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Super-resolved spatial transcriptomics by deep data fusion.
Bergenstråhle, Ludvig; He, Bryan; Bergenstråhle, Joseph; Abalo, Xesús; Mirzazadeh, Reza; Thrane, Kim; Ji, Andrew L; Andersson, Alma; Larsson, Ludvig; Stakenborg, Nathalie; Boeckxstaens, Guy; Khavari, Paul; Zou, James; Lundeberg, Joakim; Maaskola, Jonas.
Affiliation
  • Bergenstråhle L; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
  • He B; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Bergenstråhle J; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Abalo X; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Mirzazadeh R; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Thrane K; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Ji AL; Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
  • Andersson A; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Larsson L; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Stakenborg N; Department of Chronic Diseases and Metabolism, Katholieke Universiteit te Leuven, Leuven, Belgium.
  • Boeckxstaens G; Department of Chronic Diseases and Metabolism, Katholieke Universiteit te Leuven, Leuven, Belgium.
  • Khavari P; Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
  • Zou J; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Lundeberg J; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden. joakim.lundeberg@scilifelab.se.
  • Maaskola J; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Nat Biotechnol ; 40(4): 476-479, 2022 04.
Article in En | MEDLINE | ID: mdl-34845373
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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Transcriptome Type of study: Prognostic_studies Language: En Journal: Nat Biotechnol Journal subject: BIOTECNOLOGIA Year: 2022 Type: Article Affiliation country: Sweden
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Transcriptome Type of study: Prognostic_studies Language: En Journal: Nat Biotechnol Journal subject: BIOTECNOLOGIA Year: 2022 Type: Article Affiliation country: Sweden