Current whole-genome spatial transcriptomics (ST) platforms, which range from multicellular (e.g., Visium) to subcellular resolution (e.g., Visium HD, Stereo-seq), face significant challenges in accurately decomposing or aggregating spots into single cells—the fundamental units of biological activity. To date, most existing computational methods operate at the spot level, and no method has effectively reconstructed transcriptomes at single-cell resolution. In this study, we present STARS (Spatial Transcriptomics across Resolutions for Single Cells). Leveraging Vision Transformer model and contrastive learning, STARS combines high-resolution histology images with spot-level transcriptomics data to reconstruct single-cell-level gene expression from multicellular and subcellular platforms. We demonstrate the advantage of our single-cell-level method using in-house datasets of mouse lung from 3 ST platforms (Visium, Visium HD and Stereo-seq) and additional public datasets. STARS is applied at tissue, individual cell, and molecular levels. At the tissue level, STARS identifies regions of interest, such as specific tissue structures or immune regions. At the individual cell level, STARS identifies immune cells—including CD4/CD8 T cells and cancer-associated fibroblasts (CAFs)—distinguishing two CAF subtypes and macrophage subtypes, including the rare SPP1+ macrophages. Additionally, STARS identifies tertiary lymphoid structures in colorectal cancer, linked to improved clinical outcomes, and detects shifts in immune cell populations between influenza infection and bacterial superinfection in mouse lung tissue. At the molecular level, we demonstrate improved separation of cell types, and differential gene expression analysis identified cell-type-specific markers with greater accuracy. These results underscore the robustness of our method, providing biologically relevant insights into tissue architecture and gene expression at the single-cell level, advancing the downstream analysis of spatial transcriptomics across resolutions. Spatial transcriptomics platforms span multicellular to subcellular resolutions but lack methods to reconstruct single-cell transcriptomes across them. Here, the authors introduce STARS, a unified deep learning framework that integrates histology and transcriptomics to infer single-cell gene expression across resolutions.
Zhao et al. (Mon,) studied this question.