Vis2Cell-Bench: A Systematic Benchmark of Spatial Transcriptomics Super-Resolution

Spatial transcriptomics super-resolution aims to infer cell-level gene expression from spatial measurements that aggregate signals across multiple cells, enabling downstream analyses of cell types and states. However, agreement with coarse observations does not establish whether reconstructed profiles preserve the cell-specific transcriptomic variation required for such analyses. We introduce Vis2Cell-Bench, a benchmark built from measured Xenium data for direct evaluation of cell-level recovery. Measured cellular profiles are spatially aggregated into controlled pseudo-Visium observations while being retained as ground truth. Across 87 datasets spanning 13 tissue and organ categories, four spatial partitions and three spatial offsets yield 1,044 benchmark instances for comparing seven methods under a common evaluation protocol. We evaluate two complementary aspects of recovery: gene-expression reconstruction and preservation of cell-specific information across gene panels and spatial partitions. These capabilities are not necessarily aligned: strong gene-wise reconstruction does not consistently translate into faithful recovery of cellular organization, and relative method performance varies across panels and partitions. In an annotated breast cancer dataset, high gene-wise correlation can coexist with limited cell-type separation. To quantify cellular recovery more directly, we introduce the Cellular Recovery Score (CRS), which combines matched-cell expression fidelity with preservation of within-spot transcriptomic relationships without requiring cell-type labels. Vis2Cell-Bench thus provides a unified framework for distinguishing expression reconstruction from preservation of cell-specific information in spatial transcriptomics super-resolution.