ArticleCell reports methods2025
Generation of super-resolution images from barcode-based spatial transcriptomics by deep image prior.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Spatially resolved transcriptomics (ST) has revolutionized the field of biology by providing a powerful tool for analyzing gene expression in situ. However, current ST methods, particularly barcode-based methods, have limitations in reconstructing high-resolution images from barcodes sparsely distributed in slides. Here, we present SuperST, an algorithm that enables the reconstruction of dense matrices (higher-resolution and non-zero-inflated matrices) from low-resolution ST libraries. SuperST is based on deep image prior, which reconstructs spatial gene expression patterns as image matrices. Compared with previous methods, SuperST generated output images that more closely resembled immunofluorescence images for given gene expression maps. Furthermore, we demonstrated how one can combine images created by SuperST with computer vision algorithms. In this context, we proposed a method for extracting features from the images, which can aid in spatial clustering of genes. By providing a dense matrix for each gene in situ, SuperST can successfully address the resolution and zero-inflation issue.
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