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Visualization of image data from cells to organisms

Nature Methods volume 7, pages S26–S41 (2010)Cite this article

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A Corrigendum to this article was published on 01 June 2010

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Abstract

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.

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Figure 1: Imaging techniques.
Figure 2: Visualization of high–dimensional image data.
Figure 3: Visualization of anatomical features in MRI.
Figure 4: Visualization of high–throughput data.

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  • 30 April 2010

    In the version of this article initially published, Carl Zeiss Microimaging was not acknowledged for providing access to the SPIM prototype used to generate images in the article. The error has been corrected in the HTML and PDF versions of the article.

References

  1. Goldberg, I.G. et al. The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 6, R47 (2005).

    PubMed  PubMed Central  Google Scholar 

  2. Moore, J. et al. Open tools for storage and management of quantitative image data. Methods Cell Biol. 85, 555–570 (2008).

    PubMed  Google Scholar 

  3. Swedlow, J.R., Goldberg, I.G. & Eliceiri, K.W. & the OME Consortium. Bioimage informatics for experimental biology. Annu. Rev. Biophys. 38, 327–346 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Cox, R. et al. A (sort of) new image data format standard: NifTI-1. Neuroimage 22, 99 (2004).

    Google Scholar 

  5. Saalfeld, S., Cardona, A., Hartenstein, V. & Tomancák, P. CATMAID: Collaborative Annotation Toolkit for Massive Amounts of Image Data. Bioinformatics 25, 1984–1986 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Levoy, M. Display of surfaces from volume data. IEEE Comput. Graph. Appl. 8, 29–37 (1988). This paper is a seminal work on the rendering of volumetric data by directly shading each voxel value and projecting it onto the viewing plane. The method provides realistic volumetric rendering without the need to model the data with geometric primitives.

    Google Scholar 

  7. Pieper, S.D., Halle, M. & Kikinis, R. 3D Slicer. in IEEE International Symposium on Biomedical Imaging: From Nano to Macro 632–635 (2004).

    Google Scholar 

  8. Pieper, S., Lorensen, B., Schroeder, W. & Kikinis, R. The na-mic kit: Itk, vtk, pipelines, grids and 3d slicer as an open platform for the medical image computing community. in IEEE International Symposium on Biomedical Imaging: From Nano to Macro 698–701 (2006).

    Google Scholar 

  9. Gordon, J.L., Buguliskis, J.S., Buske, P.J. & Sibley, L.D. Actin-like protein 1 (ALP1) is a component of dynamic, high molecular weight complexes in Toxoplasma gondii . Cell Motil. Cytoskeleton 67, 23–31 (2009).

    Google Scholar 

  10. Friston, K.J. et al. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189–210 (1994).

    Google Scholar 

  11. Smith, S.M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 suppl. 1, S208–S219 (2004).

    PubMed  Google Scholar 

  12. Dufour, A. et al. Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans. Image Process. 14, 1396–1410 (2005).

    PubMed  Google Scholar 

  13. Jaqaman, K. et al. Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods 5, 695–702 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Keller, P.J., Schmidt, A.D., Wittbrodt, J. & Stelzer, E.H. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008).

    CAS  PubMed  Google Scholar 

  15. Fischl, B. & Dale, A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. USA 97, 11050–11055 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Fischl, B. et al. Sequence-independent segmentation of magnetic resonance images. Neuroimage 23 suppl. 1, S69–S84 (2004).

    PubMed  Google Scholar 

  17. Salat, D.H. et al. Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage 48, 21–28 (2009).

    CAS  PubMed  Google Scholar 

  18. Schubert, W. et al. Analyzing proteome topology and function by automated multidimensional fluorescence microscopy. Nat. Biotechnol. 24, 1270–1278 (2006).

    CAS  PubMed  Google Scholar 

  19. Le Bihan, D. et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161, 401–407 (1986).

    CAS  PubMed  Google Scholar 

  20. Basser, P.J., Mattiello, J. & LeBihan, D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B. 103, 247–254 (1994).

    CAS  PubMed  Google Scholar 

  21. Tuch, D.S. et al. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48, 577–582 (2002).

    PubMed  Google Scholar 

  22. Wedeen, V.J., Hagmann, P., Tseng, W.-Y.I., Reese, T.G. & Weisskoff, R.M. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 54, 1377–1386 (2005).

    PubMed  Google Scholar 

  23. Hebert, B., Costantino, S. & Wiseman, P.W. Spatiotemporal image correlation spectroscopy (STICS) theory, verification, and application to protein velocity mapping in living CHO cells. Biophys. J. 88, 3601–3614 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Lorensen, W.E. & Cline, H.E. Marching cubes: a high resolution 3D surface construction algorithm. SIGGRAPH '87: Proc. 14th Ann. Conf. Computer Graphics and Interactive Techniques 21, 163–169 (1987). This paper presented a fast algorithm for computing a triangular mesh corresponding to an isosurface in a 3D data volume.

