Super-resolved spatial transcriptomics by deep data fusion

被引：81

作者：

Bergenstrahle, Ludvig ^{[1
]}

He, Bryan ^{[2
]}

Bergenstrahle, Joseph ^{[1
]}

Abalo, Xesus ^{[1
]}

Mirzazadeh, Reza ^{[1
]}

Thrane, Kim ^{[1
]}

Ji, Andrew L. ^{[3
]}

Andersson, Alma ^{[1
]}

Larsson, Ludvig ^{[1
]}

Stakenborg, Nathalie ^{[4
]}

Boeckxstaens, Guy ^{[4
]}

Khavari, Paul ^{[3
]}

Zou, James ^{[2
]}

Lundeberg, Joakim ^{[1
]}

Maaskola, Jonas ^{[1
,5
]}

机构：

[1] KTH Royal Inst Technol, Dept Gene Technol, SciLifeLab, Stockholm, Sweden

[2] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA

[3] Stanford Univ, Stanford Canc Inst, Stanford, CA 94305 USA

[4] Katholieke Univ Leuven, Dept Chron Dis & Metab, Leuven, Belgium

[5] Stockholm Univ, Dept Biochem & Biophys, SciLifeLab, Stockholm, Sweden

来源：

NATURE BIOTECHNOLOGY | 2022年 / 40卷 / 04期

基金：

瑞典研究理事会;

关键词：

CELL RNA-SEQ; SINGLE-CELL; GENE-EXPRESSION; TISSUE; VISUALIZATION;

D O I：

10.1038/s41587-021-01075-3

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

The low resolution of spatial transcriptomics is substantially improved by including histology images. Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone.

引用

页码：476 / +

页数：17

共 40 条

[1] High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin [J].

Achim, Kaia ;

Pettit, Jean-Baptiste ;

Saraiva, Luis R. ;

Gavriouchkina, Daria ;

Larsson, Tomas ;

Arendt, Detlev ;

Marioni, John C. .

NATURE BIOTECHNOLOGY, 2015, 33 (05) :503-U215

[2] Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography [J].

Andersson, Alma ;

Bergenstrahle, Joseph ;

Asp, Michaela ;

Bergenstrahle, Ludvig ;

Jurek, Aleksandra ;

Fernandez Navarro, Jose ;

Lundeberg, Joakim .

COMMUNICATIONS BIOLOGY, 2020, 3 (01)

[3]

Bingham E, 2019, J MACH LEARN RES, V20

[4] Variational Inference: A Review for Statisticians [J].

Blei, David M. ;

Kucukelbir, Alp ;

McAuliffe, Jon D. .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2017, 112 (518) :859-877

[5] C1q acts in the tumour microenvironment as a cancer-promoting factor independently of complement activation [J].

Bulla, Roberta ;

Tripodo, Claudio ;

Rami, Damiano ;

Ling, Guang Sheng ;

Agostinis, Chiara ;

Guarnotta, Carla ;

Zorzet, Sonia ;

Durigutto, Paolo ;

Botto, Marina ;

Tedesco, Francesco .

NATURE COMMUNICATIONS, 2016, 7

[6] Integrating single-cell transcriptomic data across different conditions, technologies, and species [J].

Butler, Andrew ;

Hoffman, Paul ;

Smibert, Peter ;

Papalexi, Efthymia ;

Satija, Rahul .

NATURE BIOTECHNOLOGY, 2018, 36 (05) :411-+

[7] Robust decomposition of cell type mixtures in spatial transcriptomics [J].

Cable, Dylan M. ;

Murray, Evan ;

Zou, Luli S. ;

Goeva, Aleksandrina ;

Macosko, Evan Z. ;

Chen, Fei ;

Irizarry, Rafael A. .

NATURE BIOTECHNOLOGY, 2022, 40 (04) :517-+

[8] Spatially resolved, highly multiplexed RNA profiling in single cells [J].

Chen, Kok Hao ;

Boettiger, Alistair N. ;

Moffitt, Jeffrey R. ;

Wang, Siyuan ;

Zhuang, Xiaowei .

SCIENCE, 2015, 348 (6233)

[9] SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes [J].

Elosua-Bayes, Marc ;

Nieto, Paula ;

Mereu, Elisabetta ;

Gut, Ivo ;

Heyn, Holger .

NUCLEIC ACIDS RESEARCH, 2021, 49 (09) :E50

[10] Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH [J].

Eng, Chee-Huat Linus ;

Lawson, Michael ;

Zhu, Qian ;

Dries, Ruben ;

Koulena, Noushin ;

Takei, Yodai ;

Yun, Jina ;

Cronin, Christopher ;

Karp, Christoph ;

Yuan, Guo-Cheng ;

Cai, Long .

NATURE, 2019, 568 (7751) :235-+

← 1 2 3 4 →