团队揭示了三维厚组织块中可扩展的空间单细胞转录组学和翻译组学。这一研究成果发表在2025年11月24日出版的国际学术期刊《自然—方法学》上。

该课题组研究人员开发了Deep-STARmap和Deep-RIBOmap,它们能够在60–200-µm厚的组织块内分别对基因转录物的需求及其相应的翻译活性进行3D原位定量。这是通过可扩展的探针合成,水凝胶嵌入有效的探针锚定和机器人cDNA交联来实现的。研究人员首次将Deep-STARmap与多色荧光蛋白成像相结合,在无主题脑中同时进行分子细胞分型和3D神经元形态追踪。课题组研究人员还证明,3D空间分析有助于对人类皮肤癌中肿瘤免疫相互作用进行全面和定量分析。这项工作介绍了Deep-STARmap和Deep-RIBOmap,这是基于成像的测序平台,旨在分析厚组织块中的转录和翻译活性。

研究人员表示,在单细胞水平的三维(3D)组织环境中表征转录和翻译基因表达模式对于揭示基因如何塑造健康和疾病中的组织结构和功能至关重要。然而,大多数现有的空间剖面技术仅限于5–20µm的薄组织切片。

附:英文原文

Title: Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks

Author: Sui, Xin, Lo, Jennifer A., Luo, Shuchen, He, Yichun, Tang, Zefang, Lin, Zuwan, Barabsi, Dniel L., Zhou, Yiming, Wang, Wendy Xueyi, Liu, Jia, Wang, Xiao

Issue&Volume: 2025-11-24

Abstract: Characterizing the transcriptional and translational gene expression patterns at the single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape tissue structure and function in health and disease. However, most existing spatial profiling techniques are limited to 5–20 µm thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D in situ quantification of thousands of gene transcripts and their corresponding translation activities, respectively, within 60–200-µm thick tissue blocks. This is achieved through scalable probe synthesis, hydrogel embedding with efficient probe anchoring and robust cDNA crosslinking. We first utilized Deep-STARmap in combination with multicolor fluorescent protein imaging for simultaneous molecular cell typing and 3D neuron morphology tracing in the mouse brain. We also demonstrate that 3D spatial profiling facilitates comprehensive and quantitative analysis of tumor–immune interactions in human skin cancer. This work introduces Deep-STARmap and Deep-RIBOmap, imaging-based sequencing platforms designed to profile transcriptional and translational activity in thick tissue blocks.

DOI: 10.1038/s41592-025-02867-0

Source: https://www.nature.com/articles/s41592-025-02867-0

期刊信息

Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:47.99
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex