Using CellX to Quantify Intracellular Events
互联网
- Abstract
- Table of Contents
- Figures
- Literature Cited
Abstract
Methods to quantify features of individual cells using light microscopy have become widely used in biology. A multitude of computational tools has been developed for image analysis; however, they are often only for specific cell types and microscopy techniques. This unit describes CellX, an open?source software package for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images. CellX can perform cell segmentation largely independently of cell shapes, and can also cope with images that are crowded with cells. The basic protocol describes how to use CellX for cell segmentation and quantification. This protocol remains the same whether there is a collection of images to be analyzed or whether cell tracking on a sequence of images is to be performed. The CellX output comprises control images for visual validation, text files for post?processing statistics, and MATLAB objects for advanced subsequent analysis. Curr. Protoc. Mol. Biol. 101:14.22.1?14.22.20. © 2013 by John Wiley & Sons, Inc.
Keywords: image analysis; segmentation; tracking; microscopy; single cell quantification
Table of Contents
- Introduction
- Basic Protocol 1: CellX Installation, Calibration, and Batch Processing
- Alternate Protocol 1: Run in Terminal
- Alternate Protocol 2: Run on Cluster
- Commentary
- Literature Cited
- Figures
- Tables
Materials
Figures
-
Figure 14.22.1 Overview of the calibration steps. Blue boxes indicate protocol steps, the red decision box corresponds to visual inspection of the control images by the user, the green circles cover possible troubleshooting steps and adjustment of advanced parameters. View Image -
Figure 14.22.2 Screenshot of CellX parameter calibration tab. (A ) Image area, (B ) membrane profile chart, (C ) mode toggle switch and shapes list, (D ) general parameters table, (E ) advanced parameters table, (F ) test buttons and add buttons for optional quantification image(s). View Image -
Figure 14.22.3 Screenshots of image areas of budding yeast cells (left panel) and fission yeast cells (right panel) with seeds (cyan circles) placed in the smallest and largest cells. The result is acquired by execution of step 8 of the protocol. View Image -
Figure 14.22.4 Screenshots of image areas of budding yeast cells (left panel) and fission yeast cells (right panel) with green line segments denoting the maximum cell length . The result is acquired by execution of step 9 of the protocol. View Image -
Figure 14.22.5 Screenshots of image areas of budding yeast cells (left panel, middle panel) and fission yeast cells (right panel) with membrane profile calibration rays (red arrows) drawn across their membranes. The result is acquired by execution of step 10 of the protocol. View Image -
Figure 14.22.6 Screenshot of the membrane chart. Left panel: Selection of the part of the signal that corresponds to the membrane. Right panel: The sub‐region of the profile with the membrane offset and width adjusted. The result is acquired by execution of step 11 of the protocol. View Image -
Figure 14.22.7 Screenshots of control images. Seed control image (upper left panel), segmentation control images (upper right panel: budding yeast, lower left panel: fission yeast), and quantification control image (lower right panel). View Image -
Figure 14.22.8 Screenshot of CellX batch processing tab. The top‐down order of text fields and buttons corresponds to the order of the protocol steps 18 to 27. View Image -
Figure 14.22.9 Screenshots of successful seeding results. Left panel: almost perfect seeding (only two false positive seeds appear). Right panel: perfect seeding (no false positive seeds appear). View Image -
Figure 14.22.10 Screenshot of bad seeding results. Left panel: few seeds appear. Right panel: many false positive seeds appear. View Image -
Figure 14.22.11 Screenshot of bad segmentation results superimposed on the segmentation image. Left panel: few cells get segmented. Middle panel: many false positive segmentation results appear. Right panel: many cells are segmented incorrectly. View Image -
Figure 14.22.12 Screenshot of bad segmentation results superimposed on the quantification image. Left panel: segmentation result is inside the cells. Middle panel: segmentation result is outside the cells. Right panel: membrane areas exceed the true cell membranes. View Image
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. 2009. CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7:R100. | |
| Collins, T.J. 2007. ImageJ for microscopy. BioTechniques 43:S25‐S30. | |
| Gordon, A., Colman‐Lerner, A., Chin, T.E., Benjamin, K.R., Yu, R.C., and Brent, R. 2007. Single‐cell quantification of molecules and rates using open‐source microscope‐based cytometry. Nat. Methods 4:175‐181. | |
| Kvarnstrom, M., Logg, K., Diez, A., Bodvard, K., and Kall, M. 2008. Image analysis algorithms for cell contour recognition in budding yeast. Opt. Express 17:12943–12957. | |
| Locke, J.C.W. and Elowitz, M.B. 2006. Using movies to analyse gene circuit dynamics in single cells. Nat. Rev. Microbiol. 7:383–392. |
