iMeta最新文献

{"title":"The role of human microbiota in breast cancer pathogenesis and treatment","authors":"Jie Qiu,&nbsp;Xiaojia Wang,&nbsp;Man Li,&nbsp;Jing Guo,&nbsp;Shipeng Ning,&nbsp;Yi-Zhou Gao,&nbsp;Xinyi Zhang,&nbsp;Xuli Meng,&nbsp;Yiding Chen,&nbsp;Yunxiang Zhou","doi":"10.1002/imt2.70142","DOIUrl":"https://doi.org/10.1002/imt2.70142","url":null,"abstract":"<p>The human microbiota contributes to breast cancer pathogenesis and treatment response through interactions involving the oral, gut, and breast microbial niches. (Left) Multi-site dysbiosis is characterized by altered microbial composition and abundance across different anatomical sites. (Middle) Microbial components and metabolites influence tumor progression and the tumor microenvironment through mechanisms including immune, metabolic, and inflammatory regulation. (Right) The microbiota and their metabolites further affect therapeutic efficacy and toxicity in immunotherapy, chemotherapy, endocrine therapy, and human epidermal growth factor receptor 2 (HER2)-targeted therapy, highlighting the translational potential of microbiota-targeted strategies in breast cancer.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"5 4","pages":""},"PeriodicalIF":44.4,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.70142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148816574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gut microbiota-mediated immune and metabolic dysregulation in coronary artery disease progression and prognosis","authors":"Honghong Liu,&nbsp;Siqin Feng,&nbsp;Lifeng Liang,&nbsp;Muyun Tang,&nbsp;Yifei Wang,&nbsp;Yiyang Wang,&nbsp;Zhiyu Zhang,&nbsp;Haiping Xing,&nbsp;Chenhong Zhang,&nbsp;Ran Tian,&nbsp;Shuyang Zhang","doi":"10.1002/imt2.70147","DOIUrl":"https://doi.org/10.1002/imt2.70147","url":null,"abstract":"<p>The gut microbiota has emerged as a metabolically active endocrine-like organ with a crucial role in coronary artery disease (CAD), yet its contribution to adverse clinical outcomes remains incompletely understood. In this prospective cohort of 319 participants, we integrated metagenomic and metabolomic profiling with longitudinal follow-up over a median of 1.85 years. Fecal microbiota transplantation from patients with CAD transmitted susceptibility to atherosclerosis in antibiotic-treated <i>ApoE</i>^−/− mice, accompanied by microbiota-induced vascular inflammation mediated through LPS–TLR4 signaling. Gut microbiota-derived aromatic amino acid metabolism was associated with thrombotic risk and major adverse cardiac events (MACE). Machine learning identified gut microbial features that improved prospective prediction of MACE. Together, these findings implicate the gut microbiome in CAD progression and support the development of microbiome-based strategies for cardiovascular risk prediction and prevention.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"5 4","pages":""},"PeriodicalIF":44.4,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.70147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148816459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperoside alleviates endometrial stromal cell senescence in unexplained recurrent spontaneous abortion via DHX9-mediated R-loop resolution","authors":"Yuepeng Jiang,&nbsp;Hongli Zhao,&nbsp;Xinyi Ding,&nbsp;Haoling Zhang,&nbsp;Yadong Guo,&nbsp;Yiming Ma,&nbsp;Lingyi Cai,&nbsp;Qingnan Fan,&nbsp;Ruisi Peng,&nbsp;Fuyuan Yang,&nbsp;Doblin Sandai,&nbsp;Xianling Cao,&nbsp;Jiali Yao,&nbsp;Wenyi Wang,&nbsp;Zhiheng Lin,&nbsp;Wangzheqi Zhang,&nbsp;Jialin He,&nbsp;Aihua Zhang,&nbsp;Xiaoxuan Zhao","doi":"10.1002/imt2.70150","DOIUrl":"10.1002/imt2.70150","url":null,"abstract":"<p>Decidualization deficiency is a hallmark pathology of unexplained recurrent spontaneous abortion (URSA), but the undefined molecular drivers hinder the development of effective therapies. Hyperoside, a bioactive flavonoid from Hypericum perforatum, exhibits therapeutic potential against URSA, yet its underlying mechanism of action remains unknown. In this study, we employed an integrated multi-omics approach coupled with a multi-dimensional validation framework that spanned URSA patient decidual tissues, in vivo mouse models, and in vitro telomerase-immortalized human endometrial stromal cell (T-hESC) decidualization system, to systematically investigate hyperoside's mechanism in URSA, with a focus on R-loop-driven endometrial stromal cell senescence. We found that hyperoside dose-dependently reduced embryo resorption and rescued decidualization deficiency by preventing stromal cell senescence. Mechanistically, hyperoside effectively alleviated aberrant intracellular R-loop accumulation, thereby suppressing excessive activation of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway which contributes to the initiation of the cellular senescence program. Further target identification and validation experiments confirmed that DExH-box helicase 9 (DHX9) was the functional molecular target of hyperoside, with the Thr419 residue serving as the critical binding site. Functional validation