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A retinoic acid:YAP1 signaling axis controls atrial lineage commitment

Cell Reports 2025年第5期44卷 115687页

作者： Abraham, Elizabeth Kostina, Aleksandra Volmert, Brett Roule, Thomas Huang, Ling Yu, Jingting Williams, April E. Megill, Emily Douglas, Aidan Pericak, Olivia M. Morris, Alex Stronati, Eleonora Larrinaga-Zamanillo, Arantza Fueyo, Raquel Zubillaga, Mikel Andrake, Mark D. Akizu, Naiara Aguirre, Aitor Estaras, Conchi Department of Cardiovascular Sciences Aging + Cardiovascular Discovery Center Lewis Katz School of Medicine Temple University Philadelphia 19140 PA United States Institute for Quantitative Health Science and Engineering Division of Developmental and Stem Cell Biology Michigan State University East Lansing 48824 MI United States Department of Biomedical Engineering College of Engineering Michigan State University East Lansing 48824 MI United States Raymond G. Perelman Center for Cellular and Molecular Therapeutics The Children's Hospital of Philadelphia Philadelphia 19104 PA United States Integrative Genomics and Bioinformatics Core Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Child and Adolescence Psychiatry The Children's Hospital of Philadelphia Philadelphia 19104 PA United States Department of Chemical and Systems Biology Stanford University School of Medicine Stanford 94305 CA United States Cancer Epigenetics Institute Fox Chase Cancer Center Philadelphia 19111 PA United States Molecular Modeling Facility Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia 19111 PA United States

In cardiac progenitor cells (CPCs), retinoic acid (RA) signaling induces atrial lineage gene expression and acquisition of an atrial cell fate. To achieve this, RA coordinates a complex regulatory network of downstream effectors that is not fully identified. To address this gap, we applied a functional genomics approach (i.e., scRNA-seq and snATAC-seq) to untreated and RA-treated human embryonic stem cell (hESC)-derived CPCs. Unbiased analysis revealed that the Hippo effectors YAP1 and TEAD4 are integrated with the atrial transcription factor enhancer network and that YAP1 activates RA enhancers in CPCs. Furthermore, Yap1 deletion in mouse embryos compromises the expression of RA-induced genes, such as Nr2f2, in the CPCs of the second heart field. Accordingly, in hESC-derived patterned heart organoids, YAP1 regulates the formation of an atrial chamber but is dispensable for the formation of a ventricle. Overall, our findings revealed that YAP1 cooperates with RA signaling to induce atrial lineages during cardiogenesis. © 2025 The Author(s)

关键词： atrial differentiation cardiac enhancers cardiac progenitors chamber development CP: Developmental biology heart organoids NR2F2 retinoic acid signaling SHF TEAD4 YAP1

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Digital is the New Blood: Enabling the Present and the Future

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Alzheimer's & Dementia 2023年第S21期19卷

作者： Rhoda Au Zachary T Popp Spencer Low Phillip H Hwang Ileana De Anda-Duran Stephanie Li Salman Rahman Huitong Ding Akwaugo Igwe Cody Karjadi Ting Fang Alvin Ang Sherral A. Devine Ashita S Gurnani Jesse B. Mez Lindsay A. Farrer Preeti Sunderaraman Honghuang Lin Vijaya B. Kolachalama Boston University School of Public Health Boston MA USA Framingham Heart Study Boston University School of Medicine Boston MA USA Davos Alzheimer’s Collaborative Philadelphia PA USA Department of Epidemiology Boston University School of Public Health Boston MA USA Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University School of Public Heatlh Boston MA USA Tulane School of Public Health and Tropical Medicine New Orleans LA USA Slone Epidemiology Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framigham Heart Study - Boston University Framingham MA USA Boston University Bioinformatics Program Boston MA USA Departments of Medicine (Biomedical Genetics) Neurology Ophthalmology Biostatistics and Epidemiology Boston University Schools of Medicine and Public Health Boston MA USA Department of Neurology - Boston University Chobanian & Avedisian School of Medicine Boston MA USA University of Massachusetts Medical School Worcester MA USA Framingham Heart Study/Boston University BOSTON MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA College of Arts & Sciences Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current applications, analogous to previously stored blood currently be used for omics/biomarker research. Today digital technologies are used to measure Alzheimer’s disease (AD)-related clinical symptoms (e.g., cognition, function, and behavior), but future possibilities remain to be determined. Importantly, digital data collection can go beyond current intermittent sampling by employing internet-connected applications and devices that allow real-time monitoring and accurate detection of changes, even within a widely heterogeneous spectrum. Method Beginning in 2005, the Framingham Heart Study (FHS) began digitally recording participants’ spoken responses to neuropsychological (NP) test questions, and in 2011 extended to a digital pen to record written responses. The emergence of wearable devices and smartphone applications has led to an expanded use of digital technologies beyond recordings during NP assessments. The Boston University Alzheimer’s Disease Research center (BU ADRC) leverages digital sensors in multiple internet-connected devices that longitudinally collect digital data, in both in-person and remote settings. Result The first publication involving FHS digital data was in 2016; the remaining (n> 17) have been from 2020-current. Digital data measuring cognition, function and behavior are being collected through the BU ADRC, where results to date indicate nearly 100% and 75% uptake of active and passive engagement applications, respectively, and 95% uptake for wearable devices (n = 60). Longitudinal assessments include > 3 months for ∼50 participants; > 1 year for n∼21 participants. Conclusion Opportunities for improved trial outcome measurement are evident in the broad range of outcomes digital sensors capture beyond traditional research methodologi

