Pub Date : 2026-03-25DOI: 10.1038/s41551-026-01637-3
Rohan Shad, Cyril Zakka, Dhamanpreet Kaur, Mrudang Mathur, Robyn Fong, Joseph Cho, Ross Warren Filice, John Mongan, Kimberly Kallianos, Nishith Khandwala, David Eng, Matthew Leipzig, Walter R. Witschey, Alejandro de Feria, Victor A. Ferrari, Euan A. Ashley, Michael A. Acker, Curtis Langlotz, William Hiesinger
Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank and two additional publicly available external datasets. We explore emergent capabilities of our system and demonstrate remarkable performance across a range of tasks, including the problem of left-ventricular ejection fraction regression and the diagnosis of 39 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep-learning system is capable of not only contextualizing the staggering complexity of human cardiovascular disease but can be directed towards clinical problems of interest, yielding impressive, clinical-grade diagnostic accuracy with a fraction of the training data typically required for such tasks.
{"title":"A generalizable deep learning system for cardiac MRI","authors":"Rohan Shad, Cyril Zakka, Dhamanpreet Kaur, Mrudang Mathur, Robyn Fong, Joseph Cho, Ross Warren Filice, John Mongan, Kimberly Kallianos, Nishith Khandwala, David Eng, Matthew Leipzig, Walter R. Witschey, Alejandro de Feria, Victor A. Ferrari, Euan A. Ashley, Michael A. Acker, Curtis Langlotz, William Hiesinger","doi":"10.1038/s41551-026-01637-3","DOIUrl":"https://doi.org/10.1038/s41551-026-01637-3","url":null,"abstract":"Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank and two additional publicly available external datasets. We explore emergent capabilities of our system and demonstrate remarkable performance across a range of tasks, including the problem of left-ventricular ejection fraction regression and the diagnosis of 39 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep-learning system is capable of not only contextualizing the staggering complexity of human cardiovascular disease but can be directed towards clinical problems of interest, yielding impressive, clinical-grade diagnostic accuracy with a fraction of the training data typically required for such tasks.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"19 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506135","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 : 2026-03-25DOI: 10.1038/s41551-026-01618-6
Lin Qi, Yuchen He, Alexandra Sviercovich, Xiaoyue Mei, Erzhen Chen, Yihan Xia, Michael J. Conboy, Irina M. Conboy, Andreas Stahl
The search for biological mechanisms of human aging is stalled by a lack of suitable models, and it remains unknown whether and to what degree rejuvenation reported in rodents translates to people. Here we report a human induced pluripotent stem cell-derived microphysiological system modelling the white adipose tissue–liver axis in the presence of heterochronic human serum to study aging and rejuvenation in humans. We reveal changes in functional and molecular hallmarks of aging and rejuvenation. We also investigate unknown biomarkers and mechanisms of plasticity in human tissue aging and potential rejuvenation strategies. The microphysiological chip recapitulates, in 4 days, aging-associated hallmarks that occur after decades of aging in people, including gerontic shifts in gene expression and oxidative DNA damage. We uncover unknown signalling networks in human aging, knock-on effects of aging in fat on liver, sexual polymorphisms of aging and tissue memory of age, and develop a custom machine learning model for biological age. Combining heterochronic human serum with the microphysiological system allows for rapidly establishing human tissue aging, discovering clinically relevant mechanisms and biomarkers, and testing of anti-geronic approaches.
