Using CellProfiler for Automatic Identification and Measurement of Biological Objects in Images

Martha S. Vokes1, Anne E. Carpenter1

1 Broad Institute Imaging Platform, Cambridge, Massachusetts
Publication Name:  Current Protocols in Molecular Biology
Unit Number:  Unit 14.17
DOI:  10.1002/0471142727.mb1417s82
Online Posting Date:  April, 2008
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Abstract

Visual analysis is required to perform many biological experiments, from counting yeast colonies to measuring the size and shape of individual cells or the intensity of fluorescently labeled proteins within them. This unit outlines the use of CellProfiler, a free, open-source image analysis tool that extracts quantitative information from biological images. It includes a step-by-step protocol for automated analysis of the number, color, and size of yeast colonies growing on agar plates, but the methods can be adapted to identify and measure any objects in images. The flexibility of the software allows users to tailor pipelines of adjustable modules to fit different biological experiments, to generate accurate measurements from dozens or even hundreds of thousands of images. Curr. Protoc. Mol. Biol. 82:14.17.1-14.17.12. © 2008 by John Wiley & Sons, Inc.

Keywords: automatic image analysis; yeast colonies; open-source software; morphology; colony counting

     
 
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Table of Contents

  • Introduction
  • Basic Protocol: Setting Up and Using CellProfiler
  • Alternate Protocol: Analyzing Images on a Computing Cluster
  • Commentary
  • Literature Cited
  • Figures
     
 
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Materials

Basic Protocol: Setting Up and Using CellProfiler

 Materials
  • Images of yeast plates to be processed
    • Images can be taken with a flatbed scanner or digital camera (Dahle et al., 2004; Memarian et al., 2007; see Critical Parameters for guidance). The images can be located within subfolders and need not be in a particular order or follow a particular naming convention. While this example only analyzes one image, it is possible to analyze hundreds of images on a single computer, or tens of thousands of images using a computing cluster (see Alternate Protocol). A variety of file formats are currently readable by CellProfiler, including bmp, cur, fts, fits, gif, hdf, ico, jpg, jpeg, pbm, pcx, pgm, png, pnm, ppm, ras, tif, tiff, xwd, dib, mat, fig, and zvi. See Critical Parameters for more information about acquiring images and image file types.
  • Computer with at least 1 Gb of RAM and 1 GHz processor (recommended)
    • CellProfiler is available for Macintosh, Windows, and Unix/Linux. A complete list of compatible operating systems can be found at http://www.CellProfiler.org/download.htm. The example image pipeline demonstrated here will be processed in <1 min/image on a single computer with a 2.4 GHz processor and 3 Gb RAM. Large image sets (greater than ~500 images) will likely require a computing cluster (see Alternate Protocol).
  • Decompression software (e.g., Winzip, http://www.winzip.com, or Stuffit, http://www.stuffit.com) for unpacking compressed files
  • CellProfiler software (see step 1)
  • Example images and corresponding CellProfiler pipeline (see step 4)
  • CellProfiler manual (http://www.cellprofiler.org/install.htm)
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Figures

  •  FigureFigure 14.17.1 An overview of the CellProfiler example pipeline. The names of the images created or objects identified appear in italics below each image, whereas the module names appear in a larger regular font.
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  •  FigureFigure 14.17.2 (A) The original plate image. (B) The colonies identified. The colors are arbitrary. (C) The SubtractedRed image (or the red channel). (D) All identified colonies outlined. (E) The colonies classified by area. (F) The colonies classified by redness.
    View Image

Videos

Literature Cited

Literature Cited
    Bailey, S.N., Ali, S.M., Carpenter, A.E., Higgins, C.O., and Sabatini, D.M. 2006. Microarrays of lentiviruses for gene function screens in immortalized and primary cells. Nat. Methods 3: 117-122.
    Carpenter, A.E. 2008. Data analysis: Extracting rich information from images. Methods Mol. Biol. In press.
    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., and Sabatini, D.M. 2006. CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7: R100.
    Cowen, L.E., Carpenter, A.E., Matangkasombut, O., Fink, G.R., and Lindquist, S. 2006. Genetic architecture of Hsp90-dependent drug resistance. Eukaryot. Cell 5: 2184-2188.
    Dahle, J., Kakar, M., Steen, H.B., and Kaalhus, O. 2004. Automated counting of mammalian cell colonies by means of a flat bed scanner and image processing. Cytometry A 60: 182-188.
    Hartwell, K.A., Muir, B., Reinhardt, F., Carpenter, A.E., Sgroi, D.C., and Weinberg, R.A. 2006. The Spemann organizer gene, Goosecoid, promotes tumor metastasis. Proc. Natl. Acad. Sci. U.S.A. 103: 18969-18974.
    Lamprecht, M.R., Sabatini, D.M., and Carpenter, A.E. 2007. CellProfiler: Free, versatile software for automated biological image analysis. Biotechniques 42: 71-75.
    Memarian, N., Jessulat, M., Alirezaie, J., Mir-Rashed, N., Xu, J., Zareie, M., Smith, M., and Golshani, A. 2007. Colony size measurement of the yeast gene deletion strains for functional genomics. BMC Bioinformatics 8: 117.
    Moffat, J., Grueneberg, D.A., Yang, X., Kim, S.Y., Kloepfer, A.M., Hinkle, G., Piqani, B., Eisenhaure, T.M., Luo, B., Grenier, J.K., Carpenter, A.E., Foo, S.Y., Stewart, S.A., Stockwell, B.R., Hacohen, N., Hahn, W.C., Lander, E.S., Sabatini, D.M., and Root, D.E. 2006. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124: 1283-1298.
    Pearson, H. 2007. The good, the bad and the ugly. Nature 447: 138-140.
    R Development Core Team. 2007. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org.
 Internet Resources
    http://www.CellProfiler.org

The CellProfiler home page allows free access to the software, example pipelines, and the discussion forum.

    http://en.wikipedia.org/wiki/Lossy_data_compression

A detailed Web page discussing lossless versus lossy image compression. Example images are available that demonstrate the difference in quality in lossless images versus lossy images.

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