Automated image analysis for high-content screening and analysis

Journal of Biomolecular Screening

Volumn 15, Issue 7, 2010, Pages 726-734

  Shariff, Aabid ^a,b   Kangas, Joshua ^a,b   Coelho, Luis Pedro ^a,b   Quinn, Shannon ^a,b   Murphy, Robert F ^a,b,c  

^a Carnegie Mellon University   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c UNIVERSITY OF FREIBURG   (Germany)

Author keywords

bioimage informatics; high content screening; image analysis; machine learning; subcellular location

Indexed keywords

AUTOMATION; CELL CULTURE; COMPUTER ANALYSIS; CYTOLOGY; HIGH CONTENT SCREENING; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MACHINE LEARNING; MICROSCOPE; MICROSCOPY; PIPELINE; PRIORITY JOURNAL; REGISTRATION; REVIEW; SCREENING;

ANIMALS; AUTOMATION; DRUG EVALUATION, PRECLINICAL; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LEARNING; STATISTICS AS TOPIC;

EID: 77956015614     PISSN: 10870571     EISSN: 1552454X     Source Type: Journal    
DOI: 10.1177/1087057110370894     Document Type: Review

References (56)

1. Giuliano K., DeBiasio R., Dunlay T., Gough A., Volosky J., Zock J.,, et al. High-content screening: a new approach to easing key bottlenecks in the drug discovery process. J Biomol Screen 1997 ; 2: 249-259.
2. Boland MV, Markey MK, Murphy RF: Classification of protein localization patterns obtained via fluorescence light microscopy. Proc IEEE Int Conf EMBS Soc 1997: 594-597.
3. Bleicher KH, Bohm HJ, Muller K., Alanine AI: Hit and lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2003 ; 2: 369-378.
4. Taylor DL: Past, present and future of high content screening and the field of cellomics. Methods Mol Biol 2007 ; 356: 3-18.
5. Carpenter AE, Sabatini DM: Systematic genome-wide screens of gene function. Nat Rev Genet 2004 ; 5: 11-22.
6. Daub A., Sharma P., Finkbeiner S.: High-content screening of primary neurons: ready for prime time. Curr Opin Neurobiol 2009 ; 19: 537-543.
7. Glory E., Murphy RF: Automated subcellular location determination and high-throughput microscopy. Dev Cell 2007 ; 12: 7-16.
8. Jackson C., Murphy RF, Kovacevic J.: Efficient acquisition and learning of fluorescence microscopy data models. IEEE Int Conf Image Proc 2007 ; 6: 245-248.
9. Jackson C., Murphy RF, Kovacevic J.: Intelligent acquisition and learning of fluorescence microscope data models. IEEE Trans Image Proc 2009 ; 18: 2071-2084.
10. Chan TF, Vese LA: Active contours without edges. IEEE Trans Image Proc 2001 ; 10: 266-277.
11. Jones TR, Carpenter AE, Golland P.: Voronoi-based segmentation of cells on image manifolds. In Liu Y, Jiang T, Zhang C (eds): Computer Vision for Biomedical Image Applications. Berlin: Springer, 2005: 535-543.
12. Chen S-C., Zhao T., Gordon GJ, Murphy RF: A novel graphical model approach to segmenting cell images. Proc IEEE Symp Comput Intell Bioinform Comput Biol 2006: 1-8.
13. Srinivasa G., Fickus MC, Guo Y., Linstedt AD, Kovacevic J.: Active mask segmentation of fluorescence microscope images. IEEE Trans Image Proc 2009 ; 18: 1817-1829.
14. Gould S., Gao T., Koller D.: Region-based segmentation and object detection. In Advances in Neural Information Processing Systems (NIPS 2009). La Jolla, CA: Neural Information Processing Systems Foundation, 2009.
15. Lin G., Adiga U., Olson K., Guzowski JF, Barnes CA, Roysam B.: A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry Part A 2003 ; 56A: 23-36.
16. Cohen AR, Roysam B., Turner JN: Automated tracing and volume measurements of neurons from 3-D confocal fluorescence microscopy data. J Microsc 1994 ; 173: 103-114.