{"title":"Human microphysiological systems of aging recreate the in vivo process expediting evaluation of anti-geronic strategies","authors":"Lin Qi, Yuchen He, Alexandra Sviercovich, Xiaoyue Mei, Erzhen Chen, Yihan Xia, Michael J. Conboy, Irina M. Conboy, Andreas Stahl","doi":"10.1038/s41551-026-01618-6","DOIUrl":"https://doi.org/10.1038/s41551-026-01618-6","url":null,"abstract":"The search for biological mechanisms of human aging is stalled by a lack of suitable models, and it remains unknown whether and to what degree rejuvenation reported in rodents translates to people. Here we report a human induced pluripotent stem cell-derived microphysiological system modelling the white adipose tissue–liver axis in the presence of heterochronic human serum to study aging and rejuvenation in humans. We reveal changes in functional and molecular hallmarks of aging and rejuvenation. We also investigate unknown biomarkers and mechanisms of plasticity in human tissue aging and potential rejuvenation strategies. The microphysiological chip recapitulates, in 4 days, aging-associated hallmarks that occur after decades of aging in people, including gerontic shifts in gene expression and oxidative DNA damage. We uncover unknown signalling networks in human aging, knock-on effects of aging in fat on liver, sexual polymorphisms of aging and tissue memory of age, and develop a custom machine learning model for biological age. Combining heterochronic human serum with the microphysiological system allows for rapidly establishing human tissue aging, discovering clinically relevant mechanisms and biomarkers, and testing of anti-geronic approaches.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"45 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506134","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}
Frameshift mutations, responsible for >20% of Mendelian inherited diseases, pose substantial therapeutic challenges. Here we developed Template-Independent Genome Editing for Restoration (TIGER), a platform for the efficient and precise correction of frameshift mutations across various models. By identifying reproducible nucleotide-level factors that influence therapeutic efficacy across cells and tissues, we developed a scoring system for guide RNA (gRNA)–Cas9 outcomes. Approximately 75% of deletion and 50% of insertion mutations produced ≥30% in-frame products, sufficient for phenotypic restoration, with 38% and 65% achieving wild-type correction, respectively. To expand the applicability of TIGER across species and genome wide, we retrained the inDelphi algorithm to predict therapeutic gRNAs for single-nucleotide frameshifts. In a mouse model of deafness, delivery of SpCas9 and optimal gRNA via dual adeno-associated virus restored hearing thresholds to wild-type levels, with ~90% of in-frame edits being wild type. TIGER provides a robust and broadly applicable strategy for in vivo correction of inherited frameshift diseases.
{"title":"Template-independent genome editing and restoration for correcting frameshift disorders","authors":"Shiwei Qiu, Lian Liu, Bin Xiang, Ziqin Jin, Yahong Li, Dong Li, Hanqing Hou, Kuan Li, Gege Wei, Jiangping Xie, Shang Li, Shuang Liu, Chunlai Chen, Xin Liang, Qianwen Sun, Wei Xiong","doi":"10.1038/s41551-026-01635-5","DOIUrl":"https://doi.org/10.1038/s41551-026-01635-5","url":null,"abstract":"Frameshift mutations, responsible for >20% of Mendelian inherited diseases, pose substantial therapeutic challenges. Here we developed Template-Independent Genome Editing for Restoration (TIGER), a platform for the efficient and precise correction of frameshift mutations across various models. By identifying reproducible nucleotide-level factors that influence therapeutic efficacy across cells and tissues, we developed a scoring system for guide RNA (gRNA)–Cas9 outcomes. Approximately 75% of deletion and 50% of insertion mutations produced ≥30% in-frame products, sufficient for phenotypic restoration, with 38% and 65% achieving wild-type correction, respectively. To expand the applicability of TIGER across species and genome wide, we retrained the inDelphi algorithm to predict therapeutic gRNAs for single-nucleotide frameshifts. In a mouse model of deafness, delivery of SpCas9 and optimal gRNA via dual adeno-associated virus restored hearing thresholds to wild-type levels, with ~90% of in-frame edits being wild type. TIGER provides a robust and broadly applicable strategy for in vivo correction of inherited frameshift diseases.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"190 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496842","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}
Efficient mRNA delivery to specific tissues requires optimized ionizable lipids, yet the role of lipid spatial conformation in organ targeting and endosomal escape remains underexplored. Here we developed a library of lipids with diverse amino heads, degradable linkers and hydrophobic tails, generating distinct three-dimensional conformations. Molecular dynamics simulations revealed the dynamic conformations of these lipids during organic-aqueous phase transitions, and experimental validation confirmed that head and tail arrangements are key determinants of delivery efficiency and organ specificity. To accelerate lipid discovery, dynamic conformation data were converted into 2D density images to train machine learning models for lipid selection. AI-guided candidates, notably lipid P1, adopted stable three-tail cone-shaped conformations that promoted IgM protein corona formation and enabled spleen-targeted mRNA delivery. In preclinical models, P1-based mRNA vaccines triggered strong antibody and T-cell responses, leading to marked tumour suppression. These results highlight the pivotal role of lipid spatial conformation and the potential of AI-driven strategies to optimize lipid nanoparticles for organ-specific mRNA delivery.
