Neuroimaging is a ubiquitous tool for evaluating patients with neurological diseases. The global demand for magnetic resonance imaging (MRI) studies has risen steadily, placing substantial strain on health systems, prolonging turnaround times and intensifying physician burnout. These challenges disproportionately impact patients in low-resource and rural settings. Here we utilize data from a large academic health system to develop Prima, an AI foundation model for neuroimaging that supports real-world, clinical MRI studies as input. Trained on over 220,000 MRI studies, Prima uses a hierarchical vision architecture that provides general and transferable MRI features. Prima was tested in a 1-year health system-wide study that included 29,431 MRI studies. Across 52 radiologic diagnoses from major neurologic disorders, Prima achieved a mean diagnostic area under the curve (AUC) of 92.0%, outperforming other state-of-the-art general and medical AI models. Prima offers explainable differential diagnoses, worklist priority for radiologists and clinical referral recommendations. Prima demonstrates algorithmic fairness across sensitive groups. These findings highlight the transformative potential of health system-scale AI training and Prima's role in advancing AI-driven healthcare.
{"title":"Learning neuroimaging models from health system-scale data.","authors":"Yiwei Lyu, Samir Harake, Asadur Chowdury, Soumyanil Banerjee, Rachel Gologorsky, Shixuan Liu, Anna-Katharina Meissner, Akshay Rao, Chenhui Zhao, Akhil Kondepudi, Cheng Jiang, Xinhai Hou, Rushikesh S Joshi, Volker Neuschmelting, Ashok Srinivasan, Dawn Kleindorfer, Brian Athey, Vikas Gulani, Aditya Pandey, Honglak Lee, Todd Hollon","doi":"10.1038/s41551-025-01608-0","DOIUrl":"https://doi.org/10.1038/s41551-025-01608-0","url":null,"abstract":"<p><p>Neuroimaging is a ubiquitous tool for evaluating patients with neurological diseases. The global demand for magnetic resonance imaging (MRI) studies has risen steadily, placing substantial strain on health systems, prolonging turnaround times and intensifying physician burnout. These challenges disproportionately impact patients in low-resource and rural settings. Here we utilize data from a large academic health system to develop Prima, an AI foundation model for neuroimaging that supports real-world, clinical MRI studies as input. Trained on over 220,000 MRI studies, Prima uses a hierarchical vision architecture that provides general and transferable MRI features. Prima was tested in a 1-year health system-wide study that included 29,431 MRI studies. Across 52 radiologic diagnoses from major neurologic disorders, Prima achieved a mean diagnostic area under the curve (AUC) of 92.0%, outperforming other state-of-the-art general and medical AI models. Prima offers explainable differential diagnoses, worklist priority for radiologists and clinical referral recommendations. Prima demonstrates algorithmic fairness across sensitive groups. These findings highlight the transformative potential of health system-scale AI training and Prima's role in advancing AI-driven healthcare.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":" ","pages":""},"PeriodicalIF":26.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146132442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1038/s41551-025-01602-6
Tianyu Liu, Tinglin Huang, Tong Ding, Hao Wu, Peter Humphrey, Sudhir Perincheri, Kurt Schalper, Rex Ying, Hua Xu, James Zou, Faisal Mahmood, Hongyu Zhao
Recent advances in pathology foundation models, pre-trained on large-scale histopathology images, have greatly advanced disease-focused applications. At the same time, spatial multi-omic technologies now measure gene and protein expression with high spatial resolution, offering valuable insights into tissue context. Yet, existing models struggle to integrate these complementary data types. Here, to address this challenge, we present spEMO, a computational framework that unifies embeddings from pathology foundation models and large language models for spatial multi-omic analysis. By leveraging multi-modal representations, spEMO surpasses single-modality models across diverse downstream tasks, including spatial domain identification, spot-type classification, whole-slide disease prediction and interpretation, multicellular interaction inference and automated medical reporting. These results highlight spEMO's strength in both biological discovery and clinical applications. Furthermore, we introduce a new benchmark task-multi-modal alignment-to evaluate how effectively pathology foundation models retrieve complementary information. Together, spEMO establishes a powerful step towards holistic, interpretable and generalizable AI for spatial biology and pathology.
