Spatial transcriptomics (ST) enables transcriptome-wide profiling while preserving the spatial context of tissues, offering unprecedented opportunities to study tissue organization and cell-cell interactions in situ. Despite recent advances, existing methods often lack effective integration of histological morphology with molecular profiles, relying on shallow fusion strategies or omitting tissue images altogether, which limits their ability to resolve ambiguous spatial domain boundaries. To address this challenge, we propose MultiST, a unified multimodal framework that jointly models spatial topology, gene expression, and tissue morphology through cross-attention-based fusion. MultiST employs graph-based gene encoders with adversarial alignment to learn robust spatial representations, while integrating color-normalized histological features to capture molecular-morphological dependencies and refine domain boundaries. We evaluated the proposed method on 13 diverse ST datasets spanning two organs, including human brain cortex and breast cancer tissue. MultiST yields spatial domains with clearer and more coherent boundaries than existing methods, leading to more stable pseudotime trajectories and more biologically interpretable cell-cell interaction patterns. The MultiST framework and source code are available at https://github.com/LabJunBMI/MultiST.git.
ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics
National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康与医学数据科学国家研究院）；School of Informatics, Xiamen University（厦门大学信息学院）；Aginome Scientific（Aginome科技公司）；School of Life Sciences, Xiamen University（厦门大学生命科学学院）
Generating crossmodal gene expression from cancer histopathology improves multimodal AI predictions
School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science（数学与计算科学学院，印度科学培养协会）；The Alan Turing Institute（艾伦·图灵研究所）；Comprehensive Cancer Center, King’s College London（国王学院综合癌症中心）；Department of Computer Science and Engineering, Jadavpur University（计算机科学与工程系，贾瓦德pur大学）；MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计学单位）；Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University（生物统计学、生物信息学与生物数学系，杰斐逊大学）；UCL Cancer Institute, Dept of Medical Physics & Biomedical Engineering, University College London（伦敦大学学院癌症研究所，医学物理与生物医学工程系）
PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）
Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics
Academy for Engineering and Technology, Fudan University（复旦大学工程技术研究院）；Cognition and Intelligent Technology Laboratory, Fudan University（复旦大学认知与智能技术实验室）；Shanghai Jiaotong University（上海交通大学）；Fudan University（复旦大学）
Multimodal Prototyping for cancer survival prediction
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）
Prototype-driven fusion of pathology and spatial transcriptomics for interpretable survival prediction
Department of Biostatistics, MD Anderson Cancer Center（生物统计学系，MD安德森癌症中心）；Department of Translational Molecular Pathology, MD Anderson Cancer Center（转化分子病理学系，MD安德森癌症中心）；The Institute for Data Science in Oncology (IDSO), MD Anderson Cancer Center（肿瘤学数据科学研究所（IDSO），MD安德森癌症中心）
CellSymphony: Deciphering the molecular and phenotypic orchestration of cells with single-cell pathomics
Translational Molecular Pathology Department, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心转化分子病理学部门）；Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心肿瘤数据科学研究所）
Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics
International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan；AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan；Medical Sciences Division, University of Oxford, Oxford, United Kingdom；Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA；Department of Computer Science, Emory University, Atlanta, GA, USA；Department of Chemistry, Emory University, Atlanta, GA, USA；International Master Program for Translational Science, College of Medical Science；Technology, Taipei Medical University, Taipei 110, Taiwan；In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan；Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology
College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）；Fysics Intelligence Technologies Co., Ltd. (Fysics AI)（菲茨斯智能科技有限公司（菲茨斯AI））；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Tencent Youtu Lab（腾讯优图实验室）；ByteDance（字节跳动）
SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space
Biomedical AI Research Lab, University of California, Los Angeles（生物医学人工智能研究实验室，加州大学洛杉矶分校）；Department of Pathology, University of California, Los Angeles（病理学系，加州大学洛杉矶分校）；Department of Radiology, University of California, Los Angeles（放射学系，加州大学洛杉矶分校）；Department of Bioengineering, University of California, Los Angeles（生物工程系，加州大学洛杉矶分校）；UCLA Medical Informatics Home Area, University of California, Los Angeles（UCLA医学信息学家庭区域，加州大学洛杉矶分校）；Department of Computational Medicine, University of California, Los Angeles（计算医学系，加州大学洛杉矶分校）
HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；ETH Zurich（苏黎世联邦理工学院）；MIT（麻省理工学院）；HST MIT（麻省理工学院卫生科学与技术部）；TUM（慕尼黑工业大学）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；Emory School of Medicine（埃默里医学院）；Pusan National University（釜山国立大学）
Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder
Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health（慕尼黑亥姆霍兹中心人工智能健康研究所（德国环境健康研究中心））；University of Trento（特伦托大学）；Technical University of Munich（慕尼黑工业大学）；Ludwig-Maximilians University Munich（慕尼黑大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction
AI Technology for Life, Department of Information and Computing Sciences, Department of Biology, Utrecht University, Utrecht, The Netherlands（AI技术与生命、信息与计算科学系、生物学系、乌得勒支大学、乌得勒支、荷兰）；Computational Pathology group, Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands（计算病理组、病理学系、荷兰癌症研究所、阿姆斯特丹、荷兰）
Towards Spatial Transcriptomics-driven Pathology Foundation Models
Department of Pathology, Mass General Brigham, Harvard Medical School, Boston, MA, USA（病理学系，马萨诸塞州总医院与哈佛医学院，波士顿，马萨诸塞州，美国）；Department of Computer Science & Technology, University of Cambridge, Cambridge, UK（计算机科学与技术系，剑桥大学，剑桥，英国）；Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA（癌症计划，哈佛与麻省理工联合学院，剑桥，马萨诸塞州，美国）；Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA（数据科学计划，达纳-法伯癌症研究所，波士顿，马萨诸塞州，美国）；Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA（哈佛-麻省理工健康科学与技术 division，麻省理工学院，剑桥，马萨诸塞州，美国）；Télécom Paris, Institut Polytechnique de Paris, Paris, France（巴黎电信学院，巴黎理工学院，巴黎，法国）；Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA（哈佛大学数据科学倡议，哈佛大学，剑桥，马萨诸塞州，美国）
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理重点实验室）；Beihang University（北京航空航天大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；AresoX
SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes
Department of Biomedical Informatics, Harvard Medical School（哈佛医学学校生物医学信息学系）；Department of Computer Science, University of North Carolina（北卡罗来纳大学计算机科学系）；Department of Computer Science, Massachusetts Institute of Technology（麻省理工学院计算机科学系）
