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

Mark‐Anthony Bray1, 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.mb1417s109
Online Posting Date:  January, 2015
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Abstract

Visual analysis is required to perform many biological experiments, from counting colonies to measuring the size or fluorescence intensity of individual cells or organisms. 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 many other types of objects in images. The flexibility of the software allows experimenters to create pipelines of adjustable modules to fit different biological experiments and to generate accurate measurements from dozens or even hundreds of thousands of images. © 2015 by John Wiley & Sons, Inc.

Keywords: automatic image analysis; yeast colonies; open‐source software; phenotypes; colony counting

     
 
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Table of Contents

  • Introduction
  • Basic Protocol 1: Setting up and Using CellProfiler
  • Alternate Protocol 1: Analyzing Images on a Computing Cluster
  • Commentary
  • Figures
     
 
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Materials

Basic Protocol 1: Setting up and Using CellProfiler

  Materials
  • Images of yeast plates to be processed
  • Computer with at least 2 GB of RAM and preferably containing multiple processors
  • Decompression software (e.g., WinZip, Stuffit) for unpacking compressed files, if not already included in your operating system
  • CellProfiler software (see step 1; this protocol was written for CellProfiler version 2.1.0)
  • Example images and corresponding CellProfiler pipeline (see step 4)
NOTE: Images can be taken with a flatbed scanner or digital camera (Dahle et al., ; Memarian et al., ); 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 hundreds of thousands of images using a computing cluster (see protocol 2Alternate Protocol). More than 100 file formats are currently readable by CellProfiler, including BMP, GIF, JPG, PNG, TIF, DIB, LSM, and FLEX. See Critical Parameters for more information about acquiring images and image file types.NOTE: A 64‐bit operating system is strongly 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.shtml. The example image pipeline demonstrated here will be processed in ∼1 min per image on a single computer with a 2.9 GHz processor and 4 GB RAM. CellProfiler is optimized to take advantage of multiple computing processors on a single computer, but large image sets (greater than ∼500 images) will likely require a computing cluster (see protocol 2Alternate Protocol).
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Figures

Videos

Literature Cited

Literature Cited
  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.
  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.
  Jones, T.R., Kang, I.H., Wheeler, D.B., Lindquist, R.A., Papallo, A., Sabatini, D.M., Golland, P., and Carpenter, A.E. 2008. CellProfiler Analyst: Data exploration and analysis software for complex image‐based screens. BMC Bioinformatics 9:482.
  Kamentsky, L., Jones, T.R., Fraser, A., Bray, M.‐A., Logan, D.J., Madden, K.L., Ljosa, V., Rueden, C., Eliceiri, K.W., and Carpenter, A.E. 2011. Improved structure, function, and compatibility for CellProfiler: Modular high‐throughput image analysis software. Bioinformatics 27:1179‐1180.
  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.
  Pearson, H. 2007. The good, the bad and the ugly. Nature 447:138‐140.
  R Development Core Team. 2014. 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 relevant to the choice of image file formats which discusses 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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