  Deep learning has proven to successfully learn variations in tissue and cell
morphology. Training of such models typically relies on expensive manual
annotations. Here we conjecture that spatially resolved gene expression, e.i.,
the transcriptome, can be used as an alternative to manual annotations. In
particular, we trained five convolutional neural networks with patches of
different size extracted from locations defined by spatially resolved gene
expression. The network is trained to classify tissue morphology related to two
different genes, general tissue, as well as background, on an image of
fluorescence stained nuclei in a mouse brain coronal section. Performance is
evaluated on an independent tissue section from a different mouse brain,
reaching an average Dice score of 0.51. Results may indicate that novel
techniques for spatially resolved transcriptomics together with deep learning
may provide a unique and unbiased way to find genotype-phenotype relationships.
