Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.
Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Nat Biotechnol. 2022 Apr;40(4):517-526. doi: 10.1038/s41587-021-00830-w. Epub 2021 Feb 18.
A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .
空间转录组学技术的一个局限性是，单个测量值可能包含多个细胞的贡献，这阻碍了对细胞类型特异性定位和表达空间模式的发现。在这里，我们开发了稳健的细胞类型分解（RCTD），这是一种计算方法，利用从单细胞 RNA-seq 中学习到的细胞类型特征来分解细胞类型混合物，同时纠正不同测序技术之间的差异。我们证明了 RCTD 在模拟数据集上检测混合物和识别细胞类型的能力。此外，RCTD 准确地再现了已知的细胞类型和亚型在小鼠大脑的 Slide-seq 和 Visium 数据集上的定位模式。最后，我们展示了 RCTD 恢复细胞类型定位的能力如何能够发现表达依赖于空间环境的细胞类型内的基因。使用 RCTD 对细胞类型进行空间映射可以定义细胞身份的空间成分，揭示生物组织中细胞组织的新原则。RCTD 作为一个开源 R 包在 https://github.com/dmcable/RCTD 上公开提供。
