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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

SSRN

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SSRN 2024年

作者： Johansen, Nelson J. Kempynck, Niklas Zemke, Nathan R. Somasundaram, Saroja Winter, Seppe De Hooper, Marcus Dwivedi, Deepanjali Lohia, Ruchi Wehbe, Fabien Li, Bocheng Abaffyová, Darina Armand, Ethan J. Man, Julie De Eksi, Eren Can Hecker, Nikolai Hulselmans, Gert Konstantakos, Vasilis Mauduit, David Mich, John K. Partel, Gabriele Daigle, Tanya L. Levi, Boaz P. Zhang, Kai Tanaka, Yoshiaki Gillis, Jesse Ting, Jonathan T. Ben-Simon, Yoav Miller, Jeremy Ecker, Joseph R. Ren, Bing Aerts, Stein Lein, Ed S. Tasic, Bosiljka Bakken, Trygve E. Allen Institute for Brain Science SeattleWA98109 United States VIB Center for AI & Computational Biology VIB-KU Leuven Center for Brain and Disease Research KU Leuven Department of Human Genetics Leuven Belgium Center for Epigenomics Department of Cellular and Molecular Medicine University of California La Jolla San DiegoCA92093 United States Physiology Department Donnelly Centre for Cellular and Biomolecular Research University of Toronto TorontoON Canada Maisonneuve-Rosemont Hospital Research Centre University of Montreal MontrealQC Canada School of Life Sciences Westlake University Zhejiang Hangzhou China Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA92093 United States Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Physiology and Biophysics University of Washington SeattleWA98195 United States Salk Institute for Biological Studies La Jolla CA92037 United States

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi- Omics" to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for the identification of functional enhancers for mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian brain and help us design more efficient genetic tools and potential gene therapies for human neurological diseases. © 2024, The Authors. All rights reserved.

关键词： Mammals

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Proteome Analysis Using Gel-LC-MS/MS

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Current Protocols in Protein Science 2019年第1期96卷 e93页

作者： Goldman, Aaron R. Beer, Lynn A. Tang, Hsin-Yao Hembach, Peter Zayas-Bazan, Delaine Speicher, David W. Proteomics and Metabolomics Facility The Wistar Institute Philadelphia PA United States Center for Systems and Computational Biology and Molecular and Cellular Oncogenesis Program The Wistar Institute Philadelphia PA United States Biochemistry and Biophysics Graduate Group Perelman School of Medicine University of Pennsylvania Philadelphia PA United States

This article describes processing of protein samples using 1D SDS gels prior to protease digestion for proteomics workflows that subsequently utilize reversed-phase nanocapillary ultra-high-pressure liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). The resulting LC-MS/MS data are used to identify peptides and thereby infer proteins present in samples ranging from simple mixtures to very complex proteomes. Bottom-up proteome studies usually involve quantitative comparisons across several or many samples. For either situation, 1D SDS gels represent a simple, widely available technique that can be used to either fractionate complex proteomes or rapidly clean up low microgram samples with minimal losses. After gel separation and staining/destaining, appropriate gel slices are excised, and in-gel reduction, alkylation, and protease digestion are performed. Digests are then processed for LC-MS/MS analysis. Protocols are described for either sample fractionation with high-throughput processing of many samples or simple cleanup without fractionation. An optional strategy is to conduct in-solution reduction and alkylation prior to running gels, which is advantageous when a large number of samples will be separated into large numbers of fractions. Optimization of trypsin digestion parameters and comparison to in-solution protease digestion are also described. © 2019 by John Wiley & Sons, Inc. © 2019 John Wiley & Sons, Inc.

关键词： gel-LC-MS/MS in-gel digestion LC-MS/MS mass spectrometry proteome fractionation proteomics SDS gels

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Conserved multi-tissue transcriptomic adaptations to exercise training in humans and mice

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Cell Reports 2023年第5期42卷 112499页

