{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:26:20Z","timestamp":1787023580730,"version":"build-2736575974"},"reference-count":79,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Project of Guangzhou National Laboratory","award":["GZNL2024A03001"],"award-info":[{"award-number":["GZNL2024A03001"]}]}],"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>The rapid growth of single-cell and spatial omics has outpaced computational methods capable of unifying these data into a cohesive framework for tissue atlas construction and cross-sample analysis. A critical bottleneck lies in the inability of existing tools to co-embed cells from diverse technologies\u2014spanning transcriptomics, epigenomics, and proteomics\u2014into a shared reference space while preserving spatial architecture and molecular specificity. Here, we present Garfield (Graph-based Contrastive Learning Enables Fast Single-Cell Embedding), a geometric deep-learning framework that addresses these challenges through spatially or molecularly aware cell embedding. Leveraging a graph contrastive learning framework, Garfield learns a shared embedding space for data generated by diverse technologies, enabling seamless construction and querying of spatial reference atlases. Our results show that Garfield consistently outperforms state-of-the-art benchmark models in identifying spatial niches across multiple datasets. We further demonstrate Garfield\u2019s versatility by applying it to multimodal spatial data, including gene expression and chromatin accessibility, where it successfully identifies distinct niches in the mouse brain. Notably, Garfield reveals tumor microenvironment heterogeneity in non-small cell lung cancer and breast cancer, uncovered conserved, barrier-like immune niches at tumor margins orchestrating CD80-mediated T cell\u2013B cell\u2013dendritic cell interactions and IFN-$\\gamma$\/B cell activation pathways, forming spatially coordinated immune surveillance hubs. These findings underscore Garfield\u2019s potential to advance spatial omics research by offering a robust, scalable solution for integrating and interpreting complex spatial data across diverse tissue types and modalities.<\/jats:p>","DOI":"10.1093\/bib\/bbag432","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T12:01:26Z","timestamp":1784721686000},"source":"Crossref","is-referenced-by-count":0,"title":["Graph-based contrastive learning enables unified integration and niche transfer across single-cell and spatial multi-omics"],"prefix":"10.1093","volume":"27","author":[{"given":"Weige","family":"Zhou","sequence":"first","affiliation":[{"name":"GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University , No. 1 Xinzao Road, Xinzao Town, Panyu District, Guangzhou, Guangdong 511436 ,","place":["China"]},{"name":"Guangzhou National Laboratory , No. 9 Xingdao Huanbei Road, Guangzhou International Bio Island, Haizhu District, Guangzhou, Guangdong 510005 ,","place":["China"]},{"name":"School of Life Sciences, Westlake University , No. 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang 310030 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueying","family":"Fan","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences , 320 Yueyang Road, Xuhui District, Shanghai 200031 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanxiang","family":"Li","sequence":"additional","affiliation":[{"name":"GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University , No. 1 Xinzao Road, Xinzao Town, Panyu District, Guangzhou, Guangdong 511436 ,","place":["China"]},{"name":"Guangzhou National Laboratory , No. 9 Xingdao Huanbei Road, Guangzhou International Bio Island, Haizhu District, Guangzhou, Guangdong 510005 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianrong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Guangzhou National Laboratory , No. 9 Xingdao Huanbei Road, Guangzhou International Bio Island, Haizhu District, Guangzhou, Guangdong 510005 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Liu","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences , 320 Yueyang Road, Xuhui District, Shanghai 200031 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenfei","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Westlake University , No. 600 Dunyu Road, Xihu District, Hangzhou, Zhejiang 310030 ,","place":["China"]},{"name":"School of Life Sciences, Southern University of Science and Technology , Shenzhen 518055 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyi","family":"Tian","sequence":"additional","affiliation":[{"name":"GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University , No. 1 Xinzao Road, Xinzao Town, Panyu District, Guangzhou, Guangdong 511436 ,","place":["China"]},{"name":"Guangzhou National Laboratory , No. 9 Xingdao Huanbei Road, Guangzhou International Bio Island, Haizhu District, Guangzhou, Guangdong 510005 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"2026081722594823700_ref1","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1038\/nmeth.4380","article-title":"Simultaneous epitope and transcriptome measurement in single cells","volume":"14","author":"Stoeckius","year":"2017","journal-title":"Nat 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