Multi-task spatial distillation reveals cell-type-resolved programmed cell death landscapes in the human kidney.

Kidney injury and chronic kidney disease progression are accompanied by spatially heterogeneous activation of programmed cell death (PCD), yet existing approaches have limited ability to jointly infer cell-type composition, death-program activity, and their spatial organization from spatial transcriptomics (ST) data. We present CoDeST (Confidence-weighted Dual-teacher Spatial Training), a multi-task spatial inference framework that predicts, for each ST spot, a 34-class kidney cell-type composition vector together with continuous activity scores for four PCD programs: apoptosis, pyroptosis, necroptosis, and ferroptosis. CoDeST uses a three-stage pseudo-to-real training strategy that combines self-supervised pretraining on real ST slices, supervised deconvolution on donor-matched pseudo-spots, and real-ST domain adaptation with teacher-student distillation, marker-based weak constraints, confidence-weighted AUCell/ssGSEA PCD supervision, and boundary-preserving spatial regularization. In donor-held-out pseudo-spot benchmarks, CoDeST shows competitive recovery of cell-type proportions compared with representative deconvolution methods. On real kidney ST data, marker-consistency analysis and a Visium HD-derived benchmark further support its ability to transfer deconvolution signals from pseudo-spots to real spatial tissue settings. Ablation and sensitivity analyses indicate that the three-stage design, confidence weighting, and spatial graph modeling contribute to stable deconvolution and PCD mapping while balancing spatial coherence with boundary contrast. CoDeST provides a kidney-focused framework for joint spatial mapping of cell composition and PCD-related transcriptional programs.