转录组
计算生物学
反褶积
RNA序列
核糖核酸
单细胞分析
空间分析
生物
计算机科学
细胞
基因
基因表达
遗传学
算法
遥感
地质学
作者
Jie Liao,Jingyang Qian,Fang Yin,Zhuo Chen,Xiang Zhuang,Ningyu Zhang,Xin Shao,Yining Hu,Penghui Yang,Jiajing Cheng,Yang Hu,Lingqi Yu,Hua Yang,Jinlu Zhang,Xiaoyan Lu,Shao Li,Dan Wu,Yue Gao,Huajun Chen,Xiaohui Fan
标识
DOI:10.1038/s41467-022-34271-z
摘要
Abstract Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms’ biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space ( https://github.com/ZJUFanLab/bulk2space ), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.
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