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Corrigendum to "European contribution to the study of ROS: A summary of the findings and prospects for the future from the COST action BM1203 (EU-ROS)" [Redox Biol. 13 (2017) 94-162]

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Redox biology 2018年 14卷 694-696页

作者： J Egea I Fabregat Y M Frapart P Ghezzi A Görlach T Kietzmann K Kubaichuk U G Knaus M G Lopez G Olaso-Gonzalez A Petry R Schulz J Vina P Winyard K Abbas O S Ademowo C B Afonso I Andreadou H Antelmann F Antunes M Aslan M M Bachschmid R M Barbosa V Belousov C Berndt D Bernlohr E Bertrán A Bindoli S P Bottari P M Brito G Carrara A I Casas A Chatzi N Chondrogianni M Conrad M S Cooke J G Costa A Cuadrado P My-Chan Dang B De Smet B Debelec-Butuner I H K Dias J D Dunn A J Edson M El Assar J El-Benna P Ferdinandy A S Fernandes K E Fladmark U Förstermann R Giniatullin Z Giricz A Görbe H Griffiths V Hampl A Hanf J Herget P Hernansanz-Agustín M Hillion J Huang S Ilikay P Jansen-Dürr V Jaquet J A Joles B Kalyanaraman D Kaminskyy M Karbaschi M Kleanthous L O Klotz B Korac K S Korkmaz R Koziel D Kračun K H Krause V Křen T Krieg J Laranjinha A Lazou H Li A Martínez-Ruiz R Matsui G J McBean S P Meredith J Messens V Miguel Y Mikhed I Milisav L Milković A Miranda-Vizuete M Mojović M Monsalve P A Mouthuy J Mulvey T Münzel V Muzykantov I T N Nguyen M Oelze N G Oliveira C M Palmeira N Papaevgeniou A Pavićević B Pedre F Peyrot M Phylactides G G Pircalabioru A R Pitt H E Poulsen I Prieto M P Rigobello N Robledinos-Antón L Rodríguez-Mañas A P Rolo F Rousset T Ruskovska N Saraiva S Sasson K Schröder K Semen T Seredenina A Shakirzyanova G L Smith T Soldati B C Sousa C M Spickett A Stancic M J Stasia H Steinbrenner V Stepanić S Steven K Tokatlidis E Tuncay B Turan F Ursini J Vacek O Vajnerova K Valentová F Van Breusegem L Varisli E A Veal A S Yalçın O Yelisyeyeva N Žarković M Zatloukalová J Zielonka R M Touyz A Papapetropoulos T Grune S Lamas H H H W Schmidt F Di Lisa A Daiber Institute Teofilo Hernando Department of Pharmacology School of Medicine Univerisdad Autonoma de Madrid Spain. Bellvitge Biomedical Research Institute (IDIBELL) and University of Barcelona (UB) L'Hospitalet Barcelona Spain. LCBPT UMR 8601 CNRS - Paris Descartes University Sorbonne Paris Cité Paris France. Brighton & Sussex Medical School Brighton UK. Experimental and Molecular Pediatric Cardiology German Heart Center Munich at the Technical University Munich Munich Germany DZHK (German Centre for Cardiovascular Research) partner site Munich Heart Alliance Munich Germany. Faculty of Biochemistry and Molecular Medicine and Biocenter Oulu University of Oulu Oulu Finland. Conway Institute School of Medicine University College Dublin Dublin Ireland. Department of Physiology University of Valencia Spain. Experimental and Molecular Pediatric Cardiology German Heart Center Munich at the Technical University Munich Munich Germany. Institute of Physiology JLU Giessen Giessen Germany. University of Exeter Medical School St Luke's Campus Exeter EX1 2LU UK. Life & Health Sciences and Aston Research Centre for Healthy Ageing Aston University Aston Triangle Birmingham B4 7ET UK. School of Life & Health Sciences Aston University Aston Triangle Birmingham B47ET UK. Laboratory of Pharmacology Faculty of Pharmacy National and Kapodistrian University of Athens Greece. Institute for Biology-Microbiology Freie Universität Berlin Berlin Germany. Departamento de Química e Bioquímica and Centro de Química e Bioquímica Faculdade de Ciências Portugal. Department of Medical Biochemistry Faculty of Medicine Akdeniz University Antalya Turkey. Vascular Biology Section & Whitaker Cardiovascular Institute Boston University School of Medicine Boston MA USA. Center for Neurosciences and Cell Biology University of Coimbra and Faculty of Pharmacy University of Coimbra Coimbra Portugal. Molecular technologies laboratory Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry Miklukho

