Single-cell transcriptomes are sparse, unordered measurements of coordinated biological activity, yet many self-supervised models define their learning target at the level of individual genes or reconstructed expression values. Here we introduce GB-JEPA, a joint-embedding predictive architecture in which graph-connected gene blocks form the latent prediction units. A student encoder observes the genes outside each target block, whereas an exponential-moving-average teacher represents the full observed gene set. Teacher states are aggregated within a block before the prediction error is evaluated, converting thousands of sparse gene-level targets into a small collection of intermediate-scale latent targets. The graph therefore serves not only as prior information, but also as a mechanism for controlling the structure, scale and predictability of self-supervision. We evaluate the resulting frozen cell representations through objective diagnostics, cell-state annotation, continuous-expression and pathway probes, perturbation-response prediction, computational measurements and direct biological interrogation. This study presents a graph-structured route for adapting JEPA learning to molecular observations and a general framework for designing prediction units from relationships native to a biological modality.

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