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Cited by 5,157

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CellProfiler: image analysis software for identifying and quantifying cell phenotypes

Open access
Carpenter, Anne E, Jones, Thouis R, Lamprecht, Michael R, Clarke, Colin, Kang, In Han, Friman, Ola, Guertin, David A, Chang, Joo Han, Lindquist, Robert A, Moffat, Jason, Golland, Polina, Sabatini, David M·2006·Genome Biology
DOI 10.1186/gb-2006-7-10-r100
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Abstract

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

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Research works printed but not linked: 55

  1. Moffat J, Grueneberg DA, Yang X, Kim SY, Kloepfer AM, Hinkle G, Piqani B, Eisenhaure TM, Luo B, Grenier JK, et al: A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell. 2006, 124: 1283-1298. · DOI · Paper page
  2. Dasgupta R, Perrimon N: Using RNAi to catch Drosophila genes in a web of interactions: insights into cancer research. Oncogene. 2004, 23: 8359-8365. · DOI
  3. Carpenter AE, Sabatini DM: Systematic genome-wide screens of gene function. Nat Rev Genet. 2004, 5: 11-22. · DOI · Paper page
  4. Vanhecke D, Janitz M: Functional genomics using high-throughput RNA interference. Drug Discov Today. 2005, 10: 205-212. · DOI
  5. Kiger A, Baum B, Jones S, Jones M, Coulson A, Echeverri C, Perrimon N: A functional genomic analysis of cell morphology using RNA interference. J Biol. 2003, 2: 27- · DOI · Paper page
  6. Kim JK, Gabel HW, Kamath RS, Tewari M, Pasquinelli A, Rual JF, Kennedy S, Dybbs M, Bertin N, Kaplan JM, et al: Functional genomic analysis of RNA interference in C. elegans. Science. 2005, 308: 1164-1167. · DOI · Paper page
  7. Mitchison TJ: Small-molecule screening and profiling by using automated microscopy. Chembiochem. 2005, 6: 33-39. · DOI
  8. Perlman ZE, Mitchison TJ, Mayer TU: High-content screening and profiling of drug activity in an automated centrosome-duplication assay. Chembiochem. 2005, 6: 145-151. · DOI
  9. Taylor DL, Giuliano KA: Multiplexed high content screening assays create a systems cell biology approach to drug discovery. Drug Discov Today: Technologies. 2005, 2: 149-154. · DOI
  10. Abraham VC, Taylor DL, Haskins JR: High content screening applied to large-scale cell biology. Trends Biotechnol. 2004, 22: 15-22. · DOI · Paper page
  11. Bjorklund M, Taipale M, Varjosalo M, Saharinen J, Lahdenpera J, Taipale J: Identification of pathways regulating cell size and cell-cycle progression by RNAi. Nature. 2006, 439: 1009-1013. · DOI · Paper page
  12. Ohya Y, Sese J, Yukawa M, Sano F, Nakatani Y, Saito TL, Saka A, Fukuda T, Ishihara S, Oka S, et al: High-dimensional and large-scale phenotyping of yeast mutants. Proc Natl Acad Sci USA. 2005, 102: 19015-19020. · DOI · Paper page
  13. Levsky JM, Singer RH: Gene expression and the myth of the average cell. Trends Cell Biol. 2003, 13: 4-6. · DOI
  14. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP: Causal protein-signaling networks derived from multiparameter single-cell data. Science. 2005, 308: 523-529. · DOI · Paper page
  15. Gil J, Wu H, Wang BY: Image analysis and morphometry in the diagnosis of breast cancer. Microsc Res Tech. 2002, 59: 109-118. · DOI
  16. Chen X, Murphy RF: Objective clustering of proteins based on subcellular location patterns. J Biomed Biotechnol. 2005, 2005: 87-95. · DOI
  17. Doudkine A, Macaulay C, Poulin N, Palcic B: Nuclear texture measurements in image cytometry. Pathologica. 1995, 87: 286-299.