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Introduction to noninvasive cardiac mapping

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Cardiac Electrophysiology Clinics 2015年第1期7卷 1-16页

作者： Bear, Laura Cuculich, Phillip S. Bernus, Olivier Efimov, Igor Dubois, Rémi IHU LIRYC Electrophysiology and Heart Modeling Institute Fondation Bordeaux Université Bordeaux France Inserm U1045 Cardiothoracic Research Center 146 rue Léo-Saignat Bordeaux Cedex 33076 France Cardiovascular Diseases and Electrophysiology Barnes-Jewish Hospital Washington University School of Medicine 660 South Euclid Avenue Campus Box 8086 St Louis 63110 MO United States Department of Biomedical Engineering Washington University School of Medicine 390E Whitaker Hall One Brookings Drive St. Louis 63130 MO United States

From the dawn of the twentieth century, the electrocardiogram (ECG) has revolutionized the way clinical cardiology has been practiced, and it has become the cornerstone of modern medicine today. Driven by clinical and research needs for a more precise understanding of cardiac electrophysiology beyond traditional ECG, inverse solution electrocardiography has been developed, tested, and validated. This article outlines the important progress from ECG development, through more extensive measurement of body surface potentials, and the fundamental leap to solving the inverse problem of electrocardiography, with a focus on mathematical methods and experimental validation. © 2015 Elsevier Inc.

关键词： Electrocardiogram Forward problem Inverse solution Mapping

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Diffeomorphic matching of diffusion tensor images

Diffeomorphic matching of diffusion tensor images

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2006 Conference on Computer Vision and Pattern Recognition Workshops

作者： Cao, Yan Miller, Michael I. Mori, Susumu Winslow, Raimond L. Younes, Laurent Center for Imaging Science Johns Hopkins University Department of Radiology Johns Hopkins University School of Medicine Center for Cardiovascular Bioinformatics and Modeling Whitaker Biomedical Engineering Institute Johns Hopkins University

ISBN: (纸本)0769526462

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two diffusion tensor images. A coarse to fine multi-resolution and multikernel-width scheme is detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI brain and images. © 2006 IEEE.

关键词： Pattern matching

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Diffeomorphic Matching of Diffusion Tensor Images

Diffeomorphic Matching of Diffusion Tensor Images

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yan Cao M.I. Miller S. Mori R.L. Winslow L. Younes Center for Imaging Science Johns Hopkins University USA Department of Radiology and Kennedy Krieger Institute School of Medicine Johns Hopkins University USA Center for Cardiovascular Bioinformatics & Modeling and the Whitaker Biomedical Engineering Institute Johns Hopkins University USA

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two diffusion tensor images. A coarse to fine multi-resolution and multikernel- width scheme is detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI brain and images.

关键词： Tensile stress Diffusion tensor imaging biomedical imaging Optimization methods Magnetic field measurement Extraterrestrial measurements Deformable models Magnetic resonance Joining processes Geophysics computing

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Large deformation diffeomorphic metric mapping of fiber orientations

Large deformation diffeomorphic metric mapping of fiber orie...

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Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005

作者： Cao, Yan Miller, Michael I. Winslow, Raimond L. Younes, Laurent Center for Imaging Science Johns Hopkins University Baltimore MD 21218 Center for Cardiovascular Bioinformatics and Modeling Whitaker Biomedical Engineering Institute Johns Hopkins University Baltimore MD 21218

ISBN: (纸本)076952334X

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the Large Deformation Diffeomorphic Metric Mapping framework to this type ofdataset, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart and brain images. © 2005 IEEE.

关键词： Fiber reinforced materials

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Large deformation diffeomorphic metric mapping of fiber orientations

Large deformation diffeomorphic metric mapping of fiber orie...

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International Conference on Computer Vision (ICCV)

作者： Yan Cao M.I. Miller R.L. Winslow L. Younes Center for Imaging Science Johns Hopkins University Baltimore MD USA Center for Cardiovascular Bioinformatics & Modeling and Whitaker Biomedical Engineering Institute Johns Hopkins University Baltimore MD USA

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the large deformation diffeomorphic metric-mapping framework to this type of dataset, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart and brain images

关键词： Diffusion tensor imaging Tensile stress Heart Cardiac disease Magnetic resonance imaging Image registration biomedical imaging Biological tissues cardiovascular diseases Cardiology

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