{"title":"Artificial intelligence-guided design of LNPs for in vivo targeted mRNA delivery via analysis of the spatial conformation of ionizable lipids.","authors":"Lin-Jia Su,Nan-Nan Wang,Rui Luo,Zi-Han Ji,Haiyan Gu,Chao Yang,Mo-Xi Xu,Juchen Zhang,Qinghua Chen,Meng-Zhen Yu,Chenglin Li,Kelong Fan,Lin Mei,Yuliang Zhao,Yi Wang,Yurui Gao,Hao Wang,Yao-Xin Lin","doi":"10.1038/s41551-026-01640-8","DOIUrl":"https://doi.org/10.1038/s41551-026-01640-8","url":null,"abstract":"Efficient mRNA delivery to specific tissues requires optimized ionizable lipids, yet the role of lipid spatial conformation in organ targeting and endosomal escape remains underexplored. Here we developed a library of lipids with diverse amino heads, degradable linkers and hydrophobic tails, generating distinct three-dimensional conformations. Molecular dynamics simulations revealed the dynamic conformations of these lipids during organic-aqueous phase transitions, and experimental validation confirmed that head and tail arrangements are key determinants of delivery efficiency and organ specificity. To accelerate lipid discovery, dynamic conformation data were converted into 2D density images to train machine learning models for lipid selection. AI-guided candidates, notably lipid P1, adopted stable three-tail cone-shaped conformations that promoted IgM protein corona formation and enabled spleen-targeted mRNA delivery. In preclinical models, P1-based mRNA vaccines triggered strong antibody and T-cell responses, leading to marked tumour suppression. These results highlight the pivotal role of lipid spatial conformation and the potential of AI-driven strategies to optimize lipid nanoparticles for organ-specific mRNA delivery.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"51 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147478963","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 : 2026-03-17DOI: 10.1038/s41551-026-01615-9
Zeru Tian, Xu Wang, Sumanta Chatterjee, William Miller, Erick Guerrero, Yun-Chieh Sung, Alexis Pacheco Benitez, Sean Dilliard, Xiaoyan Bian, Amogh Vaidya, Xizhen Lian, Stephen Moore, Yehui Sun, Minjeong Kim, Yufen Xiao, Shiying Wu, Bret M. Evers, Jeon Lee, Lukas Farbiak, Daniel J. Siegwart
Developing lung-targeting delivery systems is essential for treating pulmonary conditions such as genetic respiratory diseases, infections, fibrosis and cancer. We synthesized and evaluated 444 lung-targeting lipids (LuT lipids) that form lipid nanoparticles (LNPs) to efficiently deliver messenger RNA and CRISPR–Cas9 genome editors to lungs with minimal side effects. Empirical analyses revealed structure–activity relationships, with top-performing LuT lipids possessing a unique ‘tripod-like’ structure consisting of a quaternary amine head, three long alkyl chains as legs and a short chain as a handle. LuT lipids improved endosomal escape, cargo release and endogenous targeting via adsorption of plasma proteins. Lead 1A7B13 LNPs showed a 25.5-fold improvement in mRNA delivery and a 9.2-fold increase in CRISPR–Cas9 gene-editing efficiency compared to benchmark DOTAP SORT LNPs, achieving over 90% selectivity to the lungs. 1A7B13 LNPs effectively delivered IL-10 mRNA in a therapeutic model of acute lung injury. This study reveals the relationship between lipid structure and lung-targeting activity, enriching the toolkit for lung-specific carriers.
