College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Nat Commun. 2024 Sep 6;15(1):7794. doi: 10.1038/s41467-024-49457-w.
Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.
基于成像的空间转录组学技术，如多重纠错荧光原位杂交（MERFISH），可以以前所未有的细节捕捉细胞过程。然而，需要严格和强大的分析工具来释放它们在发现亚细胞生物学模式方面的全部潜力。我们提出了细胞内空间转录组学分析工具包（InSTAnT），这是一个用于从空间转录组学数据中以单分子分辨率提取分子关系的计算工具包。InSTAnT 采用专门的统计检验和算法来检测基因对和模块，这些基因对和模块在单个细胞内和整个细胞景观中表现出有趣的共定位模式。我们在五个不同的数据集上展示了该工具包，这些数据集代表了两种不同的细胞系、两种大脑结构、两种物种和三种不同的技术。我们对发现的共定位模式进行了严格的统计评估，从数据库和 RNA 相互作用中找到支持证据，并确定相关的亚细胞结构域。我们在大脑中发现了几个具有细胞类型和区域特异性的基因共定位。InSTAnT 发现的细胞内空间模式反映了多种分子关系，包括 RNA 相互作用以及共享的亚细胞定位或功能，为有关分子功能的可测试假设提供了丰富的纲要。
