Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning

Spatial transcriptomics (ST) has revolutionized biomedical research by enabling high resolution gene expression profiling within tissues. However, the high cost and scarcity of high resolution ST data remain significant challenges. We present Single-shot Sparser-to-Sparse (S2S-ST), a novel framework for accurate ST imputation that requires only a single and low-cost sparsely sampled ST dataset alongside widely available natural images for co-training. Our approach integrates three key innovations: (1) a sparser-to-sparse self-supervised learning strategy that leverages intrinsic spatial patterns in ST data, (2) cross-domain co-learning with natural images to enhance feature representation, and (3) a Cascaded Data Consistent Imputation Network (CDCIN) that iteratively refines predictions while preserving sampled gene data fidelity. Extensive experiments on diverse tissue types, including breast cancer, liver, and lymphoid tissue, demonstrate that our method outperforms state-of-the-art approaches in imputation accuracy. By enabling robust ST reconstruction from sparse inputs, our framework significantly reduces reliance on costly high resolution data, facilitating potential broader adoption in biomedical research and clinical applications. Keywords: Spatial Transcriptomics, Gene Expression Imputation, Single-shot Learning, Natural Image Co-training, Cost Reduction 1. Introduction Spatial transcriptomics (ST) is a cutting-edge technology that enables the investigation of spatially resolved gene expression within tissues (Asp et al., 2020). Traditional transcriptomic approaches, such as single-cell RNA sequencing (scRNA-seq), provide high-throughput, high resolution gene expression profiles but inherently lack spatial context (Aung et al., 2024; Boe et al., 2024; Sankar et al., 2024). However, spatial information is crucial for identifying disease biomarkers, understanding disease progression, and developing personalized treatment strategies.