{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T13:14:21Z","timestamp":1783689261634,"version":"3.55.0"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFA1004801"],"award-info":[{"award-number":["2022YFA1004801"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program"},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XDB1350203"],"award-info":[{"award-number":["XDB1350203"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Eric and Wendy Schmidt Center"},{"DOI":"10.13039\/100013114","name":"Broad Institute of MIT and Harvard","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013114","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Spatial transcriptomics enables the dissection of tissue heterogeneity within native contexts, yet current platforms are inherently constrained by high sparsity and low signal-to-noise ratios that obscure fine-grained biological signals. Current efforts to recover these signals are limited by image registration dependencies or the inherent context-blindness of implicit neural representations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce the Cell Positioning System (CPS), a context-aware implicit neural representation framework designed to map physical coordinates to high-fidelity spatial transcriptomics via a privileged multi-scale context distillation strategy. CPS treats multi-scale tissue niches as privileged information, employing a teacher network equipped with a multi-scale niche attention mechanism to capture adaptive biological interactions during training. This structural knowledge is explicitly distilled into a student coordinate network, enabling the generation of context-aware expression landscapes solely from spatial coordinates during inference. Benchmarking on the DLPFC dataset demonstrates that CPS achieves state-of-the-art performance in spatial and gene expression imputation and denoising. Furthermore, CPS enables super-resolution to recover high-resolution mouse brain anatomical details and offers interpretability by identifying the scale effective size of biological interactions within human breast cancer tissues. Finally, the framework exhibits superior scalability for large-scale datasets with linear computational complexity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>Software is available online at https:\/\/github.com\/tju-zl\/CPS.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag218","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:33:27Z","timestamp":1783442007000},"source":"Crossref","is-referenced-by-count":0,"title":["CPS: mapping physical coordinates to high-fidelity spatial transcriptomics via privileged multi-scale context distillation"],"prefix":"10.1093","volume":"42","author":[{"given":"Lei","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University , Shanghai, 201804,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Cao","sequence":"additional","affiliation":[{"name":"Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard , Cambridge, MA 02142,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqiao","family":"Zheng","sequence":"additional","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing, 100190,","place":["China"]},{"name":"School of Mathematical Sciences, University of Chinese Academy of Sciences , Beijing, 100049,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University , Shanghai, 201804,","place":["China"]},{"name":"Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University , Shanghai, 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