Nature Biomedical Engineering最新文献_第3页

A selection pipeline for switchable aptamer beacons in broad-spectrum protein detection. 广谱蛋白检测中可切换适配体信标的选择管道。

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-18 DOI: 10.1038/s41551-025-01519-0

Maxim V Berezovski

引用次数: 0

Systematic functional screening of switchable aptamer beacon probes. 可切换适体信标探针的系统功能筛选。

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-18 DOI: 10.1038/s41551-025-01503-8

Xuan Cheng,Panzhu Yao,Chongyu Jin,Jinchen Long,Xueling Yan,Xuyang Zhao,Tongxuan Wei,Qinguo Liu,Yifan Chen,Huang Su,Hong Xuan,Siqi Bian,Jun Li,Wenlang Liu,Zheng Zheng,Liqin Zhang

{"title":"Systematic functional screening of switchable aptamer beacon probes.","authors":"Xuan Cheng,Panzhu Yao,Chongyu Jin,Jinchen Long,Xueling Yan,Xuyang Zhao,Tongxuan Wei,Qinguo Liu,Yifan Chen,Huang Su,Hong Xuan,Siqi Bian,Jun Li,Wenlang Liu,Zheng Zheng,Liqin Zhang","doi":"10.1038/s41551-025-01503-8","DOIUrl":"https://doi.org/10.1038/s41551-025-01503-8","url":null,"abstract":"Immunoassays using affinity binders such as antibodies and aptamers are crucial for molecular biology. However, the advancement of analytical methods based on these affinity probes is often hampered by complex operational steps that can introduce errors, particularly in intricate environments such as intracellular settings and microfluidic systems. There is growing interest in developing molecular probes for wash-free assays that activate signals upon target detection. Here we report a systematic functional screening platform for switchable aptamer beacon probes that can achieve target-responsive detection. A stem-loop, hairpin-shaped beacon library was constructed on microbeads and screened using target-responsive fluorescence-activated sorting. The selected aptamer beacons exhibit strong affinities, triggering fluorescence only upon binding, thus enabling wash-free immunoassays for the detection of intracellular and membrane proteins. Computational modelling offers insights into aptamer binding and structural switching mechanisms, revealing how specific protein-aptamer interactions drive stem-loop unwinding and postbinding conformational changes critical for functional activation. This approach establishes a standardized platform for generating switchable aptameric tools, supporting their potential in advanced diagnostics and research.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"17 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145083391","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}

引用次数: 0

Label-free metabolic imaging monitors the fitness of chimeric antigen receptor T cells 无标签代谢成像监测嵌合抗原受体T细胞的适应性

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-16 DOI: 10.1038/s41551-025-01504-7

Dan L. Pham, Dan Cappabianca, Matthew H. Forsberg, Cole Weaver, Katherine P. Mueller, Anna Tommasi, Jolanta Vidugiriene, Anthony Lauer, Kayla Sylvester, Jorgo Lika, Madison Bugel, Jing Fan, Christian M. Capitini, Krishanu Saha, Melissa C. Skala

{"title":"Label-free metabolic imaging monitors the fitness of chimeric antigen receptor T cells","authors":"Dan L. Pham, Dan Cappabianca, Matthew H. Forsberg, Cole Weaver, Katherine P. Mueller, Anna Tommasi, Jolanta Vidugiriene, Anthony Lauer, Kayla Sylvester, Jorgo Lika, Madison Bugel, Jing Fan, Christian M. Capitini, Krishanu Saha, Melissa C. Skala","doi":"10.1038/s41551-025-01504-7","DOIUrl":"https://doi.org/10.1038/s41551-025-01504-7","url":null,"abstract":"Chimeric antigen receptor (CAR) T cell therapy for solid tumours is challenging because of the immunosuppressive tumour microenvironment and a complex manufacturing process. Cellular manufacturing protocols directly impact CAR T cell yield, phenotype and metabolism, which correlates with in vivo potency and persistence. Although metabolic fitness is a critical quality attribute, how T cell metabolic requirements vary throughout the manufacturing process remains unexplored. Here we use optical metabolic imaging (OMI), a non-invasive, label-free method to evaluate single-cell metabolism. Using OMI, we identified the impacts of media composition on CAR T cell metabolism, activation strength and kinetics, and phenotype. We demonstrate that OMI parameters can indicate cell cycle stage and optimal gene transfer conditions for both viral transduction and electroporation-based CRISPR/Cas9. In a CRISPR-edited anti-GD2 CAR T cell model, OMI measurements allow accurate prediction of an oxidative metabolic phenotype that yields higher in vivo potency against neuroblastoma. Our data support OMI as a robust, sensitive analytical tool to optimize manufacturing conditions and monitor cell metabolism for increased CAR T cell yield and metabolic fitness.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"84 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145067764","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}

