Abdulraouf Abdulraouf, Jiang Weirong, Xu Zihan, Zhang Zehao, Isakov Samuel, Raihan Tanvir, Zhou Wei, Cao Junyue
Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA.
The Tri-Institutional M.D-Ph.D Program, New York, NY, USA.
bioRxiv. 2024 Aug 8:2024.08.06.606712. doi: 10.1101/2024.08.06.606712.
Spatial transcriptomics has revolutionized our understanding of cellular network dynamics in aging and disease by enabling the mapping of molecular and cellular organization across various anatomical locations. Despite these advances, current methods face significant challenges in throughput and cost, limiting their utility for comprehensive studies. To address these limitations, we introduce (Imaging Reconstruction using Indexed Sequencing), a optics-free spatial transcriptomics platform that eliminates the need for predefined capture arrays or extensive imaging, allowing for the rapid and cost-effective processing of multiple tissue sections simultaneously. Its capacity to reconstruct images based solely on sequencing local DNA interactions allows for profiling of tissues without size constraints and across varied resolutions. Applying , we examined gene expression and cellular dynamics in thirty brain regions of both adult and aged mice, uncovering region-specific changes in gene expression associated with aging. Further cell type-centric analysis further identified age-related cell subtypes and intricate changes in cell interactions that are distinct to certain spatial niches, emphasizing the unique aspects of aging in different brain regions. The affordability and simplicity of position it as a versatile tool for mapping region-specific gene expression and cellular interactions across various biological systems.
空间转录组学通过实现跨各种解剖位置的分子和细胞组织图谱绘制,彻底改变了我们对衰老和疾病中细胞网络动态的理解。尽管取得了这些进展,但目前的方法在通量和成本方面面临重大挑战,限制了它们在综合研究中的效用。为了解决这些限制,我们引入了(使用索引测序的成像重建),这是一种无光学的空间转录组学平台,无需预定义的捕获阵列或广泛的成像,允许同时对多个组织切片进行快速且经济高效的处理。它仅基于对局部DNA相互作用进行测序来重建图像的能力,使得能够对不受大小限制且具有不同分辨率的组织进行分析。应用该技术,我们研究了成年和老年小鼠三十个脑区的基因表达和细胞动态,发现了与衰老相关的基因表达区域特异性变化。进一步以细胞类型为中心的分析进一步确定了与年龄相关的细胞亚型以及特定空间生态位特有的细胞相互作用的复杂变化,强调了不同脑区衰老的独特方面。该技术的经济性和简便性使其成为绘制跨各种生物系统的区域特异性基因表达和细胞相互作用图谱的通用工具。