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Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure.
González-Solares, Eduardo A; Dariush, Ali; González-Fernández, Carlos; Küpcü Yoldas, Aybüke; Molaeinezhad, Alireza; Al Sa'd, Mohammad; Smith, Leigh; Whitmarsh, Tristan; Millar, Neil; Chornay, Nicholas; Falciatori, Ilaria; Fatemi, Atefeh; Goodwin, Daniel; Kuett, Laura; Mulvey, Claire M; Páez Ribes, Marta; Qosaj, Fatime; Roth, Andrew; Vázquez-García, Ignacio; Watson, Spencer S; Windhager, Jonas; Aparicio, Samuel; Bodenmiller, Bernd; Boyden, Ed; Caldas, Carlos; Harris, Owen; Shah, Sohrab P; Tavaré, Simon; Bressan, Dario; Hannon, Gregory J; Walton, Nicholas A.
Afiliação
  • González-Solares EA; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Dariush A; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • González-Fernández C; CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom.
  • Küpcü Yoldas A; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Molaeinezhad A; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Al Sa'd M; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Smith L; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Whitmarsh T; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Millar N; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Chornay N; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Falciatori I; Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom.
  • Fatemi A; CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom.
  • Goodwin D; CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom.
  • Kuett L; McGovern Institute, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Mulvey CM; McGovern Institute, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Páez Ribes M; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Qosaj F; Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
  • Roth A; CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom.
  • Vázquez-García I; CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom.
  • Watson SS; CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom.
  • Windhager J; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
  • Aparicio S; Herbert and Florence Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA.
  • Bodenmiller B; Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Boyden E; Department of Oncology and Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
  • Caldas C; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Harris O; Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
  • Shah SP; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
  • Tavaré S; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Bressan D; McGovern Institute, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Hannon GJ; McGovern Institute, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Walton NA; Howard Hughes Medical Institute, Department of Physics, Harvard University, Cambridge, MA, USA.
Biol Imaging ; 3: e11, 2023.
Article em En | MEDLINE | ID: mdl-38487685
ABSTRACT
With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Biol Imaging Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Biol Imaging Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido
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