引用次数: 0

Leveraging large language and vision models for knowledge extraction from large-scale image–text colonoscopy records 利用大型语言和视觉模型从大规模图像-文本结肠镜检查记录中提取知识

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-16 DOI: 10.1038/s41551-025-01500-x

Shuo Wang, Yan Zhu, Zhiwei Yang, Xiaoyuan Luo, Yizhe Zhang, Peiyao Fu, Haoran Wang, Manning Wang, Zhijian Song, Quanlin Li, Pinghong Zhou, Yike Guo

{"title":"Leveraging large language and vision models for knowledge extraction from large-scale image–text colonoscopy records","authors":"Shuo Wang, Yan Zhu, Zhiwei Yang, Xiaoyuan Luo, Yizhe Zhang, Peiyao Fu, Haoran Wang, Manning Wang, Zhijian Song, Quanlin Li, Pinghong Zhou, Yike Guo","doi":"10.1038/s41551-025-01500-x","DOIUrl":"https://doi.org/10.1038/s41551-025-01500-x","url":null,"abstract":"The development of artificial intelligence systems for colonoscopy analysis often necessitates expert-annotated image datasets. However, limitations in dataset size and diversity impede model performance and generalization. Image–text colonoscopy records from routine clinical practice, comprising millions of images and text reports, serve as a valuable data source, although annotating them is labour intensive. Here we leverage recent advancements in large language and vision models and propose EndoKED, a data mining paradigm for deep knowledge extraction and distillation. EndoKED automates the transformation of raw colonoscopy records into image datasets with pixel-level annotation. We apply EndoKED to multicentre datasets of raw colonoscopy records (~1 million images), showing its superior performance in detecting polyps at the report and image levels, as well as annotating polyps at the pixel level. The state-of-the-art performance and generalization ability of polyp segmentation models are achieved through EndoKED pretraining. Furthermore, the EndoKED vision backbone enables data-efficient learning for optical biopsy, achieving expert-level performance in internal, external and prospective validation datasets.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"35 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145067763","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}

引用次数: 0

Engineering of a bespoke CRISPR base editor to treat a severe vascular disease 一种定制的CRISPR碱基编辑器用于治疗一种严重的血管疾病

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-15 DOI: 10.1038/s41551-025-01510-9

引用次数: 0

Treatment of a severe vascular disease using a bespoke CRISPR–Cas9 base editor in mice 在小鼠中使用定制的CRISPR-Cas9碱基编辑器治疗严重血管疾病

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-11 DOI: 10.1038/s41551-025-01499-1

Christiano R. R. Alves, Sabyasachi Das, Vijai Krishnan, Leillani L. Ha, Lauren R. Fox, Hannah E. Stutzman, Claire E. Shamber, Pazhanichamy Kalailingam, Siobhan McCarthy, Christian L. Lino Cardenas, Claire E. Fong, Takahiko Imai, Sunayana Mitra, Shuqi Yun, Rachael K. Wood, Friederike M. C. Benning, Kangsan Roh, Joseph Lawton, Nahye Kim, Rachel A. Silverstein, Joana Ferreira da Silva, Demitri de la Cruz, Rashmi Richa, Jun Xie, Heather L. Gray-Edwards, Rajeev Malhotra, David Y. Chung, Luke H. Chao, Shengdar Q. Tsai, Casey A. Maguire, Mark E. Lindsay, Benjamin P. Kleinstiver, Patricia L. Musolino