作者： Moore, Timothy M. Lee, Sindre Olsen, Thomas Morselli, Marco Strumwasser, Alexander R. Lin, Amanda J. Zhou, Zhenqi Abrishami, Aaron Garcia, Steven M. Bribiesca, Jennifer Cory, Kevin Whitney, Kate Ho, Theodore Ho, Timothy Lee, Joseph L. Rucker, Daniel H. Nguyen, Christina Q.A. Anand, Akshay T.S. Yackly, Aidan Mendoza, Lorna Q. Leyva, Brayden K. Aliman, Claudia Artiga, Daniel J. Meng, Yonghong Charugundla, Sarada Pan, Calvin Jedian, Vida Seldin, Marcus M. Ahn, In Sook Diamante, Graciel Blencowe, Montgomery Yang, Xia Mouisel, Etienne Pellegrini, Matteo Turcotte, Lorraine P. Birkeland, Kåre I. Norheim, Frode Drevon, Christian A. Lusis, Aldons J. Hevener, Andrea L. Division of Cardiology Diabetes and Hypertension Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States Division of Endocrinology Diabetes and Hypertension Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States Department of Transplantation Institute of Clinical Medicine Faculty of Medicine University of Oslo Oslo Norway Department of Nutrition Institute of Basic Medical Sciences Faculty of Medicine University of Oslo Oslo Norway Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA United States UCLA-DOE Institute for Genomics and Proteomics University of California Los Angeles Los Angeles CA United States Institute for Quantitative and Computational Biosciences – The Collaboratory University of California Los Angeles Los Angeles CA United States Department of Chemical and Systems Biology Stanford School of Medicine Stanford CA United States Department of Biological Chemistry and Center for Epigenetics and Metabolism University of California Irvine Irvine CA United States Molecular Cellular and Integrative Physiology Interdepartmental Program University of California Los Angeles Los Angeles CA United States Department of Integrative Biology and Physiology University of California Los Angeles Los Angeles CA United States Institute for Quantitative and Computational Biosciences University of California Los Angeles Los Angeles CA United States Institute of Metabolic and Cardiovascular Diseases UMR1297 Inserm Paul Sabatier University Toulouse France Department of Biological Sciences Dana & David Dornsife College of Letters Arts and Sciences University of Southern California Los Angeles CA United States Department of Human Genetics University of California Los Angeles Los Angeles CA United States Department of Microbiology Immunology and Molecular Geneti

Physical activity is associated with beneficial adaptations in human and rodent metabolism. We studied over 50 complex traits before and after exercise intervention in middle-aged men and a panel of 100 diverse strains of female mice. Candidate gene analyses in three brain regions, muscle, liver, heart, and adipose tissue of mice indicate genetic drivers of clinically relevant traits, including volitional exercise volume, muscle metabolism, adiposity, and hepatic lipids. Although ∼33% of genes differentially expressed in skeletal muscle following the exercise intervention are similar in mice and humans independent of BMI, responsiveness of adipose tissue to exercise-stimulated weight loss appears controlled by species and underlying genotype. We leveraged genetic diversity to generate prediction models of metabolic trait responsiveness to volitional activity offering a framework for advancing personalized exercise prescription. The human and mouse data are publicly available via a user-friendly Web-based application to enhance data mining and hypothesis development. © 2023 The Authors

关键词： CP: Metabolism CP: molecular biology exercise genetics HMDP multi-omics physical activity physiology systems biology

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Correction to: CpG‑creating mutations are costly in many human viruses

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Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex

Cell genomics

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Cell genomics 2025年 100879页

作者： Nelson J Johansen Niklas Kempynck Nathan R Zemke Saroja Somasundaram Seppe De Winter Marcus Hooper Deepanjali Dwivedi Ruchi Lohia Fabien Wehbe Bocheng Li Darina Abaffyová Ethan J Armand Julie De Man Eren Can Ekşi Nikolai Hecker Gert Hulselmans Vasilis Konstantakos David Mauduit John K Mich Gabriele Partel Tanya L Daigle Boaz P Levi Kai Zhang Yoshiaki Tanaka Jesse Gillis Jonathan T Ting Yoav Ben-Simon Jeremy Miller Joseph R Ecker Bing Ren Stein Aerts Ed S Lein Bosiljka Tasic Trygve E Bakken Allen Institute for Brain Science Seattle WA 98109 USA. VIB Center for AI & Computational Biology VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics Leuven Belgium. Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA 92093 USA. Physiology Department and Donnelly Centre for Cellular and Biomolecular Research University of Toronto Toronto ON Canada. Maisonneuve-Rosemont Hospital Research Centre University of Montreal Montreal QC Canada. School of Life Sciences Westlake University Hangzhou Zhejiang China. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA 92093 USA. School of Life Sciences Westlake University Hangzhou Zhejiang China Westlake Laboratory of Life Sciences and Biomedicine Hangzhou Zhejiang China. Allen Institute for Brain Science Seattle WA 98109 USA Department of Physiology and Biophysics University of Washington Seattle WA 98195 USA. Salk Institute for Biological Studies La Jolla CA 92037 USA. Allen Institute for Brain Science Seattle WA 98109 USA. Electronic address: trygveb@***.

