增强的小鼠肺组织空间转录组学分析揭示了与肺动脉高压相关的细胞特异性基因表达变化。

Hanqiu Zhao, Xiaokuang Ma, Peng Chen, Bin Liu, Jing Wei, John Zhang, Ankit A Desai, Andrea L Frump, Olga Rafikova, Michael B Fallon, Shenfeng Qiu, Zhiyu Dai
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引用次数: 0

摘要

空间转录组学技术已经成为理解组织自然空间背景下细胞身份和功能的有力工具。传统的转录组学技术,如大量和单细胞RNA测序,失去了这种空间信息,这对解决许多生物学问题至关重要。在这里,我们提出了一种高分辨率空间转录组学的方案,使用固定的冷冻小鼠肺切片安装在10X Genomics Xenium载玻片上。该方法将多路荧光原位杂交(FISH)与高通量成像相结合,揭示肺组织切片中mRNA分子的空间分布,从而详细分析肺动脉高压(PH)小鼠模型中基因表达的变化。比较了固定冷冻和新鲜冷冻两种组织制备方法与Xenium平台的相容性。我们的固定冷冻方法,利用自由漂浮技术在室温下将薄肺切片贴装到Xenium载玻片上,保存了组织完整性并最大化了成像区域,从而获得高保真的空间转录组学数据。使用预先设计的379个基因小鼠小组,我们确定了40种主要的肺细胞类型。我们检测到PH的关键细胞变化,包括动脉内皮细胞(AECs)和成纤维细胞的增加,以及毛细血管内皮细胞(CAP1和CAP2)的减少。通过差异基因表达分析,我们观察了PH肺中内皮细胞向间充质细胞转化和成纤维细胞活化的标记。高分辨率空间制图进一步证实远端微血管动脉化增加。这些发现强调了空间转录组学在保存天然组织结构和增强我们对疾病细胞异质性的理解方面的效用。我们的方案为使用固定冷冻肺组织整合空间和转录组数据提供了可靠的方法,为未来复杂疾病(如PH)的研究提供了重大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Spatial Transcriptomics Analysis of Mouse Lung Tissues Reveals Cell-Specific Gene Expression Changes Associated with Pulmonary Hypertension.

Spatial transcriptomics technologies have emerged as powerful tools for understanding cellular identity and function within the natural spatial context of tissues. Traditional transcriptomics techniques, such as bulk and single-cell RNA sequencing, lose this spatial information, which is critical for addressing many biological questions. Here, we present a protocol for high-resolution spatial transcriptomics using fixed frozen mouse lung sections mounted on 10X Genomics Xenium slides. This method integrates multiplexed fluorescent in situ hybridization (FISH) with high-throughput imaging to reveal the spatial distribution of mRNA molecules in lung tissue sections, allowing detailed analysis of gene expression changes in a mouse model of pulmonary hypertension (PH). We compared two tissue preparation methods, fixed frozen and fresh frozen, for compatibility with the Xenium platform. Our fixed frozen approach, utilizing a free-floating technique to mount thin lung sections onto Xenium slides at room temperature, preserved tissue integrity and maximized the imaging area, resulting in high-fidelity spatial transcriptomics data. Using a predesigned 379-gene mouse panel, we identified 40 major lung cell types. We detected key cellular changes in PH, including an increase in arterial endothelial cells (AECs) and fibroblasts, alongside a reduction in capillary endothelial cells (CAP1 and CAP2). Through differential gene expression analysis, we observed markers of endothelial-to-mesenchymal transition and fibroblast activation in PH lungs. High-resolution spatial mapping further confirmed increased arterialization in the distal microvasculature. These findings underscore the utility of spatial transcriptomics in preserving the native tissue architecture and enhancing our understanding of cellular heterogeneity in disease. Our protocol provides a reliable method for integrating spatial and transcriptomic data using fixed frozen lung tissues, offering significant potential for future studies in complex diseases such as PH.

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