Cell Plot

Cell Plot is used to measure centrosomal and nuclear positions (in relative to the cell centroid). Previously measuring those numbers of a cell was done with MetaMorph and Excel and required many operations, including rotating the image several times and drawing and logging multiple ROIs. On average it took >2 minutes to measure positions in a cell and there was no way to review the results. With Cell Plot most of the time consuming works are done by the computer. It automatically detects the positions of nuclei and centrosomes, draws cell boundaries, looks for wound edge and determines the direction of cell migration. Cell boundaries detection is not perfect so Cell Plot allows manual correction of the boundaries. With Cell Plot it takes less than 10 seconds to analyze a cell.

Features

• Cell Plot contains only one single .exe file, and doesn’t require installation. It also doesn’t rely on any external software bundles, like MATLAB or Java virtual machine.
• Cell Plot has a built-in color mixer, allowing users to change channels to display.
• User can select cells to be included in the final results – simply double click a cell to include/exclude it.
• Cell boundary can be modified by moving the boundary points. Clicking on the lines adds end points. Double clicking on a point removes it.
• Holding the Alt key and draw a shape around a nucleus, the cell boundary will be replaced with the shape.
• Direction of cell migration (the arrow) can be changed by moving the arrow. The arrow is used as the Y-axis of the cell in all measurements.
• Centrosome position (white dot) can be moved.
• All images in an experiment can be saved as stacks and can be analyzed at the same time. Use PgUp and PgDn to navigate between images.
• Final results can be copied to the clipboard. Simply paste them in to Excel for further statistical analysis.
• All results, including manually changed cell boundaries, are automatically saved (in a file named CellPlot.dat), so that a user can always go back and review/modify their analysis. Note: a single CellPlot.dat is created in a folder, and is used for all images in that folder.
• A data inspector provides real time summary of the results. Top of the inspector is a plot, in which each point represents result from a cell. Clicking on a point moves to the image with the cell and highlights the cell.

Notes

• Cell Plot can open regular .tif files and .stk files (a TIFF variant used by Metamorph). TIFF files with a single image or a stack of images are supported. When using images with single channel, the file name should ended with -G, -R, -B, -I, -Green, -Red, -Blue, or -FarRed (case-insensitive). When opening a file whose name ended with them, Cell Plot will look for the other files and open them as a multi-channel image.
• Cell Plot can open old .lsm files from Zeiss microscopes.
• Cell Plot can open new .czi files from Zeiss microscopes.
• Nikon considers the ND2 file format private and will not publish its specification. Cell Plot can open .nd2 files from Nikon microscopes, but it requires the installation of a DLL library from Nikon’s SDK. Please request the SDK from Nikon and extract its DLL files in to a subfolder named Nd2Reader next to CellPlot.exe. It may not work though, as my experiences with the DLL library varied, especially on Windows 10.

How to use

Please download Cell Plot with sample images and extract the files to a folder. Inside the extracted folder are the execute file CellPlot.exe and three images stack files: Test-LPA-G.tif (microtubule stainging), Test-LPA-R.tif (pericentrin) and Test-LPA-B.tif (DAPI) channels. A lcData.ini file will be generated after analysis is done.

1. Double click to run CellPlot.exe.
2. Drag and drop one of the .stk files to on Cell Plot.
3. Click the “Cell” button and Cell Plot will start to analyse the images.
4. Use the mouse to play around with the boudaries, the centrosome dots and the arrows.
5. Have fun!

Reference

• Chang W, Antoku S, Gundersen GG. Wound-Healing Assays to Study Mechanisms of Nuclear Movement in Fibroblasts and Myoblasts. Methods Mol Biol, 2016; 1411:255-67.