{"title":"Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data.","authors":"Tianyu Liu, Tinglin Huang, Tong Ding, Hao Wu, Peter Humphrey, Sudhir Perincheri, Kurt Schalper, Rex Ying, Hua Xu, James Zou, Faisal Mahmood, Hongyu Zhao","doi":"10.1038/s41551-025-01602-6","DOIUrl":"https://doi.org/10.1038/s41551-025-01602-6","url":null,"abstract":"<p><p>Recent advances in pathology foundation models, pre-trained on large-scale histopathology images, have greatly advanced disease-focused applications. At the same time, spatial multi-omic technologies now measure gene and protein expression with high spatial resolution, offering valuable insights into tissue context. Yet, existing models struggle to integrate these complementary data types. Here, to address this challenge, we present spEMO, a computational framework that unifies embeddings from pathology foundation models and large language models for spatial multi-omic analysis. By leveraging multi-modal representations, spEMO surpasses single-modality models across diverse downstream tasks, including spatial domain identification, spot-type classification, whole-slide disease prediction and interpretation, multicellular interaction inference and automated medical reporting. These results highlight spEMO's strength in both biological discovery and clinical applications. Furthermore, we introduce a new benchmark task-multi-modal alignment-to evaluate how effectively pathology foundation models retrieve complementary information. Together, spEMO establishes a powerful step towards holistic, interpretable and generalizable AI for spatial biology and pathology.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":" ","pages":""},"PeriodicalIF":26.8,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146125952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1038/s41551-025-01609-z
Xiangling Li, Shibo Liu, Jingshan Mo, Cheng Yang, Gen Li, Mingwei Zhou, Jaehyeon Ryu, Matthew Morales, Hui Fang, Wei Ouyang
Comprehensive and continuous assessment of organ physiology and biochemistry, beyond the capabilities of conventional monitoring tools, can enable timely interventions for perioperative complications such as organ ischaemia and transplant rejection. Here we present an integrated bioresorbable system that enables multiplexed, real-time and spatially mapped electrochemical monitoring of deep organs throughout the surgical course. Using a 3D printing-based, photolithography-free fabrication process, the system features a flexible, 3D programmed, individually addressable microneedle sensor array with backward-facing barbs for conformal and stable organ interfacing and 3D parenchymal probing. Electrochemical functionalization of microneedle tips enable concurrent monitoring and spatial mapping of key biochemical markers, such as electrolytes, metabolites and oxygenation, in deep organs for at least 7 days. An electrically programmable self-destruction mechanism offers controllability over the degradation process, eliminating the need for device retrieval. Demonstrations in clinically relevant complications such as kidney ischaemia and gut disorders in animal models highlight the broad applications of this device in intra- and postoperative monitoring, advancing perioperative care and critical care medicine.
{"title":"A programmable bioresorbable electrochemical microneedle sensor array for perioperative monitoring of organ health","authors":"Xiangling Li, Shibo Liu, Jingshan Mo, Cheng Yang, Gen Li, Mingwei Zhou, Jaehyeon Ryu, Matthew Morales, Hui Fang, Wei Ouyang","doi":"10.1038/s41551-025-01609-z","DOIUrl":"https://doi.org/10.1038/s41551-025-01609-z","url":null,"abstract":"Comprehensive and continuous assessment of organ physiology and biochemistry, beyond the capabilities of conventional monitoring tools, can enable timely interventions for perioperative complications such as organ ischaemia and transplant rejection. Here we present an integrated bioresorbable system that enables multiplexed, real-time and spatially mapped electrochemical monitoring of deep organs throughout the surgical course. Using a 3D printing-based, photolithography-free fabrication process, the system features a flexible, 3D programmed, individually addressable microneedle sensor array with backward-facing barbs for conformal and stable organ interfacing and 3D parenchymal probing. Electrochemical functionalization of microneedle tips enable concurrent monitoring and spatial mapping of key biochemical markers, such as electrolytes, metabolites and oxygenation, in deep organs for at least 7 days. An electrically programmable self-destruction mechanism offers controllability over the degradation process, eliminating the need for device retrieval. Demonstrations in clinically relevant complications such as kidney ischaemia and gut disorders in animal models highlight the broad applications of this device in intra- and postoperative monitoring, advancing perioperative care and critical care medicine.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"290 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1038/s41551-025-01601-7
Hyun-Kyung Woo, Changhyun Kim, Yoonjeong Choi, Young Kwan Cho, Luu-Ngoc Do, Hyunho Kim, Dae-Han Jung, Matt Allen, Jueun Jeon, Seok Chung, Soo Yeun Park, Ilwoo Park, Cesar M. Castro, Jun Seok Park, Hakho Lee
Circulating extracellular vesicles can be used for tumour diagnostics. However, current isolation methods are time consuming, require manual handling and are prone to contamination. Here we report on SpinEx (separation-processing integration for extracellular vesicles), a compact disc device for automatic isolation and multiplex immunolabelling of whole-blood samples. SpinEx integrates on-disc chromatography, centripetal liquid transfer and bead-based vesicle capture with antibody labelling. The system processes 150 µl of whole blood, enriching and labelling vesicles for 16 protein targets in under 75 minutes. Detection is performed by measuring dual fluorescence signals from labelled extracellular vesicles captured on microbeads. In a pilot clinical study, SpinEx was used to process 221 plasma samples for multiplex profiling of 30 vesicle-associated proteins. Using fluorescence flow cytometry to analyse cancer-specific biomarker expression, we found that vesicles processed by SpinEx distinguished cancer from non-cancer samples with 90% accuracy and 97% specificity, and classified 5 tumour types with 96% accuracy. SpinEx enables automated and multiplex processing of extracellular vesicles from blood, which may support the development of clinically viable assays for cancer detection and classification.
