CellHIST: A Cell-Centric Dataset and Benchmark for Weakly-Supervised Virtual Spatial Transcriptomics from Histopathology

Spatial Transcriptomics (ST) at single-cell resolution has revolutionized our understanding of spatial biology. However, high experimental costs impede its widespread adoption, driving virtual ST from histopathology. Current approaches either require expensive single-cell labels for training or rely on paired spot labels at inference. An emerging paradigm, weakly-supervised learning (WSL), overcomes this by training a model on cost-effective spot-level ST data to infer single-cell spatial gene expression solely from images. Although offering a highly scalable alternative, a single-cell-level dataset and benchmark framework are still lacking. To bridge this gap, this paper presents CellHIST. It builds the first *cell-centric* dataset for virtual single-cell ST from histopathology. CellHIST-Data curates 169 samples across 14 organs, encompassing around 36 million cells and 1 million image patches, standardized to support model development across various paradigms. Furthermore, a comprehensive benchmark, CellHIST-Bench, is introduced to systematically evaluate weakly-supervised virtual single-cell ST in terms of predictive performance, data scaling behavior, single-cell prediction specificity, biological relevance, and downstream utility. Results uncover the practicability and limitations of WSL for this task. This cell-centric study can bridge an important gap and pave the way for histopathology-driven virtual single-cell ST.