    Google Scholar 

  25. Lindig, T.M. et al. Spiny versus stubby: 3D reconstruction of human myenteric (type I) neurons. Histochem. Cell Biol. 131, 1–12 (2009).

    CAS  PubMed  Google Scholar 

  26. McAuliffe, M. et al. Medical image processing, analysis and visualization in clinical research. in Proc. 14th IEEE Symp. Computer-based Medical Systems (CBMS2001) 381–386 (2001).

    Google Scholar 

  27. Shattuck, D.W. & Leahy, R.M. BrainSuite: an automated cortical surface identification tool. Med. Image Anal. 6, 129–142 (2002).

    PubMed  Google Scholar 

  28. Fischl, B., Sereno, M.I. & Dale, A.M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).

    CAS  PubMed  Google Scholar 

  29. Goebel, R., Esposito, F. & Formisano, E. Analysis of functional image analysis contest (FIAC) data with Brainvoyager QX: from single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Hum. Brain Mapp. 27, 392–401 (2006).

    PubMed  PubMed Central  Google Scholar 

  30. Cointepas, Y., Mangin, J.-F., Garnero, L., Poline, J.-B. & Benali, H. BrainVISA: software platform for visualization and analysis of multi-modality brain data. Neuroimage 13, S98 (2001).

    Google Scholar 

  31. Visel, A., Thaller, C. & Eichele, G. GenePaint.org: an atlas of gene expression patterns in the mouse embryo. Nucleic Acids Res. 32, D552–D556 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Gray, P.A. et al. Mouse brain organization revealed through direct genome-scale TF expression analysis. Science 306, 2255–2257 (2004).

    CAS  PubMed  Google Scholar 

  33. Christiansen, J.H. et al. EMAGE: a spatial database of gene expression patterns during mouse embryo development. Nucleic Acids Res. 34, D637–D641 (2006).

    CAS  PubMed  Google Scholar 

  34. Tomancak, P. et al. Global analysis of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 8, R145 (2007).

    PubMed  PubMed Central  Google Scholar 

  35. Lein, E.S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Fowlkes, C.C. et al. A quantitative spatiotemporal atlas of gene expression in the Drosophila blastoderm. Cell 133, 364–374 (2008).

    CAS  PubMed  Google Scholar 

  37. Hill, D.L.G., Batchelor, P.G., Holden, M. & Hawkes, D.J. Medical image registration. Phys. Med. Biol. 46, R1–R45 (2001).

    CAS  PubMed  Google Scholar 

  38. Ng, L. et al. Neuroinformatics for genome-wide 3D gene expression mapping in the mouse brain. IEEE/ACM Trans. Comput. Biol. Bioinform. 4, 382–393 (2007).

    CAS  PubMed  Google Scholar 

  39. Klein, A. et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46, 786–802 (2009).

    PubMed  Google Scholar 

  40. Lowe, D. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004).

    Google Scholar 

  41. Preibisch, S., Saalfeld, S., Rohlfing, T. & Tomancak, P. Bead-based mosaicing of single plane illumination microscopy images using geometric local descriptor matching. Proc. SPIE 7259 (2009).

  42. Lindeberg, T. Scale-space for discrete signals. IEEE Trans. Pattern Anal. Mach. Learn. 12, 234–254 (1990).

    Google Scholar 

  43. Tharin, S. & Golby, A. Functional brain mapping and its applications to neurosurgery. Neurosurgery 60, 185–201; discussion 201–202 (2007).

    PubMed  Google Scholar 

  44. Lau, C. et al. Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain. BMC Bioinformatics 9, 153 (2008).

    PubMed  PubMed Central  Google Scholar 

  45. Bertrand, L. & Nissanov, J. The Neuroterrain 3D mouse brain atlas. Front. Neuroinformatics 2, 3 (2008).

    PubMed Central  Google Scholar 

  46. Carpenter, A.E. & Sabatini, D.M. Systematic genome-wide screens of gene function. Nat. Rev. Genet. 5, 11–22 (2004).

    CAS  PubMed  Google Scholar 

  47. Pepperkok, R. & Ellenberg, J. High-throughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell Biol. 7, 690–696 (2006).

    CAS  PubMed  Google Scholar 

  48. Glory, E. & Murphy, R.F. Automated subcellular location determination and high-throughput microscopy. Dev. Cell 12, 7–16 (2007).