{"title":"‘Tripod-like’ lung-targeting (LuT) lipids for highly efficient and selective LNPs for gene delivery and editing","authors":"Zeru Tian, Xu Wang, Sumanta Chatterjee, William Miller, Erick Guerrero, Yun-Chieh Sung, Alexis Pacheco Benitez, Sean Dilliard, Xiaoyan Bian, Amogh Vaidya, Xizhen Lian, Stephen Moore, Yehui Sun, Minjeong Kim, Yufen Xiao, Shiying Wu, Bret M. Evers, Jeon Lee, Lukas Farbiak, Daniel J. Siegwart","doi":"10.1038/s41551-026-01615-9","DOIUrl":"https://doi.org/10.1038/s41551-026-01615-9","url":null,"abstract":"Developing lung-targeting delivery systems is essential for treating pulmonary conditions such as genetic respiratory diseases, infections, fibrosis and cancer. We synthesized and evaluated 444 lung-targeting lipids (LuT lipids) that form lipid nanoparticles (LNPs) to efficiently deliver messenger RNA and CRISPR–Cas9 genome editors to lungs with minimal side effects. Empirical analyses revealed structure–activity relationships, with top-performing LuT lipids possessing a unique ‘tripod-like’ structure consisting of a quaternary amine head, three long alkyl chains as legs and a short chain as a handle. LuT lipids improved endosomal escape, cargo release and endogenous targeting via adsorption of plasma proteins. Lead 1A7B13 LNPs showed a 25.5-fold improvement in mRNA delivery and a 9.2-fold increase in CRISPR–Cas9 gene-editing efficiency compared to benchmark DOTAP SORT LNPs, achieving over 90% selectivity to the lungs. 1A7B13 LNPs effectively delivered IL-10 mRNA in a therapeutic model of acute lung injury. This study reveals the relationship between lipid structure and lung-targeting activity, enriching the toolkit for lung-specific carriers.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"59 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465218","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 : 2026-03-17DOI: 10.1038/s41551-026-01629-3
Yan-Ruide Li, Haochen Nan, Zeyang Liu, Ying Fang, Yichen Zhu, Zibai Lyu, Zhengyao Shao, Enbo Zhu, Bo Zhang, Youcheng Yang, Xinyuan Shen, Yuning Chen, Tzung Hsiai, Lili Yang, Song Li
Invariant natural killer T (iNKT) cells are a unique subset of T lymphocytes with allogeneic potential and strong solid tumour-homing capacity, making them attractive for cancer immunotherapy. Unlike conventional T cells, iNKT cells recognize lipid antigens presented by the non-polymorphic CD1d molecule. Chimaeric antigen receptor (CAR)-redirected iNKT (CAR-iNKT) cells have shown promise; however, their clinical efficacy is limited by insufficient activation and poor long-term persistence within the tumour microenvironment. Here we describe the iNKT cell-targeted microparticle recruitment and activation system (iMRAS), a biomimetic platform engineered to locally recruit, activate and expand CAR-iNKT cells in vivo. Acting as an in vivo ‘charging station’, iMRAS provides chemotactic and activating cues that enhance CAR-iNKT cell functionality, improving persistence and tumour control in preclinical lymphoma and melanoma models. Through its biomimetic design and localized immunostimulatory effects, iMRAS helps overcome the limitations of current therapies for solid tumours, establishing a robust platform for advancing CAR-iNKT cell-based cancer immunotherapy.
