  Spatial transcriptomics (ST) enables the visualization of gene expression
within the context of tissue morphology. This emerging discipline has the
potential to serve as a foundation for developing tools to design precision
medicines. However, due to the higher costs and expertise required for such
experiments, its translation into a regular clinical practice might be
challenging. Despite the implementation of modern deep learning to enhance
information obtained from histological images using AI, efforts have been
constrained by limitations in the diversity of information. In this paper, we
developed a model, HistoSPACE that explore the diversity of histological images
available with ST data to extract molecular insights from tissue image. Our
proposed study built an image encoder derived from universal image autoencoder.
This image encoder was connected to convolution blocks to built the final
model. It was further fine tuned with the help of ST-Data. This model is
notably lightweight in compared to traditional histological models. Our
developed model demonstrates significant efficiency compared to contemporary
algorithms, revealing a correlation of 0.56 in leave-one-out cross-validation.
Finally, its robustness was validated through an independent dataset, showing a
well matched preditction with predefined disease pathology.