    CAS  PubMed  Google Scholar 

  49. Simpson, J.C., Wellenreuther, R., Poustka, A., Pepperkok, R. & Wiemann, S. Systematic subcellular localization of novel proteins identified by large-scale cDNA sequencing. EMBO Rep. 1, 287–292 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Lécuyer, E. & Tomancak, P. Mapping the gene expression universe. Curr. Opin. Genet. Dev. 18, 506–512 (2008).

    PubMed  Google Scholar 

  51. Sönnichsen, B. et al. Full-genome RNAi profiling of early embryogenesis in Caenorhabditis elegans . Nature 434, 462–469 (2005).

    PubMed  Google Scholar 

  52. Tomancak, P. et al. Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 3, research0088.1–0088.14 (2002).

  53. Han, L., Hemert, J., Baldock, R. & Atkinson, M. Automating gene expression annotation for mouse embryo. in Proceedings of the 5th International Conference on Advanced Data Mining and Applications 469–478 (Springer, Beijing, 2009).

    Google Scholar 

  54. Newberg, J. & Murphy, R.F. A framework for the automated analysis of subcellular patterns in human protein atlas images. J. Proteome Res. 7, 2300–2308 (2008).

    CAS  PubMed  Google Scholar 

  55. Ji, S., Sun, L., Jin, R., Kumar, S. & Ye, J. Automated annotation of Drosophila gene expression patterns using a controlled vocabulary. Bioinformatics 24, 1881–1888 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Peng, H. et al. Automatic image analysis for gene expression patterns of fly embryos. BMC Cell Biol. 8 suppl. 1, S7 (2007).

    PubMed  PubMed Central  Google Scholar 

  57. Gehlenborg, N. et al. Visualization of omics data for systems biology. Nat. Methods 7, S56–S68 (2010).

    CAS  PubMed  Google Scholar 

  58. Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    PubMed  PubMed Central  Google Scholar 

  59. Jones, T.R. et al. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics 9, 482 (2008).

    PubMed  PubMed Central  Google Scholar 

  60. Kruskal, J.B. & Wish, M. Multidimensional Scaling (Sage Publications, Beverly Hills, California, USA and London, 1978).

    Google Scholar 

  61. Sammon, J.W. A nonlinear mapping for data structure analysis. IEEE Trans. Comput. C-18, 401–409 (1969).

    Google Scholar 

  62. Hamilton, N.A., Wang, J.T.H., Kerr, M.C. & Teasdale, R.D. Statistical and visual differentiation of subcellular imaging. BMC Bioinformatics 10, 94 (2009).

    PubMed  PubMed Central  Google Scholar 

  63. McComb, T. et al. Illoura: a software tool for analysis, visualization and semantic querying of cellular and other spatial biological data. Bioinformatics 25, 1208–1210 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Walter, T. et al. Automatic identification and clustering of chromosome phenotypes in a genome wide RNAi screen by time-lapse imaging. J. Struct. Biol. published online, doi: 10.1016/j.jsb.2009.10.004 (23 October 2009).

  65. Ringwald, M. et al. A database for mouse development. Science 265, 2033–2034 (1994).

    CAS  PubMed  Google Scholar 

  66. Richardson, L. et al. EMAGE mouse embryo spatial gene expression database: 2010 update. Nucleic Acids Res. 38, D703–D709 (2010).

    CAS  PubMed  Google Scholar 

  67. Baldock, R.A. et al. EMAP and EMAGE: a framework for understanding spatially organized data. Neuroinformatics 1, 309–325 (2003).

    PubMed  Google Scholar 

  68. Mueller, S.G. et al. Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement. 1, 55–66 (2005).

    PubMed  PubMed Central  Google Scholar 

  69. Mazziotta, J. et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Phil. Trans. R. Soc. Lond. B 356, 1293–1322 (2001).

    CAS  Google Scholar 

  70. Toga, A.W. Neuroimage databases: the good, the bad and the ugly. Nat. Rev. Neurosci. 3, 302–309 (2002).

    CAS  PubMed  Google Scholar 

  71. Van Horn, J.D. et al. The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies. Phil. Trans. R. Soc. Lond. B 356, 1323–1339 (2001).

    CAS  Google Scholar 

  72. Marcus, D.S. et al. Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19, 1498–1507 (2007).

    PubMed  Google Scholar 

  73. Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

  74. Tufte, E.R. The Visual Display of Quantitative Information (Graphics Press, Cheshire, Connecticut, USA, 2001). The classic text on the science of data visualization

  75. Moffat, J. et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283–1298 (2006).

    CAS  PubMed  Google Scholar 

  76. Wurdinger, T. et al. A secreted luciferase for ex vivo monitoring of in vivo processes. Nat. Methods 5, 171–173 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Ejsmont, R.K., Sarov, M., Winkler, S., Lipinski, K.A. & Tomancak, P. A toolkit for high-throughput, cross-species gene engineering in Drosophila . Nat. Methods 6, 435–437 (2009).