{"title":"Engineering an in vivo charging station for CAR-redirected invariant natural killer T cells to enhance cancer therapy","authors":"Yan-Ruide Li, Haochen Nan, Zeyang Liu, Ying Fang, Yichen Zhu, Zibai Lyu, Zhengyao Shao, Enbo Zhu, Bo Zhang, Youcheng Yang, Xinyuan Shen, Yuning Chen, Tzung Hsiai, Lili Yang, Song Li","doi":"10.1038/s41551-026-01629-3","DOIUrl":"https://doi.org/10.1038/s41551-026-01629-3","url":null,"abstract":"Invariant natural killer T (iNKT) cells are a unique subset of T lymphocytes with allogeneic potential and strong solid tumour-homing capacity, making them attractive for cancer immunotherapy. Unlike conventional T cells, iNKT cells recognize lipid antigens presented by the non-polymorphic CD1d molecule. Chimaeric antigen receptor (CAR)-redirected iNKT (CAR-iNKT) cells have shown promise; however, their clinical efficacy is limited by insufficient activation and poor long-term persistence within the tumour microenvironment. Here we describe the iNKT cell-targeted microparticle recruitment and activation system (iMRAS), a biomimetic platform engineered to locally recruit, activate and expand CAR-iNKT cells in vivo. Acting as an in vivo ‘charging station’, iMRAS provides chemotactic and activating cues that enhance CAR-iNKT cell functionality, improving persistence and tumour control in preclinical lymphoma and melanoma models. Through its biomimetic design and localized immunostimulatory effects, iMRAS helps overcome the limitations of current therapies for solid tumours, establishing a robust platform for advancing CAR-iNKT cell-based cancer immunotherapy.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"18 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465216","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 : 2026-03-11DOI: 10.1038/s41551-026-01627-5
Jonathan S Calvert,Samuel R Parker,Lakshmi N Govindarajan,Radu Darie,Elias Shaaya,Ryan Solinsky,Lily M Del Valle,Priyanka Miranda,Jaeson Jang,Ekta Tiwari,Sohail Syed,Raymond M Villalobos,Liza M Aguiar,J Andrew Taylor,Hanlin Tang,Sean McPherson,Wenzhe Xue,Alexios G Carayannopoulos,Adetokunbo A Oyelese,Ziya L Gokaslan,Arjun K Bansal,Linda J Resnik,Thomas Serre,Jared S Fridley,David A Borton
Spinal cord injury (SCI) results in permanent impairment of sensory, motor and autonomic function. Epidural electrical stimulation (EES) applied below the lesion can restore voluntary movement, autonomic function and locomotion following chronic SCI. However, impaired sensation below the SCI does not improve during the application of sublesional EES. Here we present first-in-human results demonstrating simultaneous lower extremity motor activation and somatosensory feedback in three participants with motor complete, chronic SCI enabled by perilesional EES. We determined motor- and sensory-specific EES parameters by leveraging modern deep learning methods and participant-directed control of stimulation. Supralesional EES evoked sensations were synchronized with leg movement, enabling participants to accurately report leg position. We then applied simultaneous supralesional and sublesional EES, enabling intentional control over leg movements and somatosensory feedback during functional tasks. Overall, we demonstrate a perilesional EES framework to modulate sensorimotor function that may improve quality of life in individuals with SCI.
