Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Cancer immunoprevention leverages the immune system’s surveillance mechanisms to mitigate tumor development. Vaccines that constitute a tumor antigen and an immune adjuvant are perceived as immunoprevention modalities. However, relevant tumor antigens are unknown for non-viral cancers, which constitute most human cancers. Our group has recently shown that SA–4–1BBL, a novel agonist of CD137 receptor, but not antibodies, shows immunoprevention efficacy against various tumors. Advanced bioinformatics analyses of bulk RNA-seq data were conducted to elucidate mechanisms underlying cancer immunoprevention. Mice received subcutaneous injections of SA–4–1BBL or agonistic 3H3 antibody, and the injection-site tissue (IS) and draining lymph nodes (LN) were analyzed for differential gene expression. SA–4–1BBL induced a compartmentalized and temporally dynamic immune program characterized by early effector activation at IS and sustained immune regulation in draining LN. K-means clustering of 4564 DEGs identified eight functionally distinct clusters. IS-enriched clusters contained activation genes for CD4⁺ T and NK cells, including Cd28, Klra1, Cd4, Cd40, and Cd40l, while LN clusters were enriched for regulatory genes (Tnfaip3, Irf5, Col1a2) that ensure immune priming and homeostatic restraint for a balanced response. SA–4–1BBL generated a more selective and durable activation of adaptive immunity, TCR signaling, Th1/Th2 differentiation, and NK cytotoxicity. 3H3 activated broader innate inflammatory programs, including Toll-like receptor and neurodegeneration-linked pathways. IMPRes analysis showed that SA–4–1BBL activates sequential immune-regulatory circuits centered on Stat1, Cd247, and Ifng and modulates the CD151–TGF-β axis. These findings demonstrate that SA–4–1BBL elicits a balanced immune response, ensuring both safety and efficacy in preventing cancer development.