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arXiv 2022年

作者： Agterberg, Joshua Zhang, Anru R. Departments of Electrical and Systems Engineering and Statistics and Data Science University of Pennsylvania United States Departments of Biostatistics & Bioinformatics Computer Science Mathematics and Statistical Science Duke University United States

Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this paper we propose the tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the 2,∞ tensor perturbation bound for HOOI under independent, heteroskedastic, subgaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships. © 2022, CC BY.

关键词： Tensors

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Acid–base Homeostasis and Implications to the Phenotypic Behaviors of Cancer

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Genomics, Proteomics & bioinformatics 2023年第6期21卷 1133-1148页

作者： Yi Zhou Wennan Chang Xiaoyu Lu Jin Wang Chi Zhang Ying Xu Cancer Systems Biology Center China–Japan Union HospitalJilin UniversityChangchun 130033China Department of Biochemistry and Molecular Biology and Institute of Bioinformatics University of GeorgiaAthensGA 30602USA Department of Medical and Molecular Genetics Indiana University School of MedicineIndianapolisIN 46202USA Department of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907USA Department of Biohealth Informatics Indiana University–Purdue University IndianapolisIndianapolisIN 46202USA Departments of Chemistry and of Physics and Astronomy Stony Brook UniversityStony BrookNY 11794USA

Acid–base homeostasis is a fundamental property of living cells,and its persistent disruption in human cells can lead to a wide range of *** this study,we conducted a computational modeling analysis of transcriptomic data of 4750 human tissue samples of 9 cancer types in The Cancer Genome Atlas(TCGA)*** on our previous study,we quantitatively estimated the average production rate of OHby cytosolic Fenton reactions,which continuously disrupt the intracellular pH(pHi)*** predictions indicate that all or at least a subset of 43 reprogrammed metabolisms(RMs)are induced to produce net protons(H+)at comparable rates of Fenton reactions to keep the pHi *** then discovered that a number of wellknown phenotypes of cancers,including increased growth rate,metastasis rate,and local immune cell composition,can be naturally explained in terms of the Fenton reaction level and the induced *** study strongly suggests the possibility to have a unified framework for studies of cancerinducing stressors,adaptive metabolic reprogramming,and cancerous *** addition,strong evidence is provided to demonstrate that a popular view that Na+/H+exchangers along with lactic acid exporters and carbonic anhydrases are responsible for the intracellular alkalization and extracellular acidification in cancer may not be justified.

关键词： Acid-basehomeostasis Cancer microenvironment Metabolic reprogramming Fenton reaction Ironmetabolism

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Guidance on the use of complex systems models for economic evaluations of public health interventions

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HEALTH ECONOMICS 2023年第7期32卷 1603-1625页

作者： Breeze, Penny R. Squires, Hazel Ennis, Kate Meier, Petra Hayes, Kate Lomax, Nik Shiell, Alan Kee, Frank de Vocht, Frank O'Flaherty, Martin Gilbert, Nigel Purshouse, Robin Robinson, Stewart Dodd, Peter J. Strong, Mark Paisley, Suzy Smith, Richard Briggs, Andrew Shahab, Lion Occhipinti, Jo-An Lawson, Kenny Bayley, Thomas Smith, Robert Boyd, Jennifer Kadirkamanathan, Visakan Cookson, Richard Hernandez-Alava, Monica Jackson, Christopher H. Karapici, Amanda Sassi, Franco Scarborough, Peter Siebert, Uwe Silverman, Eric Vale, Luke Walsh, Cathal Brennan, Alan School of Health and Related Research University of Sheffield Sheffield UK British Medical Journal Technology Appraisal Group London UK MRC/CSO Social and Public Health Sciences Unit University of Glasgow Scotland UK School of Geography University of Leeds Leeds UK Department of Public Health LaTrobe University Melbourne Australia Centre for Public Health Queen's University Belfast Belfast UK Population Health Sciences Bristol Medical School University of Bristol Bristol UK NIHR Applied Research Collaboration West (ARC West) Bristol UK Department of Public Health Policy and Systems University of Liverpool Liverpool UK CRESS University of Surrey Guildford UK Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Business School University of Newcastle Newcastle UK Lumanity-HEOR South Yorkshire Sheffield UK College of Medicine and Health University of Exeter Exeter UK London School of Hygiene & Tropical Medicine London UK Department of Behavioural Science and Health UCL London UK Brain and Mind Centre University of Sydney New South Wales Camperdown Australia United Kingdom Health Security Agency Birmingham UK MRC/CSO Social and Public Health Sciences Unit University of Glasgow Glasgow UK Centre for Health Economics University of York Heslington UK MRC Biostatistics Unit University of Cambridge Cambridge UK NIHR SPHR London School of Hygiene and Tropical Medicine London UK Centre for Health Economics & Policy Innovation Imperial College Business School London UK Nuffield Department of Population Health University of Oxford Oxfordshire Oxford UK Department of Public Health Health Services Research and Health Technology Assessment UMIT TIROL - University for Health Sciences and Technology Hall in Tirol Tyrol Austria Division of Health Technology Assessment and Bioinformatics ONCOTYROL - Center for Personalized Cancer Medicine Innsbruck Austria Center for Health Decision Science Departments of Epidemio