{"title":"Automated disc device for multiplexed extracellular vesicle isolation and labelling from liquid biopsies in cancer diagnostics","authors":"Hyun-Kyung Woo, Changhyun Kim, Yoonjeong Choi, Young Kwan Cho, Luu-Ngoc Do, Hyunho Kim, Dae-Han Jung, Matt Allen, Jueun Jeon, Seok Chung, Soo Yeun Park, Ilwoo Park, Cesar M. Castro, Jun Seok Park, Hakho Lee","doi":"10.1038/s41551-025-01601-7","DOIUrl":"https://doi.org/10.1038/s41551-025-01601-7","url":null,"abstract":"Circulating extracellular vesicles can be used for tumour diagnostics. However, current isolation methods are time consuming, require manual handling and are prone to contamination. Here we report on SpinEx (separation-processing integration for extracellular vesicles), a compact disc device for automatic isolation and multiplex immunolabelling of whole-blood samples. SpinEx integrates on-disc chromatography, centripetal liquid transfer and bead-based vesicle capture with antibody labelling. The system processes 150 µl of whole blood, enriching and labelling vesicles for 16 protein targets in under 75 minutes. Detection is performed by measuring dual fluorescence signals from labelled extracellular vesicles captured on microbeads. In a pilot clinical study, SpinEx was used to process 221 plasma samples for multiplex profiling of 30 vesicle-associated proteins. Using fluorescence flow cytometry to analyse cancer-specific biomarker expression, we found that vesicles processed by SpinEx distinguished cancer from non-cancer samples with 90% accuracy and 97% specificity, and classified 5 tumour types with 96% accuracy. SpinEx enables automated and multiplex processing of extracellular vesicles from blood, which may support the development of clinically viable assays for cancer detection and classification.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"30 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1038/s41551-025-01607-1
Wei Qin, Sheng-Jia Lin, Yu Zhang, Kevin Huang, Cassidy Petree, Pratishtha Varshney, Gaurav K. Varshney
Many missense mutations identified in genetic testing are variants of uncertain significance (VUS), not yet classified as either benign or pathogenic. Systematic determination of their functional relevance is a pressing clinical need. CRISPR-mediated base editing can precisely introduce precise variants into model organisms for functional testing, but current editors face efficiency and targeting constraints. We developed TCBE-Umax, a family of TadA-derived cytosine base editors optimized for zebrafish. Engineering the TadA deaminase domain improved editing efficiency and reduced sequence-context bias, expanded PAM compatibility, and minimized bystander edits and indel formation. Our editors achieved efficient biallelic editing, enabling rapid functional assessment of genetic variants in the F0 (founding) zebrafish. As a proof of concept, we evaluated 15 VUS linked to hereditary hearing loss, determining pathogenicity through phenotypic analysis. With high efficiency and versatility, TCBE-Umax base editors provide a powerful platform for studying genetic variants and disease in vivo.
