{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T15:15:59Z","timestamp":1763651759245,"version":"3.45.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":19,"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":["2024YFA1307702"],"award-info":[{"award-number":["2024YFA1307702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Innovation Action Plan in Computational Biology","award":["24JS2840200"],"award-info":[{"award-number":["24JS2840200"]}]},{"name":"Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The widespread application of spatial transcriptomics in uncovering disease mechanisms remains limited by the scarcity of samples and the high experimental costs, which have not declined substantially in recent years. Unlocking the vast resources of clinical H&amp;E-stained images could provide an efficient and cost-effective alternative for large-scale spatial analysis. However, predicting spatial gene expression from histopathological images remains challenging, as existing end-to-end frameworks often fail to capture the intrinsic transcriptomic structures observed in real transcriptomics data. To address this, we developed SciSt, a deep learning framework that predicts spatial gene expression by integrating pathological features with biologically informed initial gene expressions. These initial expressions are generated through a weighted strategy combining cell segmentation and single-cell reference data, thereby enhancing biological interpretability. SciSt achieved state-of-the-art performance across three benchmark datasets, outperforming the second-best models by 21.4% and 13.7%, respectively, and demonstrated robust generalization on the TCGA-BRCA and TCGA-LIHC cohorts. Beyond accurate prediction, SciSt enables cross-modal translation between morphology and gene expression, offering new avenues for mining the untapped potential of clinical image archives. This work highlights how prior biological knowledge can substantially advance the interpretability and scalability of biomedical AI models.<\/jats:p>","DOI":"10.1093\/bib\/bbaf613","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T15:10:58Z","timestamp":1763651458000},"source":"Crossref","is-referenced-by-count":0,"title":["SciSt: single-cell reference-informed spatial gene expression prediction from pathological images"],"prefix":"10.1093","volume":"26","author":[{"given":"Yixin","family":"Li","sequence":"first","affiliation":[{"name":"Institutes of Biomedical Sciences, Fudan University , 130 Dong'an Road, Xuhui District, Shanghai 200032 ,","place":["China"]}]},{"given":"Fan","family":"Zhong","sequence":"additional","affiliation":[{"name":"Intelligent Medicine Institute, Fudan University , 138 Yixueyuan Road, Xuhui District, Shanghai 200032 \u00a0","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9995-9080","authenticated-orcid":false,"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"Intelligent Medicine Institute, Fudan University , 138 Yixueyuan Road, Xuhui District, Shanghai 200032 \u00a0","place":["China"]},{"name":"Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai East Hospital, Tongji University , 1500 Yuntai Road, Pudong New Area, Shanghai 200120 ,","place":["China"]},{"name":"Shanghai Institute of Infectious Disease and Biosecurity, Fudan University , Shanghai 200032, 130 Dong'an Road, Xuhui District ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"2025112010075511300_ref1","doi-asserted-by":"publisher","first-page":"2060","DOI":"10.1038\/s12276-022-00896-9","article-title":"Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics","volume":"54","author":"Zhu","year":"2022","journal-title":"Exp Mol Med"},{"key":"2025112010075511300_ref2","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1038\/s41588-021-00911-1","article-title":"A single-cell and spatially resolved atlas of human breast cancers","volume":"53","author":"Wu","year":"2021","journal-title":"Nat Genet"},{"key":"2025112010075511300_ref3","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1038\/s41586-021-03634-9","article-title":"Exploring tissue architecture using spatial transcriptomics","volume":"596","author":"Rao","year":"2021","journal-title":"Nature"},{"key":"2025112010075511300_ref4","doi-asserted-by":"publisher","first-page":"103631","DOI":"10.1016\/j.ebiom.2021.103631","article-title":"Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: a retrospective multicentre