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Quantum Mechanical Tunnelling Probes with Redox Cycling for Ultra-Sensitive Detection of Biomolecules

Angewandte Chemie 2025年

作者： Long Yi Tao Jiang Dr. Ren Ren Prof. Ji Cao Prof. Joshua B. Edel Prof. Aleksandar P. Ivanov Prof. Longhua Tang State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou 310027 China Nanhu Brain-Computer Interface Institute Second Affiliated Hospital School of Medicine Hangzhou Zhejiang 311100 China Department of Chemistry Molecular Science Research Hub Imperial College London White City Campus 82 Wood Lane London W12 0BZ UK Institute of Pharmacology and Toxicology Zhejiang Province Key Laboratory of Anti-Cancer Drug Research College of Pharmaceutical Sciences Zhejiang University Hangzhou China

Quantum mechanical tunnelling sensors (QMTs) have emerged as a promising technology for next-generation single-molecule detection. Furthermore, QMT sensors can be combined with redox species resulting in repeated oxidation and reduction (redox cycling).. We developed robust QMT probes with electrode gap distances below 2 nm. Using the generator-collector (GC) mode, we verified that redox cycling of the ferrocyanide/ferricyanide (Fe(CN) 6 3−/4− ) couple occurs both in the tunnelling regime and on the electrode surface. Our findings indicated that the current enhancement is affected by both the gap distance and surface modifications of the probes. These QMT probes exhibited remarkable sensitivity, capable of detecting Fe(CN) 6 3−/4− concentrations down to sub-picomolar levels. Utilising this ability to modulate redox reactions, we adapted the QMT probes to serve as electrochemical sensors for detecting viral proteins. By modifying the electrode surfaces, our functionalised QMT probes achieved sub-pM detection limits with high selectivity in biofluids such as nasopharyngeal secretions. These findings highlight the potential of QMT probes to develop into a new class of electrochemical tunnelling sensors, offering significant advancements in biomedical diagnostics.

关键词： quantum tunnelling sensors redox cycling reaction single-molecular detection biosensor

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Mapping global lake dynamics reveals the emerging roles of small lakes (October, 10.1038/s41467-022-33239-3, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-12页

作者： Pi, Xuehui Luo, Qiuqi Feng, Lian Xu, Yang Tang, Jing Liang, Xiuyu Ma, Enze Cheng, Ran Fensholt, Rasmus Brandt, Martin Cai, Xiaobin Gibson, Luke Liu, Junguo Zheng, Chunmiao Li, Weifeng Bryan, Brett A. Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) The University of Hong Kong Department of Urban Planning and Design Hong Kong SAR China (GRID:grid.194645.b) (ISNI:***) The University of Hong Kong Urban Systems Institute Hong Kong SAR China (GRID:grid.194645.b) (ISNI:***) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) University of Copenhagen Department of Geosciences and Natural Resource Management Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Biology Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Lund University Department of Physical Geography and Ecosystem Science Lund Sweden (GRID:grid.4514.4) (ISNI:0000 0001 0930 2361) Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) University of Copenhagen Department of Geosciences and Natural Resource Management Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Chinese Academy of Sciences Institute of Geodesy and Geophysics Wuhan China (GRID:grid.9227.e) (ISNI:***) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) North China University of Water Resources and Electric Power School of Water Conservancy Zhengzhou China (GRID:grid.412224.3) (ISNI:0000 0004 1759 6955) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263

Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984–1999) to the end (2010–2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km2, and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km2) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon *** are essential components of the hydrological and biogeochemical cycles. Here, Pi et al develop a global lake dataset called GLAKES via high-resolution satellite images and deep learning to examine global lake changes over four decades.

关键词： Hydrology Limnology

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Safety and efficacy of Angong Niuhuang Pills in patients with moderate-to-severe acute ischemic stroke (ANGONG TRIAL): A randomized double-blind placebo-controlled pilot clinical trial

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中华医学杂志(英文版) 2025年第5期138卷 579-588页

作者： Li Shengde Wang Anxin Shi Lin Liu Qin Guo Xiaoling Liu Kun Wang Xiaoli Li Jie Zhu Jianming Wu Qiuyi Yang Qingcheng Zhuang Xianbo You Hui Feng Feng Luo Yishan Li Huiling Ni Jun Peng Bin Department of Neurology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical Science and Peking Union Medical College Beijing China Department of Neurology Beijing Tiantan Hospital Capital Medical University Beijing China BrainNow Research Institute Shenzhen Guangdong China Department of Neurology The Second People’s Hospital of Yibin Yibin Sichuan China Department of Neurology The First Affiliated Hospital of Anhui University of Technology Huainan Anhui China Department of Neurology Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province) Changsha Hunan China Department of Neurology Hengshui People’s Hospital Hengshui Hebei China Department of Neurology The First People’s Hospital of Yibin Yibin Sichuan China Department of Neurology The First People’s Hospital of Changde City Changde Hunan China Department of Neurology The First People’s Hospital of Zhangjiagang City Suzhou Jiangsu China Department of Neurology The People’s Hospital of Anyang City Anyang Henan China Department of Neurology Liaocheng People’s Hospital Liaocheng Shandong China Department of Radiology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical Science and Peking Union Medical College Beijing China Department of Occupational Medicine and Toxicologym Beijing Chaoyang Hospital Capital Medical University Beijing China

