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Visualization of Image Data from Cells to Organisms
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Walter T.1, Shattuck D.W.2, Baldock R.3, Bastin M.E.4, Carpenter A.E.5, Duce S.6, Ellenberg J.1, Fraser A.5, Hamilton N.7, Pieper S.8, Ragan M.A.7, Schneider J.E.9, Tomancak P.10, Hériché J-K.1
Institution: |
1European Molecular Biology Laboratory, Heidelberg, Germany. 2Laboratory of Neuro Imaging, University of California, Los Angeles, Los Angeles, California, USA. 3Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK. 4Medical and Radiological Sciences (Medical Physics), University of Edinburgh, Edinburgh, UK. 5Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. 6Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee, UK. 7The University of Queensland, Institute for Molecular Bioscience, Brisbane, Australia. 8Isomics, Inc., Cambridge, Massachusetts, USA. 9British Heart Foundation Experimental Magnetic Resonance Unit (BMRU), Department of Cardiovascular Medicine, University of Oxford, Oxford, UK. 10Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. |
Publisher: |
Nature Publishing Group |
Publication Date: |
Mar-2010 |
Journal: |
Nature |
Volume Number: |
7 |
Issue Number: |
3 |
Pages: |
S26-S41 |
Citation: |
Nature Methods Supplement 2010 March; 7(3):S27-S41. |
PubMed ID: |
20195255 |
Appears in Collections: |
NA-MIC, NAC, SLICER |
Sponsors: |
Mitocheck European Integrated Project LSHG-CT 2004-503464 NIH P41 RR013642 NIH R01 EB004155-03 NIH P41 RR13218 European Network of Excellence LSHG-CT-2005-518254 NIH RL1 CA133834-03 NIH U54 RR021813 NIH U54 EB005149 |
Generated Citation: |
Walter T., Shattuck D.W., Baldock R., Bastin M.E., Carpenter A.E., Duce S., Ellenberg J., Fraser A., Hamilton N., Pieper S., Ragan M.A., Schneider J.E., Tomancak P., Hériché J-K. Visualization of Image Data from Cells to Organisms. Nature Methods Supplement 2010 March; 7(3):S27-S41. PMID: 20195255. |
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Advances in imaging techniques and high-throughput technologies are providing scientists with unprecedented possibilities to visualize internal structures of cells, organs and organisms and to collect systematic image data characterizing genes and proteins on a large scale. To make the best use of these increasingly complex and large image data resources, the scientific community must be provided with methods to query, analyze and crosslink these resources to give an intuitive visual representation of the data. This review gives an overview of existing methods and tools for this purpose and highlights some of their limitations and challenges.
Additional Material
1 File (155.259kB)
Walter-NMethS2010-fig1.jpg (155.259kB)