revealed that DHX9 knockdown or introduction of the T419A point mutation markedly attenuated the anti-senescence and pro-decidualization effects of hyperoside. In vivo experiments further confirmed that uterine-specific knockdown of DHX9 reduced hyperoside's protective effects against R-loop accumulation, cGAS-STING pathway activation, and embryo loss. Collectively, these findings demonstrate that hyperoside alleviates stromal cell senescence and decidualization deficiency in URSA through DHX9-dependent resolution of R-loops and subsequent suppression of cGAS-STING-associated senescence signaling. More broadly, this work identifies R-loop-mediated genomic stress as a previously underappreciated contributor to URSA-associated decidual dysfunction and provides a mechanistic basis for the protective effects of hyperoside through DHX9-dependent R-loop homeostasis.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"5 4","pages":""},"PeriodicalIF":44.4,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148594758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A gut microbiome-lipid axis in early pregnancy is associated with metabolic dysregulation and diabetes risk","authors":"Zhonghan Sun,&nbsp;Chang He,&nbsp;Xiaonan Ma,&nbsp;Ping Wu,&nbsp;Tianlei Wang,&nbsp;Jiaying Yuan,&nbsp;Yanni Pu,&nbsp;Xiaofeng Zhou,&nbsp;Zhendong Mei,&nbsp;Huiling Song,&nbsp;Yetong Wang,&nbsp;Haiyan Yue,&nbsp;Yuanqing Fu,&nbsp;Jusheng Zheng,&nbsp;An Pan,&nbsp;Da Chen,&nbsp;Shangyu Hong,&nbsp;Xiong-Fei Pan,&nbsp;Yan Zheng","doi":"10.1002/imt2.70166","DOIUrl":"10.1002/imt2.70166","url":null,"abstract":"<p>Gestational diabetes mellitus (GDM) reflects metabolic dysregulation that becomes clinically apparent during pregnancy and shares key pathophysiological features with broader forms of diabetes. Gut microbiome-host metabolic interactions may contribute to this process, yet their role in early pregnancy remains incompletely understood. In this prospective nested case-control study within the Tongji-Huaxi-Shuangliu Birth Cohort, 784 pregnant women, including 222 who developed GDM, underwent first-trimester gut metagenomic and plasma lipidomic profiling. Cross-omics analyses were performed to identify microbiome-lipid associations and potential mediation patterns. Women who later developed GDM showed reduced gut microbial diversity and altered microbial profiles in early pregnancy. We identified 26 microbial species associated with GDM risk, with seven species, including <i>Ruminococcus bicirculans</i> (<i>R. bicirculans</i>), showing concordant associations in external type 2 diabetes populations. Microbial pathways related to fatty acid and lipid biosynthesis were enriched in women at higher risk. Plasma lipidomics revealed widespread alterations, particularly among glycosphingolipid-related metabolites. Integrated analyses suggested that lipidomic variation statistically accounted for part of the microbiome-GDM association. A class-level dihexosylceramide feature, DHC 24:1, consistent with lactosylceramide-related metabolites, emerged as a potential mediator and was prioritized for exploratory follow-up. Experimental analyses provided functional support for a microbiome-lipid-host interaction axis. <i>R. bicirculans</i> promoted lactosylceramide 24:1 production <i>in vitro</i>, bacterial colonization and metabolite administration improved insulin tolerance <i>in vivo</i>, and lactosylceramide 24:1 modulated insulin-stimulated AKT signaling dynamics in hepatocytes. These findings identify a gut microbiome-lipid axis associated with metabolic dysregulation in pregnancy and suggest a potential mechanism linking microbial metabolism to host insulin signaling.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"5 4","pages":""},"PeriodicalIF":44.4,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-omics-driven precision medicine.","authors":"Huibo Li, Zhe Zhao, Yifan Zhang, Yao Ma, Gaofei Hu, Min Zeng, Zhanqun Yang, Zixuan Zhao, Xin Zhou, Wei Hu, Yuxuan Sun, Meng Su, Jun Li, Matthew Whiteman, Wei Fu, Chao Zhong, Lemin Zheng, Long Chen, Hairong Lv, Rongsheng Zhao, Yi Zhun Zhu","doi":"10.1002/imt2.70165","DOIUrl":"10.1002/imt2.70165","url":null,"abstract":"<p><p>Precision medicine is increasingly constrained not by a lack of molecular data but by the absence of frameworks that can translate multidimensional biological information into actionable clinical decisions. Multi-omics-driven precision medicine (MODPM) addresses this lack by integrating genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome, and clinical context into a multiscale framework that links molecular mechanisms, tissue organization, and patient trajectories. In this review, we propose a conceptual framework for MODPM and examine how advances in multi-omics technologies, artificial intelligence (AI), and foundation models are reshaping disease modeling, drug development, and precision intervention. We summarize the biological contributions of major omics layers and discuss how AI supports cross-modal representation learning, contextual modeling, and