Background: Preclinical studies have indicated that Angong Niuhuang Pills (ANP) reduce cerebral infarct and edema volumes. This study aimed to investigate whether ANP safely reduces cerebral infarct and edema volumes in patients with moderate to severe acute ischemic ***: This randomized, double-blind, placebo-controlled pilot trial included patients with acute ischemic stroke with National Institutes of Health Stroke Scale (NIHSS) scores ranging from 10 to 20 in 17 centers in China between April 2021 and July 2022. Patients were allocated within 36 h after onset via block randomization to receive ANP or placebo (3 g/day for 5 days). The primary outcomes were changes in cerebral infarct and edema volumes after 14 days of treatment. The primary safety outcome was severe adverse events (SAEs) for 90 ***: There were 57 and 60 patients finally included in the ANP and placebo groups, respectively for modified intention-to-treat analysis. The median age was 66.0 years, and the median NIHSS score at baseline was 12.0. The changes in cerebral infarct volume at day 14 were 0.3 mL and 0.4 mL in the ANP and placebo groups, respectively (median difference: -7.1 mL; interquartile range [IQR]: -18.3 to 2.3 mL,P = 0.30). The changes in cerebral edema volume of the ANP and placebo groups on day 14 were 11.4 mL and 4.0 mL, respectively (median difference: 3.0 mL, IQR: -1.3 to 9.9 mL,P = 0.15). The rates of SAE within 90 days were similar in the ANP (3/57, 5%) and placebo (7/60, 12%) groups (P = 0.36). Changes in serum mercury and arsenic concentrations were comparable. In patients with large artery atherosclerosis, ANP reduced the cerebral infarct volume at 14 days (median difference: -12.3 mL; IQR: -27.7 to -0.3 mL,P = 0.03).Conclusions: ANP showed a similar safety profile to placebo and non-significant tendency to reduce cerebral infarct volume in patients with moderate-to-severe stroke. Further studies are warranted to assess the efficacy of ANP in reducing ce

关键词： Angong Niuhuang Pill Acute ischemic stroke Cerebral infarct Cerebral edema Pharmacology

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Transfer Learning for EEG-Based brain-computer interfaces: A Review of Progress Made Since 2016

arXiv

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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： brain computer interface

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Combination of NMF and IVA for improving the quality of EOG signals

Combination of NMF and IVA for improving the quality of EOG ...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Wenhui Sun Bing Xue Jingyi Xue Shengli Zhang Zhao Lv School of Computer Science and Technology Anhui University Hefei China Suzhou Xiaoma E-Commerce Co. Ltd Suzhou China Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Eye movements play an important role in the implementation of human computer interface (HCI) system, which could be recorded by the electrooculography (EOG) technique. However, the recorded EOG signals are always contaminated by various latent artifacts. Aiming at obtaining high-quality EOG signals, a blind source separation (BSS) method combined with non-negative matrix factorization (NMF) and independent vector analysis (IVA), is used in the separation problem of EOG signals. The effectiveness of the addition of NMF is validated on the classification tasks, and the corresponding average classification accuracy is higher than that of extended Infomax independent component analysis (ICA) and IVA, with the improvement of 4.42% and 3.26%, respectively. This result indicates that the combination of NMF and IVA can achieve better separation performance, in which the NMF could provide more information close to the source signals in the iteration step.

关键词： Human computer interaction Electrooculography Image processing Independent component analysis Blind source separation Classification algorithms Task analysis

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Who can benefit more from its twelve-week treatment:A prospective cohort study of blonanserin for patients with schizophrenia

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World Journal of Psychiatry 2024年第11期14卷 1735-1745页

