美国哈佛大学Rafael A. Irizarry、Fei Chen等研究人员合作开发在空间转录组学中解析细胞类型混合物的新方法。2021年2月18日，《自然—生物技术》在线发表了这项成果。

研究人员开发了一种名为RCTD（robust cell type decomposition）的计算方法，可利用从单细胞RNA-seq中获得的细胞类型概况来分解细胞类型混合物，同时校正测序技术之间的差异。研究人员证明了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可在小鼠大脑的Slide-seq和Visium数据集中准确地重复已知的细胞类型和亚型定位模式。最后，研究人员展示了RCTD获得的细胞类型定位的方式如何发现其表达取决于空间环境的细胞类型中的基因。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，从而揭示生物组织中细胞组织的新原理。RCTD可作为开源R软件包可在https://github.com/dmcable/RCTD上公开获得。 

据介绍，空间转录组学技术的局限性在于单个测量可能包含多个细胞的贡献，从而阻碍了细胞类型特定的定位和表达空间模式的发现。

附：英文原文

Title: Robust decomposition of cell type mixtures in spatial transcriptomics

Author: Dylan M. Cable, Evan Murray, Luli S. Zou, Aleksandrina Goeva, Evan Z. Macosko, Fei Chen, Rafael A. Irizarry

Issue&Volume: 2021-02-18

Abstract: 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.

DOI: 10.1038/s41587-021-00830-w

Source: https://www.nature.com/articles/s41587-021-00830-w