{"title":"Perilesional neuromodulation replaces lost sensorimotor function in persons with spinal cord injury.","authors":"Jonathan S Calvert,Samuel R Parker,Lakshmi N Govindarajan,Radu Darie,Elias Shaaya,Ryan Solinsky,Lily M Del Valle,Priyanka Miranda,Jaeson Jang,Ekta Tiwari,Sohail Syed,Raymond M Villalobos,Liza M Aguiar,J Andrew Taylor,Hanlin Tang,Sean McPherson,Wenzhe Xue,Alexios G Carayannopoulos,Adetokunbo A Oyelese,Ziya L Gokaslan,Arjun K Bansal,Linda J Resnik,Thomas Serre,Jared S Fridley,David A Borton","doi":"10.1038/s41551-026-01627-5","DOIUrl":"https://doi.org/10.1038/s41551-026-01627-5","url":null,"abstract":"Spinal cord injury (SCI) results in permanent impairment of sensory, motor and autonomic function. Epidural electrical stimulation (EES) applied below the lesion can restore voluntary movement, autonomic function and locomotion following chronic SCI. However, impaired sensation below the SCI does not improve during the application of sublesional EES. Here we present first-in-human results demonstrating simultaneous lower extremity motor activation and somatosensory feedback in three participants with motor complete, chronic SCI enabled by perilesional EES. We determined motor- and sensory-specific EES parameters by leveraging modern deep learning methods and participant-directed control of stimulation. Supralesional EES evoked sensations were synchronized with leg movement, enabling participants to accurately report leg position. We then applied simultaneous supralesional and sublesional EES, enabling intentional control over leg movements and somatosensory feedback during functional tasks. Overall, we demonstrate a perilesional EES framework to modulate sensorimotor function that may improve quality of life in individuals with SCI.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"16 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394014","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 : 2026-03-11DOI: 10.1038/s41551-026-01614-w
Yang C. Zeng, Olivia J. Young, Qiancheng Xiong, Longlong Si, Min Wen Ku, Sylvie G. Bernier, Hawa Dembele, Giorgia Isinelli, Tal Gilboa, Zoe Swank, Su Hyun Seok, Anjali Rajwar, Amanda Jiang, Yunhao Zhai, LaTonya D. Williams, Caleb A. Hellman, Chris M. Wintersinger, Amanda R. Graveline, Andyna Vernet, Melinda Sanchez, Sarai Bardales, Georgia D. Tomaras, Ju Hee Ryu, Ick Chan Kwon, Girija Goyal, Donald E. Ingber, William M. Shih
Current SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) vaccines have shown robust induction of neutralizing antibodies and CD4+ T cell activation; however, CD8+ responses are variable, and the duration of immunity and protection against variants are limited. Here we repurpose our DNA origami vaccine nanotechnology DoriVac to target infectious viruses, namely, SARS-CoV-2, HIV and Ebola. The DNA origami nanoparticle, conjugated with infectious-disease-specific heptad repeat 2 peptides, which act as highly conserved antigens, and CpG adjuvant at precise nanoscale spacing, induces neutralizing antibodies, Th1 CD4+ T cells and CD8+ T cells in naive mice, with significant improvement over a bolus control. Pre-clinical studies using lymph-node-on-a-chip systems validate that DoriVac, when conjugated with antigenic peptides or proteins, induces promising cellular and humoral immune responses in human cells. Moreover, DoriVac bearing full-length SARS-CoV-2 spike protein achieves immune responses comparable to current mRNA vaccine platforms while potentially reducing storage constraints. These results suggest that DoriVac holds potential as a versatile, modular vaccine platform, capable of inducing both humoral and cellular immunities, underscoring its potential future use.
