TY - JOUR
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
N1 - Funding Information:
This work was made possible by generous support from the Knut and Alice Wallenberg Foundation, the Erling-Persson Family Foundation, the Swedish Cancer Society, the Swedish Foundation for Strategic Research, the Swedish Research Council and the Helmsley Charitable Trust.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
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 - http://www.scopus.com/inward/record.url?scp=85120033599&partnerID=8YFLogxK
U2 - 10.1038/s41587-021-01075-3
DO - 10.1038/s41587-021-01075-3
M3 - Article
C2 - 34845373
AN - SCOPUS:85120033599
VL - 40
SP - 476
EP - 479
JO - Nature Biotechnology
JF - Nature Biotechnology
SN - 1087-0156
IS - 4
ER -