Solano, A. N.; Yip, R. K. H.; Wang, C.; Amann-Zalcenstein, D.; Rajasekhar, P.; Zaman, I.; Motyer, A.; Cmero, M.; Xu, Y.; Pan, Y.; Anttila, C. J. A.; Studniberg, S. I.; Hickey, P. F.; Wang, L.; Sargeant, C. J.; Ling, L.; Chen, Y.; Mishi, R. D.; Ioannidis, L. J.; Good-Jacobson, K. L.; King, H. W.; Rogers, K. L.; Hansen, D. S.; Bowden, R.; Ritchie, M. E.

Spatial transcriptomics (ST) has rapidly expanded with the introduction of multiple high-resolution platforms, yet cross-platform benchmarking remains limited and has largely focused on technical performance. Here we present SpatialBench, a matched multi-platform resource comprising Visium HD, Xenium and MERSCOPE data together with single-cell and single-nucleus references from a malaria-challenged wild-type and B cell-specific Tbx21 knockout mouse spleen model. In this system, loss of T-bet in B cells disrupts germinal center (GC) polarization and antibody maturation, providing a biologically grounded benchmark for technology comparison. We leveraged this system to systematically evaluate ST platform performance using both technical and biological readouts. Across platforms, immune organization and Tbx21-associated programs were consistently recovered, indicating that major biological signals are robust to platform-specific technical differences. Platforms instead differed in the level of biological resolution accessible. Visium HD enabled transcriptome-scale GC characterization and, together with Xenium, resolved dark and light zone organization, whereas GC zonation was not resolved in MERSCOPE, consistent with differences in transcript detection sensitivity. SpatialBench provides a biologically defined reference dataset for evaluation of ST technologies, computational benchmarking, method development and studies of GC spatial organization in lymphoid tissue.