STEP: Deciphering Spatial Atlas at Single-Cell Level with Whole-Transcriptome Coverage

Abstract

Recent advances in spatial transcriptomics have revolutionized our understanding of tissue spatial architecture and biological processes. However, many of these technologies face significant challenges in achieving either single-cell resolution or comprehensive whole-transcriptome profiling, hindering their capacity to fully elucidate intercellular interactions and the tissue microenvironment. To address these limitations, we present STEP, a hybrid framework that synergistically integrates probabilistic models with deep learning techniques for spatial transcriptome analysis. Through innovations in model and algorithm design, STEP not only enhances sequencing-based spatial transcriptome data to single-cell resolution but accurately infers transcriptome-wide expression levels for image-based spatial transcriptomic. By leveraging the nuclear features extracted from histological images, STEP achieves precise predictions of cell type and gene expression and effectively diffuses the discriminative ability to serial sections for modeling solid tissue landscapes. The capability is particularly advantageous for analyzing cells with distinctive characteristics, such as cancer cells, enabling cross-sample inference. In addition, STEP simulates intercellular communication through a spatially resolved cell-cell interaction network, uncovering intrinsic biological processes. Overall, STEP equips researchers with a powerful tool for understanding biological functions and unveiling spatial gene expression patterns, paving the way for advancements in spatial transcriptomics research. Code is available at https://github.com/childishHU/STEP.