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Readings in Bioimage Informatics

Description: Imaging experiments have become one of the main sources of data for researchers to test and validate hypothesis in biology, medicine, and related fields. Computational methods for automated interpretation and information extraction from biological images have augmented the impact of imaging experiments and are quickly becoming a valuable extension of imaging devices (microscopes, MR scanners, etc.). The course will consist of readings covering automated image analysis and interpretation methods centered around key modern biological problems. Representative topics include: location proteomics, digital histopathology, high content screening, morphological analysis, fluorescence microscopy, deconvolution, image-based modeling, amongst others. Course work will consist of critical readings, presentations, as well as student reports on current representative papers in each topic.

Prerequisite: Prior exposure to image analysis and cell biology, or permission of the instructor.

Instructor: Gustavo K. Rohde, HH C 122, gustavor@cmu.edu.

Office hours: Thursdays, 2-3pm.

Grading:

  • 60% Presentations
  • 20% Final report
  • 20% Class participation

Papers:

- Week 1 (1/20/09): Overview, general concepts.

Supplemental reading (these are to be used as background reading, not to be presented):

  • J. Kovacevic, G. K. Rohde, Overview of Image Analysis Tools and Tasks for Microscopy, in Microscopic Image Analysis for Life Science Applications, J. Rittscher, R. Machiraju and S.T.C. Wong, Eds., Artech House, 2008. ( pdf )
  • W. M. Ahmed, A. Ghafoor, J. P. Robinson, Knowledge extraction for high-throughput biological imaging. IEEE Multimedia, vol 14, no 4, pp. 52-62, 2007. ( pdf )
  • R. Eils, C. Athale, Computational imaging in cell biology. Journal of Cell Biology, vol 161, no. 3, pp. 447-481, 2003. ( pdf )

- Week 2 (1/27/09): Automated, image-based, location proteomics.

  • M. V. Boland and R. F. Murphy (2001). A Neural Network Classifier Capable of Recognizing the Patterns of all Major Subcellular Structures in Fluorescence Microscope Images of HeLa Cells. Bioinformatics 17:1213-1223. ( pdf )
  • T. Zhao, M. Velliste, M.V. Boland, and R.F. Murphy (2005). Object Type Recognition for Automated Analysis of Protein Subcellular Location. IEEE Trans. Image Proc. 14:1351-1359. ( pdf )

- Week 3 (2/3/09): Location proteomics, continuation.

  • X. Chen and R. F. Murphy (2005). Objective Clustering of Proteins Based on Subcellular Location Patterns. J. Biomed. Biotech. 2005(2):87-95. ( pdf )
  • T. Zhao and R. F. Murphy (2007). Automated learning of generative models for subcellular location: Building blocks for systems biology. Cytometry 71A:978-990. ( pdf )

Supplemental reading:

  • R.F. Murphy (2006). Putting proteins on the map [News and Views]. Nature Biotech. 24:1223-1224. ( pdf )
  • E. Glory and R.F. Murphy (2007). Automated Subcellular Location Determination and High Throughput Microscopy. Developmental Cell 12:7-16. ( pdf )

- Week 4 (2/10/09): Computation aided localization, resolution.

  • E. Betzig et al., 2006, Imaging intracellular florescent proteins at nanometer resolution. Science 313: 1642-1645. ( pdf )
  • S. Ram, E.S. Ward, R. J. Ober, Beyond Rayleigh's criterion: a resolution measure with application to single-molecule microscopy. PNAS 103: 4457-4462. ( pdf ). Supplement ( pdf )

Supplemental reading:

  • C Vonesch, F. Aguet, J-L. Vonesch, M Unser (2006). The colored revolution of bioimaging. IEEE Signal Processing Magazine, 23:20:31. ( pdf )
  • J. W. Lichtman and J-A. Cohcnello. Fluorescence microscopy (2005). Nature Methods, 2:910-919. ( pdf )
  • B. N. G. Giepmans, et al., The fluorescent toolbox for assessing protein location and function. Science 312: 217-224. ( pdf )
  • Intro to optics/system analysis slides: ( pdf )

- Week 5 (2/17/09): High-throughput screening, drug profiling.

