arXiv cs.AI· Minh H. N. Le, Thanh-Huy Nguyen, Tao Li, Bao Quang Gia Le, Han H. Huynh, Monika Raj, Carl Yang, Min Xu, Tuan Vinh, Nguyen Quoc Khanh Le·· 2 天前AI 评分18
以影像为锚点的多组学如何整合心脏影像与转录组数据
Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics
AI 导读
一篇综述提出"以影像为锚点"的视角，将超声心动图、CMR 和 CT 定义的心脏空间表型，与 bulk、单细胞和空间转录组学提供的细胞类型及位置分子信息相连接。文章定义了各模态的表示需求，比较多模态融合策略，并梳理了放射基因组学、空间对齐和基于影像的基因表达预测的整合流程及其验证要求与失效模式。
正文 · 原文
Abstract:Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed clinically through imaging. Cardiac MRI (CMR), computed tomography (CT), and echocardiography are integral to routine cardiovascular care, while bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics are providing increasingly detailed molecular characterization of cardiac tissue. Yet, these imaging and molecular data are still analysed in largely separate pipelines. This review examines joint representations that link cardiac imaging phenotypes to transcriptomic and spatially resolved molecular states. An imaging-anchored perspective is adopted in which echocardiography, CMR, and CT define a spatial phenotype of the heart, and bulk, single-cell and spatial transcriptomics provide cell-type- and location-specific molecular context. We define the representation requirements of each modality, compare multimodal fusion strategies, and synthesize integrative pipelines for radiogenomics, spatial alignment, and image-based gene-expression prediction, together with their validation requirements, limitations, and failure modes. Spatial multiomic maps of human myocardium and atherosclerotic plaque, together with single-cell, spatial, and multimodal medical foundation models, are advancing imaging-anchored multiomics; however, cost, scalability, and tissue availability remain substantial barriers to large-scale cardiovascular translation.
| Comments: | 23 pages, 4 figures, 5 tables. Review article |
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2601.07871 [q-bio.QM] |
| (or arXiv:2601.07871v3 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2601.07871 arXiv-issued DOI via DataCite |
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| Journal reference: | Briefings in Bioinformatics, Volume 27, Issue 4, bbag365 (2026) |
| Related DOI: | https://doi.org/10.1093/bib/bbag365
DOI(s) linking to related resources |
Submission history
From: Tuan Vinh [view email]
[v1]
Sat, 10 Jan 2026 23:30:49 UTC (16,995 KB)
[v2]
Wed, 14 Oct 2026 14:28:24 UTC (1 KB) (withdrawn)
[v3]
Thu, 15 Oct 2026 15:18:49 UTC (20,570 KB)
来源：arXiv cs.AI · arxiv.org