study","volume":"73","author":"Huang","year":"2021","journal-title":"EBioMedicine"},{"key":"2025112010075511300_ref5","doi-asserted-by":"publisher","first-page":"i79","DOI":"10.1093\/bioinformatics\/btae236","article-title":"PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images","volume":"40","author":"Yan","year":"2024","journal-title":"Bioinformatics"},{"key":"2025112010075511300_ref6","doi-asserted-by":"publisher","first-page":"11080","DOI":"10.7150\/thno.49864","article-title":"Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer","volume":"10","author":"Cao","year":"2020","journal-title":"Theranostics"},{"key":"2025112010075511300_ref7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1038\/s41698-021-00225-9","article-title":"Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning. Npj precision","volume":"5","author":"Qu","year":"2021","journal-title":"Oncology"},{"key":"2025112010075511300_ref8","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac294","article-title":"Contrastive learning-based computational histopathology predict differential expression of cancer driver genes","volume":"23","author":"Huang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025112010075511300_ref9","doi-asserted-by":"publisher","first-page":"102824","DOI":"10.1016\/j.media.2023.102824","article-title":"Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning","volume":"87","author":"Yan","year":"2023","journal-title":"Med Image Anal"},{"key":"2025112010075511300_ref10","doi-asserted-by":"publisher","first-page":"4133","DOI":"10.1038\/s41598-022-07685-4","article-title":"Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation","volume":"12","author":"Monjo","year":"2022","journal-title":"Sci Rep"},{"key":"2025112010075511300_ref11","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-030-93733-1_32","article-title":"All you need is color: image based spatial gene expression prediction using neural stain learning","volume":"1525","author":"Dawood","year":"2021","journal-title":"Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Pt Ii"},{"key":"2025112010075511300_ref12","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1038\/s41551-020-0578-x","article-title":"Integrating spatial gene expression and breast tumour morphology via deep learning","volume":"4","author":"He","year":"2020","journal-title":"Nat Biomed Eng"},{"key":"2025112010075511300_ref13","doi-asserted-by":"publisher","DOI":"10.1101\/2021.11.28.470212","article-title":"Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors","author":"Pang","journal-title":"bioRxiv"},{"key":"2025112010075511300_ref14","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac297","article-title":"Spatial transcriptomics prediction from histology jointly through transformer and graph neural networks","volume":"23","author":"Zeng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025112010075511300_ref15","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad464","article-title":"THItoGene: a deep learning method for predicting spatial transcriptomics from histological images","volume":"25","author":"Jia","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025112010075511300_ref16","doi-asserted-by":"publisher","first-page":"103040","DOI":"10.1016\/j.media.2023.103040","article-title":"Transformer with convolution and graph-node co-embedding: an accurate and interpretable vision backbone for predicting gene expressions from local histopathological image","volume":"91","author":"Xiao","year":"2024","journal-title":"Med Image Anal"},{"key":"2025112010075511300_ref17","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1038\/s41587-022-01448-2","article-title":"The expanding vistas of spatial transcriptomics","volume":"41","author":"Tian","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2025112010075511300_ref18","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s12530-023-09491-3","article-title":"A survey on recent trends in deep learning for nucleus segmentation from histopathology images","volume":"15","author":"Basu","year":"2024","journal-title":"Evol Syst"},{"key":"2025112010075511300_ref19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s12276-018-0071-8","article-title":"Single-cell RNA sequencing technologies and bioinformatics pipelines","volume":"50","author":"Hwang","year":"2018","journal-title":"Exp Mol Med"},{"key":"2025112010075511300_ref20","doi-asserted-by":"publisher","first-page":"6012","DOI":"10.1038\/s41467-021-26271-2","article-title":"Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions","volume":"12","author":"Andersson","year":"2021","journal-title":"Nat Commun"},{"key":"2025112010075511300_ref21","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.cell.2020.05.039","article-title":"Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma","volume":"182","author":"Ji","year":"2020","journal-title":"Cell"},{"key":"2025112010075511300_ref22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2406.16192","article-title":"HEST-1k: