HistoSPACE: Histology-Inspired Spatial Transcriptome Prediction And Characterization Engine

TL;DR

HistoSPACE is a lightweight deep learning model that predicts spatial transcriptomics from histological images, enabling molecular insights with high efficiency and robustness, potentially aiding clinical applications.

Contribution

The paper introduces HistoSPACE, a novel, efficient deep learning model that leverages histological images to predict spatial transcriptomics, enhancing molecular understanding without extensive experimental costs.

Findings

Achieved a correlation of 0.56 in cross-validation.

Demonstrated robustness on independent datasets.

Outperformed existing algorithms in efficiency.

Abstract

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.