{"title":"Treatment of a severe vascular disease using a bespoke CRISPR–Cas9 base editor in mice","authors":"Christiano R. R. Alves, Sabyasachi Das, Vijai Krishnan, Leillani L. Ha, Lauren R. Fox, Hannah E. Stutzman, Claire E. Shamber, Pazhanichamy Kalailingam, Siobhan McCarthy, Christian L. Lino Cardenas, Claire E. Fong, Takahiko Imai, Sunayana Mitra, Shuqi Yun, Rachael K. Wood, Friederike M. C. Benning, Kangsan Roh, Joseph Lawton, Nahye Kim, Rachel A. Silverstein, Joana Ferreira da Silva, Demitri de la Cruz, Rashmi Richa, Jun Xie, Heather L. Gray-Edwards, Rajeev Malhotra, David Y. Chung, Luke H. Chao, Shengdar Q. Tsai, Casey A. Maguire, Mark E. Lindsay, Benjamin P. Kleinstiver, Patricia L. Musolino","doi":"10.1038/s41551-025-01499-1","DOIUrl":"https://doi.org/10.1038/s41551-025-01499-1","url":null,"abstract":"Pathogenic missense mutations in the alpha actin isotype 2 (ACTA2) gene cause multisystemic smooth muscle dysfunction syndrome (MSMDS), a genetic vasculopathy that is associated with stroke, aortic dissection and death in childhood. Here we perform mutation-specific protein engineering to develop a bespoke CRISPR–Cas9 enzyme with enhanced on-target activity against the most common MSMDS-causative mutation ACTA2 R179H. To directly correct the R179H mutation, we screened dozens of configurations of base editors to develop a highly precise corrective A-to-G edit with minimal deleterious bystander editing that is otherwise prevalent when using wild-type SpCas9 base editors. We create a murine model of MSMDS that shows phenotypes consistent with human patients, including vasculopathy and premature death, to explore the in vivo therapeutic potential of this strategy. Delivery of the customized base editor via an engineered smooth muscle-tropic adeno-associated virus (AAV-PR) vector substantially prolongs survival and rescues systemic phenotypes across the lifespan of MSMDS mice, including in the vasculature, aorta and brain. Our results highlight how bespoke mutant-specific CRISPR–Cas9 enzymes can improve mutation correction with base editors.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"16 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032191","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}

引用次数: 0

Combinatorial prediction of therapeutic perturbations using causally inspired neural networks 使用因果启发神经网络的治疗扰动组合预测

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-09 DOI: 10.1038/s41551-025-01481-x

Guadalupe Gonzalez, Xiang Lin, Isuru Herath, Kirill Veselkov, Michael Bronstein, Marinka Zitnik

{"title":"Combinatorial prediction of therapeutic perturbations using causally inspired neural networks","authors":"Guadalupe Gonzalez, Xiang Lin, Isuru Herath, Kirill Veselkov, Michael Bronstein, Marinka Zitnik","doi":"10.1038/s41551-025-01481-x","DOIUrl":"https://doi.org/10.1038/s41551-025-01481-x","url":null,"abstract":"Phenotype-driven approaches identify disease-counteracting compounds by analysing the phenotypic signatures that distinguish diseased from healthy states. Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem and predicts the perturbagens needed to achieve a desired response by embedding disease cell states into networks, learning a latent representation of these states, and identifying optimal combinatorial perturbations. In experiments in nine cell lines with chemical perturbations, PDGrapher identifies effective perturbagens in more testing samples than competing methods. It also shows competitive performance on ten genetic perturbation datasets. An advantage of PDGrapher is its direct prediction, in contrast to the indirect and computationally intensive approach common in phenotype-driven models. It trains up to 25× faster than existing methods, providing a fast approach for identifying therapeutic perturbations and advancing phenotype-driven drug discovery.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"16 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017496","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}

引用次数: 0

A generalist foundation model and database for open-world medical image segmentation 开放世界医学图像分割的通用基础模型和数据库

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-05 DOI: 10.1038/s41551-025-01497-3

