Nature Biotechnology
Publication date:
2022-04-01
Volume:
40
Pages:
476 -
Publisher:
Nature Research
Author:
Bergenstrahle, Ludvig
He, Bryan ; Bergenstrahle, Joseph ; Abalo, Xesus ; Mirzazadeh, Reza ; Thrane, Kim ; Ji, Andrew L ; Andersson, Alma ; Larsson, Ludvig ; Stakenborg, Nathalie ; Boeckxstaens, Guy ; Khavari, Paul ; Zou, James ; Lundeberg, Joakim ; Maaskola, Jonas
Keywords:
Science & Technology, Life Sciences & Biomedicine, Biotechnology & Applied Microbiology, CELL RNA-SEQ, SINGLE-CELL, GENE-EXPRESSION, TISSUE, VISUALIZATION, Transcriptome
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.