{"title":"DNA origami vaccine nanoparticles improve humoral and cellular immune responses to infectious diseases","authors":"Yang C. Zeng, Olivia J. Young, Qiancheng Xiong, Longlong Si, Min Wen Ku, Sylvie G. Bernier, Hawa Dembele, Giorgia Isinelli, Tal Gilboa, Zoe Swank, Su Hyun Seok, Anjali Rajwar, Amanda Jiang, Yunhao Zhai, LaTonya D. Williams, Caleb A. Hellman, Chris M. Wintersinger, Amanda R. Graveline, Andyna Vernet, Melinda Sanchez, Sarai Bardales, Georgia D. Tomaras, Ju Hee Ryu, Ick Chan Kwon, Girija Goyal, Donald E. Ingber, William M. Shih","doi":"10.1038/s41551-026-01614-w","DOIUrl":"https://doi.org/10.1038/s41551-026-01614-w","url":null,"abstract":"Current SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) vaccines have shown robust induction of neutralizing antibodies and CD4+ T cell activation; however, CD8+ responses are variable, and the duration of immunity and protection against variants are limited. Here we repurpose our DNA origami vaccine nanotechnology DoriVac to target infectious viruses, namely, SARS-CoV-2, HIV and Ebola. The DNA origami nanoparticle, conjugated with infectious-disease-specific heptad repeat 2 peptides, which act as highly conserved antigens, and CpG adjuvant at precise nanoscale spacing, induces neutralizing antibodies, Th1 CD4+ T cells and CD8+ T cells in naive mice, with significant improvement over a bolus control. Pre-clinical studies using lymph-node-on-a-chip systems validate that DoriVac, when conjugated with antigenic peptides or proteins, induces promising cellular and humoral immune responses in human cells. Moreover, DoriVac bearing full-length SARS-CoV-2 spike protein achieves immune responses comparable to current mRNA vaccine platforms while potentially reducing storage constraints. These results suggest that DoriVac holds potential as a versatile, modular vaccine platform, capable of inducing both humoral and cellular immunities, underscoring its potential future use.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"43 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394091","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 : 2026-03-11DOI: 10.1038/s41551-026-01632-8
Sonu Kumar,John Alex Sinclair,Tiger Shi,Han-Sheng Chuang,Satyajyoti Senapati,Hsueh-Chia Chang
Detecting small extracellular vesicles is critical for understanding disease biology and developing diagnostic tools, yet current methods require lengthy isolation steps and lack sensitivity owing to interference from abundant proteins. Here we report on an assay that uses Janus particles that enable rapid, isolation-free detection by exploiting Brownian rotation-induced blinking changes. When vesicles bind, their size significantly alters the blinking frequency, while smaller proteins produce no signal, ensuring selectivity. Using less than 10 μl of sample, the assay detects approximately 200 vesicles per microlitre and works directly on plasma, serum, urine and cell media in under 1 h. In a blind study of 87 subjects with colorectal cancer, pancreatic ductal adenocarcinoma, glioblastoma, Alzheimer's disease and healthy controls, the method identified disease type with an area under the curve of 0.90-0.99. Compared with ultracentrifugation combined with surface plasmon resonance, which requires 24 h, our approach delivers 2 orders of magnitude better sensitivity and dynamic range, offering a fast and robust platform for clinical and research applications.
{"title":"Rapid and sensitive detection of cancer-derived small extracellular vesicles using Janus particles.","authors":"Sonu Kumar,John Alex Sinclair,Tiger Shi,Han-Sheng Chuang,Satyajyoti Senapati,Hsueh-Chia Chang","doi":"10.1038/s41551-026-01632-8","DOIUrl":"https://doi.org/10.1038/s41551-026-01632-8","url":null,"abstract":"Detecting small extracellular vesicles is critical for understanding disease biology and developing diagnostic tools, yet current methods require lengthy isolation steps and lack sensitivity owing to interference from abundant proteins. Here we report on an assay that uses Janus particles that enable rapid, isolation-free detection by exploiting Brownian rotation-induced blinking changes. When vesicles bind, their size significantly alters the blinking frequency, while smaller proteins produce no signal, ensuring selectivity. Using less than 10 μl of sample, the assay detects approximately 200 vesicles per microlitre and works directly on plasma, serum, urine and cell media in under 1 h. In a blind study of 87 subjects with colorectal cancer, pancreatic ductal adenocarcinoma, glioblastoma, Alzheimer's disease and healthy controls, the method identified disease type with an area under the curve of 0.90-0.99. Compared with ultracentrifugation combined with surface plasmon resonance, which requires 24 h, our approach delivers 2 orders of magnitude better sensitivity and dynamic range, offering a fast and robust platform for clinical and research applications.