Biological Imaging (Jan 2023)
Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure
- Eduardo A. González-Solares,
- Ali Dariush,
- Carlos González-Fernández,
- Aybüke Küpcü Yoldaş,
- Alireza Molaeinezhad,
- Mohammad Al Sa’d,
- Leigh Smith,
- Tristan Whitmarsh,
- Neil Millar,
- Nicholas Chornay,
- Ilaria Falciatori,
- Atefeh Fatemi,
- Daniel Goodwin,
- Laura Kuett,
- Claire M. Mulvey,
- Marta Páez Ribes,
- Fatime Qosaj,
- Andrew Roth,
- Ignacio Vázquez-García,
- Spencer S. Watson,
- Jonas Windhager,
- Samuel Aparicio,
- Bernd Bodenmiller,
- Ed Boyden,
- Carlos Caldas,
- Owen Harris,
- Sohrab P. Shah,
- Simon Tavaré,
- CRUK IMAXT Grand Challenge Team,
- Dario Bressan,
- Gregory J. Hannon,
- Nicholas A. Walton
Affiliations
- Eduardo A. González-Solares
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Ali Dariush
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom
- Carlos González-Fernández
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Aybüke Küpcü Yoldaş
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Alireza Molaeinezhad
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Mohammad Al Sa’d
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Leigh Smith
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Tristan Whitmarsh
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Neil Millar
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Nicholas Chornay
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- Ilaria Falciatori
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom
- Atefeh Fatemi
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom
- Daniel Goodwin
- McGovern Institute, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA McGovern Institute, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Laura Kuett
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- Claire M. Mulvey
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom
- Marta Páez Ribes
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom
- Fatime Qosaj
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom
- Andrew Roth
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
- Ignacio Vázquez-García
- Herbert and Florence Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Spencer S. Watson
- Department of Oncology and Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Jonas Windhager
- ORCiD
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- Samuel Aparicio
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- Ed Boyden
- McGovern Institute, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA McGovern Institute, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA Howard Hughes Medical Institute, Department of Physics, Harvard University, Cambridge, MA, USA Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Carlos Caldas
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom Cambridge Breast Unit, Addenbrooke’s Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
- Owen Harris
- Súil Interactive, Dublin, Ireland
- Sohrab P. Shah
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Simon Tavaré
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom Herbert and Florence Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA New York Genome Center, New York, NY, USA
- CRUK IMAXT Grand Challenge Team
- Dario Bressan
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom
- Gregory J. Hannon
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom
- Nicholas A. Walton
- Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom
- DOI
- https://doi.org/10.1017/S2633903X23000090
- Journal volume & issue
-
Vol. 3
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.
Keywords