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed;principles for designing a complex systems model;and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

关键词： complex systems economic modeling public health

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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MET fusions and splicing variants convergently defne a subgroup of glioma sensitive to MET inhibitors

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Holistic Integrative Oncology 2022年第1期1卷 244-254页

作者： Ke-Nan Zhang Zheng Zhao Jing Chen Zhaoshi Bao Rui-Chao Chai Zhiyan Sun Lingxiang Wu Zhiliang Wang Hanjie Liu Quanhua Mu Huimin Hu Fan Zeng Zheng Wang Guanzhang Li Yuanhao Chang Qiangwei Wang Fan Wu Ying Zhang Yuqing Liu Chunjie Jiang Ulf Dietrich Kahlert Do-Hyun Nam Wei Zhang Chunsheng Kang Jiguang Wang Rongjie Tao Qianghu Wang Tao Jiang Beijing Neurosurgical Institute Capital Medical UniversityBeijingChina Department of Neurosurgery Beijing Tiantan HospitalCapital Medical UniversityBeijingChina Department of Bioinformatics Nanjing Medical UniversityNanjingJiangsuChina Department of Chemical and Biological Engineering Divison of Life ScienceCenter for Systems Biology and Human Healthand State Key Laboratory of Molecular NeuroscienceThe Hong Kong University of Science and TechnologyHong KongChina Division of Diabe‑tes Endocrinologyand MetabolismDepartment of MedicineBaylor College of MedicineHoustonTXUSA Molecular and Experimental Surgery Faculty of Medicine and University Medicine MagdeburgMagdeburgGermany Institute for Refractory Cancer Research Samsung Medical CenterSeoulRepublic of Korea Department of Health Science&Technology Samsung Advanced Institute for Health Sciences&Technology(SAIHST)Sungkyunkwan UniversitySeoulRepublic of Korea Department of Neurosurgery Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea Laboratory of Neuro‑Oncology Ministry of Education and Tianjin CityTianjin Neurological InstituteTianjin Medical University General HospitalKey Laboratory of PostNeuro Injury Neuro-Repair and Regeneration in Central Nervous SystemTianjinChina Departments of Neurosurgery Shandong Cancer Hospital and InstituteShandong UniversityJinanShandongChina Center of Brain Tumor Beijing Institute for Brain DisordersBeijingChina China National Clinical Research Center for Neurological Diseases BeijingChina Research Unit of Accurate Diagnosis Treatmentand Translational Medicine of Brain TumorsChinese Academy of Medical SciencesBeijingChina

Purpose:Our previous study has shown that PTPRZ1-MET(ZM)fusion is a viable target for MET inhibitors in ***,the diversity and prevalence of somatic MET alterations in difuse gliomas are still elusive and need to be extensively characterized for identifying novel therapeutic ***:Totally,1,350 glioma patients and 31 patient-derived cells were collected from the Chinese Glioma Genome Atlas(CGGA)and published *** kinds of MET fusions and/or splicing variants(MET F/SVs)were identifed by bioinformatical ***-cell RNA sequencing(scRNA-seq)were used for *** vitro experiments of drug resistance were conducted for the possibility of MET-targeted ***:MET F/SVs but not genomic amplifcation,were highly enriched in the secondary glioblastomas(sGBM)and marked worse *** molecular and scRNA-seq analysis revealed that MET F/SVs were induced in the course of glioma evolution and highly associated with MET *** in vitro and the clinical study showed that cells and patients harboring MET F/SVs have better response to MET ***:Our fndings expanded the percentage of gliomas with abnormal MET alterations and suggested that a subgroup of gliomas harboring MET F/SVs may beneft from MET-targeted therapy.

