{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T11:08:32Z","timestamp":1783595312794,"version":"3.55.0"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,2,11]],"date-time":"2024-02-11T00:00:00Z","timestamp":1707609600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Generative Adversarial Nets (GAN) achieve impressive performance for text-guided editing of natural images. However, a comparable utility of GAN remains understudied for spatial transcriptomics (ST) technologies with matched gene expression and biomedical image data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose In\u2002Silico\u2002Spatial Transcriptomic editing that enables gene expression-guided editing of immunofluorescence images. Using cell-level spatial transcriptomics data extracted from normal and tumor tissue slides, we train the approach under the framework of GAN (Inversion). To simulate cellular state transitions, we then feed edited gene expression levels to trained models. Compared to normal cellular images (ground truth), we successfully model the transition from tumor to normal tissue samples, as measured with quantifiable and interpretable cellular features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/CTPLab\/SST-editing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae077","type":"journal-article","created":{"date-parts":[[2024,2,11]],"date-time":"2024-02-11T05:20:32Z","timestamp":1707628832000},"source":"Crossref","is-referenced-by-count":4,"title":["SST-editing:\n                    <i>in silico<\/i>\n                    spatial transcriptomic editing at single-cell resolution"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6898-8698","authenticated-orcid":false,"given":"Jiqing","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Pathology and Molecular Pathology, Computational and Translational Pathology Laboratory (CTP), University Hospital of Zurich, University of Zurich , Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9206-4885","authenticated-orcid":false,"given":"Viktor H","family":"Koelzer","sequence":"additional","affiliation":[{"name":"Department of Pathology and Molecular Pathology, Computational and Translational Pathology Laboratory (CTP), University Hospital of Zurich, University of Zurich , Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"key":"2024030523340909700_btae077-B1","first-page":"6711","author":"Alaluf","year":"2021"},{"key":"2024030523340909700_btae077-B2","doi-asserted-by":"crossref","first-page":"100534","DOI":"10.1016\/j.crmeth.2023.100534","article-title":"Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models","volume":"3","author":"Carrillo-Perez","year":"2023","journal-title":"Cell Rep Methods"},{"key":"2024030523340909700_btae077-B3","first-page":"2672","volume-title":"Advances in Neural Information Processing Systems 27","author":"Goodfellow","year":"2014"},{"key":"2024030523340909700_btae077-B4","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1038\/s41587-022-01483-z","article-title":"High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular 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