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.

翻译：空间转录组学（ST）能够在组织形态学背景下实现基因表达的可视化。这一新兴学科有潜力作为开发精准医疗工具的基础。然而，由于此类实验成本较高且需要专业知识，将其转化为常规临床实践可能面临挑战。尽管现代深度学习技术已应用于通过人工智能增强从组织学图像中获取的信息，但相关努力一直受限于信息多样性的不足。本文开发了一个名为HistoSPACE的模型，该模型利用与ST数据配套的组织学图像的多样性，从组织图像中提取分子层面的信息。我们提出的研究构建了一个源自通用图像自编码器的图像编码器，该编码器通过卷积块连接以构建最终模型，并借助ST数据进行了进一步微调。相较于传统的组织学模型，本模型显著轻量化。与当前主流算法相比，我们开发的模型展现出显著效能，在留一法交叉验证中显示出0.56的相关性。最后，通过独立数据集验证了其稳健性，显示其预测结果与预设疾病病理特征高度吻合。