关键词： MET variation Secondary glioblastoma Biomarker MET inhibitor Precision neuro-oncology

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Cholesterol metabolism regulated by CAMKK2-CREB signaling promotes castration-resistant prostate cancer

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Cell Reports 2025年第6期44卷 115792页

作者： Lin, Chenchu Pulliam, Thomas L. Han, Jenny J. Xu, Jiaqian Recio, Carlos Vera Wilkenfeld, Sandi R. Shi, Yan Kushwaha, Manoj Bench, Sarah Ruiz, Eduardo Senthilkumar, Sanjanaa Dileep, Jayasurya Shepherd, Peter D.A. Navone, Nora M. Klekers, Albert R. Whitley, Elizabeth M. Ittmann, Michael M. Eberlin, Livia S. Wang, Wenyi Frigo, Daniel E. Department of Cancer Systems Imaging The University of Texas MD Anderson Cancer Center Houston 77054 TX United States UTHealth Graduate School of Biomedical Sciences The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Surgery Baylor College of Medicine Houston 77030 TX United States Mayo Clinic Alix School of Medicine Phoenix 85054 AZ United States McKetta Department of Chemical Engineering University of Texas at Austin Austin 78712 TX United States Department of Genitourinary Medical Oncology The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Abdominal Radiology The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Veterinary Medicine and Surgery The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Departments of Pathology and Immunology Baylor College of Medicine Houston 77030 TX United States Dan L. Duncan Cancer Center Houston 77030 TX United States Michael E. DeBakey Veterans Affairs Medical Center Houston 77030 TX United States Department of Chemistry The University of Texas at Austin Austin 78712 TX United States Center for Nuclear Receptors and Cell Signaling University of Houston Houston 77204 TX United States Department of Biology and Biochemistry University of Houston Houston 77004 TX United States

Castration-resistant prostate cancer (CRPC) remains an incurable disease in need of improved treatments. CAMKK2 is an emerging therapeutic target whose oncogenic effects in prostate cancer have, to date, been largely attributed to its activation of AMP-activated protein kinase (AMPK). Here, we demonstrate that CAMKK2 promotes prostate cancer growth through an alternative downstream pathway involving CAMKI and CREB. Unbiased transcriptomics identify CREB-mediated transcription as a CAMKK2-regulated process, findings that we validate using diverse molecular, genetic, and pharmacological approaches in vitro and in vivo. CAMKK2 promotes CREB phosphorylation/activation through CAMKIα independently of AMPK, CAMKIV, or other CAMKI isoforms. Functionally, the CREB family members CREB1 and ATF1 exhibit close redundancy, necessitating co-targeting for optimal anti-tumor efficacy. An inhibitor of CREB1/ATF1 blocks CRPC with minimal side effects. Mechanistically, CAMKK2 and CREB increase CRPC growth through augmenting cholesterol metabolism. Together, these findings identify an oncogenic pathway that could be exploited for the treatment of CRPC. © 2025 The Author(s)

关键词： AMPK androgen receptor CAMKI CAMKK2 cholesterol CP: Cancer CP: Metabolism CREB metabolism prostate cancer

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Building an international consortium for tracking coronavirus health status (vol 78, pg 931, 2020)

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NATURE MEDICINE 2020年第8期26卷 1309-1309页

作者： Segal, Eran Zhang, Feng Lin, Xihong King, Gary Shalem, Ophir Shilo, Smadar Allen, William E. Alquaddoomi, Faisal Altae-Tran, Han Anders, Simon Balicer, Ran Bauman, Tal Bonilla, Ximena Booman, Gisel Chan, Andrew T. Cohen, Ori Coletti, Silvano Davidson, Natalie Dor, Yuval Drew, David A. Elemento, Olivier Evans, Georgina Ewels, Phil Gale, Joshua Gavrieli, Amir Geiger, Benjamin Grad, Yonatan H. Greene, Casey S. Hajirasouliha, Iman Jerala, Roman Kahles, Andre Kallioniemi, Olli Keshet, Ayya Kocarev, Ljupco Landua, Gregory Meir, Tomer Muller, Aline Nguyen, Long H. Oresic, Matej Ovchinnikova, Svetlana Peterson, Hedi Prodanova, Jana Rajagopal, Jay Raetsch, Gunnar Rossman, Hagai Rung, Johan Sboner, Andrea Sigaras, Alexandros Spector, Tim Steinherz, Ron Stevens, Irene Vilo, Jaak Wilmes, Paul Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Howard Hughes Medical Institute Chevy Chase Maryland USA Broad Institute of MIT and Harvard Cambridge MA USA Departments of Biostatistics and Statistics Harvard T.H. Chan School of Public Health Harvard University Cambridge MA USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Pediatric Diabetes Unit Ruth Rappaport Children’s Hospital Rambam Healthcare Campus Haifa Israel Harvard Society of Fellows Harvard University Cambridge MA USA ETH Zurich NEXUS Personalized Health Technologies Zurich Switzerland Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Center for Molecular Biology (ZMBH) University of Heidelberg Heidelberg Germany Clalit Research Institute Clalit Health Services Ramat Gan Israel Mapping and Geo-Information Engineering Civil and Environmental Engineering Faculty The Technion Haifa Israel ETH Zurich Department for Computer Science Zurich University Hospital Zurich Medical Informatics Zurich and SIB Swiss Institute of Bioinformatics Zurich Switzerland Regen Network Mar del Plata Argentina Massachusetts General Hospital (MGH) Boston MA USA Chelonia Applied Science Allschwil Switzerland IMRIC Developmental Biology and Cancer Research School of Medicine The Hebrew University Jerusalem Israel Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Science for Life Laboratory (SciLifeLab) Department of Biochemistry and Biophysics Stockholm University Stockholm Sweden http://*** Canada Department of Immunology Weizmann Institute of Scienc