perturbation-aware prediction. We highlight drug development as a key translational application of MODPM and further discuss its clinical relevance across three major disease contexts: cancer, autoimmune diseases, and metabolic disorders, including cardiometabolic and renal-metabolic diseases. These examples illustrate how MODPM can support target discovery, disease endotyping, treatment response prediction, and clinical monitoring by analyzing shared mechanisms such as immune dysregulation, metabolic remodeling, chronic inflammation, tissue microenvironmental changes, and gene-environment interactions. Across these settings, MODPM enables finer molecular stratification, the identification of pathway-dominant disease states, improved response prediction, and dynamic treatment monitoring. We also discuss key barriers to implementation, including data heterogeneity, limited cohort diversity, polygenic complexity, workflow constraints, cost, and ethical issues related to privacy, consent, and data ownership. Overall, the value of MODPM lies not in stacking additional data layers but in building a multiscale, continuously learnable framework to link biological heterogeneity to clinically interpretable and actionable decisions.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":" ","pages":"e70165"},"PeriodicalIF":44.4,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13507694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148835405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human biomarker navigator.","authors":"Meng-Yao Li, Qian Zhang, Zijie Wang, Zeping Gui, Jing Liu, Jingda Li, Maojun Jiang, Zukang Chang, Qingqing Han, Bendong Yang, Tariq Saeed, Jun Li, Jinghan Jia, Chenghu Song, Tong Shao, Tianhuan Peng, Xueying Zhang, Pingping Guo, Jun Luo, Gangao Yang, Yuan Wan, Gokhan Zengin, Quan Yuan, Lingru Li, Xiaohui Fan, Yang Luo, Lvyun Zhu, Yanfei Zheng, Wenjun Mao, Wenlong Sun","doi":"10.1002/imt2.70157","DOIUrl":"10.1002/imt2.70157","url":null,"abstract":"<p><p>Biomarkers play a pivotal role in contemporary medical research, particularly in the early diagnosis of diseases and personalized treatment. Although previous studies have systematically reviewed markers across various disease domains, an integrated framework that connects major physiological systems, encompasses multiple organs, and spans a broad spectrum of diseases is still lacking. In the context of modern health challenges, marked by the high prevalence of chronic diseases and widespread comorbidities, establishing a panoramic biomarker navigation system is imperative. This review offers the initial comprehensive elucidation of the biomarker interaction networks across diverse systems, organs, and diseases, and delineates the operational framework for establishing a \"biomarker navigation system\". The core contribution of this work is a transition from 'knowledge enumeration' to a more integrated, systems-level approach. This provides new perspectives in systems biology for understanding the shared pathological foundations of comorbidities. Furthermore, it provides a theoretical basis for a reorientation in medicine, from a focus on 'treating existing diseases' to 'preventive interventions' and from 'single-disease management' to a comprehensive, systems-based approach. To facilitate the exploration and application of this system-organ-disease-biomarker network, we developed the \"Human Biomarker Navigator\" web platform (http://www.hbiomarker.com), which allows researchers and clinicians to efficiently retrieve the functional characteristics and clinical significance of diverse biomarkers across different disease contexts.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":" ","pages":"e70157"},"PeriodicalIF":44.4,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to \"Prophylactic supplementation with <i>Bifidobacterium infantis</i> or its metabolite inosine attenuates cardiac ischemia/reperfusion injury\".","authors":"","doi":"10.1002/imt2.70162","DOIUrl":"10.1002/imt2.70162","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1002/imt2.220.].</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":" ","pages":"e70162"},"PeriodicalIF":44.4,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13423747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond omics: From descriptive profiling to causal and scalable design of fermentation microbiomes.","authors":"Dongze Niu, Huili Pang, Xuekai Wang, Liang Song, Jing Zhang, Ansah Terry, Junfeng Li, Jingwen Cao, Qinhua Liu, Dayong Han, Jidong Wang, Jong Geun Kim, Kazuo Ataku, Smerjai Bureenok, Zhongfang Tan, Fengyan Bai, Tao Shao, Taoli Huhe, Lifeng Wang, Peijie Han, Chuncheng Xu, Fuyu Yang, Yimin Cai","doi":"10.1002/imt2.70161","DOIUrl":"10.1002/imt2.70161","url":null,"abstract":"<p><p>Fermentation microbiomes play essential roles in food production, feed preservation, waste valorization, and diverse sustainable industrial processes. Although multi-omics and systems biology have substantially advanced our understanding of their assembly, interactions, and functional dynamics, industrial translation remains constrained by fragmented datasets, limited causal validation, and transport constraints during scale-up. Large language models act as upper-level knowledge and workflow orchestrators, accelerating data integration, hybrid AI-mechanistic modeling, hypothesis generation, and perturbation-guided learning. Collectively, these advances enable fermentation microbiome research to move beyond descriptive omics toward mechanism-guided synthetic microbial community design, causal validation, and scalable biomanufacturing.