a dataset for spatial","author":"Guillaume","year":"2024"},{"key":"2025112010075511300_ref23","doi-asserted-by":"publisher","first-page":"101563","DOI":"10.1016\/j.media.2019.101563","article-title":"Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images","volume":"58","author":"Graham","year":"2019","journal-title":"Med Image Anal"},{"key":"2025112010075511300_ref24","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-030-23937-4_2","article-title":"PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification","volume":"11435","author":"Gamper1","year":"2019","journal-title":"European congress on digital pathology"},{"key":"2025112010075511300_ref25","doi-asserted-by":"publisher","first-page":"3992","DOI":"10.1109\/ICCV51070.2023.00371","volume-title":"2023 Ieee\/Cvf International Conference on Computer Vision","author":"Kirillov","year":"2023"},{"key":"2025112010075511300_ref26","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s12916-025-03977-4","article-title":"Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC","volume":"23","author":"Li","year":"2025","journal-title":"BMC Med"},{"key":"2025112010075511300_ref27","doi-asserted-by":"publisher","first-page":"5575","DOI":"10.2147\/IJN.S256022","article-title":"Fatty acid synthase (FASN) siRNA-encapsulated-Her-2 targeted fab'-Immunoliposomes for gene silencing in breast cancer cells","volume":"15","author":"Khan","year":"2020","journal-title":"Int J Nanomedicine"},{"key":"2025112010075511300_ref28","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s12094-019-02042-w","article-title":"Elevated expression of GNAS promotes breast cancer cell proliferation and migration via the PI3K\/AKT\/Snail1\/E-cadherin axis","volume":"21","author":"Jin","year":"2019","journal-title":"Clin Transl Oncol"},{"key":"2025112010075511300_ref29","doi-asserted-by":"publisher","first-page":"4484","DOI":"10.1038\/s41598-019-40826-w","article-title":"Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis","volume":"9","author":"Hsu","year":"2019","journal-title":"Sci Rep"},{"key":"2025112010075511300_ref30","doi-asserted-by":"publisher","first-page":"825","DOI":"10.3748\/wjg.v28.i8.825","article-title":"Differential DNA methylation analysis of SUMF2, ADAMTS5, and PXDN provides novel insights into colorectal cancer prognosis prediction in Taiwan","volume":"28","author":"Su","year":"2022","journal-title":"World J Gastroenterol"},{"key":"2025112010075511300_ref31","doi-asserted-by":"publisher","first-page":"1702","DOI":"10.1158\/2767-9764.CRC-23-0334","article-title":"PTP4A2 promotes glioblastoma progression and macrophage polarization under microenvironmental pressure","volume":"4","author":"Chouleur","year":"2024","journal-title":"Cancer Research Communications"},{"key":"2025112010075511300_ref32","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1111\/1759-7714.13976","article-title":"DDX10 promotes human lung carcinoma proliferation by U3 small nucleolar ribonucleoprotein IMP4","volume":"12","author":"Liu","year":"2021","journal-title":"Thoracic Cancer"},{"key":"2025112010075511300_ref33","doi-asserted-by":"publisher","first-page":"4302","DOI":"10.1038\/s41467-022-31950-9","article-title":"Single-cell transcriptome atlas of the human corpus cavernosum","volume":"13","author":"Zhao","year":"2022","journal-title":"Nat Commun"},{"key":"2025112010075511300_ref34","doi-asserted-by":"publisher","first-page":"103143","DOI":"10.1016\/j.media.2024.103143","article-title":"CellViT: vision transformers for precise cell segmentation and classification","volume":"94","author":"H\u00f6rst","year":"2024","journal-title":"Med Image Anal"},{"key":"2025112010075511300_ref35","doi-asserted-by":"publisher","first-page":"12471","DOI":"10.1117\/12.2654117","article-title":"Predicting cell type counts in whole slide histology images using evidential multi-task learning","volume":"2023","author":"Gudhe","year":"2023","journal-title":"Medical Imaging"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf613\/65408490\/bbaf613.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf613\/65408490\/bbaf613.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T15:10:58Z","timestamp":1763651458000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf613\/8329263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":35,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf613","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,11]]},"published":{"date-parts":[[2025,11,1]]},"article-number":"bbaf613"}}