Super-resolved spatial transcriptomics by deep data fusion

• 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. 

Subject headings

NATURVETENSKAP  -- Fysik -- Subatomär fysik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Subatomic Physics (hsv//eng)

NATURVETENSKAP  -- Biologi -- Genetik (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Genetics (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

NATURVETENSKAP  -- Biologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences (hsv//eng)

Keyword

Gene expression

'current

Gene Expression Data

Generative model

High resolution

Histological images

Image data

Spatial resolution

Tissue sections

Transcriptomes

Transcriptomics

Data fusion

transcriptome

Publication and Content Type

ref (subject category)

art (subject category)