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393742","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 : 2026-03-09DOI: 10.1038/s41551-026-01633-7
Gregory M Chen,Ankita Jain,David T Gering,Javier Satulovsky,Sarbani Datta,Peng Lai,Jayashree Karar,Vanessa E Gonzalez,Kathleen Alexander,Anne Chew,Julie K Jadlowsky,Marco Ruella,Luca Paruzzo,Kevin R Amses,Andrew J Rech,Edward A Stadtmauer,Noelle V Frey,Elizabeth O Hexner,David L Porter,Adam D Cohen,Saar I Gill,Alfred L Garfall,Stephen J Schuster,Kelvin C Mo,Samantha I Liang,Marko Spasic,Bruce L Levine,Don L Siegel,Angel Ramírez-Fernández,Christopher R Cabanski,EnJun Yang,Crystal L Mackall,Frederic D Bushman,Zinaida Good,E John Wherry,Carl H June,Joseph A Fraietta
Chimeric antigen receptor (CAR) T-cell therapy holds great promise for patients with cancer, and the identification of predictive biomarkers is crucial in finding new ways to guide therapy. Major challenges to the application of informatics and machine learning in CAR T-cell therapy include limited sample sizes and non-uniformity in data generation across cancer indications and trials. Here we took a global, pan-haematologic cancer approach, analysing 256 patients across 5 cancer types and 13 clinical trials. We generated data using a framework that included pre-infusion clinical features, over 2 million apheresis T cells analysed by flow cytometry using 17 unique markers, ex vivo T-cell expansion during CAR T-cell manufacture, more than 90,000 measurements of 30 serum markers and serial tracking of circulating CAR T cells using qPCR. From this data resource, we demonstrate the potential of pan-cancer predictive biomarkers that capture generalizable characteristics of treatment response and non-response in CAR T-cell therapy.
{"title":"Predictive biomarkers of response to chimeric antigen receptor (CAR) T-cell therapy for pan-haematologic cancer.","authors":"Gregory M Chen,Ankita Jain,David T Gering,Javier Satulovsky,Sarbani Datta,Peng Lai,Jayashree Karar,Vanessa E Gonzalez,Kathleen Alexander,Anne Chew,Julie K Jadlowsky,Marco Ruella,Luca Paruzzo,Kevin R Amses,Andrew J Rech,Edward A Stadtmauer,Noelle V Frey,Elizabeth O Hexner,David L Porter,Adam D Cohen,Saar I Gill,Alfred L Garfall,Stephen J Schuster,Kelvin C Mo,Samantha I Liang,Marko Spasic,Bruce L Levine,Don L Siegel,Angel Ramírez-Fernández,Christopher R Cabanski,EnJun Yang,Crystal L Mackall,Frederic D Bushman,Zinaida Good,E John Wherry,Carl H June,Joseph A Fraietta","doi":"10.1038/s41551-026-01633-7","DOIUrl":"https://doi.org/10.1038/s41551-026-01633-7","url":null,"abstract":"Chimeric antigen receptor (CAR) T-cell therapy holds great promise for patients with cancer, and the identification of predictive biomarkers is crucial in finding new ways to guide therapy. Major challenges to the application of informatics and machine learning in CAR T-cell therapy include limited sample sizes and non-uniformity in data generation across cancer indications and trials. Here we took a global, pan-haematologic cancer approach, analysing 256 patients across 5 cancer types and 13 clinical trials. We generated data using a framework that included pre-infusion clinical features, over 2 million apheresis T cells analysed by flow cytometry using 17 unique markers, ex vivo T-cell expansion during CAR T-cell manufacture, more than 90,000 measurements of 30 serum markers and serial tracking of circulating CAR T cells using qPCR. From this data resource, we demonstrate the potential of pan-cancer predictive biomarkers that capture generalizable characteristics of treatment response and non-response in CAR T-cell therapy.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"87 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381251","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：