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

Abstract

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 learns to predict partially observed sparse regions from even sparser subsets within the same sample, leveraging 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. Code is available at: https://github.com/Advanced-AI-in-Medicine-and-Physics-Lab/S2S-ST.