Affordable Access

Super-resolved spatial transcriptomics by deep data fusion

Authors
  • Bergenstrahle, Ludvig;
  • He, Bryan;
  • Bergenstrahle, Joseph;
  • Abalo, Xesus;
  • Mirzazadeh, Reza;
  • Thrane, Kim;
  • Ji, Andrew L;
  • Andersson, Alma;
  • Larsson, Ludvig;
  • Stakenborg, Nathalie; 85528;
  • Boeckxstaens, Guy; 54713;
  • Khavari, Paul;
  • Zou, James;
  • Lundeberg, Joakim;
  • Maaskola, Jonas;
Publication Date
Nov 29, 2021
Source
Lirias
Keywords
License
Unknown
External links

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. / status: published

Report this publication

Statistics

Seen <100 times