作者： Bao-Yan Xu Kun Jin Hai-Shan Wu Xue-Jun Liu Xi-Jin Wang Hong Sang Ke-Qing Li Mei-Juan Sun Hua-Qing Meng Huai-Li Deng Zhi-Yuan Xun Xiao-Dong Yang Lin Zhang Guan-Jun Li Rui-Ling Zhang Duan-Fang Cai Jia-Hong Liu Gui-Jun Zhao Long-Fa Liu Gang Wang Chang-Lie Zhao Bin Guo Sheng-Chun Jin Ling-Yun Huang Fu-De Yang Jian-Min Zheng Gui-Lai Zhan Mao-Sheng Fang Xiang-Jun Meng Guang-Ya Zhang Hai-Min Li Xiang-Lai Liu Ju-Hong Li Bin Wu Hai-Yun Li Jin-Dong Chen Department of Psychiatry National Clinical Research Center for Mental DisordersNational Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangsha 410011Hunan ProvinceChina Department of Psychiatry Hebei Provincial Mental Health CenterHebei Key Laboratory of Major Mental and Behavioral DisordersThe Sixth Clinical Medical College of Hebei UniversityBaoding 071000Hebei ProvinceChina Department of Psychiatry Brain Hospital of Hunan ProvinceChangsha 410000Hunan ProvinceChina Department of Psychiatry The First Psychiatric Hospital of HarbinHarbin 150000Heilongjiang ProvinceChina Mental Health Center Changchun Sixth HospitalChangchun 130000Jilin ProvinceChina Department of Pharmacy Daqing Third HospitalDaqing 163000Heilongjiang ProvinceChina Department of Physical and Psychological Rehabilitation Guangda Rehabilitation HospitalChongqing 400000China Department of Psychology Shanxi Provincial Mental Health CenterTaiyuan 030000Shanxi ProvinceChina Department of Psychiatry Tianjin Anding HospitalTianjin 300000China Department of Psychiatry Shandong Mental Health CenterJinan 250000Shandong ProvinceChina Department of Psychiatry Fuzhou Shenkang HospitalFuzhou 350000Fujian ProvinceChina Department of Psychiatry Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghai 200000China Department of Psychiatry The Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang 453000Henan ProvinceChina Department of Psychiatry The Fifth People’s Hospital of ZigongZigong 643000Sichuan ProvinceChina Department of Psychiatry The Affiliated Kangning Hospital of Wenzhou Medical UniversityWenzhou 325000Zhejiang ProvinceChina Department of Psychiatry Guangyuan Mental Health CenterGuangyuan 628000Sichuan ProvinceChina Department of Psychiatry Jilin Sixth People’s HospitalJilin 132000Jilin ProvinceChina The National Clinical Research Center for Mental Disorders&Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital

BACKGROUND Blonanserin(BNS)is a well-tolerated and effective drug for treating *** To investigate which types of patients would obtain the most benefit from BNS *** A total of 3306 participants were evaluated in a 12-week,prospective,multicenter,open-label post-marketing surveillance study of *** psychiatric rating scale(BPRS)scores were calculated to evaluate the effectiveness of BNS,and its safety was assessed with the incidence of adverse drug *** regression was used to screen the influencing factors for the reduction of BPRS total score,and logistic regression was used to identify patients with a better response to *** The baseline BPRS total score(48.8±15.03)decreased to 27.7±10.08 at 12 weeks(P<0.001).Extrapyramidal symptoms(14.6%)were found to be the most frequent adverse drug *** acute phase,baseline BPRS total score,current episode duration,number of previous episodes,dose of concomitant antipsychotics,and number of types of sedative-hypnotic agents were found to be independent factors affecting the reduction of BPRS total score after treatment ***,patients in the acute phase with baseline BPRS total score≥45,current episode duration<3 months,and≤3 previous episodes derived greater benefit from 12-week treatment with *** Patients in the acute phase with more severe symptoms,shorter current episode duration,fewer previous episodes,and a lower psychotropic drug load derived the greatest benefit from treatment with BNS.

关键词： Schizophrenia Blonanserin Effectiveness Psychosocial functioning Prospective cohort study

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Micropatterned piezoelectric films induced by modulated corona field for weak signal detection

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Chemical Engineering Journal 2025年 517卷

作者： Fu Lv Chenghang Yu Bing Liu Jiamin Lin Zijian Hong Yongjun Wu Zhaohui Ren Qilong Zhang Shurong Dong Jikui Luo Juan Li Yuhui Huang School of Materials Science and Engineering Cyrus Tang Center for Sensor Materials and Applications Innovation Center of Yangtze Delta Institute of Fundamental and Transdisciplinary Research Zhejiang University Hangzhou Zhejiang 310058 China Nanhu Brain-computer Interface Institute Hangzhou Zhejiang 311100 China School of Engineering Hangzhou City University Hangzhou Zhejiang 310015 China College of Electronic Information and Engineering Hangzhou Dianzi University Hangzhou Zhejiang 310018 China Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Taizhou Institute of Zhejiang University Taizhou Zhejiang 318000 China College of Information Science and Electronic Engineering Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou Zhejiang 310027 China College of Materials and Science Engineering Zhejiang University of Technology Hangzhou Zhejiang 310014 China

