Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Nat Commun. 2024 Aug 30;15(1):7559. doi: 10.1038/s41467-024-51728-5.
In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level and whole-slide scale deconvolution, convolution, and clustering for both low- and high-resolution spots. STIE characterizes cell-type-specific gene expression and demonstrates outperforming concordance with true cell-type-specific transcriptomic signatures than the other spot- and subspot-level methods. Furthermore, STIE reveals the single-cell level insights, for instance, lower actual spot resolution than its reported spot size, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatial cell-cell interactions at the single-cell level other than spot level.
在基于原位捕获的空间转录组学中，大小相同且固定位置打印的斑点不能精确捕获随机位置的单细胞，因此无法在单细胞水平上进行转录组分析。为此，我们提出了 STIE，这是一种期望最大化算法，它将空间转录组与其匹配的组织学图像核形态对齐，并从~70%的缺口区域中恢复缺失的细胞，从而实现真正的单细胞水平和全幻灯片尺度的去卷积、卷积和聚类，适用于低分辨率和高分辨率的斑点。STIE 描述了细胞类型特异性基因表达，并证明与真实细胞类型特异性转录组特征的一致性优于其他斑点和亚斑点水平的方法。此外，STIE 揭示了单细胞水平的见解，例如，实际斑点分辨率低于其报道的斑点大小，对细胞类型共定位的无偏评估，高分辨率斑点在区分细微细胞类型方面的优越能力，以及单细胞水平的空间细胞-细胞相互作用，而不是斑点水平。