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Computational platforms and environments Databases Infectious diseases

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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

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NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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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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Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution

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Nature metabolism 2025年第5期7卷 875-894页

作者： Loft, Anne Emont, Margo P. Weinstock, Ada Divoux, Adeline Ghosh, Adhideb Wagner, Allon Hertzel, Ann V. Maniyadath, Babukrishna Deplancke, Bart Liu, Boxiang Scheele, Camilla Lumeng, Carey Ding, Changhai Ma, Chenkai Wolfrum, Christian Strieder-Barboza, Clarissa Li, Congru Truong, Danh D. Bernlohr, David A. Stener-Victorin, Elisabet Kershaw, Erin E. Yeger-Lotem, Esti Shamsi, Farnaz Hui, Hannah X. Camara, Henrique Zhong, Jiawei Kalucka, Joanna Ludwig, Joseph A. Semon, Julie A. Jalkanen, Jutta Whytock, Katie L. Dumont, Kyle D. Sparks, Lauren M. Muir, Lindsey A. Fang, Lingzhao Massier, Lucas Saraiva, Luis R. Beyer, Marc D. Jeschke, Marc G. Mori, Marcelo A. Boroni, Mariana Walsh, Martin J. Patti, Mary-Elizabeth Lynes, Matthew D. Blüher, Matthias Rydén, Mikael Hamda, Natnael Solimini, Nicole L. Mejhert, Niklas Gao, Peng Gupta, Rana K. Murphy, Rinki Pirouzpanah, Saeed Corvera, Silvia Tang, Su'an Das, Swapan K. Schmidt, Søren F Zhang, Tao Nelson, Theodore M. Efthymiou, Vissarion Wang, Wenjing Tong, Yihan Tseng, Yu-Hua Mandrup, Susanne Rosen, Evan D. Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark Section of Endocrinology Diabetes and Metabolism University of Chicago Chicago IL United States Department of Medicine Section of Genetic Medicine University of Chicago Chicago IL United States Translational Research Institute Orlando FL United States Department of Health Sciences and Technology Institute of Food Nutrition and Health ETH Zurich Schwerzenbach Switzerland Department of Electrical Engineering and Computer Sciences University of California Berkeley CA United States Department of Molecular and Cell Biology University of California Berkeley CA United States Center for Computational Biology University of California Berkeley CA United States Department of Biochemistry Molecular Biology and Biophysics Institute on the Biology of Aging and Metabolism University of Minnesota-Twin Cities Minneapolis MN United States Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark Laboratory of Systems Biology and Genetics Institute of Bioengineering School of Life Sciences École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Pharmacy and Pharmaceutical Sciences Faculty of Science National University of Singapore Singapore Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Genome Institute of Singapore (GIS) Agency for Science Technology and Research (A*STAR) Singapore Precision Medicine Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Cardiovascular-Metabolic Disease Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore NUS Centre for Cancer Research Yong Loo Lin School of Medicine Nationa

Adipose tissue (AT) is a complex connective tissue with a high relative proportion of adipocytes, which are specialized cells with the ability to store lipids in large droplets. AT is found in multiple discrete depots throughout the body, where it serves as the primary repository for excess calories. In addition, AT has an important role in functions as diverse as insulation, immunity and regulation of metabolic homeostasis. The Human Cell Atlas Adipose Bionetwork was established to support the generation of single-cell atlases of human AT as well as the development of unified approaches and consensus for cell annotation. Here, we provide a first roadmap from this bionetwork, including our suggested cell annotations for humans and mice, with the aim of describing the state of the field and providing guidelines for the production, analysis, interpretation and presentation of AT single-cell data. © 2025. Springer Nature Limited.

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