Super-resolved spatial transcriptomics by deep data fusion

Ludvig Bergenstråhle; Bryan He; Joseph Bergenstråhle; Xesús Abalo; Reza Mirzazadeh; Kim Thrane; Andrew L. Ji; Alma Andersson; Ludvig Larsson; Nathalie Stakenborg; Guy Boeckxstaens; Paul Khavari; James Zou; Joakim Lundeberg; Jonas Maaskola

doi:10.1038/s41587-021-01075-3

Super-resolved spatial transcriptomics by deep data fusion

Ludvig Bergenstråhle
, Bryan He
, Joseph Bergenstråhle
, Xesús Abalo
, Reza Mirzazadeh
, Kim Thrane
, Andrew L. Ji
, Alma Andersson
, Ludvig Larsson
, Nathalie Stakenborg
, Guy Boeckxstaens
, Paul Khavari
, James Zou
, Joakim Lundeberg
, Jonas Maaskola

Research output: Contribution to journal › Article › peer-review

124 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	476-479
Number of pages	4
Journal	Nature Biotechnology
Volume	40
Issue number	4
DOIs	https://doi.org/10.1038/s41587-021-01075-3
State	Published - Apr 2022
Externally published	Yes

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10.1038/s41587-021-01075-3

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title = "Super-resolved spatial transcriptomics by deep data fusion",

abstract = "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.",

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note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.",

year = "2022",

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doi = "10.1038/s41587-021-01075-3",

language = "English",

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T1 - Super-resolved spatial transcriptomics by deep data fusion

AU - Bergenstråhle, Ludvig

AU - He, Bryan

AU - Bergenstråhle, Joseph

AU - Abalo, Xesús

AU - Mirzazadeh, Reza

AU - Thrane, Kim

AU - Ji, Andrew L.

AU - Andersson, Alma

AU - Larsson, Ludvig

AU - Stakenborg, Nathalie

AU - Boeckxstaens, Guy

AU - Khavari, Paul

AU - Zou, James

AU - Lundeberg, Joakim

AU - Maaskola, Jonas

PY - 2022/4

Y1 - 2022/4

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85120033599

U2 - 10.1038/s41587-021-01075-3

DO - 10.1038/s41587-021-01075-3

M3 - Article

C2 - 34845373

AN - SCOPUS:85120033599

SN - 1087-0156

VL - 40

SP - 476

EP - 479

JO - Nature Biotechnology

JF - Nature Biotechnology

IS - 4

ER -

Super-resolved spatial transcriptomics by deep data fusion

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