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":" ","pages":"e70161"},"PeriodicalIF":44.4,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13420143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spatiotemporal single-cell atlas of porcine development reveals regulatory dynamics and cellular targets of domestication","authors":"Rong Zhou,&nbsp;Zishuai Wang,&nbsp;Chenghao Hu,&nbsp;Shuhan Deng,&nbsp;Changyun Cai,&nbsp;Yanfang Wang,&nbsp;Shang-Tong Li,&nbsp;Lijing Bai,&nbsp;Kui Li","doi":"10.1002/imt2.70135","DOIUrl":"https://doi.org/10.1002/imt2.70135","url":null,"abstract":"<p>Systematic characterization of cellular gene expression in livestock tissues during development is essential for understanding the regulation of complex traits. Despite the comprehensive profiling of cell atlases in livestock, a dynamic view of tissue development remains lacking. Here, using both single-cell and single-nucleus RNA sequencing, we present a comprehensive single-cell transcriptomic landscape of 252,033 cells/nuclei, mapping 83 distinct cell types across five pig tissues from prenatal to postnatal developmental stages. Our findings highlight the coordinated remodeling of tissue architecture through stem/progenitor cell proliferation, lineage specification, and functional maturation during organogenesis. We identified key transcription factors and regulatory networks that drive lineage-specific and spatiotemporally dynamic transcriptional programs. Developmental trajectory analysis identified a conserved bifurcated transcriptional organization of immune cells, accompanied by dynamic changes in transcription factors associated with immune cell maturation. Integrative analysis utilizing multi-omic, single-cell, and pig population genomics data identified a muscle-specific enhancer of the <i>MYOT</i> gene as a target of artificial selection, underlying meat-quality divergence between Asian and European pig breeds. Moreover, cross-species comparison between pigs and humans revealed conserved cell types, underscoring the evolutionary link. In summary, the comprehensive pig developmental cell atlas serves as a key resource for understanding livestock development, provides insights for precision breeding, and highlights the value of the reference pig cell atlas as a powerful resource for biomedical research.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"5 3","pages":""},"PeriodicalIF":44.4,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.70135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148532524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives","authors":"Haoxiu Wang,&nbsp;Xinwang Yang,&nbsp;Siheng Wang,&nbsp;Zhe Yang,&nbsp;Xiuhui Yang,&nbsp;Yutong Yang,&nbsp;Zirong Li,&nbsp;Yuqi Ren,&nbsp;Qianqian Zhang,&nbsp;Bowen Zhao,&nbsp;Jingming Xiao,&nbsp;Yidong Wang,&nbsp;Junhao Dong,&nbsp;Zhenhao Kou,&nbsp;Jie Li,&nbsp;Liqun Yang,&nbsp;Erhu Zhao,&nbsp;Gregory Fonseca,&nbsp;Ruibang Luo,&nbsp;Mingyu Yang,&nbsp;Hongjuan Cui,&nbsp;Gengjie Jia,&nbsp;Dan Wang,&nbsp;Haoyang Li,&nbsp;Jun Ding,&nbsp;Zhiyuan Yuan,&nbsp;Haojing Shao","doi":"10.1002/imt2.70146","DOIUrl":"https://doi.org/10.1002/imt2.70146","url":null,"abstract":"<p>Spatial omics technologys help overcome key limitations of conventional omics approaches that lack spatial information, by providing a panoramic perspective from the molecular level to the microenvironment scale for addressing spatially resolved biological questions in life sciences. With the rapid advancement of this field, there are significant differences among technology platforms, algorithms, and research workflows, which bring three core challenges to interdisciplinary researchers: the detailed explanation of technical principles, the selection of appropriate algorithms, and the future development directions. This review systematically summarizes the technical platforms and analytical algorithms of spatial omics, compares their advantages and disadvantages in the context of specific tasks and presents application cases across multiple biological fields. It also outlines the emerging research directions and advances in large model integration. It ultimately aims to provide a reference for researchers from diverse disciplines to design and implement spatial omics studies.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"5 3","pages":""},"PeriodicalIF":44.4,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.70146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148532599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}