{"title":"High-efficiency TadA cytosine base editors for precise modelling of human disease variants","authors":"Wei Qin, Sheng-Jia Lin, Yu Zhang, Kevin Huang, Cassidy Petree, Pratishtha Varshney, Gaurav K. Varshney","doi":"10.1038/s41551-025-01607-1","DOIUrl":"https://doi.org/10.1038/s41551-025-01607-1","url":null,"abstract":"Many missense mutations identified in genetic testing are variants of uncertain significance (VUS), not yet classified as either benign or pathogenic. Systematic determination of their functional relevance is a pressing clinical need. CRISPR-mediated base editing can precisely introduce precise variants into model organisms for functional testing, but current editors face efficiency and targeting constraints. We developed TCBE-Umax, a family of TadA-derived cytosine base editors optimized for zebrafish. Engineering the TadA deaminase domain improved editing efficiency and reduced sequence-context bias, expanded PAM compatibility, and minimized bystander edits and indel formation. Our editors achieved efficient biallelic editing, enabling rapid functional assessment of genetic variants in the F0 (founding) zebrafish. As a proof of concept, we evaluated 15 VUS linked to hereditary hearing loss, determining pathogenicity through phenotypic analysis. With high efficiency and versatility, TCBE-Umax base editors provide a powerful platform for studying genetic variants and disease in vivo.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"42 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1038/s41551-025-01585-4
Jan Sellner, Alexander Studier-Fischer, Ahmad Bin Qasim, Silvia Seidlitz, Nicholas Schreck, Minu Tizabi, Manuel Wiesenfarth, Annette Kopp-Schneider, Janne Heinecke, Jule Brandt, Samuel Knoedler, Caelan Max Haney, Gabriel Salg, Berkin Özdemir, Maximilian Dietrich, Maurice Stephan Michel, Felix Nickel, Karl-Friedrich Kowalewski, Lena Maier-Hein
Optical imaging techniques, such as hyperspectral imaging combined with machine learning-based analysis, have the potential to revolutionize clinical surgical imaging. However, these modalities face a shortage of large-scale, representative clinical data for training machine learning-based algorithms. While preclinical animal data are abundantly available through standardized experiments and allow for controlled induction of pathological tissue states, it is not ethically possible to obtain similar data from patients. To leverage this situation, we propose ‘xeno-learning’, a cross-species knowledge-transfer concept inspired by xeno-transplantation. Here, using a total of 14,013 hyperspectral images from humans as well as porcine and rat models, we show that, although spectral signatures of organs differ substantially across species, relative changes resulting from pathologies or surgical manipulation such as malperfusion or injection of contrast agent are comparable. Such changes learnt in one species can be transferred to a new species through a ‘physiology-based data augmentation’ method, enabling the large-scale secondary use of preclinical animal data for human application. The resulting benefits promise a high impact of the proposed knowledge-transfer concept on future developments in the field.
{"title":"Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis","authors":"Jan Sellner, Alexander Studier-Fischer, Ahmad Bin Qasim, Silvia Seidlitz, Nicholas Schreck, Minu Tizabi, Manuel Wiesenfarth, Annette Kopp-Schneider, Janne Heinecke, Jule Brandt, Samuel Knoedler, Caelan Max Haney, Gabriel Salg, Berkin Özdemir, Maximilian Dietrich, Maurice Stephan Michel, Felix Nickel, Karl-Friedrich Kowalewski, Lena Maier-Hein","doi":"10.1038/s41551-025-01585-4","DOIUrl":"https://doi.org/10.1038/s41551-025-01585-4","url":null,"abstract":"Optical imaging techniques, such as hyperspectral imaging combined with machine learning-based analysis, have the potential to revolutionize clinical surgical imaging. However, these modalities face a shortage of large-scale, representative clinical data for training machine learning-based algorithms. While preclinical animal data are abundantly available through standardized experiments and allow for controlled induction of pathological tissue states, it is not ethically possible to obtain similar data from patients. To leverage this situation, we propose ‘xeno-learning’, a cross-species knowledge-transfer concept inspired by xeno-transplantation. Here, using a total of 14,013 hyperspectral images from humans as well as porcine and rat models, we show that, although spectral signatures of organs differ substantially across species, relative changes resulting from pathologies or surgical manipulation such as malperfusion or injection of contrast agent are comparable. Such changes learnt in one species can be transferred to a new species through a ‘physiology-based data augmentation’ method, enabling the large-scale secondary use of preclinical animal data for human application. The resulting benefits promise a high impact of the proposed knowledge-transfer concept on future developments in the field.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"2 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1038/s41551-025-01586-3
