{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T19:07:28Z","timestamp":1784056048562,"version":"3.55.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2013student 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.<\/jats:p>","DOI":"10.1093\/bib\/bbag362","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T18:33:57Z","timestamp":1784054037000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-task spatial distillation reveals cell-type-resolved programmed cell death landscapes in the human kidney"],"prefix":"10.1093","volume":"27","author":[{"given":"Chunling","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Rheumatology and Immunology, The First Hospital of China Medical University , No.155 Nanjing Street of Heping District, 110001 Liaoning Province,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomeng","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Rheumatology and Immunology, The First Hospital of China Medical University , No.155 Nanjing Street of Heping District, 110001 Liaoning Province,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuansong","family":"Zhao","sequence":"additional","affiliation":[{"name":"The University of Texas Health Science Center at Houston , 7000 Fannin Street, Houston, TX 77030,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Nephrology, The First Hospital of China Medical University , No. 155 Nanjing Street of Heping District, 110001 Liaoning Province,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Zuo","sequence":"additional","affiliation":[{"name":"Department of Nephrology, The First Hospital of China Medical University , No. 155 Nanjing Street of Heping District, 110001 Liaoning Province,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"2026071414335025500_ref1","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1038\/s41420-024-01979-4","article-title":"The emerging role of regulated cell death in ischemia and reperfusion-induced acute kidney injury: current evidence and future perspectives","volume":"10","author":"Li","year":"2024","journal-title":"Cell Death Discov"},{"key":"2026071414335025500_ref2","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1038\/s41581-023-00694-0","article-title":"Regulated cell death pathways in kidney disease","volume":"19","author":"Sanz","year":"2023","journal-title":"Nat Rev Nephrol"},{"key":"2026071414335025500_ref3","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbag043","article-title":"GATCL: graph attention network meets contrastive learning for spatial domain identification","volume":"27","author":"Mu","year":"2026","journal-title":"Brief Bioinform"},{"key":"2026071414335025500_ref4","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1093\/ndt\/gfae125","article-title":"Emerging high-resolution spatial transcriptomic technologies in kidney research","volume":"39","author":"Xuanyuan","year":"2024","journal-title":"Nephrol. 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