Siqi Zhang, Qizhe Zhang, Shanghang Zhang, Xiaohong Liu, Jingkun Yue, Ming Lu, Huihuan Xu, Jiaxin Yao, Xiaobao Wei, Jiajun Cao, Xiang Zhang, Ming Gao, Jun Shen, Yichang Hao, Yinkui Wang, Xingcai Zhang, Song Wu, Ping Zhang, Shuguang Cui, Guangyu Wang

{"title":"A generalist foundation model and database for open-world medical image segmentation","authors":"Siqi Zhang, Qizhe Zhang, Shanghang Zhang, Xiaohong Liu, Jingkun Yue, Ming Lu, Huihuan Xu, Jiaxin Yao, Xiaobao Wei, Jiajun Cao, Xiang Zhang, Ming Gao, Jun Shen, Yichang Hao, Yinkui Wang, Xingcai Zhang, Song Wu, Ping Zhang, Shuguang Cui, Guangyu Wang","doi":"10.1038/s41551-025-01497-3","DOIUrl":"https://doi.org/10.1038/s41551-025-01497-3","url":null,"abstract":"Vision foundation models have demonstrated vast potential in achieving generalist medical segmentation capability, providing a versatile, task-agnostic solution through a single model. However, current generalist models involve simple pre-training on various medical data containing irrelevant information, often resulting in the negative transfer phenomenon and degenerated performance. Furthermore, the practical applicability of foundation models across diverse open-world scenarios, especially in out-of-distribution (OOD) settings, has not been extensively evaluated. Here we construct a publicly accessible database, MedSegDB, based on a tree-structured hierarchy and annotated from 129 public medical segmentation repositories and 5 in-house datasets. We further propose a Generalist Medical Segmentation model (MedSegX), a vision foundation model trained with a model-agnostic Contextual Mixture of Adapter Experts (ConMoAE) for open-world segmentation. We conduct a comprehensive evaluation of MedSegX across a range of medical segmentation tasks. Experimental results indicate that MedSegX achieves state-of-the-art performance across various modalities and organ systems in in-distribution (ID) settings. In OOD and real-world clinical settings, MedSegX consistently maintains its performance in both zero-shot and data-efficient generalization, outperforming other foundation models.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"49 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144995781","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}

引用次数: 0

Phagocytic clearance of targeted cells with a synthetic ligand 用合成配体对目标细胞的吞噬清除

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-03 DOI: 10.1038/s41551-025-01483-9

Yuki Yamato, Jun Suzuki

{"title":"Phagocytic clearance of targeted cells with a synthetic ligand","authors":"Yuki Yamato, Jun Suzuki","doi":"10.1038/s41551-025-01483-9","DOIUrl":"https://doi.org/10.1038/s41551-025-01483-9","url":null,"abstract":"During the process of engulfment, phosphatidylserine is exposed on the surface of dead cells as an ‘eat-me’ signal and is recognized by Protein S (ProS), a secreted factor that also binds to the Mer tyrosine kinase (MerTK) on phagocytes. Despite its robust activity, this engulfment mechanism has not been exploited for therapeutic purposes. Here we develop a synthetic protein modality called Crunch (connector for removal of unwanted cell habitat) by modifying ProS, inspired by the high engulfment capability of the ProS–MerTK pathway. In Crunch, the phosphatidylserine-binding motif of ProS is replaced with a nanobody or single-chain variable fragment that recognizes the surface proteins of targeted cells. Green fluorescent protein nanobody-conjugated Crunch eliminates green fluorescent protein-expressing melanoma cells in transplantation mouse models. In addition, CD19+B cells are eliminated by anti-CD19 single-chain variable fragment-conjugated Crunch, resulting in a therapeutic effect on systemic lupus erythematosus. Both mouse and human versions of Crunch are effective, establishing this synthetic ligand as a promising tool for the elimination of targeted cells.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"58 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144930269","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}

引用次数: 0

Magnetic materials power networks of tiny implants in the body 磁性材料为体内微小植入物的网络提供动力

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-09-03 DOI: 10.1038/s41551-025-01493-7

引用次数: 0