Skip to content

Latest commit

 

History

History
38 lines (30 loc) · 2.63 KB

README.md

File metadata and controls

38 lines (30 loc) · 2.63 KB
Raw

foci2D

Adam Tyson | 2018-05-08 | adam.tyson@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.