  18. Guillaud M, Adler-Storthz K, Malpica A, Staerkel G, Matisic J, Van Niekirk D, Cox D, Poulin N, Follen M, Macaulay C: Subvisual chromatin changes in cervical epithelium measured by texture image analysis and correlated with HPV. Gynecol Oncol. 2005, 99: S16-23. · doi:10.1016/j.ygyno.2005.07.037
  19. Abramoff MD, Magalhaes PJ, Ram SJ: Image processing with ImageJ. Biophotonics International. 2004, 11: 36-42.
  20. Zhou X, Cao X, Perlman Z, Wong ST: A computerized cellular imaging system for high content analysis in Monastrol suppressor screens. J Biomed Inform. 2006, 39: 115-125. · DOI
  21. Lindblad J, Wahlby C, Bengtsson E, Zaltsman A: Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation. Cytometry A. 2004, 57: 22-33. · DOI
  22. Garippa RJ: A multi-faceted approach to the advancement of cell-based drug discovery. Drug Discovery World. 2004, 6: 43-55.
  23. Harada JN, Bower KE, Orth AP, Callaway S, Nelson CG, Laris C, Hogenesch JB, Vogt PK, Chanda SK: Identification of novel mammalian growth regulatory factors by genome-scale quantitative image analysis. Genome Res. 2005, 15: 1136-1144. · DOI
  24. Wheeler DB, Bailey SN, Guertin DA, Carpenter AE, Higgins CO, Sabatini DM: RNAi living-cell microarrays for loss-of-function screens in Drosophila melanogaster cells. Nat Methods. 2004, 1: 127-132. · DOI
  25. Armknecht S, Boutros M, Kiger A, Nybakken K, Mathey-Prevot B, Perrimon N: High-throughput RNA interference screens in Drosophila tissue culture cells. Methods Enzymol. 2005, 392: 55-73. · DOI
  26. Price JH, Goodacre A, Hahn K, Hodgson L, Hunter EA, Krajewski S, Murphy RF, Rabinovich A, Reed JC, Heynen S: Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools. J Cell Biochem. 2002, 39 (Suppl): 194-210. · DOI
  27. Eggert US, Mitchison TJ: Small molecule screening by imaging. Curr Opin Chem Biol. 2006, 10: 232-237. · DOI
  28. Murphy RF, Meijering E, Danuser G: Special issue on molecular and cellular bioimaging. Ieee T Image Process. 2005, 14: 1233-1236. · DOI
  29. Jones TR, Carpenter AE, Sabatini DM, Golland P: Methods for high-content, high-throughput image-based cell screening. Proceedings of the Workshop on Microscopic Image Analysis with Applications in Biology held in association with MICCAI06 (Medical Image Computing and Computer-Assisted Intervention) held in Copenhagen, Denmark, October 5. Edited by: Metaxas DN, Whitaker RT, Rittcher J, Sebastian T. 2006, 65-72.
  30. Wahlby C: Algorithms for Applied Digital Image Cytometry. Acta Universitatis Upsaliensis. 2003, Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology Uppsala, 896: 75-
  31. Malpica N, de Solorzano CO, Vaquero JJ, Santos A, Vallcorba I, Garcia-Sagredo JM, del Pozo F: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry. 1997, 28: 289-297. · DOI
  32. Wahlby C, Sintorn IM, Erlandsson F, Borgefors G, Bengtsson E: Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J Microsc. 2004, 215: 67-76. · DOI · Paper page
  33. Ortiz de Solorzano C, Rodriguez EG, Jones A, Pinkel D, Gray JW, Sudar D, Lockett SJ: Segmentation of confocal microscope images of cell nuclei in thick tissue sections. J Microsc Oxford. 1999, 193: 212-226. · DOI
  34. Meyer F, Beucher S: Morphological segmentation. J Visual Communication Image Representation. 1990, 1: 21-46. · DOI · Paper page
  35. Jones TR, Carpenter AE, Golland P: Voronoi-based segmentation of cells on image manifolds. ICCV Workshop on Computer Vision for Biomedical Image Applications. 2005, Springer-Verlag, Berlin, 2005: 535-543. · DOI
  36. 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. · DOI · Paper page
  37. Rodenacker K, Bengtsson E: A feature set for cytometry on digitized microscopic images. Anal Cell Pathol. 2003, 25: 1-36. · DOI