  • Perlman et al, Multidimensional drug profiling by automated microscopy (2004). Science 306: 1194-1198. ( pdf )
  • L.-H. Loo, W. F. Wu, S. Altschuler, Image-based multivariate profiling of drug responses from single cells (2007). Nature Methods 4:445-453. ( pdf ), Supplement, ( pdf )

Supplemental reading:

  • X. Zhou, S.T.C. Wong, High content cellular imaging for drug development (2006). IEEE Signal Processing Magazine, 170-174. ( pdf )
  • X. Zhou, S.T.C. Wong, Informatics challenges of high-throughput microscopy (2006). IEEE Signal Processing Magazine, 63-72. ( pdf )

- Week 6 (2/24/09): Single molecule conformational studies.

  • Watkins et al, Quantitative Single-Molecule Conformational Distributions: a Case Study with Poly(L-proline). ( pdf )
  • Hanson et al,Illuminating the mechanistic roles of enzyme conformational dynamics. PNAS 104(46): 18055-18060. ( pdf ). Supplement ( pdf )

Supplemental reading:

  • Watkins, L.P., H. Yang (2004), Information bounds and optimal analysis of dynamic single molecule measurements. Biophysical Journal, 88:4015-4029. ( pdf )
  • Watkins, L.P., H. Yahng (2005), Detection of intensity change points in time-resolved single-molecule measurements. J. Phys. Chem B, 109:617-628. ( pdf )

- Week 7 (3/3/09): Shape analysis.

  • Pincus, Z., J. A. Theriot 2007, Comparison of quantitative methods for cell-shape analysis. Journal of Microscopy, 227(2):140-156. ( pdf )
  • Rohde et al, Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa cells (2008). Cytometry A, 73:341-350. ( pdf ).

- Week 8 (3/17/09): Neuroimaging.

  • Friston, K.J. et al. (1995), Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2:189-210. ( pdf )
  • Ashburner, J., and K. J. Friston (2000), Voxel-based morphometry -- the methods. Neuroimage, 11:805-821. ( pdf ).

- Week 9 (3/24/09): modeling cell deformation.

  • Germain et al, Characterization of Cell Deformation and Migration Using a Parametric Estimation of Image Motion. IEEE Transactions on Biomedical Engineering, 46(5):1999. ( pdf )
  • Rieu et al, Diffusion and Deformations in Single Hydra Cells in Cellular Aggregates. Biophysical Journal 79, 2000, pp 1903-1914. ( pdf )

- Week 10 (4/31/09): pathology, morphometry

  • S. Basu, S.T. Acton, Estimation of cell statistics on a cell manifold for high content screening with automated cell segmentation.Forty-first asilomar conference on signals systems and computers, pp:1851-1855, 2007. ( pdf )
  • P. Wolfe et al, Using nuclear morphometry to discriminate the tumorigenic potential of cells: a comparison of statistical methods. Cancer epidemiology, Biomarkers & Prevention 13(6), 976-988, 2004. ( pdf )

- Week 11 (5/14/09): drosophila gene expression

  • Hendriks et al, Three-dimensional morphoogy and gene expression in the Drosophila blastoderm at cellular resolution I: data acquisition pipeline. Genome Biology 7(12), R123, 2006. ( pdf )
  • J. Zhou and H. Peng, Automatic recognition and annotation of gene expression patterns of fly embryos. Bioinformatics, 23(5), pp. 589-596, 2007. ( pdf )

- Week 12 (5/21/09): morphogenesis

  • Gibson et al, The emergence of geometric order in proliferating metazoan epithelia. Nature 442(31), pp. 1038-1041, 2006. ( pdf )
  • P. Vitorino and T. Meyer, Modular control of endothelial sheet migration, Genes Dev. pp. 3268-3281, 2008. ( pdf )