foci2D

Adam Tyson | 2018-05-08 | adam.tyson01@icr.ac.uk

Takes a two channel .lsm image. Channel 1 (nuclei) is maximum projected, and segmented. The nuclear segmentation is used to estimate cytoplasmic boundaries (midpoints between nuclei). Foci in the 2nd channel (sum projection) are then quantified on a cell by cell basis.

N.B. needs a recent version of MATLAB and the Image Processing Toolbox. Also required bioformats toolbox (included). Should work on Windows/OSX/Linux.

Instructions:

1. Save images as .lsm, nuclear marker in channel 1, foci in 2nd channel.

2. Clone or download repository (e.g. Clone or download -> Download ZIP, then unzip foci2D-master.zip).

3. Place whole directory in the MATLAB path (e.g. C:\Users\User\Documents\MATLAB).

4. Open foci2D\foci2D and run (F5 or the green "Run" arrow under the "EDITOR" tab). Alternatively, type foci2D into the Command Window and press ENTER.

5. Choose a directory that contains the images.

6. Choose various options:

   • Save results as csv - all the results will be exported as a .csv for plotting and statistics
   • Display segmentation - displays segmentation of nuclei, estimation of cytoplasmic boundaries and the segmented foci.

7. Confirm or change options: (the defaults can be changed under function vars=getVars in cell_coloc_3D.m

   • Nuclear segmentation threshold - increase to be more stringent on what is a cell (and vice versa)
   • Foci segmentation threshold - increase to be more stringent on what are foci (and vice versa)
   • Maximum hole size - how big a "hole" inside a cell should be filled
   • Largest object to remove - how big can bright spots outside the main mass of cells be and still be ignored by the analysis
   • Smoothing sigma (nucleus) - how much to smooth before thresholding
   • Smoothing sigma (foci) - how much to smooth before thresholding

8. The script will then loop through all the images in the chosen folder. Each image will be processed in turn, and a number of parameters will be saved (if specified):

• fociNumbers_TIMESTAMP.csv - number of foci per cell, per image
• fociTotalInten_TIMESTAMP.csv - total intensity of all pixels in foci, per cell, per image
• fociTotalArea_TIMESTAMP.csv - total area of all foci, per cell, per image
• summaryResults.csv - includes various parameters per image (but not per cell), these include:
  • Mean foci number per cell
  • Mean total foci area per cell
  • Mean total foci intensity per cell
  • Number of cells

Once the first image has been analysed, the progress bar will give an estimate of the remaining time.