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.

本研究针对空间转录组学在临床实践中应用的挑战，提出了一种新颖的模型HistoSPACE，旨在通过提取组织图像中的分子信息解决信息多样性不足的问题。该模型利用通用图像自编码器构建了轻量级图像编码器，并通过空间转录组数据进行微调，显示出优于现有算法的效率与准确性，具有提升精准医疗潜力的重要性。

HistoSPACE：启发于组织学的空间转录组预测与表征引擎