Zhang Chao, Chen Renchao, Zhang Yi
Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA.
Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA.
Sci Adv. 2022 Aug 26;8(34):eabq0990. doi: 10.1126/sciadv.abq0990.
Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Because of the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here, we developed a new algorithm named iSpatial to derive the spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other existing methods, iSpatial has higher accuracy in predicting gene expression and spatial distribution. Furthermore, it reduces false-positive and false-negative signals in the original datasets. By testing iSpatial with multiple spatial transcriptomic datasets, we demonstrate its wide applicability to datasets from different tissues and by different techniques. Thus, we provide a computational approach to reveal spatial organization of the entire transcriptome at single-cell resolution. With numerous high-quality datasets available in the public domain, iSpatial provides a unique way to understand the structure and function of complex tissues and disease processes.
空间分辨转录组分析能够揭示组织结构以及依赖于上下文的细胞间或细胞与环境相互作用背后的分子见解。由于当前技术限制，以单细胞分辨率获取全基因组空间转录组具有挑战性。在此，我们开发了一种名为iSpatial的新算法，通过整合空间转录组和单细胞RNA测序数据集来推导整个转录组的空间模式。与其他现有方法相比，iSpatial在预测基因表达和空间分布方面具有更高的准确性。此外，它减少了原始数据集中的假阳性和假阴性信号。通过使用多个空间转录组数据集测试iSpatial，我们证明了其对来自不同组织和不同技术的数据集具有广泛的适用性。因此，我们提供了一种计算方法来在单细胞分辨率下揭示整个转录组的空间组织。鉴于公共领域有大量高质量数据集，iSpatial提供了一种独特的方式来理解复杂组织的结构和功能以及疾病过程。