    CAS  PubMed  Google Scholar 

  78. Howles, G.P. & Ghaghada, K.B., Qi, Y., Munkundan, S. & Johnson, G.A. High-resolution magnetic resonance angiography in the mouse using a nanoparticle blood-pool contrast agent. Magn. Reson. Med. 62, 1447–1456 (2009).

    PubMed  PubMed Central  Google Scholar 

  79. Maudsley, A.A. et al. Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging. NMR Biomed. 19, 492–503 10.1002/nbm.1025 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Thompson, P.M. et al. Dynamics of gray matter loss in Alzheimer's disease. J. Neurosci. 23, 994–1005 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Plank, G. et al. Generation of histo-anatomically representative models of the individual heart: tools and application. Phil. Transact. A Math. Phys. Eng. Sci. 367, 2257–2292 (2009).

    Google Scholar 

  82. Chiang, M.C. et al. Fluid registration of diffusion tensor images using information theory. IEEE Trans. Med. Imaging 27, 442–456 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Lichtman, J.W. & Conchello, J.A. Fluorescence microscopy. Nat. Methods 2, 910–919 (2005).

    CAS  PubMed  Google Scholar 

  84. Conchello, J.-A. & Lichtman, J.W. Optical sectioning microscopy. Nat. Methods 2, 920–931 (2005).

    CAS  PubMed  Google Scholar 

  85. Hell, S.W. Toward fluorescence nanoscopy. Nat. Biotechnol. 21, 1347–1355 (2003).

    CAS  PubMed  Google Scholar 

  86. Helmchen, F. & Denk, W. Deep tissue two-photon microscopy. Nat. Methods 2, 932–940 (2005).

    CAS  PubMed  Google Scholar 

  87. Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).

    CAS  PubMed  Google Scholar 

  88. Rust, M.J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–795 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Sharpe, J. et al. Optical projection tomography as a tool for 3D microscopy and gene expression studies. Science 296, 541–545 (2002).

    CAS  PubMed  Google Scholar 

  90. Huisken, J., Swoger, J., Del Bene, F., Wittbrodt, J. & Stelzer, E.H. Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305, 1007–1009 (2004).

    CAS  PubMed  Google Scholar 

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Acknowledgements

P.T. thanks Carl Zeiss Microimaging for SPIM prototype access. T.W. was supported by a grant to J.E. (within the Mitocheck European Integrated Project LSHG-CT-2004-503464). D.W.S. was partially supported by US National Institutes of Health (NIH) grant P41 RR013642. M.E.B. was partly supported by NIH grant R01 EB004155-03. S.P. was partially supported by NIH grant P41 RR13218. S.D. was supported by the Wellcome Trust. J.-K.H. was supported by the ENFIN European Network of Excellence (contract LSHG-CT-2005-518254) awarded to J.E. A.E.C. and A.F. were supported by NIH grant 5 RL1 CA133834-03. J.E.S. was supported by the British Heart Foundation (grant BS/06/001) and the BBSRC (grant E003443). This work was funded in part through the NIH Roadmap for Medical Research grants U54 RR021813 (D.W.S.) and U54 EB005149 (S.P.). Information on the US National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics/.

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Authors and Affiliations

  1. European Molecular Biology Laboratory, Heidelberg, Germany

    Thomas Walter, Jan Ellenberg & Jean-Karim Hériché

  2. Laboratory of Neuro Imaging, University of California, Los Angeles, Los Angeles, California, USA

    David W Shattuck

  3. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK

    Richard Baldock

  4. Medical and Radiological Sciences (Medical Physics), University of Edinburgh, Edinburgh, UK

    Mark E Bastin

  5. Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Anne E Carpenter & Adam Fraser

  6. Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee, UK

    Suzanne Duce

  7. The University of Queensland, Institute for Molecular Bioscience, Brisbane, Australia

    Nicholas Hamilton & Mark A Ragan

  8. Isomics, Inc., Cambridge, Massachusetts, USA

    Steve Pieper

  9. Department of Cardiovascular Medicine, British Heart Foundation Experimental Magnetic Resonance Unit (BMRU), University of Oxford, Oxford, UK

    Jurgen E Schneider

  10. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

    Pavel Tomancak

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Correspondence to Jean-Karim Hériché.

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Walter, T., Shattuck, D., Baldock, R. et al. Visualization of image data from cells to organisms. Nat Methods 7 (Suppl 3), S26–S41 (2010). https://doi.org/10.1038/nmeth.1431

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