  38. Boland MV, Markey MK, Murphy RF: Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry. 1998, 33: 366-375. · doi:10.1002/(sici)1097-0320(19981101)33:3<366::aid-cyto12>3.0.co;2-r
  39. Haralick RM, Shanmuga K, Dinstein I: Textural features for image classification. Ieee T Syst Man Cyb. 1973, SMC3: 610-621. · DOI · Paper page
  40. Gabor D: Theory of communication. J Institute Electrical Engineers. 1946, 93: 429-441.
  41. Turner MR: Texture discrimination by Gabor functions. Biol Cybern. 1986, 55: 71-82. · DOI · Paper page
  42. Zhou X, Liu KY, Bradley P, Perrimon N, Wong ST: Towards automated cellular image segmentation for RNAi genome-wide screening. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2005, 8: 885-892.
  43. Lockett SJ, Jacobson K, Herman B: Quantitative precision of an automated, fluorescence-based image cytometer. Anal Quant Cytol Histol. 1992, 14: 187-202.
  44. Poulin NM, Matthews JB, Skov KA, Palcic B: Effects of fixation method on image cytometric measurement of DNA content and distribution in cells stained for fluorescence with propidium iodide. J Histochem Cytochem. 1994, 42: 1149-1156. · doi:10.1177/42.8.8027534
  45. Bailey SN, Ali SM, Carpenter AE, Higgins CO, Sabatini DM: Microarrays of lentiviruses for gene function screens in immortalized and primary cells. Nat Methods. 2006, 3: 117-122. · DOI
  46. Porter KR: Changes in cell topography associated with transformation to malignancy. Adv Pathobiol. 1975, 1: 29-47.
  47. Ravkin I, Temov V: Poster PO2025: Comparison of Several Classes of Algorithms for Cytoplasm to Nucleus Translocation. Society for Biomolecular Screening Annual Meeting: 2005. 2005
  48. Sigal A, Milo R, Cohen A, Geva-Zatorsky N, Klein Y, Alaluf I, Swerdlin N, Perzov N, Danon T, Liron Y, et al: Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins. Nat Methods. 2006, 3: 525-531. · DOI
  49. Philips JA, Rubin EJ, Perrimon N: Drosophila RNAi screen reveals CD36 family member required for mycobacterial infection. Science. 2005, 309: 1251-1253. · DOI · Paper page
  50. Flockhart I, Booker M, Kiger A, Boutros M, Armknecht S, Ramadan N, Richardson K, Xu A, Perrimon N, Mathey-Prevot B: FlyRNAi: the Drosophila RNAi screening center database. Nucleic Acids Res. 2006, 34: D489-494. · DOI
  51. Swedlow JR, Goldberg I, Brauner E, Sorger PK: Informatics and quantitative analysis in biological imaging. Science. 2003, 300: 100-102. · DOI · Paper page
  52. Stevens B, Alvarez CM, Bohman R, O'Connor JD: An ecdysteroid-induced alteration in the cell cycle of cultured Drosophila cells. Cell. 1980, 22: 675-682. · DOI
  53. Zhang JH, Chung TD, Oldenburg KR: A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen. 1999, 4: 67-73. · DOI · Paper page
  54. Ravkin I: Quality measures for imaging-based cellular assays. Society Biomol Screen Conference Posters. 2004, [http://www.ravkin.net/posters/P12024-Quality Measures for Imaging-based Cellular Assays.pdf] :#P12024
  55. Ravkin I, Temov V, Nelson AD, Zarowitz MA, Hoopes M, Verhovsky Y, Ascue G, Goldbard S, Beske O, Bhagwat B, et al: Multiplexed high-throughput image cytometry using encoded carriers. Proc SPIE. 2004, 5322: 52-63. · doi:10.1117/12.528071

Cited by

Items 101–196 of 196

Metadata

Authors
Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., Golland, P., & Sabatini, D. M.
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2006-10-31
Volume
7
Issue
10
Pages
R100-R100
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/gb-2006-7-10-r100
OpenAlex
W2107554012
Cited by
5,157
References
3
FWCI
28.53
Type
journal article
Language
en
Open access
gold
Licence

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