De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution.
De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution.
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DOI：
10.1038/s41467-022-34271-z
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发表时间：
2022-10-30
影响因子：
16.6
通讯作者：
Fan, Xiaohui
中科院分区：
文献类型：
--
作者：
Liao, Jie;Qian, Jingyang;Fang, Yin;Chen, Zhuo;Zhuang, Xiang;Zhang, Ningyu;Shao, Xin;Hu, Yining;Yang, Penghui;Cheng, Junyun;Hu, Yang;Yu, Lingqi;Yang, Haihong;Zhang, Jinlu;Lu, Xiaoyan;Shao, Li;Wu, Dan;Gao, Yue;Chen, Huajun;Fan, Xiaohui
以单细胞分辨率揭示组织分子结构可以帮助更好地理解生物体的生物学和病理学过程。然而，批量RNA-seq只能测量细胞混合物中的基因表达，而不能揭示单个细胞的转录异质性和空间模式。在此，我们介绍Bulk 2Space（https：//github.com/ZJufanLab/bulk2space），这是一种基于深度学习框架的空间去卷积算法，可以使用现有的单细胞和空间转录组学参考文献同时揭示批量RNA-seq数据的空间和细胞异质性。使用批量转录组学来验证Bulk 2Space揭示了不同肿瘤区域中免疫细胞的空间差异，炎症诱导的肿瘤发生期间组织的分子和空间异质性，以及不同细胞类型中新基因的空间模式。此外，Bulk 2Space用于对来自我们内部开发的称为Spatial-seq的测序方法的两个不同小鼠脑区域的批量转录组数据进行空间解卷积分析。我们不仅重建了小鼠同皮质的层次结构，还进一步注释了小鼠下丘脑中未通过原始方法鉴定的细胞类型。目前以空间分辨的单细胞分辨率重新分析批量RNA-seq的方法具有局限性。在这里，作者开发了Bulk 2Space，这是一种使用单细胞和空间转录组学作为参考的空间去卷积算法，为大块组织内的空间异质性提供了新的见解。
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. Current methods to reanalyze bulk RNA-seq at spatially resolved single-cell resolution have limitations. Here, the authors develop Bulk2Space, a spatial deconvolution algorithm using single-cell and spatial transcriptomics as references, providing new insights into spatial heterogeneity within bulk tissue.
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影响因子：
9.3
作者：
Baron M;Veres A;Wolock SL;Faust AL;Gaujoux R;Vetere A;Ryu JH;Wagner BK;Shen-Orr SS;Klein AM;Melton DA;Yanai I
通讯作者：
Yanai I
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
5.8
作者：
Gangalum RK;Kim D;Kashyap RK;Mangul S;Zhou X;Elashoff D;Bhat SP
通讯作者：
Bhat SP
影响因子：
11.8
作者：
Green CD;Ma Q;Manske GL;Shami AN;Zheng X;Marini S;Moritz L;Sultan C;Gurczynski SJ;Moore BB;Tallquist MD;Li JZ;Hammoud SS
通讯作者：
Hammoud SS
影响因子：
5.9
作者：
Andersson A;Bergenstråhle J;Asp M;Bergenstråhle L;Jurek A;Fernández Navarro J;Lundeberg J
通讯作者：
Lundeberg J