Details

Autor(en) / Beteiligte

• 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

Titel

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution

Ist Teil von

• Nature communications, 2022-10, Vol.13 (1), p.6498-19, Article 6498

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2022

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• 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.