{"title":"AI learns across species to address human clinical imaging data sparsity","authors":"","doi":"10.1038/s41551-025-01586-3","DOIUrl":"https://doi.org/10.1038/s41551-025-01586-3","url":null,"abstract":"","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1038/s41551-025-01605-3
Hedan Bai, Jianlin Zhou, Mingzheng Wu, Steven Papastefan, Xiuyuan Li, Haohui Zhang, Kaiyu Zhao, Zhuoran Zhang, Wei Ouyang, Catherine R. Redden, Amir M. Alhajjat, Heyang Wang, Yibo Zhou, Kenneth Madsen, Shuo Li, Andrew I. Efimov, Katelyn Ma, Lisa Kovacs, Sahdev Patel, Daniel R. Liesman, Katherine C. Ott, Rinaldo Garziera, Steffen Sammet, Wenming Zhang, Yonggang Huang, Aimen F. Shaaban, John A. Rogers
{"title":"A filamentary soft robotic probe for multimodal in utero monitoring of fetal health","authors":"Hedan Bai, Jianlin Zhou, Mingzheng Wu, Steven Papastefan, Xiuyuan Li, Haohui Zhang, Kaiyu Zhao, Zhuoran Zhang, Wei Ouyang, Catherine R. Redden, Amir M. Alhajjat, Heyang Wang, Yibo Zhou, Kenneth Madsen, Shuo Li, Andrew I. Efimov, Katelyn Ma, Lisa Kovacs, Sahdev Patel, Daniel R. Liesman, Katherine C. Ott, Rinaldo Garziera, Steffen Sammet, Wenming Zhang, Yonggang Huang, Aimen F. Shaaban, John A. Rogers","doi":"10.1038/s41551-025-01605-3","DOIUrl":"https://doi.org/10.1038/s41551-025-01605-3","url":null,"abstract":"","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"12 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1038/s41551-025-01553-y
Brendan C Jones,Giada Benedetti,Giuseppe Calà,Ramin Amiri,Lucinda Tullie,Roberto Lutman,Jahangir Sufi,Lucy Holland,Daniyal J Jafree,Monika Balys,Glenn Anderson,Ian C Simcock,Owen J Arthurs,Simon Eaton,Nicola Elvassore,Vivian Sw Li,Christopher J Tape,Kelsey Dj Jones,Camilla Luni,Giovanni Giuseppe Giobbe,Paolo De Coppi
Patient-derived human organoids have the capacity to self-organize into more complex structures. However, to what extent gastric organoids can recapitulate differentiated cell types and mucosal functions remains unexplored. Here we report on how region-specific gastric organoids can self-assemble into complex multi-regional assembloids. These assembloids show increased complexity and cross-communication between different gastric regions, allowing for the emergence of the elusive parietal cell type that is responsible for the production of gastric acid and shows a functional response to drugs targeting the H+/K+ ATPase pump. We generate assembloids from paediatric patients with a genetic condition found to be associated with unusual antral foveolar hyperplasia and hyperplastic polyposis. Our multi-regional assembloid efficiently recapitulates hyperplastic-like antral regions, with decreased mucin secretion and glycosylated H+/K+ ATPase subunit beta, which results in impaired gastric acid secretion. Multi-regional gastric assembloids, generated using paediatric-stem-cell-derived organoids, successfully recapitulate the structural and functional characteristics of the human stomach, offering a promising tool for studying gastric epithelial interactions and disease mechanisms that were previously challenging to investigate in primary models.
{"title":"Human gastric multi-regional assembloids for functional parietal maturation and patient-specific modelling of antral foveolar hyperplasia.","authors":"Brendan C Jones,Giada Benedetti,Giuseppe Calà,Ramin Amiri,Lucinda Tullie,Roberto Lutman,Jahangir Sufi,Lucy Holland,Daniyal J Jafree,Monika Balys,Glenn Anderson,Ian C Simcock,Owen J Arthurs,Simon Eaton,Nicola Elvassore,Vivian Sw Li,Christopher J Tape,Kelsey Dj Jones,Camilla Luni,Giovanni Giuseppe Giobbe,Paolo De Coppi","doi":"10.1038/s41551-025-01553-y","DOIUrl":"https://doi.org/10.1038/s41551-025-01553-y","url":null,"abstract":"Patient-derived human organoids have the capacity to self-organize into more complex structures. However, to what extent gastric organoids can recapitulate differentiated cell types and mucosal functions remains unexplored. Here we report on how region-specific gastric organoids can self-assemble into complex multi-regional assembloids. These assembloids show increased complexity and cross-communication between different gastric regions, allowing for the emergence of the elusive parietal cell type that is responsible for the production of gastric acid and shows a functional response to drugs targeting the H+/K+ ATPase pump. We generate assembloids from paediatric patients with a genetic condition found to be associated with unusual antral foveolar hyperplasia and hyperplastic polyposis. Our multi-regional assembloid efficiently recapitulates hyperplastic-like antral regions, with decreased mucin secretion and glycosylated H+/K+ ATPase subunit beta, which results in impaired gastric acid secretion. Multi-regional gastric assembloids, generated using paediatric-stem-cell-derived organoids, successfully recapitulate the structural and functional characteristics of the human stomach, offering a promising tool for studying gastric epithelial interactions and disease mechanisms that were previously challenging to investigate in primary models.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"29 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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