17. He W., Hamilton TA, Cohen AR, Holmes TJ, Pace C., Szarowski DH,, et al. Automated three-dimensional tracing of neurons in confocal and bright-field images. Microsc Microanal 2003 ; 9: 296-310.
18. Al-Kofahi K., Lasek S., Szarowski DH, Page CJ, Nagy G., Turner JN,, et al. Rapid automated three-dimensional tracing of neurons from confocal image stacks. IEEE Trans Inform Tech Biomed 2002 ; 6: 171-187.
19. Tyrrell JA, di Tomaso E., Fuja D., Tong R., Kozak K., Jain RK,, et al. Robust 3-D modeling of vasculature imagery using superellipsoids. IEEE Trans Med Imaging 2007 ; 26: 223-237.
20. Sigal A., Milo R., Cohen A., Geva-Zatorsky N., Klein Y., Alaluf I.,, et al. Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins. Nat Methods 2006 ; 3: 525-531.
21. Genovesio A., Olivo-Martin J-C.: Particle tracking in 3D+t biological imaging. In Rittscher J, Machiraju R, Wong STC (eds): Microscopic Image Analysis for Life Science Applications. Norwood, MA: Artech House, 2008: 223-282.
22. Li K., Miller ED, Chen M., Kanade T., Weiss LE, Campbell PG: Cell population tracking and lineage construction with spatiotemporal context. Med Image Anal 2008 ; 12: 546-566.
23. Smal I., Draegestein K., Galjart N., Niessen W., Meijering E.: Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: application to microtubule growth analysis. IEEE Trans Med Imaging 2008 ; 27: 789-804.
24. Fitzpatrick JM, Hill DLG, Maurer CR Jr: Image registration. In Fitzpatrick JM, Sonka M (eds): Handbook of Medical Imaging: Volume 2. Medical Image Processing and Analysis. Bellingham, WA: SPIE-The International Society for Optical Engineering, 2000: 447-513.
25. Yang S., Kohler D., Teller K., Cremer T., Le Baccon P., Heard E.,, et al. Nonrigid registration of 3-d multichannel microscopy images of cell nuclei. IEEE Trans Image Process 2008 ; 17: 493-499.
26. Mosaliganti K., Pan T., Sharp R., Ridgway R., Iyengar S., Gulacy A.,, et al. Registration and 3D visualization of large microscopy images. Proc SPIE Ann Med Imaging Meetings 2006 ; 6144: 923-934.
27. Boland MV, Murphy RF: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 2001 ; 17: 1213-1223.
28. Huang K., Murphy RF: From quantitative microscopy to automated image understanding. J Biomed Opt 2004 ; 9: 893-912.
29. Chebira A., Barbotin Y., Jackson C., Merryman T., Srinivasa G., Murphy RF,, et al. A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinform 2007 ; 8: 210.
30. Hamilton N., Pantelic R., Hanson K., Teasdale R.: Fast automated cell phenotype image classification. BMC Bioinform 2007 ; 8: 110.
31. Newberg J., Murphy RF: A framework for the automated analysis of subcellular patterns in human protein atlas images. J Proteome Res 2008 ; 7: 2300-2308.
32. Zhao T., Murphy RF: Automated learning of generative models for subcellular location: building blocks for systems biology. Cytometry Part A 2007 ; 71A: 978-990.
33. Jennrich RI: Stepwise discriminant analysis. In Ralston A, Wilf HS, Enslein K (eds): Statistical Methods for Digital Computers. Vol. 3. New York: John Wiley, 1976: 79-95.
34. Huang K., Velliste M., Murphy RF: Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images. Proc SPIE 2003 ; 4962: 307-318.
35. Peng H., Long F., Ding C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005 ; 27: 1226-1238.
36. Kotsiantis SB: Supervised machine learning: a review of classification techniques. Informatica 2007 ; 31: 249-268.