Enhancing the inherent piezoelectricity and introducing innovative structural designs are the two fundamental strategies for enhancing the pressure sensitivity of P(VDF-TrFE)-based sensors. In this study, we propose a scalable and cost-effective manufacturing approach that utilizes a modulated corona field to create micropatterns on the surface of P(VDF-TrFE) films, and enhance their piezoelectric properties during the annealing and recrystallization process. The micropatterned P(VDF-TrFE) films exhibit a distinctive “wavy” microstructure and possess ultrahigh piezoelectric properties (d 33 ∼ 35.6 pC/N), resulting in excellent sensitivity (∼477.94 mV/kPa). To unravel the underlying mechanism responsible for this superior performance, we employ finite element modeling and phase-field simulations. Our findings reveal that the modulated corona field generates an equipotential surface with a characteristic “wavy” distribution and promotes the creation of nucleation sites for the polar β phase in P(VDF-TrFE) films. A real-time monitoring system is further exploited with the obtained micropatterned P(VDF-TrFE) films for detecting radial artery pulse signals. The results obtained from this work hold great promise for the development of highly sensitive sensors, with wide-ranging applications in healthcare and environmental monitoring.

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Oracle character recognition by nearest neighbor classification with deep metric learning 15

Oracle character recognition by nearest neighbor classificat...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Zhang, Yi-Kang Zhang, Heng Liu, Yong-Ge Yang, Qing Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing China School of Computer & Information Engineering Anyang Normal University Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan China

ISBN: (纸本)9781728128610

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU. © 2019 IEEE.

关键词： Character recognition

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Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition

arXiv

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arXiv 2020年

作者： Wu, Xun Zheng, Wei-Long Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Clinical Data Animation Center Department of Neurology Massachusetts General Hospital Harvard Medical School 55 Fruit Street BostonMA United States

Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP. The strength feature achieves the best classification performance and outperforms the state-of-the-art differential entropy feature based on single-channel analysis for the EEG signals. The experimental results reveal that distinct functional connectivity patterns are exhibited for the five emotions of disgust, fear, sadness, happiness, and neutrality. Furthermore, we construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals using deep canonical correlation analysis. The classification accuracies of multimodal emotion recognition are 95.08 ± 6.42% on the SEED dataset, 84.51 ± 5.11% on the SEED-V dataset, and 85.34 ± 2.90% and 86.61 ± 3.76% for arousal and valence on the DEAP dataset, respectively. The results demonstrate the complementary representation properties of the EEG functional connectivity network features with eye movement data. In addition, we find that the brain networks constructed with fewer channels, i.e., 18 channels, achieve comparable performance with that of the 62-channel network with respect to multimodal emotion recognition and enable easier setups for BCI systems in real scenarios. Cop

关键词： Electrophysiology

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Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers

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Cell Stem Cell 2025年

作者： Luo, Tao Liu, Cong Cheng, Tao Zhao, Guo-Qin Huang, Ying Luan, Jing-Yun Guo, Junyu Liu, Xiang Wang, Yi-Fan Dong, Yang Xiao, Yu He, Enhui Sun, Rui-Zhen Chen, Xiuyu Chen, Jiekai Ma, Jun Megason, Sean Ji, Junfeng Xu, Peng-Fei Center of Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital Zhejiang University School of Medicine Zhejiang Hangzhou 310058 China Nanhu Brain-Computer Interface Institute Zhejiang Hangzhou 311100 China Center for Genetic Medicine the Fourth Affiliated Hospital of School of Medicine and International School of Medicine Zhejiang University Yiwu 322000 China Institute of Genetics Zhejiang University School of Medicine Zhejiang Hangzhou 310058 China Women's Hospital Zhejiang University School of Medicine Zhejiang Hangzhou 310006 China Precision Medicine Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou China Department of Histology and Embryology Harbin Medical University Heilongjiang Harbin 150081 China Center for Biomedical Digital Science Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences Guangzhou 510530 China Department of Systems Biology Harvard Medical School 200 Longwood Avenue Boston 02115 MA United States

Precise dorsal-ventral (D-V) patterning of the neural tube (NT) is essential for the development and function of the central nervous system. However, existing models for studying NT D-V patterning and related human diseases remain inadequate. Here, we present organizers derived from pluripotent stem cell aggregate fusion (“ORDER”), a method that establishes opposing BMP and SHH gradients within neural ectodermal cell aggregates. Using this approach, we generated NT organoids with ordered D-V patterning from both zebrafish and human pluripotent stem cells (hPSCs). Single-cell transcriptomic analysis revealed that the synthetic human NT organoids (hNTOs) closely resemble the human embryonic spinal cord at Carnegie stage 12 (CS12) and exhibit greater similarity to human NT than to mouse models. Furthermore, using the hNTO model, we demonstrated the critical role of WNT signaling in regulating intermediate progenitors, modeled TCTN2-related D-V patterning defects, and identified a rescue strategy. © 2025 Elsevier Inc.

关键词： BMP neural tube organoid pluripotent stem cells SHH

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