Identifying cell-type-specific enhancers is critical for developing genetic tools to study the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics" to evaluate machine learning and feature-based methods for nominating enhancer sequences targeting mouse cortical cell types. Methods were assessed using in vivo data from hundreds of adeno-associated virus (AAV)-packaged, retro-orbitally delivered enhancers. Open chromatin was the strongest predictor of functional enhancers, while sequence models improved prediction of non-functional enhancers and identified cell-type-specific transcription factor codes to inform in silico enhancer design. This challenge establishes a benchmark for enhancer prioritization and highlights computational and molecular features critical for identifying functional cortical enhancers, advancing efforts to map and manipulate gene regulation in the mammalian cortex.

关键词： ATAC-seq DNA sequence model TF codes cortex cross-species enhancer-AAV prediction benchmark single-cell multiomics

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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The IBEX Imaging Knowledge-Base: A Community Resource Enabling Adoption and Development of Immunofluoresence Imaging Methods

arXiv

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arXiv 2024年

作者： Yaniv, Ziv Anidi, Ifeanyichukwu U. Arakkal, Leanne Arroyo-Mejías, Armando J. Beuschel, Rebecca T. Börner, Katy Chu, Colin J. Clark, Beatrice Clatworthy, Menna R. Colautti, Jake Coscia, Fabian Croteau, Joshua Denha, Saven Dever, Rose Dutra, Walderez O. Fritzsche, Sonja Fullam, Spencer Gerner, Michael Y. Gola, Anita Gollob, Kenneth J. Hernandez, Jonathan M. Hor, Jyh Liang Ichise, Hiroshi Jing, Zhixin Jonigk, Danny Kandov, Evelyn Kastenmüller, Wolfgang Koenig, Joshua F.E. Kothurkar, Aanandita Kortekaas, Rosa K. Kreins, Alexandra Y. Lamborn, Ian T. Lin, Yuri Morais, Katia Luciano Pereira Lunich, Aleksandra Luz, Jean C.S. MacDonald, Ryan B. Makranz, Chen Maltez, Vivien I. McDonough, John E. Moriarty, Ryan V. Ocampo-Godinez, Juan M. Olyntho, Vitoria M. Oxenius, Annette Padhan, Kartika Remmert, Kirsten Richoz, Nathan Schrom, Edward C. Shang, Wanjing Shi, Lihong Shih, Rochelle M. Speranza, Emily Stierli, Salome Teichmann, Sarah A. Veres, Tibor Z. Vierhout, Megan Wachter, Brianna T. Williams, Margaret Zangger, Nathan Germain, Ronald N. Radtke, Andrea J. Bioinformatics and Computational Bioscience Branch National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Critical Care Medicine and Pulmonary Branch National Heart Lung and Blood Institute National Institutes of Health BethesdaMD United States Lymphocyte Biology Section Laboratory of Immune System Biology NIAID NIH BethesdaMD United States Department of Intelligent Systems Engineering Indiana University BloomingtonIN United States UCL Institute of Ophthalmology NIHR Moorfields Biomedical Research Centre London United Kingdom Cambridge Institute for Therapeutic Immunology and Infectious Diseases University of Cambridge Department of Medicine Molecular Immunity Unit Laboratory of Molecular Biology Cambridge United Kingdom McMaster Immunology Research Centre Schroeder Allergy and Immunology Research Institute Department of Medicine Faculty of Health Sciences McMaster University HamiltonON Canada Spatial Proteomics Group Berlin Germany Department of Business Development BioLegend Inc. San DiegoCA United States Functional Immunogenomics Unit National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health BethesdaMD United States Laboratory of Cell-Cell Interactions Department of Morphology Institute of Biological Sciences Universidade Federal de Minas Gerais MG Belo Horizonte Brazil Division of Rheumatology Rush University Medical Center ChicagoIL United States Department of Immunology University of Washington School of Medicine SeattleWA United States Robin Chemers Neustein Laboratory of Mammalian Cell Biology and Development The Rockefeller University New YorkNY United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Surgical Oncology Program National Cancer Institute National Institutes of Health BethesdaMD United States Institute of Pathology Aachen Medical University RWTH Aachen Aachen Germany Hannover Germany Würzburg Institute

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data. © 2024, CC BY.

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