37. De Vos WH, Van Neste L., Dieriks B., Joss GH, Van Oostveldt P.: High content image cytometry in the context of subnuclear organization. Cytometry Part A 2010 ; 77A: 64-75.
38. Burnham KP, Anderson DR: Multimodel inference: understanding AIC and BIC in model selection. Sociol Method Res 2004 ; 33: 261-304.
39. Chen X., Velliste M., Weinstein S., Jarvik JW, Murphy RF: Location proteomics: building subcellular location trees from high resolution 3D fluorescence microscope images of randomly-tagged proteins. Proc SPIE 2003 ; 4962: 298-306.
40. Perlman ZE, Mitchison TJ, Mayer TU: High-content screening and profiling of drug activity in an automated centrosome-duplication assay. ChemBioChem 2004 ; 2004: 1-8.
41. Zhu X., Goldberg AB: Introduction to Semi-Supervised Learning. San Rafael, CA: Morgan & Claypool, 2009.
42. Lin Y. -S, Huang Y. -H, Lin C. -C, Hsu C. -N: Feature space transformation for semi-supervised learning for protein subcellular localization in fluorescence microscopy images. IEEE Intl Symp Biomed Imaging 2009: 414-417.
43. Eggert US, Mitchison TJ: Small molecule screening by imaging. Curr Opin Chem Biol 2006 ; 10: 232-237.
44. Tanaka M., Bateman R., Rauh D., Vaisberg E., Ramachandani S., Zhang C.,, et al. An unbiased cell morphology-based screen for new, biologically active small molecules. PLoS Biol 2005 ; 3: e128.
45. Perlman ZE, Slack MD, Feng Y., Mitchison TJ, Wu LF, Altschuler SJ: Multidimensional drug profiling by automated microscopy. Science 2004 ; 306: 1194-1198.
46. Birmingham A., Selfors LM, Forster T., Wrobel D., Kennedy CJ, Shanks E.,, et al. Statistical methods for analysis of high-throughput RNA interference screens. Nat Methods 2009 ; 6: 569-575.
47. Goshima G., Wollman R., Goodwin SS, Zhang N., Scholey JM, Vale RD,, et al. Genes required for mitotic spindle assembly in Drosophila S2 cells. Science 2007 ; 316: 417-421.
48. Harder N., Mora-Bermudez F., Godinez WJ, Ellenberg J., Eils R., Rohr K.: Automated analysis of the mitotic phases of human cells in 3D fluorescence microscopy image sequences. Med Image Comput Comput Assist Interv 2006 ; 9: 840-848.
49. Bakal C., Aach J., Church G., Perrimon N.: Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 2007 ; 316: 1753-1756.
50. Matula P., Kumar A., Worz I., Erfle H., Bartenschlager R., Eils R.,, et al. Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection. Cytometry Part A 2009 ; 75A: 309-318.
51. Wang J., Zhou X., Li F., Bradley PL, Chang S. -F, Perrimon N.,, et al. An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling. J Biomed Inform 2009 ; 42: 32-40.
52. Huang K., Lin J., Gajnak JA, Murphy RF: Image content-based retrieval and automated interpretation of fluorescence microscope images via the protein subcellular location image database. IEEE Intl Symp Biomed Imaging 2002: 867-870.
53. Swedlow JR, Goldberg I., Brauner E., Sorger PK: Informatics and quantitative analysis in biological imaging. Science 2003 ; 300: 100-102.
54. Martone ME, Tran J., Wong WW, Sargis J., Fong L., Larson S.,, et al. The cell centered database project: an update on building community resources for managing and sharing 3D imaging data. J Struct Biol 2008 ; 161: 220-231.
55. Berglund L., Bjorling E., Oksvold P., Fagerberg L., Asplund A., Szigyarto CA,, et al. A genecentric Human Protein Atlas for expression profiles based on antibodies. Mol Cell Proteomics 2008 ; 7: 2019-2027.
56. Murphy RF: Location proteomics: a systems approach to subcellular location. Biochem Soc Trans 2005 ; 33: 535-538.